diff --git a/data/emnist-test.npz b/data/emnist-test.npz
new file mode 100644
index 0000000..05df1d8
Binary files /dev/null and b/data/emnist-test.npz differ
diff --git a/data/emnist-train.npz b/data/emnist-train.npz
new file mode 100644
index 0000000..177a30c
Binary files /dev/null and b/data/emnist-train.npz differ
diff --git a/data/emnist-valid.npz b/data/emnist-valid.npz
new file mode 100644
index 0000000..87183dd
Binary files /dev/null and b/data/emnist-valid.npz differ
diff --git a/mlp/data_providers.py b/mlp/data_providers.py
index 4ff2a8f..aac04ee 100644
--- a/mlp/data_providers.py
+++ b/mlp/data_providers.py
@@ -199,6 +199,75 @@ class MNISTDataProvider(DataProvider):
         one_of_k_targets[range(int_targets.shape[0]), int_targets] = 1
         return one_of_k_targets
 
+class EMNISTDataProvider(DataProvider):
+    """Data provider for EMNIST handwritten digit images."""
+
+    def __init__(self, which_set='train', batch_size=100, max_num_batches=-1,
+                 shuffle_order=True, rng=None):
+        """Create a new EMNIST data provider object.
+
+        Args:
+            which_set: One of 'train', 'valid' or 'eval'. Determines which
+                portion of the EMNIST data this object should provide.
+            batch_size (int): Number of data points to include in each batch.
+            max_num_batches (int): Maximum number of batches to iterate over
+                in an epoch. If `max_num_batches * batch_size > num_data` then
+                only as many batches as the data can be split into will be
+                used. If set to -1 all of the data will be used.
+            shuffle_order (bool): Whether to randomly permute the order of
+                the data before each epoch.
+            rng (RandomState): A seeded random number generator.
+        """
+        # check a valid which_set was provided
+        assert which_set in ['train', 'valid', 'test'], (
+            'Expected which_set to be either train, valid or eval. '
+            'Got {0}'.format(which_set)
+        )
+        self.which_set = which_set
+        self.num_classes = 47
+        # construct path to data using os.path.join to ensure the correct path
+        # separator for the current platform / OS is used
+        # MLP_DATA_DIR environment variable should point to the data directory
+        data_path = os.path.join(
+            os.environ['MLP_DATA_DIR'], 'emnist-{0}.npz'.format(which_set))
+        assert os.path.isfile(data_path), (
+            'Data file does not exist at expected path: ' + data_path
+        )
+        # load data from compressed numpy file
+        loaded = np.load(data_path)
+        print(loaded.keys())
+        inputs, targets = loaded['inputs'], loaded['targets']
+        inputs = inputs.astype(np.float32)
+        inputs = np.reshape(inputs, newshape=(-1, 28*28))
+        inputs = inputs / 255.0
+        # pass the loaded data to the parent class __init__
+        super(EMNISTDataProvider, self).__init__(
+            inputs, targets, batch_size, max_num_batches, shuffle_order, rng)
+
+    def next(self):
+        """Returns next data batch or raises `StopIteration` if at end."""
+        inputs_batch, targets_batch = super(EMNISTDataProvider, self).next()
+        return inputs_batch, self.to_one_of_k(targets_batch)
+
+    def to_one_of_k(self, int_targets):
+        """Converts integer coded class target to 1 of K coded targets.
+
+        Args:
+            int_targets (ndarray): Array of integer coded class targets (i.e.
+                where an integer from 0 to `num_classes` - 1 is used to
+                indicate which is the correct class). This should be of shape
+                (num_data,).
+
+        Returns:
+            Array of 1 of K coded targets i.e. an array of shape
+            (num_data, num_classes) where for each row all elements are equal
+            to zero except for the column corresponding to the correct class
+            which is equal to one.
+        """
+        one_of_k_targets = np.zeros((int_targets.shape[0], self.num_classes))
+        one_of_k_targets[range(int_targets.shape[0]), int_targets] = 1
+        return one_of_k_targets
+
 
 class MetOfficeDataProvider(DataProvider):
     """South Scotland Met Office weather data provider."""
diff --git a/mlp/layers.py b/mlp/layers.py
index 626d10b..6393803 100644
--- a/mlp/layers.py
+++ b/mlp/layers.py
@@ -16,7 +16,6 @@ import numpy as np
 import mlp.initialisers as init
 from mlp import DEFAULT_SEED
 
-
 class Layer(object):
     """Abstract class defining the interface for a layer."""
 
@@ -96,6 +95,76 @@ class LayerWithParameters(Layer):
         """
         raise NotImplementedError()
 
+class StochasticLayerWithParameters(Layer):
+    """Specialised layer which uses a stochastic forward propagation."""
+
+    def __init__(self, rng=None):
+        """Constructs a new StochasticLayer object.
+
+        Args:
+            rng (RandomState): Seeded random number generator object.
+        """
+        if rng is None:
+            rng = np.random.RandomState(DEFAULT_SEED)
+        self.rng = rng
+
+    def fprop(self, inputs, stochastic=True):
+        """Forward propagates activations through the layer transformation.
+
+        Args:
+            inputs: Array of layer inputs of shape (batch_size, input_dim).
+            stochastic: Flag allowing different deterministic
+                forward-propagation mode in addition to default stochastic
+                forward-propagation e.g. for use at test time. If False
+                a deterministic forward-propagation transformation
+                corresponding to the expected output of the stochastic
+                forward-propagation is applied.
+
+        Returns:
+            outputs: Array of layer outputs of shape (batch_size, output_dim).
+        """
+        raise NotImplementedError()
+    def grads_wrt_params(self, inputs, grads_wrt_outputs):
+        """Calculates gradients with respect to layer parameters.
+
+        Args:
+            inputs: Array of inputs to layer of shape (batch_size, input_dim).
+            grads_wrt_to_outputs: Array of gradients with respect to the layer
+                outputs of shape (batch_size, output_dim).
+
+        Returns:
+            List of arrays of gradients with respect to the layer parameters
+            with parameter gradients appearing in same order in tuple as
+            returned from `get_params` method.
+        """
+        raise NotImplementedError()
+
+    def params_penalty(self):
+        """Returns the parameter dependent penalty term for this layer.
+
+        If no parameter-dependent penalty terms are set this returns zero.
+        """
+        raise NotImplementedError()
+
+    @property
+    def params(self):
+        """Returns a list of parameters of layer.
+
+        Returns:
+            List of current parameter values. This list should be in the
+            corresponding order to the `values` argument to `set_params`.
+        """
+        raise NotImplementedError()
+
+    @params.setter
+    def params(self, values):
+        """Sets layer parameters from a list of values.
+
+        Args:
+            values: List of values to set parameters to. This list should be
+                in the corresponding order to what is returned by `get_params`.
+        """
+        raise NotImplementedError()
 
 class StochasticLayer(Layer):
     """Specialised layer which uses a stochastic forward propagation."""
@@ -260,6 +329,94 @@ class AffineLayer(LayerWithParameters):
         return 'AffineLayer(input_dim={0}, output_dim={1})'.format(
             self.input_dim, self.output_dim)
 
+class BatchNormalizationLayer(StochasticLayerWithParameters):
+    """Layer implementing an affine tranformation of its inputs.
+
+    This layer is parameterised by a weight matrix and bias vector.
+    """
+
+    def __init__(self, input_dim, rng=None):
+        """Initialises a parameterised affine layer.
+
+        Args:
+            input_dim (int): Dimension of inputs to the layer.
+            output_dim (int): Dimension of the layer outputs.
+            weights_initialiser: Initialiser for the weight parameters.
+            biases_initialiser: Initialiser for the bias parameters.
+            weights_penalty: Weights-dependent penalty term (regulariser) or
+                None if no regularisation is to be applied to the weights.
+            biases_penalty: Biases-dependent penalty term (regulariser) or
+                None if no regularisation is to be applied to the biases.
+        """
+        super(BatchNormalizationLayer, self).__init__(rng)
+        self.beta = np.random.normal(size=(input_dim))
+        self.gamma = np.random.normal(size=(input_dim))
+        self.epsilon = 0.00001
+        self.cache = None
+        self.input_dim = input_dim
+
+    def fprop(self, inputs, stochastic=True):
+        """Forward propagates inputs through a layer."""
+
+        raise NotImplementedError
+
+    def bprop(self, inputs, outputs, grads_wrt_outputs):
+        """Back propagates gradients through a layer.
+
+        Given gradients with respect to the outputs of the layer calculates the
+        gradients with respect to the layer inputs.
+
+        Args:
+            inputs: Array of layer inputs of shape (batch_size, input_dim).
+            outputs: Array of layer outputs calculated in forward pass of
+                shape (batch_size, output_dim).
+            grads_wrt_outputs: Array of gradients with respect to the layer
+                outputs of shape (batch_size, output_dim).
+
+        Returns:
+            Array of gradients with respect to the layer inputs of shape
+            (batch_size, input_dim).
+        """
+
+        raise NotImplementedError
+
+    def grads_wrt_params(self, inputs, grads_wrt_outputs):
+        """Calculates gradients with respect to layer parameters.
+
+        Args:
+            inputs: array of inputs to layer of shape (batch_size, input_dim)
+            grads_wrt_to_outputs: array of gradients with respect to the layer
+                outputs of shape (batch_size, output_dim)
+
+        Returns:
+            list of arrays of gradients with respect to the layer parameters
+            `[grads_wrt_weights, grads_wrt_biases]`.
+        """
+        raise NotImplementedError
+
+    def params_penalty(self):
+        """Returns the parameter dependent penalty term for this layer.
+
+        If no parameter-dependent penalty terms are set this returns zero.
+        """
+        params_penalty = 0
+
+        return params_penalty
+
+    @property
+    def params(self):
+        """A list of layer parameter values: `[gammas, betas]`."""
+        return [self.gamma, self.beta]
+
+    @params.setter
+    def params(self, values):
+        self.gamma = values[0]
+        self.beta = values[1]
+
+    def __repr__(self):
+        return 'BatchNormalizationLayer(input_dim={0})'.format(
+            self.input_dim)
+
 
 class SigmoidLayer(Layer):
     """Layer implementing an element-wise logistic sigmoid transformation."""
@@ -300,6 +457,151 @@ class SigmoidLayer(Layer):
     def __repr__(self):
         return 'SigmoidLayer'
 
+class ConvolutionalLayer(LayerWithParameters):
+    """Layer implementing a 2D convolution-based transformation of its inputs.
+    The layer is parameterised by a set of 2D convolutional kernels, a four
+    dimensional array of shape
+        (num_output_channels, num_input_channels, kernel_dim_1, kernel_dim_2)
+    and a bias vector, a one dimensional array of shape
+        (num_output_channels,)
+    i.e. one shared bias per output channel.
+    Assuming no-padding is applied to the inputs so that outputs are only
+    calculated for positions where the kernel filters fully overlap with the
+    inputs, and that unit strides are used the outputs will have spatial extent
+        output_dim_1 = input_dim_1 - kernel_dim_1 + 1
+        output_dim_2 = input_dim_2 - kernel_dim_2 + 1
+    """
+
+    def __init__(self, num_input_channels, num_output_channels,
+                 input_dim_1, input_dim_2,
+                 kernel_dim_1, kernel_dim_2,
+                 kernels_init=init.UniformInit(-0.01, 0.01),
+                 biases_init=init.ConstantInit(0.),
+                 kernels_penalty=None, biases_penalty=None):
+        """Initialises a parameterised convolutional layer.
+        Args:
+            num_input_channels (int): Number of channels in inputs to
+                layer (this may be number of colour channels in the input
+                images if used as the first layer in a model, or the
+                number of output channels, a.k.a. feature maps, from a
+                a previous convolutional layer).
+            num_output_channels (int): Number of channels in outputs
+                from the layer, a.k.a. number of feature maps.
+            input_dim_1 (int): Size of first input dimension of each 2D
+                channel of inputs.
+            input_dim_2 (int): Size of second input dimension of each 2D
+                channel of inputs.
+            kernel_dim_1 (int): Size of first dimension of each 2D channel of
+                kernels.
+            kernel_dim_2 (int): Size of second dimension of each 2D channel of
+                kernels.
+            kernels_intialiser: Initialiser for the kernel parameters.
+            biases_initialiser: Initialiser for the bias parameters.
+            kernels_penalty: Kernel-dependent penalty term (regulariser) or
+                None if no regularisation is to be applied to the kernels.
+            biases_penalty: Biases-dependent penalty term (regulariser) or
+                None if no regularisation is to be applied to the biases.
+        """
+        self.num_input_channels = num_input_channels
+        self.num_output_channels = num_output_channels
+        self.input_dim_1 = input_dim_1
+        self.input_dim_2 = input_dim_2
+        self.kernel_dim_1 = kernel_dim_1
+        self.kernel_dim_2 = kernel_dim_2
+        self.kernels_init = kernels_init
+        self.biases_init = biases_init
+        self.kernels_shape = (
+            num_output_channels, num_input_channels, kernel_dim_1, kernel_dim_2
+        )
+        self.inputs_shape = (
+            None, num_input_channels, input_dim_1, input_dim_2
+        )
+        self.kernels = self.kernels_init(self.kernels_shape)
+        self.biases = self.biases_init(num_output_channels)
+        self.kernels_penalty = kernels_penalty
+        self.biases_penalty = biases_penalty
+
+        self.cache = None
+
+    def fprop(self, inputs):
+        """Forward propagates activations through the layer transformation.
+        For inputs `x`, outputs `y`, kernels `K` and biases `b` the layer
+        corresponds to `y = conv2d(x, K) + b`.
+        Args:
+            inputs: Array of layer inputs of shape (batch_size, input_dim).
+        Returns:
+            outputs: Array of layer outputs of shape (batch_size, output_dim).
+        """
+        raise NotImplementedError
+
+    def bprop(self, inputs, outputs, grads_wrt_outputs):
+        """Back propagates gradients through a layer.
+        Given gradients with respect to the outputs of the layer calculates the
+        gradients with respect to the layer inputs.
+        Args:
+            inputs: Array of layer inputs of shape
+                (batch_size, num_input_channels, input_dim_1, input_dim_2).
+            outputs: Array of layer outputs calculated in forward pass of
+                shape
+                (batch_size, num_output_channels, output_dim_1, output_dim_2).
+            grads_wrt_outputs: Array of gradients with respect to the layer
+                outputs of shape
+                (batch_size, num_output_channels, output_dim_1, output_dim_2).
+        Returns:
+            Array of gradients with respect to the layer inputs of shape
+            (batch_size, input_dim).
+        """
+        # Pad the grads_wrt_outputs
+
+        raise NotImplementedError
+
+    def grads_wrt_params(self, inputs, grads_wrt_outputs):
+        """Calculates gradients with respect to layer parameters.
+        Args:
+            inputs: array of inputs to layer of shape (batch_size, input_dim)
+            grads_wrt_to_outputs: array of gradients with respect to the layer
+                outputs of shape
+                (batch_size, num_output-_channels, output_dim_1, output_dim_2).
+        Returns:
+            list of arrays of gradients with respect to the layer parameters
+            `[grads_wrt_kernels, grads_wrt_biases]`.
+        """
+
+        raise NotImplementedError
+
+    def params_penalty(self):
+        """Returns the parameter dependent penalty term for this layer.
+        If no parameter-dependent penalty terms are set this returns zero.
+        """
+        params_penalty = 0
+        if self.kernels_penalty is not None:
+            params_penalty += self.kernels_penalty(self.kernels)
+        if self.biases_penalty is not None:
+            params_penalty += self.biases_penalty(self.biases)
+        return params_penalty
+
+    @property
+    def params(self):
+        """A list of layer parameter values: `[kernels, biases]`."""
+        return [self.kernels, self.biases]
+
+    @params.setter
+    def params(self, values):
+        self.kernels = values[0]
+        self.biases = values[1]
+
+    def __repr__(self):
+        return (
+            'ConvolutionalLayer(\n'
+            '    num_input_channels={0}, num_output_channels={1},\n'
+            '    input_dim_1={2}, input_dim_2={3},\n'
+            '    kernel_dim_1={4}, kernel_dim_2={5}\n'
+            ')'
+            .format(self.num_input_channels, self.num_output_channels,
+                    self.input_dim_1, self.input_dim_2, self.kernel_dim_1,
+                    self.kernel_dim_2)
+        )
+
 
 class ReluLayer(Layer):
     """Layer implementing an element-wise rectified linear transformation."""
@@ -339,6 +641,102 @@ class ReluLayer(Layer):
     def __repr__(self):
         return 'ReluLayer'
 
+class LeakyReluLayer(Layer):
+    """Layer implementing an element-wise rectified linear transformation."""
+    def __init__(self, alpha=0.01):
+        self.alpha = alpha
+
+    def fprop(self, inputs):
+        """Forward propagates activations through the layer transformation.
+
+        For inputs `x` and outputs `y` this corresponds to `y = max(0, x)`.
+        """
+        positive_inputs = np.maximum(inputs, 0.)
+
+        negative_inputs = inputs
+        negative_inputs[negative_inputs>0] = 0.
+        negative_inputs = negative_inputs * self.alpha
+
+        outputs = positive_inputs + negative_inputs
+        return outputs
+
+    def bprop(self, inputs, outputs, grads_wrt_outputs):
+        """Back propagates gradients through a layer.
+
+        Given gradients with respect to the outputs of the layer calculates the
+        gradients with respect to the layer inputs.
+        """
+        positive_gradients = (outputs > 0) * grads_wrt_outputs
+        negative_gradients = self.alpha * (outputs < 0) * grads_wrt_outputs
+        gradients = positive_gradients + negative_gradients
+        return gradients
+
+    def __repr__(self):
+        return 'LeakyReluLayer'
+
+class ELULayer(Layer):
+    """Layer implementing an ELU activation."""
+    def __init__(self, alpha=1.0):
+        self.alpha = alpha
+    def fprop(self, inputs):
+        """Forward propagates activations through the layer transformation.
+
+        For inputs `x` and outputs `y` this corresponds to `y = max(0, x)`.
+        """
+        positive_inputs = np.maximum(inputs, 0.)
+
+        negative_inputs = np.copy(inputs)
+        negative_inputs[negative_inputs>0] = 0.
+        negative_inputs = self.alpha * (np.exp(negative_inputs) - 1)
+
+        outputs = positive_inputs + negative_inputs
+        return outputs
+
+    def bprop(self, inputs, outputs, grads_wrt_outputs):
+        """Back propagates gradients through a layer.
+
+        Given gradients with respect to the outputs of the layer calculates the
+        gradients with respect to the layer inputs.
+        """
+        positive_gradients = (outputs >= 0) * grads_wrt_outputs
+        outputs_to_use = (outputs < 0) * outputs
+        negative_gradients = (outputs_to_use + self.alpha)
+        negative_gradients[outputs >= 0] = 0.
+        negative_gradients = negative_gradients * grads_wrt_outputs
+        gradients = positive_gradients + negative_gradients
+        return gradients
+
+    def __repr__(self):
+        return 'ELULayer'
+
+class SELULayer(Layer):
+    """Layer implementing an element-wise rectified linear transformation."""
+    #α01 ≈ 1.6733 and λ01 ≈ 1.0507
+    def __init__(self):
+        self.alpha = 1.6733
+        self.lamda = 1.0507
+        self.elu = ELULayer(alpha=self.alpha)
+    def fprop(self, inputs):
+        """Forward propagates activations through the layer transformation.
+
+        For inputs `x` and outputs `y` this corresponds to `y = max(0, x)`.
+        """
+        outputs = self.lamda * self.elu.fprop(inputs)
+        return outputs
+
+    def bprop(self, inputs, outputs, grads_wrt_outputs):
+        """Back propagates gradients through a layer.
+
+        Given gradients with respect to the outputs of the layer calculates the
+        gradients with respect to the layer inputs.
+        """
+        scaled_outputs = outputs / self.lamda
+        gradients = self.lamda * self.elu.bprop(inputs=inputs, outputs=scaled_outputs,
+                                                grads_wrt_outputs=grads_wrt_outputs)
+        return gradients
+
+    def __repr__(self):
+        return 'SELULayer'
 
 class TanhLayer(Layer):
     """Layer implementing an element-wise hyperbolic tangent transformation."""
@@ -482,4 +880,123 @@ class RadialBasisFunctionLayer(Layer):
         ).sum(-1)
 
     def __repr__(self):
-        return 'RadialBasisFunctionLayer(grid_dim={0})'.format(self.grid_dim)
\ No newline at end of file
+        return 'RadialBasisFunctionLayer(grid_dim={0})'.format(self.grid_dim)
+
+class DropoutLayer(StochasticLayer):
+    """Layer which stochastically drops input dimensions in its output."""
+
+    def __init__(self, rng=None, incl_prob=0.5, share_across_batch=True):
+        """Construct a new dropout layer.
+
+        Args:
+            rng (RandomState): Seeded random number generator.
+            incl_prob: Scalar value in (0, 1] specifying the probability of
+                each input dimension being included in the output.
+            share_across_batch: Whether to use same dropout mask across
+                all inputs in a batch or use per input masks.
+        """
+        super(DropoutLayer, self).__init__(rng)
+        assert incl_prob > 0. and incl_prob <= 1.
+        self.incl_prob = incl_prob
+        self.share_across_batch = share_across_batch
+        self.rng = rng
+
+    def fprop(self, inputs, stochastic=True):
+        """Forward propagates activations through the layer transformation.
+
+        Args:
+            inputs: Array of layer inputs of shape (batch_size, input_dim).
+            stochastic: Flag allowing different deterministic
+                forward-propagation mode in addition to default stochastic
+                forward-propagation e.g. for use at test time. If False
+                a deterministic forward-propagation transformation
+                corresponding to the expected output of the stochastic
+                forward-propagation is applied.
+
+        Returns:
+            outputs: Array of layer outputs of shape (batch_size, output_dim).
+        """
+        if stochastic:
+            mask_shape = (1,) + inputs.shape[1:] if self.share_across_batch else inputs.shape
+            self._mask = (self.rng.uniform(size=mask_shape) < self.incl_prob)
+            return inputs * self._mask
+        else:
+            return inputs * self.incl_prob
+
+    def bprop(self, inputs, outputs, grads_wrt_outputs):
+        """Back propagates gradients through a layer.
+
+        Given gradients with respect to the outputs of the layer calculates the
+        gradients with respect to the layer inputs. This should correspond to
+        default stochastic forward-propagation.
+
+        Args:
+            inputs: Array of layer inputs of shape (batch_size, input_dim).
+            outputs: Array of layer outputs calculated in forward pass of
+                shape (batch_size, output_dim).
+            grads_wrt_outputs: Array of gradients with respect to the layer
+                outputs of shape (batch_size, output_dim).
+
+        Returns:
+            Array of gradients with respect to the layer inputs of shape
+            (batch_size, input_dim).
+        """
+        return grads_wrt_outputs * self._mask
+
+    def __repr__(self):
+        return 'DropoutLayer(incl_prob={0:.1f})'.format(self.incl_prob)
+
+class ReshapeLayer(Layer):
+    """Layer which reshapes dimensions of inputs."""
+
+    def __init__(self, output_shape=None):
+        """Create a new reshape layer object.
+
+        Args:
+            output_shape: Tuple specifying shape each input in batch should
+                be reshaped to in outputs. This **excludes** the batch size
+                so the shape of the final output array will be
+                    (batch_size, ) + output_shape
+                Similarly to numpy.reshape, one shape dimension can be -1. In
+                this case, the value is inferred from the size of the input
+                array and remaining dimensions. The shape specified must be
+                compatible with the input array shape - i.e. the total number
+                of values in the array cannot be changed. If set to `None` the
+                output shape will be set to
+                    (batch_size, -1)
+                which will flatten all the inputs to vectors.
+        """
+        self.output_shape = (-1,) if output_shape is None else output_shape
+
+    def fprop(self, inputs):
+        """Forward propagates activations through the layer transformation.
+
+        Args:
+            inputs: Array of layer inputs of shape (batch_size, input_dim).
+
+        Returns:
+            outputs: Array of layer outputs of shape (batch_size, output_dim).
+        """
+        return inputs.reshape((inputs.shape[0],) + self.output_shape)
+
+    def bprop(self, inputs, outputs, grads_wrt_outputs):
+        """Back propagates gradients through a layer.
+
+        Given gradients with respect to the outputs of the layer calculates the
+        gradients with respect to the layer inputs.
+
+        Args:
+            inputs: Array of layer inputs of shape (batch_size, input_dim).
+            outputs: Array of layer outputs calculated in forward pass of
+                shape (batch_size, output_dim).
+            grads_wrt_outputs: Array of gradients with respect to the layer
+                outputs of shape (batch_size, output_dim).
+
+        Returns:
+            Array of gradients with respect to the layer inputs of shape
+            (batch_size, input_dim).
+        """
+        return grads_wrt_outputs.reshape(inputs.shape)
+
+    def __repr__(self):
+        return 'ReshapeLayer(output_shape={0})'.format(self.output_shape)
diff --git a/mlp/models.py b/mlp/models.py
index 5e35dbc..b2be888 100644
--- a/mlp/models.py
+++ b/mlp/models.py
@@ -8,7 +8,7 @@ outputs (and intermediate states) and for calculating gradients of scalar
 functions of the outputs with respect to the model parameters.
 """
 
-from mlp.layers import LayerWithParameters
+from mlp.layers import LayerWithParameters, StochasticLayer, StochasticLayerWithParameters
 
 
 class SingleLayerModel(object):
@@ -60,7 +60,7 @@ class SingleLayerModel(object):
         return self.layer.grads_wrt_params(activations[0], grads_wrt_outputs)
 
     def __repr__(self):
-        return 'SingleLayerModel(' + str(layer) + ')'
+        return 'SingleLayerModel(' + str(self.layer) + ')'
 
 
 class MultipleLayerModel(object):
@@ -84,7 +84,7 @@ class MultipleLayerModel(object):
                 params += layer.params
         return params
 
-    def fprop(self, inputs):
+    def fprop(self, inputs, evaluation=False):
         """Forward propagates a batch of inputs through the model.
 
         Args:
@@ -97,7 +97,19 @@ class MultipleLayerModel(object):
         """
         activations = [inputs]
         for i, layer in enumerate(self.layers):
-            activations.append(self.layers[i].fprop(activations[i]))
+            if evaluation:
+                if issubclass(type(self.layers[i]), StochasticLayer) or issubclass(type(self.layers[i]),
+                                                                                   StochasticLayerWithParameters):
+                    current_activations = self.layers[i].fprop(activations[i], stochastic=False)
+                else:
+                    current_activations = self.layers[i].fprop(activations[i])
+            else:
+                if issubclass(type(self.layers[i]), StochasticLayer) or issubclass(type(self.layers[i]),
+                                                                                   StochasticLayerWithParameters):
+                    current_activations = self.layers[i].fprop(activations[i], stochastic=True)
+                else:
+                    current_activations = self.layers[i].fprop(activations[i])
+            activations.append(current_activations)
         return activations
 
     def grads_wrt_params(self, activations, grads_wrt_outputs):
diff --git a/mlp/optimisers.py b/mlp/optimisers.py
index 8222807..629144b 100644
--- a/mlp/optimisers.py
+++ b/mlp/optimisers.py
@@ -9,7 +9,7 @@ import time
 import logging
 from collections import OrderedDict
 import numpy as np
-
+import tqdm
 
 logger = logging.getLogger(__name__)
 
@@ -18,7 +18,7 @@ class Optimiser(object):
     """Basic model optimiser."""
 
     def __init__(self, model, error, learning_rule, train_dataset,
-                 valid_dataset=None, data_monitors=None):
+                 valid_dataset=None, data_monitors=None, notebook=False):
         """Create a new optimiser instance.
 
         Args:
@@ -43,6 +43,11 @@ class Optimiser(object):
         self.data_monitors = OrderedDict([('error', error)])
         if data_monitors is not None:
             self.data_monitors.update(data_monitors)
+        self.notebook = notebook
+        if notebook:
+            self.tqdm_progress = tqdm.tqdm_notebook
+        else:
+            self.tqdm_progress = tqdm.tqdm
 
     def do_training_epoch(self):
         """Do a single training epoch.
@@ -52,12 +57,15 @@ class Optimiser(object):
         respect to all the model parameters and then updates the model
         parameters according to the learning rule.
         """
-        for inputs_batch, targets_batch in self.train_dataset:
-            activations = self.model.fprop(inputs_batch)
-            grads_wrt_outputs = self.error.grad(activations[-1], targets_batch)
-            grads_wrt_params = self.model.grads_wrt_params(
-                activations, grads_wrt_outputs)
-            self.learning_rule.update_params(grads_wrt_params)
+        with self.tqdm_progress(total=self.train_dataset.num_batches) as train_progress_bar:
+            train_progress_bar.set_description("Epoch Progress")
+            for inputs_batch, targets_batch in self.train_dataset:
+                activations = self.model.fprop(inputs_batch)
+                grads_wrt_outputs = self.error.grad(activations[-1], targets_batch)
+                grads_wrt_params = self.model.grads_wrt_params(
+                    activations, grads_wrt_outputs)
+                self.learning_rule.update_params(grads_wrt_params)
+                train_progress_bar.update(1)
 
     def eval_monitors(self, dataset, label):
         """Evaluates the monitors for the given dataset.
@@ -121,17 +129,20 @@ class Optimiser(object):
             and the second being a dict mapping the labels for the statistics
             recorded to their column index in the array.
         """
-        start_train_time = time.clock()
+        start_train_time = time.time()
         run_stats = [list(self.get_epoch_stats().values())]
-        for epoch in range(1, num_epochs + 1):
-            start_time = time.clock()
-            self.do_training_epoch()
-            epoch_time = time.clock() - start_time
-            if epoch % stats_interval == 0:
-                stats = self.get_epoch_stats()
-                self.log_stats(epoch, epoch_time, stats)
-                run_stats.append(list(stats.values()))
-        finish_train_time = time.clock()
+        with self.tqdm_progress(total=num_epochs) as progress_bar:
+            progress_bar.set_description("Experiment Progress")
+            for epoch in range(1, num_epochs + 1):
+                start_time = time.time()
+                self.do_training_epoch()
+                epoch_time = time.time()- start_time
+                if epoch % stats_interval == 0:
+                    stats = self.get_epoch_stats()
+                    self.log_stats(epoch, epoch_time, stats)
+                    run_stats.append(list(stats.values()))
+                progress_bar.update(1)
+        finish_train_time = time.time()
         total_train_time = finish_train_time - start_train_time
         return np.array(run_stats), {k: i for i, k in enumerate(stats.keys())}, total_train_time
 
diff --git a/notebooks/03_Multiple_layer_models.ipynb b/notebooks/03_Multiple_layer_models.ipynb
deleted file mode 100644
index 6f681b2..0000000
--- a/notebooks/03_Multiple_layer_models.ipynb
+++ /dev/null
@@ -1,2498 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$\\newcommand{\\vct}[1]{\\boldsymbol{#1}}\n",
-    "\\newcommand{\\mtx}[1]{\\mathbf{#1}}\n",
-    "\\newcommand{\\tr}{^\\mathrm{T}}\n",
-    "\\newcommand{\\reals}{\\mathbb{R}}\n",
-    "\\newcommand{\\lpa}{\\left(}\n",
-    "\\newcommand{\\rpa}{\\right)}\n",
-    "\\newcommand{\\lsb}{\\left[}\n",
-    "\\newcommand{\\rsb}{\\right]}\n",
-    "\\newcommand{\\lbr}{\\left\\lbrace}\n",
-    "\\newcommand{\\rbr}{\\right\\rbrace}\n",
-    "\\newcommand{\\fset}[1]{\\lbr #1 \\rbr}\n",
-    "\\newcommand{\\pd}[2]{\\frac{\\partial #1}{\\partial #2}}$\n",
-    "\n",
-    "# Multiple layer models\n",
-    "\n",
-    "In this notebook we will explore network models with multiple layers of transformations. This will build upon the single-layer affine model we looked at in the previous notebook and use material covered in the [second](http://www.inf.ed.ac.uk/teaching/courses/mlp/2017-18/mlp02-sln.pdf) and [third](http://www.inf.ed.ac.uk/teaching/courses/mlp/2017-18/mlp03-mlp.pdf) lectures.\n",
-    "\n",
-    "You will need to use these models for the experiments you will be running in the first coursework so part of the aim of this lab will be to get you familiar with how to construct multiple layer models in our framework and how to train them.\n",
-    "\n",
-    "## What is a layer?\n",
-    "\n",
-    "Often when discussing (neural) network models, a network layer is taken to mean an input to output transformation of the form\n",
-    "\n",
-    "\\begin{equation}\n",
-    "    \\vct{y} = \\vct{f}(\\mtx{W} \\vct{x} + \\vct{b})\n",
-    "    \\qquad\n",
-    "    \\Leftrightarrow\n",
-    "    \\qquad\n",
-    "    y_k = f\\lpa\\sum_{d=1}^D \\lpa W_{kd} x_d \\rpa + b_k \\rpa\n",
-    "\\end{equation}\n",
-    "\n",
-    "where $\\mtx{W}$ and $\\vct{b}$ parameterise an affine transformation as discussed in the previous notebook, and $f$ is a function applied elementwise to the result of the affine transformation. For example a common choice for $f$ is the logistic sigmoid function \n",
-    "\\begin{equation}\n",
-    "  f(u) = \\frac{1}{1 + \\exp(-u)}.\n",
-    "\\end{equation}\n",
-    "\n",
-    "In the second lecture slides you were shown how to train a model consisting of an affine transformation followed by the elementwise logistic sigmoid using gradient descent. This was referred to as a 'sigmoid single-layer network'.\n",
-    "\n",
-    "In the previous notebook we also referred to single-layer models, where in that case the layer was an affine transformation, with you implementing the various necessary methods for the `AffineLayer` class before using an instance of that class within a `SingleLayerModel` on a regression problem. We could in that case consider the function $f$ to simply be the identity function $f(u) = u$. In the code for the labs we will however use a slightly different convention. Here we will consider the affine transformations and subsequent elementwise function $f$ to each be a separate transformation layer. \n",
-    "\n",
-    "This will mean we can combine our already implemented `AffineLayer` class with any non-linear function applied to the outputs just by implementing a layer object for the relevant non-linearity and then stacking the two layers together. An alternative would be to have our new layer objects inherit from `AffineLayer` and then call the relevant parent class methods in the child class however this would mean we need to include a lot of the same boilerplate code in every new class.\n",
-    "\n",
-    "To give a concrete example, in the `mlp.layers` module there is a definition for a `SigmoidLayer` equivalent to the following (documentation strings have been removed here for brevity)\n",
-    "\n",
-    "```python\n",
-    "class SigmoidLayer(Layer):\n",
-    "\n",
-    "    def fprop(self, inputs):\n",
-    "        return 1. / (1. + np.exp(-inputs))\n",
-    "\n",
-    "    def bprop(self, inputs, outputs, grads_wrt_outputs):\n",
-    "        return grads_wrt_outputs * outputs * (1. - outputs)\n",
-    "```\n",
-    "\n",
-    "As you can see this `SigmoidLayer` class has a very lightweight definition, defining just two key methods:\n",
-    "\n",
-    "  * `fprop` which takes a batch of activations at the input to the layer and forward propagates them to produce activates at the outputs (directly equivalently to the `fprop` method you implemented for then `AffineLayer` in the previous notebook),\n",
-    "  * `brop` which takes a batch of gradients with respect to the outputs of the layer and back-propagates them to calculate gradients with respect to the inputs of the layer (explained in more detail below).\n",
-    "  \n",
-    "This `SigmoidLayer` class only implements the logistic sigmoid non-linearity transformation and so does not have any parameters. Therefore unlike `AffineLayer` it is derived directly from the base `Layer` class rather than `LayerWithParameters` and does not need to implement `grads_wrt_params` or `params` methods. \n",
-    "\n",
-    "To create a model consisting of an affine transformation followed by applying an elementwise logistic sigmoid transformation we first create a list of the two layer objects (in the order they are applied from inputs to outputs) and then use this to instantiate a new `MultipleLayerModel` object:\n",
-    "\n",
-    "```python\n",
-    "from mlp.layers import AffineLayer, SigmoidLayer\n",
-    "from mlp.models import MultipleLayerModel\n",
-    "\n",
-    "layers = [AffineLayer(input_dim, output_dim), SigmoidLayer()]\n",
-    "model = MultipleLayerModel(layers)\n",
-    "```\n",
-    "\n",
-    "Because of the modular way in which the layers are defined we can also stack an arbitrarily long sequence of layers together to produce deeper models. For instance the following would define a model consisting of three pairs of affine and logistic sigmoid transformations.\n",
-    "\n",
-    "```python\n",
-    "model = MultipleLayerModel([\n",
-    "    AffineLayer(input_dim, hidden_dim), SigmoidLayer(),\n",
-    "    AffineLayer(hidden_dim, hidden_dim), SigmoidLayer(),\n",
-    "    AffineLayer(hidden_dim, output_dim), SigmoidLayer(),\n",
-    "])\n",
-    "```\n",
-    "\n",
-    "## Back-propagation of gradients\n",
-    "  \n",
-    "To allow training models consisting of a stack of multiple layers, all layers need to implement a `bprop` method in addition to the `fprop` we encountered in the previous week. \n",
-    "\n",
-    "The `bprop` method takes gradients of an error function with respect to the *outputs* of a layer and uses these gradients to calculate gradients of the error function with respect to the *inputs* of a layer. As the inputs to a non-input layer in a multiple-layer model consist of the outputs of the previous layer, this means we can calculate the gradients of the error function with respect to the outputs of every layer in the model by iteratively propagating the gradients backwards through the layers of the model (i.e. from the last to first layer), hence the term 'back-propagation' or 'bprop' for short. A block diagram illustrating this is shown for a three layer model below.\n",
-    "\n",
-    "<img src='res/fprop-bprop-block-diagram.png' />\n",
-    "\n",
-    "For a layer with parameters, the gradients with respect to the layer outputs are required to calculate gradients with respect to the layer parameters. Therefore by combining back-propagation of gradients through the model with computing the gradients with respect to parameters in the relevant layers we can calculate gradients of the error function with respect to all of the parameters of a multiple-layer model in a very efficient manner (in fact the computational cost of computing gradients with respect to all of the parameters of the model using this method will only be a constant factor times the cost of calculating the model outputs in the forward pass).\n",
-    "\n",
-    "We so far have abstractly talked about calculating gradients with respect to the inputs of a layer using gradients with respect to the layer outputs. More concretely we will be using the chain rule for derivatives to do this, similarly to how we used the chain rule in exercise 4 of the previous notebook to calculate gradients with respect to the parameters of an affine layer given gradients with respect to the outputs of the layer.\n",
-    "\n",
-    "In particular if our layer has a batch of $B$ vector inputs each of dimension $D$, $\\fset{\\vct{x}^{(b)}}_{b=1}^B$, and produces a batch of $B$ vector outputs each of dimension $K$, $\\fset{\\vct{y}^{(b)}}_{b=1}^B$,  then we can calculate the gradient with respect to the $d^\\textrm{th}$ dimension of the $b^{\\textrm{th}}$ input given the gradients with respect to the $b^{\\textrm{th}}$ output using\n",
-    "\n",
-    "\\begin{equation}\n",
-    "    \\pd{E}{x^{(b)}_d} = \\sum_{k=1}^K \\lpa \\pd{E}{y^{(b)}_k} \\pd{y^{(b)}_k}{x^{(b)}_d} \\rpa.\n",
-    "\\end{equation}\n",
-    "\n",
-    "Mathematically therefore the `bprop` method takes an array of gradients with respect to the outputs $\\pd{E}{y^{(b)}_k}$ and applies a sum-product operation with the partial derivatives of each output with respect to each input $\\pd{y^{(b)}_k}{x^{(b)}_d}$ to produce gradients with respect to the inputs of the layer $\\pd{E}{x^{(b)}_d}$.\n",
-    "\n",
-    "For the affine transformation used in the `AffineLayer` implemented last week, i.e a forward propagation corresponding to \n",
-    "\n",
-    "\\begin{equation}\n",
-    "    y^{(b)}_k = \\sum_{d=1}^D \\lpa W_{kd} x^{(b)}_d \\rpa + b_k\n",
-    "\\end{equation}\n",
-    "\n",
-    "then the corresponding partial derivatives of layer outputs with respect to inputs are\n",
-    "\n",
-    "\\begin{equation}\n",
-    "    \\pd{y^{(b)}_k}{x^{(b)}_d} = W_{kd}\n",
-    "\\end{equation}\n",
-    "\n",
-    "and so the back-propagation method for the `AffineLayer` takes the following form\n",
-    "\n",
-    "\\begin{equation}\n",
-    "    \\pd{E}{x^{(b)}_d} = \\sum_{k=1}^K \\lpa \\pd{E}{y^{(b)}_k} W_{kd} \\rpa.\n",
-    "\\end{equation}\n",
-    "\n",
-    "This can be efficiently implemented in NumPy using the `dot` function\n",
-    "\n",
-    "```python\n",
-    "class AffineLayer(LayerWithParameters):\n",
-    "\n",
-    "    # ... [implementation of remaining methods from previous week] ...\n",
-    "    \n",
-    "    def bprop(self, inputs, outputs, grads_wrt_outputs):\n",
-    "        return grads_wrt_outputs.dot(self.weights)\n",
-    "```\n",
-    "\n",
-    "An important special case applies when the outputs of a layer are an elementwise function of the inputs such that $y^{(b)}_k$ only depends on $x^{(b)}_d$ when $d = k$. In this case the partial derivatives $\\pd{y^{(b)}_k}{x^{(b)}_d}$ will be zero for $k \\neq d$ and so the above summation collapses to a single term, giving\n",
-    "\n",
-    "\\begin{equation}\n",
-    "    \\pd{E}{x^{(b)}_d} = \\pd{E}{y^{(b)}_d} \\pd{y^{(b)}_d}{x^{(b)}_d}\n",
-    "\\end{equation}\n",
-    "\n",
-    "i.e. to calculate the gradient with respect to the $b^{\\textrm{th}}$ input vector we just perform an elementwise multiplication of the gradient with respect to the $b^{\\textrm{th}}$ output vector with the vector of derivatives of the outputs with respect to the inputs. This case applies to the `SigmoidLayer` and to all other layers applying an elementwise function to their inputs.\n",
-    "\n",
-    "For the logistic sigmoid layer we have that\n",
-    "\n",
-    "\\begin{equation}\n",
-    "    y^{(b)}_d = \\frac{1}{1 + \\exp(-x^{(b)}_d)}\n",
-    "    \\qquad\n",
-    "    \\Rightarrow\n",
-    "    \\qquad\n",
-    "    \\pd{y^{(b)}_d}{x^{(b)}_d} = \n",
-    "    \\frac{\\exp(-x^{(b)}_d)}{\\lsb 1 + \\exp(-x^{(b)}_d) \\rsb^2} =\n",
-    "     y^{(b)}_d \\lsb 1 -  y^{(b)}_d  \\rsb\n",
-    "\\end{equation}\n",
-    "\n",
-    "which you should now be able relate to the implementation of `SigmoidLayer.bprop` given earlier:\n",
-    "\n",
-    "```python\n",
-    "class SigmoidLayer(Layer):\n",
-    "\n",
-    "    def fprop(self, inputs):\n",
-    "        return 1. / (1. + np.exp(-inputs))\n",
-    "\n",
-    "    def bprop(self, inputs, outputs, grads_wrt_outputs):\n",
-    "        return grads_wrt_outputs * outputs * (1. - outputs)\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercise 1: training a softmax model on MNIST\n",
-    "\n",
-    "For this first exercise we will train a model consisting of an affine transformation plus softmax on a multiclass classification task: classifying the digit labels for handwritten digit images from the MNIST data set introduced in the first notebook.\n",
-    "\n",
-    "First run the cell below to import the necessary modules and classes and to load the MNIST data provider objects. As it takes a little while to load the MNIST data from disk into memory it is worth loading the data providers just once in a separate cell like this rather than recreating the objects for every training run.\n",
-    "\n",
-    "We are loading two data provider objects here - one corresponding to the training data set and a second to use as a *validation* data set. This is data we do not train the model on but measure the performance of the trained model on to assess its ability to *generalise* to unseen data. \n",
-    "\n",
-    "If you are in the Monday or Tuesday lab sessions you will not yet have had the lecture introducing the concepts of generalisation and validation data sets (though those doing MLPR alongside this course should already be familiar with these ideas). As you will need to report both training and validation set performances in your experiments for the first coursework assignment we are providing code here to give an example of how to do this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import logging\n",
-    "from mlp.layers import AffineLayer, SoftmaxLayer, SigmoidLayer\n",
-    "from mlp.errors import CrossEntropyError, CrossEntropySoftmaxError\n",
-    "from mlp.models import SingleLayerModel, MultipleLayerModel\n",
-    "from mlp.initialisers import UniformInit\n",
-    "from mlp.learning_rules import GradientDescentLearningRule\n",
-    "from mlp.data_providers import MNISTDataProvider\n",
-    "from mlp.optimisers import Optimiser\n",
-    "%matplotlib inline\n",
-    "plt.style.use('ggplot')\n",
-    "\n",
-    "# Seed a random number generator\n",
-    "seed = 6102016 \n",
-    "rng = np.random.RandomState(seed)\n",
-    "\n",
-    "# Set up a logger object to print info about the training run to stdout\n",
-    "logger = logging.getLogger()\n",
-    "logger.setLevel(logging.INFO)\n",
-    "logger.handlers = [logging.StreamHandler()]\n",
-    "\n",
-    "# Create data provider objects for the MNIST data set\n",
-    "train_data = MNISTDataProvider('train', rng=rng)\n",
-    "valid_data = MNISTDataProvider('valid', rng=rng)\n",
-    "input_dim, output_dim = 784, 10"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To minimise replication of code and allow you to run experiments more quickly a helper function is provided below which trains a model and plots the evolution of the error and classification accuracy of the model (on both training and validation sets) over training."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def train_model_and_plot_stats(\n",
-    "        model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval):\n",
-    "\n",
-    "    # As well as monitoring the error over training also monitor classification\n",
-    "    # accuracy i.e. proportion of most-probable predicted classes being equal to targets\n",
-    "    data_monitors={'acc': lambda y, t: (y.argmax(-1) == t.argmax(-1)).mean()}\n",
-    "\n",
-    "    # Use the created objects to initialise a new Optimiser instance.\n",
-    "    optimiser = Optimiser(\n",
-    "        model, error, learning_rule, train_data, valid_data, data_monitors)\n",
-    "\n",
-    "    # Run the optimiser for 5 epochs (full passes through the training set)\n",
-    "    # printing statistics every epoch.\n",
-    "    stats, keys, run_time = optimiser.train(num_epochs=num_epochs, stats_interval=stats_interval)\n",
-    "\n",
-    "    # Plot the change in the validation and training set error over training.\n",
-    "    fig_1 = plt.figure(figsize=(8, 4))\n",
-    "    ax_1 = fig_1.add_subplot(111)\n",
-    "    for k in ['error(train)', 'error(valid)']:\n",
-    "        ax_1.plot(np.arange(1, stats.shape[0]) * stats_interval, \n",
-    "                  stats[1:, keys[k]], label=k)\n",
-    "    ax_1.legend(loc=0)\n",
-    "    ax_1.set_xlabel('Epoch number')\n",
-    "\n",
-    "    # Plot the change in the validation and training set accuracy over training.\n",
-    "    fig_2 = plt.figure(figsize=(8, 4))\n",
-    "    ax_2 = fig_2.add_subplot(111)\n",
-    "    for k in ['acc(train)', 'acc(valid)']:\n",
-    "        ax_2.plot(np.arange(1, stats.shape[0]) * stats_interval, \n",
-    "                  stats[1:, keys[k]], label=k)\n",
-    "    ax_2.legend(loc=0)\n",
-    "    ax_2.set_xlabel('Epoch number')\n",
-    "    \n",
-    "    return stats, keys, run_time, fig_1, ax_1, fig_2, ax_2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Running the cell below will create a model consisting of an affine layer follower by a softmax transformation and train it on the MNIST data set by minimising the multi-class cross entropy error function using a basic gradient descent learning rule. By using the helper function defined above, at the end of training curves of the evolution of the error function and also classification accuracy of the model over the training epochs will be plotted.\n",
-    "\n",
-    "You should try running the code for various settings of the training hyperparameters defined at the beginning of the cell to get a feel for how these affect how training proceeds. You may wish to create multiple copies of the cell below to allow you to keep track of and compare the results across different hyperparameter settings."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Varying initialisation scale\n",
-    "\n",
-    "<span style=\"color:red\">First try a few different parameter initialisation scales</span>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### `init_scale = 0.01`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 5: 6.4s to complete\n",
-      "    error(train)=3.10e-01, acc(train)=9.14e-01, error(valid)=2.91e-01, acc(valid)=9.18e-01\n",
-      "Epoch 10: 5.4s to complete\n",
-      "    error(train)=2.88e-01, acc(train)=9.20e-01, error(valid)=2.76e-01, acc(valid)=9.23e-01\n",
-      "Epoch 15: 3.5s to complete\n",
-      "    error(train)=2.78e-01, acc(train)=9.23e-01, error(valid)=2.69e-01, acc(valid)=9.24e-01\n",
-      "Epoch 20: 4.2s to complete\n",
-      "    error(train)=2.71e-01, acc(train)=9.25e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n",
-      "Epoch 25: 4.7s to complete\n",
-      "    error(train)=2.68e-01, acc(train)=9.25e-01, error(valid)=2.65e-01, acc(valid)=9.26e-01\n",
-      "Epoch 30: 3.7s to complete\n",
-      "    error(train)=2.63e-01, acc(train)=9.27e-01, error(valid)=2.62e-01, acc(valid)=9.26e-01\n",
-      "Epoch 35: 3.8s to complete\n",
-      "    error(train)=2.60e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n",
-      "Epoch 40: 4.2s to complete\n",
-      "    error(train)=2.59e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.29e-01\n",
-      "Epoch 45: 4.3s to complete\n",
-      "    error(train)=2.55e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n",
-      "Epoch 50: 4.0s to complete\n",
-      "    error(train)=2.54e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n",
-      "Epoch 55: 3.6s to complete\n",
-      "    error(train)=2.52e-01, acc(train)=9.30e-01, error(valid)=2.58e-01, acc(valid)=9.29e-01\n",
-      "Epoch 60: 4.4s to complete\n",
-      "    error(train)=2.52e-01, acc(train)=9.30e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n",
-      "Epoch 65: 3.8s to complete\n",
-      "    error(train)=2.50e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
-      "Epoch 70: 4.3s to complete\n",
-      "    error(train)=2.49e-01, acc(train)=9.31e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n",
-      "Epoch 75: 3.5s to complete\n",
-      "    error(train)=2.47e-01, acc(train)=9.31e-01, error(valid)=2.57e-01, acc(valid)=9.30e-01\n",
-      "Epoch 80: 4.0s to complete\n",
-      "    error(train)=2.46e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.31e-01\n",
-      "Epoch 85: 3.9s to complete\n",
-      "    error(train)=2.45e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
-      "Epoch 90: 3.6s to complete\n",
-      "    error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.57e-01, acc(valid)=9.29e-01\n",
-      "Epoch 95: 4.6s to complete\n",
-      "    error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.29e-01\n",
-      "Epoch 100: 3.8s to complete\n",
-      "    error(train)=2.43e-01, acc(train)=9.33e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAENCAYAAAAi8D15AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEEJ44i/5hIwFo7U1lwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc438001828>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAENCAYAAADngqfoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc435f2a5f8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Set training run hyperparameters\n",
-    "batch_size = 100  # number of data points in a batch\n",
-    "init_scale = 0.01  # scale for random parameter initialisation\n",
-    "learning_rate = 0.1  # learning rate for gradient descent\n",
-    "num_epochs = 100  # number of training epochs to perform\n",
-    "stats_interval = 5  # epoch interval between recording and printing stats\n",
-    "\n",
-    "# Reset random number generator and data provider states on each run\n",
-    "# to ensure reproducibility of results\n",
-    "rng.seed(seed)\n",
-    "train_data.reset()\n",
-    "valid_data.reset()\n",
-    "\n",
-    "# Alter data-provider batch size\n",
-    "train_data.batch_size = batch_size \n",
-    "valid_data.batch_size = batch_size\n",
-    "\n",
-    "# Create a parameter initialiser which will sample random uniform values\n",
-    "# from [-init_scale, init_scale]\n",
-    "param_init = UniformInit(-init_scale, init_scale, rng=rng)\n",
-    "\n",
-    "# Create affine + softmax model\n",
-    "model = MultipleLayerModel([\n",
-    "    AffineLayer(input_dim, output_dim, param_init, param_init),\n",
-    "    SoftmaxLayer()\n",
-    "])\n",
-    "\n",
-    "# Initialise a cross entropy error object\n",
-    "error = CrossEntropyError()\n",
-    "\n",
-    "# Use a basic gradient descent learning rule\n",
-    "learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n",
-    "\n",
-    "_ = train_model_and_plot_stats(\n",
-    "    model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### `init_scale = 0.1`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 5: 3.8s to complete\n",
-      "    error(train)=3.11e-01, acc(train)=9.13e-01, error(valid)=2.92e-01, acc(valid)=9.18e-01\n",
-      "Epoch 10: 4.0s to complete\n",
-      "    error(train)=2.89e-01, acc(train)=9.20e-01, error(valid)=2.77e-01, acc(valid)=9.23e-01\n",
-      "Epoch 15: 3.7s to complete\n",
-      "    error(train)=2.79e-01, acc(train)=9.22e-01, error(valid)=2.70e-01, acc(valid)=9.24e-01\n",
-      "Epoch 20: 5.4s to complete\n",
-      "    error(train)=2.72e-01, acc(train)=9.24e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n",
-      "Epoch 25: 4.7s to complete\n",
-      "    error(train)=2.68e-01, acc(train)=9.25e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n",
-      "Epoch 30: 4.2s to complete\n",
-      "    error(train)=2.63e-01, acc(train)=9.27e-01, error(valid)=2.62e-01, acc(valid)=9.26e-01\n",
-      "Epoch 35: 4.0s to complete\n",
-      "    error(train)=2.60e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n",
-      "Epoch 40: 4.3s to complete\n",
-      "    error(train)=2.59e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n",
-      "Epoch 45: 4.5s to complete\n",
-      "    error(train)=2.55e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n",
-      "Epoch 50: 3.7s to complete\n",
-      "    error(train)=2.54e-01, acc(train)=9.30e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n",
-      "Epoch 55: 3.7s to complete\n",
-      "    error(train)=2.52e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n",
-      "Epoch 60: 4.6s to complete\n",
-      "    error(train)=2.52e-01, acc(train)=9.29e-01, error(valid)=2.60e-01, acc(valid)=9.29e-01\n",
-      "Epoch 65: 4.3s to complete\n",
-      "    error(train)=2.50e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
-      "Epoch 70: 4.9s to complete\n",
-      "    error(train)=2.49e-01, acc(train)=9.31e-01, error(valid)=2.59e-01, acc(valid)=9.31e-01\n",
-      "Epoch 75: 4.7s to complete\n",
-      "    error(train)=2.47e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
-      "Epoch 80: 4.7s to complete\n",
-      "    error(train)=2.46e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.31e-01\n",
-      "Epoch 85: 4.2s to complete\n",
-      "    error(train)=2.45e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.31e-01\n",
-      "Epoch 90: 4.4s to complete\n",
-      "    error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
-      "Epoch 95: 4.1s to complete\n",
-      "    error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
-      "Epoch 100: 4.2s to complete\n",
-      "    error(train)=2.43e-01, acc(train)=9.33e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAENCAYAAAAi8D15AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc438001780>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAENCAYAAADngqfoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc435dd92e8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Set training run hyperparameters\n",
-    "batch_size = 100  # number of data points in a batch\n",
-    "init_scale = 0.1  # scale for random parameter initialisation\n",
-    "learning_rate = 0.1  # learning rate for gradient descent\n",
-    "num_epochs = 100  # number of training epochs to perform\n",
-    "stats_interval = 5  # epoch interval between recording and printing stats\n",
-    "\n",
-    "# Reset random number generator and data provider states on each run\n",
-    "# to ensure reproducibility of results\n",
-    "rng.seed(seed)\n",
-    "train_data.reset()\n",
-    "valid_data.reset()\n",
-    "\n",
-    "# Alter data-provider batch size\n",
-    "train_data.batch_size = batch_size \n",
-    "valid_data.batch_size = batch_size\n",
-    "\n",
-    "# Create a parameter initialiser which will sample random uniform values\n",
-    "# from [-init_scale, init_scale]\n",
-    "param_init = UniformInit(-init_scale, init_scale, rng=rng)\n",
-    "\n",
-    "# Create affine + softmax model\n",
-    "model = MultipleLayerModel([\n",
-    "    AffineLayer(input_dim, output_dim, param_init, param_init),\n",
-    "    SoftmaxLayer()\n",
-    "])\n",
-    "\n",
-    "# Initialise a cross entropy error object\n",
-    "error = CrossEntropyError()\n",
-    "\n",
-    "# Use a basic gradient descent learning rule\n",
-    "learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n",
-    "\n",
-    "_ = train_model_and_plot_stats(\n",
-    "    model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### `init_scale = 0.5`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 5: 3.7s to complete\n",
-      "    error(train)=3.38e-01, acc(train)=9.03e-01, error(valid)=3.17e-01, acc(valid)=9.11e-01\n",
-      "Epoch 10: 4.3s to complete\n",
-      "    error(train)=3.06e-01, acc(train)=9.13e-01, error(valid)=2.94e-01, acc(valid)=9.17e-01\n",
-      "Epoch 15: 3.3s to complete\n",
-      "    error(train)=2.92e-01, acc(train)=9.17e-01, error(valid)=2.83e-01, acc(valid)=9.20e-01\n",
-      "Epoch 20: 5.5s to complete\n",
-      "    error(train)=2.82e-01, acc(train)=9.20e-01, error(valid)=2.77e-01, acc(valid)=9.22e-01\n",
-      "Epoch 25: 3.8s to complete\n",
-      "    error(train)=2.77e-01, acc(train)=9.22e-01, error(valid)=2.75e-01, acc(valid)=9.22e-01\n",
-      "Epoch 30: 4.3s to complete\n",
-      "    error(train)=2.71e-01, acc(train)=9.24e-01, error(valid)=2.71e-01, acc(valid)=9.25e-01\n",
-      "Epoch 35: 3.9s to complete\n",
-      "    error(train)=2.67e-01, acc(train)=9.25e-01, error(valid)=2.69e-01, acc(valid)=9.26e-01\n",
-      "Epoch 40: 4.4s to complete\n",
-      "    error(train)=2.65e-01, acc(train)=9.27e-01, error(valid)=2.68e-01, acc(valid)=9.26e-01\n",
-      "Epoch 45: 4.2s to complete\n",
-      "    error(train)=2.61e-01, acc(train)=9.27e-01, error(valid)=2.66e-01, acc(valid)=9.27e-01\n",
-      "Epoch 50: 4.2s to complete\n",
-      "    error(train)=2.59e-01, acc(train)=9.28e-01, error(valid)=2.65e-01, acc(valid)=9.27e-01\n",
-      "Epoch 55: 4.2s to complete\n",
-      "    error(train)=2.57e-01, acc(train)=9.29e-01, error(valid)=2.64e-01, acc(valid)=9.29e-01\n",
-      "Epoch 60: 3.6s to complete\n",
-      "    error(train)=2.56e-01, acc(train)=9.28e-01, error(valid)=2.65e-01, acc(valid)=9.28e-01\n",
-      "Epoch 65: 4.6s to complete\n",
-      "    error(train)=2.54e-01, acc(train)=9.30e-01, error(valid)=2.63e-01, acc(valid)=9.28e-01\n",
-      "Epoch 70: 3.7s to complete\n",
-      "    error(train)=2.52e-01, acc(train)=9.30e-01, error(valid)=2.64e-01, acc(valid)=9.28e-01\n",
-      "Epoch 75: 5.0s to complete\n",
-      "    error(train)=2.50e-01, acc(train)=9.31e-01, error(valid)=2.62e-01, acc(valid)=9.29e-01\n",
-      "Epoch 80: 3.7s to complete\n",
-      "    error(train)=2.49e-01, acc(train)=9.31e-01, error(valid)=2.63e-01, acc(valid)=9.28e-01\n",
-      "Epoch 85: 3.8s to complete\n",
-      "    error(train)=2.48e-01, acc(train)=9.31e-01, error(valid)=2.62e-01, acc(valid)=9.30e-01\n",
-      "Epoch 90: 4.1s to complete\n",
-      "    error(train)=2.47e-01, acc(train)=9.31e-01, error(valid)=2.62e-01, acc(valid)=9.28e-01\n",
-      "Epoch 95: 4.3s to complete\n",
-      "    error(train)=2.47e-01, acc(train)=9.31e-01, error(valid)=2.62e-01, acc(valid)=9.29e-01\n",
-      "Epoch 100: 3.7s to complete\n",
-      "    error(train)=2.45e-01, acc(train)=9.32e-01, error(valid)=2.62e-01, acc(valid)=9.28e-01\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAENCAYAAAAi8D15AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc438001160>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAAENCAYAAAAbl4wiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc435c4db70>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Set training run hyperparameters\n",
-    "batch_size = 100  # number of data points in a batch\n",
-    "init_scale = 0.5  # scale for random parameter initialisation\n",
-    "learning_rate = 0.1  # learning rate for gradient descent\n",
-    "num_epochs = 100  # number of training epochs to perform\n",
-    "stats_interval = 5  # epoch interval between recording and printing stats\n",
-    "\n",
-    "# Reset random number generator and data provider states on each run\n",
-    "# to ensure reproducibility of results\n",
-    "rng.seed(seed)\n",
-    "train_data.reset()\n",
-    "valid_data.reset()\n",
-    "\n",
-    "# Alter data-provider batch size\n",
-    "train_data.batch_size = batch_size \n",
-    "valid_data.batch_size = batch_size\n",
-    "\n",
-    "# Create a parameter initialiser which will sample random uniform values\n",
-    "# from [-init_scale, init_scale]\n",
-    "param_init = UniformInit(-init_scale, init_scale, rng=rng)\n",
-    "\n",
-    "# Create affine + softmax model\n",
-    "model = MultipleLayerModel([\n",
-    "    AffineLayer(input_dim, output_dim, param_init, param_init),\n",
-    "    SoftmaxLayer()\n",
-    "])\n",
-    "\n",
-    "# Initialise a cross entropy error object\n",
-    "error = CrossEntropyError()\n",
-    "\n",
-    "# Use a basic gradient descent learning rule\n",
-    "learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n",
-    "\n",
-    "_ = train_model_and_plot_stats(\n",
-    "    model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "|`init_scale`| Final `error(train)` | Final `error(valid)` |\n",
-    "|------------|----------------------|----------------------|\n",
-    "| 0.01       | 2.43e-01             | 2.58e-01             |\n",
-    "| 0.1        | 2.43e-01             | 2.59e-01             |\n",
-    "| 0.5        | 2.45e-01             | 2.62e-01             |\n",
-    "\n",
-    "<span style=\"color:red\">\n",
-    "Larger initialisation scale of 0.5 seems to give slightly slower initial learning than smaller scales of 0.1 and 0.01 however difference is only slight suggesting for this shallow architecure training performance is not particularly sensitive to initialisation scale.\n",
-    "</span>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Varying learning rate\n",
-    "\n",
-    "<span style=\"color:red\">Now let's try some different values for learning rate.</span>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### `learning_rate = 0.05`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 5: 4.0s to complete\n",
-      "    error(train)=3.41e-01, acc(train)=9.05e-01, error(valid)=3.16e-01, acc(valid)=9.12e-01\n",
-      "Epoch 10: 4.0s to complete\n",
-      "    error(train)=3.10e-01, acc(train)=9.14e-01, error(valid)=2.92e-01, acc(valid)=9.18e-01\n",
-      "Epoch 15: 5.1s to complete\n",
-      "    error(train)=2.97e-01, acc(train)=9.18e-01, error(valid)=2.82e-01, acc(valid)=9.21e-01\n",
-      "Epoch 20: 4.1s to complete\n",
-      "    error(train)=2.88e-01, acc(train)=9.20e-01, error(valid)=2.76e-01, acc(valid)=9.23e-01\n",
-      "Epoch 25: 4.4s to complete\n",
-      "    error(train)=2.83e-01, acc(train)=9.21e-01, error(valid)=2.73e-01, acc(valid)=9.24e-01\n",
-      "Epoch 30: 3.9s to complete\n",
-      "    error(train)=2.77e-01, acc(train)=9.22e-01, error(valid)=2.69e-01, acc(valid)=9.24e-01\n",
-      "Epoch 35: 3.7s to complete\n",
-      "    error(train)=2.74e-01, acc(train)=9.24e-01, error(valid)=2.67e-01, acc(valid)=9.25e-01\n",
-      "Epoch 40: 4.0s to complete\n",
-      "    error(train)=2.72e-01, acc(train)=9.24e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n",
-      "Epoch 45: 3.7s to complete\n",
-      "    error(train)=2.68e-01, acc(train)=9.26e-01, error(valid)=2.64e-01, acc(valid)=9.27e-01\n",
-      "Epoch 50: 4.7s to complete\n",
-      "    error(train)=2.66e-01, acc(train)=9.26e-01, error(valid)=2.63e-01, acc(valid)=9.28e-01\n",
-      "Epoch 55: 3.7s to complete\n",
-      "    error(train)=2.64e-01, acc(train)=9.26e-01, error(valid)=2.62e-01, acc(valid)=9.29e-01\n",
-      "Epoch 60: 4.8s to complete\n",
-      "    error(train)=2.63e-01, acc(train)=9.26e-01, error(valid)=2.62e-01, acc(valid)=9.28e-01\n",
-      "Epoch 65: 3.8s to complete\n",
-      "    error(train)=2.61e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.27e-01\n",
-      "Epoch 70: 4.2s to complete\n",
-      "    error(train)=2.60e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n",
-      "Epoch 75: 4.3s to complete\n",
-      "    error(train)=2.58e-01, acc(train)=9.29e-01, error(valid)=2.60e-01, acc(valid)=9.29e-01\n",
-      "Epoch 80: 4.5s to complete\n",
-      "    error(train)=2.57e-01, acc(train)=9.29e-01, error(valid)=2.60e-01, acc(valid)=9.29e-01\n",
-      "Epoch 85: 4.2s to complete\n",
-      "    error(train)=2.56e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n",
-      "Epoch 90: 4.5s to complete\n",
-      "    error(train)=2.55e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n",
-      "Epoch 95: 4.0s to complete\n",
-      "    error(train)=2.54e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n",
-      "Epoch 100: 3.4s to complete\n",
-      "    error(train)=2.53e-01, acc(train)=9.30e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAENCAYAAAAi8D15AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAACGILQBADDED74JovbaoU/SAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc435d77940>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAAENCAYAAAAbl4wiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEConzVwIIYRQOWnmQgghhMr9C6RY5gyJ4mt5AAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc435aaef60>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Set training run hyperparameters\n",
-    "batch_size = 100  # number of data points in a batch\n",
-    "init_scale = 0.1  # scale for random parameter initialisation\n",
-    "learning_rate = 0.05  # learning rate for gradient descent\n",
-    "num_epochs = 100  # number of training epochs to perform\n",
-    "stats_interval = 5  # epoch interval between recording and printing stats\n",
-    "\n",
-    "# Reset random number generator and data provider states on each run\n",
-    "# to ensure reproducibility of results\n",
-    "rng.seed(seed)\n",
-    "train_data.reset()\n",
-    "valid_data.reset()\n",
-    "\n",
-    "# Alter data-provider batch size\n",
-    "train_data.batch_size = batch_size \n",
-    "valid_data.batch_size = batch_size\n",
-    "\n",
-    "# Create a parameter initialiser which will sample random uniform values\n",
-    "# from [-init_scale, init_scale]\n",
-    "param_init = UniformInit(-init_scale, init_scale, rng=rng)\n",
-    "\n",
-    "# Create affine + softmax model\n",
-    "model = MultipleLayerModel([\n",
-    "    AffineLayer(input_dim, output_dim, param_init, param_init),\n",
-    "    SoftmaxLayer()\n",
-    "])\n",
-    "\n",
-    "# Initialise a cross entropy error object\n",
-    "error = CrossEntropyError()\n",
-    "\n",
-    "# Use a basic gradient descent learning rule\n",
-    "learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n",
-    "\n",
-    "_ = train_model_and_plot_stats(\n",
-    "    model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### `learning_rate = 0.1`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 5: 5.1s to complete\n",
-      "    error(train)=3.11e-01, acc(train)=9.13e-01, error(valid)=2.92e-01, acc(valid)=9.18e-01\n",
-      "Epoch 10: 3.4s to complete\n",
-      "    error(train)=2.89e-01, acc(train)=9.20e-01, error(valid)=2.77e-01, acc(valid)=9.23e-01\n",
-      "Epoch 15: 3.8s to complete\n",
-      "    error(train)=2.79e-01, acc(train)=9.22e-01, error(valid)=2.70e-01, acc(valid)=9.24e-01\n",
-      "Epoch 20: 3.4s to complete\n",
-      "    error(train)=2.72e-01, acc(train)=9.24e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n",
-      "Epoch 25: 4.3s to complete\n",
-      "    error(train)=2.68e-01, acc(train)=9.25e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n",
-      "Epoch 30: 3.9s to complete\n",
-      "    error(train)=2.63e-01, acc(train)=9.27e-01, error(valid)=2.62e-01, acc(valid)=9.26e-01\n",
-      "Epoch 35: 4.5s to complete\n",
-      "    error(train)=2.60e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n",
-      "Epoch 40: 5.9s to complete\n",
-      "    error(train)=2.59e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n",
-      "Epoch 45: 4.0s to complete\n",
-      "    error(train)=2.55e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n",
-      "Epoch 50: 4.1s to complete\n",
-      "    error(train)=2.54e-01, acc(train)=9.30e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n",
-      "Epoch 55: 5.4s to complete\n",
-      "    error(train)=2.52e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n",
-      "Epoch 60: 4.4s to complete\n",
-      "    error(train)=2.52e-01, acc(train)=9.29e-01, error(valid)=2.60e-01, acc(valid)=9.29e-01\n",
-      "Epoch 65: 3.4s to complete\n",
-      "    error(train)=2.50e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
-      "Epoch 70: 5.4s to complete\n",
-      "    error(train)=2.49e-01, acc(train)=9.31e-01, error(valid)=2.59e-01, acc(valid)=9.31e-01\n",
-      "Epoch 75: 3.7s to complete\n",
-      "    error(train)=2.47e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
-      "Epoch 80: 4.4s to complete\n",
-      "    error(train)=2.46e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.31e-01\n",
-      "Epoch 85: 4.0s to complete\n",
-      "    error(train)=2.45e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.31e-01\n",
-      "Epoch 90: 5.1s to complete\n",
-      "    error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
-      "Epoch 95: 3.6s to complete\n",
-      "    error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
-      "Epoch 100: 3.9s to complete\n",
-      "    error(train)=2.43e-01, acc(train)=9.33e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAENCAYAAAAi8D15AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc4685a60f0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAENCAYAAADngqfoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc435b63588>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Set training run hyperparameters\n",
-    "batch_size = 100  # number of data points in a batch\n",
-    "init_scale = 0.1  # scale for random parameter initialisation\n",
-    "learning_rate = 0.1  # learning rate for gradient descent\n",
-    "num_epochs = 100  # number of training epochs to perform\n",
-    "stats_interval = 5  # epoch interval between recording and printing stats\n",
-    "\n",
-    "# Reset random number generator and data provider states on each run\n",
-    "# to ensure reproducibility of results\n",
-    "rng.seed(seed)\n",
-    "train_data.reset()\n",
-    "valid_data.reset()\n",
-    "\n",
-    "# Alter data-provider batch size\n",
-    "train_data.batch_size = batch_size \n",
-    "valid_data.batch_size = batch_size\n",
-    "\n",
-    "# Create a parameter initialiser which will sample random uniform values\n",
-    "# from [-init_scale, init_scale]\n",
-    "param_init = UniformInit(-init_scale, init_scale, rng=rng)\n",
-    "\n",
-    "# Create affine + softmax model\n",
-    "model = MultipleLayerModel([\n",
-    "    AffineLayer(input_dim, output_dim, param_init, param_init),\n",
-    "    SoftmaxLayer()\n",
-    "])\n",
-    "\n",
-    "# Initialise a cross entropy error object\n",
-    "error = CrossEntropyError()\n",
-    "\n",
-    "# Use a basic gradient descent learning rule\n",
-    "learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n",
-    "\n",
-    "_ = train_model_and_plot_stats(\n",
-    "    model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### `learning_rate = 0.2`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 5: 3.5s to complete\n",
-      "    error(train)=2.90e-01, acc(train)=9.19e-01, error(valid)=2.77e-01, acc(valid)=9.22e-01\n",
-      "Epoch 10: 3.8s to complete\n",
-      "    error(train)=2.75e-01, acc(train)=9.23e-01, error(valid)=2.69e-01, acc(valid)=9.25e-01\n",
-      "Epoch 15: 5.3s to complete\n",
-      "    error(train)=2.66e-01, acc(train)=9.26e-01, error(valid)=2.64e-01, acc(valid)=9.26e-01\n",
-      "Epoch 20: 4.3s to complete\n",
-      "    error(train)=2.60e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n",
-      "Epoch 25: 4.9s to complete\n",
-      "    error(train)=2.57e-01, acc(train)=9.28e-01, error(valid)=2.64e-01, acc(valid)=9.27e-01\n",
-      "Epoch 30: 5.0s to complete\n",
-      "    error(train)=2.53e-01, acc(train)=9.29e-01, error(valid)=2.61e-01, acc(valid)=9.30e-01\n",
-      "Epoch 35: 4.2s to complete\n",
-      "    error(train)=2.50e-01, acc(train)=9.30e-01, error(valid)=2.60e-01, acc(valid)=9.30e-01\n",
-      "Epoch 40: 4.0s to complete\n",
-      "    error(train)=2.49e-01, acc(train)=9.31e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n",
-      "Epoch 45: 4.4s to complete\n",
-      "    error(train)=2.45e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
-      "Epoch 50: 3.8s to complete\n",
-      "    error(train)=2.45e-01, acc(train)=9.32e-01, error(valid)=2.60e-01, acc(valid)=9.31e-01\n",
-      "Epoch 55: 3.9s to complete\n",
-      "    error(train)=2.43e-01, acc(train)=9.32e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n",
-      "Epoch 60: 3.9s to complete\n",
-      "    error(train)=2.44e-01, acc(train)=9.31e-01, error(valid)=2.63e-01, acc(valid)=9.29e-01\n",
-      "Epoch 65: 3.8s to complete\n",
-      "    error(train)=2.41e-01, acc(train)=9.34e-01, error(valid)=2.60e-01, acc(valid)=9.30e-01\n",
-      "Epoch 70: 4.2s to complete\n",
-      "    error(train)=2.40e-01, acc(train)=9.34e-01, error(valid)=2.62e-01, acc(valid)=9.29e-01\n",
-      "Epoch 75: 3.7s to complete\n",
-      "    error(train)=2.38e-01, acc(train)=9.34e-01, error(valid)=2.60e-01, acc(valid)=9.30e-01\n",
-      "Epoch 80: 4.3s to complete\n",
-      "    error(train)=2.38e-01, acc(train)=9.33e-01, error(valid)=2.62e-01, acc(valid)=9.29e-01\n",
-      "Epoch 85: 3.2s to complete\n",
-      "    error(train)=2.36e-01, acc(train)=9.35e-01, error(valid)=2.61e-01, acc(valid)=9.30e-01\n",
-      "Epoch 90: 4.1s to complete\n",
-      "    error(train)=2.36e-01, acc(train)=9.34e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n",
-      "Epoch 95: 3.0s to complete\n",
-      "    error(train)=2.37e-01, acc(train)=9.34e-01, error(valid)=2.63e-01, acc(valid)=9.29e-01\n",
-      "Epoch 100: 3.5s to complete\n",
-      "    error(train)=2.35e-01, acc(train)=9.35e-01, error(valid)=2.63e-01, acc(valid)=9.29e-01\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAENCAYAAAAi8D15AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nx+nSbyYrAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc435de5dd8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAAENCAYAAAAbl4wiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc43599eba8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Set training run hyperparameters\n",
-    "batch_size = 100  # number of data points in a batch\n",
-    "init_scale = 0.1  # scale for random parameter initialisation\n",
-    "learning_rate = 0.2  # learning rate for gradient descent\n",
-    "num_epochs = 100  # number of training epochs to perform\n",
-    "stats_interval = 5  # epoch interval between recording and printing stats\n",
-    "\n",
-    "# Reset random number generator and data provider states on each run\n",
-    "# to ensure reproducibility of results\n",
-    "rng.seed(seed)\n",
-    "train_data.reset()\n",
-    "valid_data.reset()\n",
-    "\n",
-    "# Alter data-provider batch size\n",
-    "train_data.batch_size = batch_size \n",
-    "valid_data.batch_size = batch_size\n",
-    "\n",
-    "# Create a parameter initialiser which will sample random uniform values\n",
-    "# from [-init_scale, init_scale]\n",
-    "param_init = UniformInit(-init_scale, init_scale, rng=rng)\n",
-    "\n",
-    "# Create affine + softmax model\n",
-    "model = MultipleLayerModel([\n",
-    "    AffineLayer(input_dim, output_dim, param_init, param_init),\n",
-    "    SoftmaxLayer()\n",
-    "])\n",
-    "\n",
-    "# Initialise a cross entropy error object\n",
-    "error = CrossEntropyError()\n",
-    "\n",
-    "# Use a basic gradient descent learning rule\n",
-    "learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n",
-    "\n",
-    "_ = train_model_and_plot_stats(\n",
-    "    model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### `learning_rate = 0.5`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 5: 3.5s to complete\n",
-      "    error(train)=2.79e-01, acc(train)=9.20e-01, error(valid)=2.74e-01, acc(valid)=9.23e-01\n",
-      "Epoch 10: 3.7s to complete\n",
-      "    error(train)=2.68e-01, acc(train)=9.24e-01, error(valid)=2.72e-01, acc(valid)=9.26e-01\n",
-      "Epoch 15: 3.9s to complete\n",
-      "    error(train)=2.55e-01, acc(train)=9.28e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n",
-      "Epoch 20: 3.5s to complete\n",
-      "    error(train)=2.49e-01, acc(train)=9.31e-01, error(valid)=2.61e-01, acc(valid)=9.29e-01\n",
-      "Epoch 25: 4.3s to complete\n",
-      "    error(train)=2.52e-01, acc(train)=9.29e-01, error(valid)=2.73e-01, acc(valid)=9.25e-01\n",
-      "Epoch 30: 3.5s to complete\n",
-      "    error(train)=2.47e-01, acc(train)=9.31e-01, error(valid)=2.70e-01, acc(valid)=9.27e-01\n",
-      "Epoch 35: 3.2s to complete\n",
-      "    error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.69e-01, acc(valid)=9.27e-01\n",
-      "Epoch 40: 4.1s to complete\n",
-      "    error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.72e-01, acc(valid)=9.26e-01\n",
-      "Epoch 45: 4.3s to complete\n",
-      "    error(train)=2.36e-01, acc(train)=9.35e-01, error(valid)=2.66e-01, acc(valid)=9.29e-01\n",
-      "Epoch 50: 3.7s to complete\n",
-      "    error(train)=2.38e-01, acc(train)=9.33e-01, error(valid)=2.69e-01, acc(valid)=9.28e-01\n",
-      "Epoch 55: 3.9s to complete\n",
-      "    error(train)=2.36e-01, acc(train)=9.34e-01, error(valid)=2.68e-01, acc(valid)=9.26e-01\n",
-      "Epoch 60: 4.0s to complete\n",
-      "    error(train)=2.46e-01, acc(train)=9.29e-01, error(valid)=2.81e-01, acc(valid)=9.22e-01\n",
-      "Epoch 65: 4.1s to complete\n",
-      "    error(train)=2.33e-01, acc(train)=9.35e-01, error(valid)=2.70e-01, acc(valid)=9.28e-01\n",
-      "Epoch 70: 3.6s to complete\n",
-      "    error(train)=2.35e-01, acc(train)=9.36e-01, error(valid)=2.75e-01, acc(valid)=9.27e-01\n",
-      "Epoch 75: 4.4s to complete\n",
-      "    error(train)=2.31e-01, acc(train)=9.36e-01, error(valid)=2.70e-01, acc(valid)=9.26e-01\n",
-      "Epoch 80: 3.6s to complete\n",
-      "    error(train)=2.35e-01, acc(train)=9.34e-01, error(valid)=2.76e-01, acc(valid)=9.25e-01\n",
-      "Epoch 85: 4.0s to complete\n",
-      "    error(train)=2.32e-01, acc(train)=9.35e-01, error(valid)=2.75e-01, acc(valid)=9.26e-01\n",
-      "Epoch 90: 3.6s to complete\n",
-      "    error(train)=2.29e-01, acc(train)=9.37e-01, error(valid)=2.74e-01, acc(valid)=9.26e-01\n",
-      "Epoch 95: 4.2s to complete\n",
-      "    error(train)=2.31e-01, acc(train)=9.35e-01, error(valid)=2.76e-01, acc(valid)=9.27e-01\n",
-      "Epoch 100: 3.4s to complete\n",
-      "    error(train)=2.31e-01, acc(train)=9.36e-01, error(valid)=2.77e-01, acc(valid)=9.27e-01\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAENCAYAAAAi8D15AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhCRtIYQQwk1I0hZCCCHcxP8BLp/xmmrveDQAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc4685a61d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAAENCAYAAAAbl4wiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nmwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc435c37438>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Set training run hyperparameters\n",
-    "batch_size = 100  # number of data points in a batch\n",
-    "init_scale = 0.1  # scale for random parameter initialisation\n",
-    "learning_rate = 0.5  # learning rate for gradient descent\n",
-    "num_epochs = 100  # number of training epochs to perform\n",
-    "stats_interval = 5  # epoch interval between recording and printing stats\n",
-    "\n",
-    "# Reset random number generator and data provider states on each run\n",
-    "# to ensure reproducibility of results\n",
-    "rng.seed(seed)\n",
-    "train_data.reset()\n",
-    "valid_data.reset()\n",
-    "\n",
-    "# Alter data-provider batch size\n",
-    "train_data.batch_size = batch_size \n",
-    "valid_data.batch_size = batch_size\n",
-    "\n",
-    "# Create a parameter initialiser which will sample random uniform values\n",
-    "# from [-init_scale, init_scale]\n",
-    "param_init = UniformInit(-init_scale, init_scale, rng=rng)\n",
-    "\n",
-    "# Create affine + softmax model\n",
-    "model = MultipleLayerModel([\n",
-    "    AffineLayer(input_dim, output_dim, param_init, param_init),\n",
-    "    SoftmaxLayer()\n",
-    "])\n",
-    "\n",
-    "# Initialise a cross entropy error object\n",
-    "error = CrossEntropyError()\n",
-    "\n",
-    "# Use a basic gradient descent learning rule\n",
-    "learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n",
-    "\n",
-    "_ = train_model_and_plot_stats(\n",
-    "    model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "|`learning_rate`| Final `error(train)` | Final `error(valid)` |\n",
-    "|---------------|----------------------|----------------------|\n",
-    "| 0.05          | $2.53\\times 10^{-1}$  | $2.59\\times 10^{-1}$|\n",
-    "| 0.1           | $2.43\\times 10^{-1}$  | $2.59\\times 10^{-1}$|\n",
-    "| 0.2           | $2.35\\times 10^{-1}$  | $2.63\\times 10^{-1}$|\n",
-    "| 0.5           | $2.31\\times 10^{-1}$  | $2.77\\times 10^{-1}$|\n",
-    "\n",
-    "<span style=\"color:red\">\n",
-    "Increasing the learning rate, as would be expected, increase the speed of learning, with the final training error reached monotonically decreasing over the learning rates tested as the learning rate was increased. Note however the validation set error increases for larger learning rates - this suggests the model is overfitting to the data, with the larger learning rates causing the model to begin overfitting sooner - we could have afforded to halt learning earlier in these cases when there was no further improvement in the validation set error. Notice also the error curves for the largest learning rate value are much more noisy suggesting learning is becoming quite unstable with this large a step size, with a lot of the gradient descent steps overshooting and causing the error function value to increase.\n",
-    "</span>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Optional extra: more efficient softmax gradient evaluation\n",
-    "\n",
-    "In the lectures you were shown that for certain combinations of error function and final output layers, that the expressions for the gradients take particularly simple forms. \n",
-    "\n",
-    "In particular it can be shown that the combinations of \n",
-    "\n",
-    "  * logistic sigmoid output layer and binary cross entropy error function\n",
-    "  * softmax output layer and cross entropy error function\n",
-    " \n",
-    "lead to particularly simple forms for the gradients of the error function with respect to the inputs to the final layer. In particular for the latter softmax and cross entropy error function case we have that\n",
-    "\n",
-    "\\begin{equation}\n",
-    "    y^{(b)}_k = \\textrm{Softmax}_k\\lpa\\vct{x}^{(b)}\\rpa = \\frac{\\exp(x^{(b)}_k)}{\\sum_{d=1}^D \\lbr \\exp(x^{(b)}_d) \\rbr}\n",
-    "    \\qquad\n",
-    "    E^{(b)} = \\textrm{CrossEntropy}\\lpa\\vct{y}^{(b)},\\,\\vct{t}^{(b)}\\rpa = -\\sum_{d=1}^D \\lbr t^{(b)}_d \\log(y^{(b)}_d) \\rbr\n",
-    "\\end{equation}\n",
-    "\n",
-    "and it can be shown (this is an instructive mathematical exercise if you want a challenge!) that\n",
-    "\n",
-    "\\begin{equation}\n",
-    "    \\pd{E^{(b)}}{x^{(b)}_d} = y^{(b)}_d - t^{(b)}_d.\n",
-    "\\end{equation}\n",
-    "\n",
-    "The combination of `CrossEntropyError` and `SoftmaxLayer` used to train the model above calculate this gradient less directly by first calculating the gradient of the error with respect to the model outputs in `CrossEntropyError.grad` and then back-propagating this gradient to the inputs of the softmax layer using `SoftmaxLayer.bprop`.\n",
-    "\n",
-    "Rather than computing the gradient in two steps like this we can instead wrap the softmax transformation in to the definition of the error function and make use of the simpler gradient expression above. More explicitly we define an error function as follows\n",
-    "\n",
-    "\\begin{equation}\n",
-    "    E^{(b)} = \\textrm{CrossEntropySoftmax}\\lpa\\vct{y}^{(b)},\\,\\vct{t}^{(b)}\\rpa = -\\sum_{d=1}^D \\lbr t^{(b)}_d \\log\\lsb\\textrm{Softmax}_d\\lpa \\vct{y}^{(b)}\\rpa\\rsb\\rbr\n",
-    "\\end{equation}\n",
-    "\n",
-    "with corresponding gradient\n",
-    "\n",
-    "\\begin{equation}\n",
-    "    \\pd{E^{(b)}}{y^{(b)}_d} = \\textrm{Softmax}_d\\lpa \\vct{y}^{(b)}\\rpa - t^{(b)}_d.\n",
-    "\\end{equation}\n",
-    "\n",
-    "The final layer of the model will then be an affine transformation which produces unbounded output values corresponding to the logarithms of the unnormalised predicted class probabilities. An implementation of this error function is provided in `CrossEntropySoftmaxError`. The cell below sets up a model with a single affine transformation layer and trains it on MNIST using this new cost. If you run it with equivalent hyperparameters to one of your runs with the alternative formulation above you should get identical error and classification curves (other than floating point error) but with a minor improvement in training speed.\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 5: 0.4s to complete\n",
-      "    error(train)=3.11e-01, acc(train)=9.13e-01, error(valid)=2.92e-01, acc(valid)=9.18e-01\n",
-      "Epoch 10: 0.4s to complete\n",
-      "    error(train)=2.89e-01, acc(train)=9.20e-01, error(valid)=2.77e-01, acc(valid)=9.23e-01\n",
-      "Epoch 15: 0.4s to complete\n",
-      "    error(train)=2.79e-01, acc(train)=9.22e-01, error(valid)=2.70e-01, acc(valid)=9.24e-01\n",
-      "Epoch 20: 0.4s to complete\n",
-      "    error(train)=2.72e-01, acc(train)=9.24e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n",
-      "Epoch 25: 0.4s to complete\n",
-      "    error(train)=2.68e-01, acc(train)=9.25e-01, error(valid)=2.66e-01, acc(valid)=9.26e-01\n",
-      "Epoch 30: 0.4s to complete\n",
-      "    error(train)=2.63e-01, acc(train)=9.27e-01, error(valid)=2.62e-01, acc(valid)=9.26e-01\n",
-      "Epoch 35: 0.4s to complete\n",
-      "    error(train)=2.60e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n",
-      "Epoch 40: 0.4s to complete\n",
-      "    error(train)=2.59e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.28e-01\n",
-      "Epoch 45: 0.4s to complete\n",
-      "    error(train)=2.55e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n",
-      "Epoch 50: 0.4s to complete\n",
-      "    error(train)=2.54e-01, acc(train)=9.30e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n",
-      "Epoch 55: 0.4s to complete\n",
-      "    error(train)=2.52e-01, acc(train)=9.29e-01, error(valid)=2.59e-01, acc(valid)=9.30e-01\n",
-      "Epoch 60: 0.4s to complete\n",
-      "    error(train)=2.52e-01, acc(train)=9.29e-01, error(valid)=2.60e-01, acc(valid)=9.29e-01\n",
-      "Epoch 65: 0.4s to complete\n",
-      "    error(train)=2.50e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
-      "Epoch 70: 0.4s to complete\n",
-      "    error(train)=2.49e-01, acc(train)=9.31e-01, error(valid)=2.59e-01, acc(valid)=9.31e-01\n",
-      "Epoch 75: 0.4s to complete\n",
-      "    error(train)=2.47e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
-      "Epoch 80: 0.4s to complete\n",
-      "    error(train)=2.46e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.31e-01\n",
-      "Epoch 85: 0.4s to complete\n",
-      "    error(train)=2.45e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.31e-01\n",
-      "Epoch 90: 0.4s to complete\n",
-      "    error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
-      "Epoch 95: 0.4s to complete\n",
-      "    error(train)=2.44e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
-      "Epoch 100: 0.4s to complete\n",
-      "    error(train)=2.43e-01, acc(train)=9.33e-01, error(valid)=2.59e-01, acc(valid)=9.29e-01\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAENCAYAAAAi8D15AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc4359d8080>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAENCAYAAADngqfoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc42c4099b0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Set training run hyperparameters\n",
-    "batch_size = 100  # number of data points in a batch\n",
-    "init_scale = 0.1  # scale for random parameter initialisation\n",
-    "learning_rate = 0.1  # learning rate for gradient descent\n",
-    "num_epochs = 100  # number of training epochs to perform\n",
-    "stats_interval = 5  # epoch interval between recording and printing stats\n",
-    "\n",
-    "# Reset random number generator and data provider states on each run\n",
-    "# to ensure reproducibility of results\n",
-    "rng.seed(seed)\n",
-    "train_data.reset()\n",
-    "valid_data.reset()\n",
-    "\n",
-    "# Alter data-provider batch size\n",
-    "train_data.batch_size = batch_size \n",
-    "valid_data.batch_size = batch_size\n",
-    "\n",
-    "# Create a parameter initialiser which will sample random uniform values\n",
-    "# from [-init_scale, init_scale]\n",
-    "param_init = UniformInit(-init_scale, init_scale, rng=rng)\n",
-    "\n",
-    "# Create affine model (outputs are logs of unnormalised class probabilities)\n",
-    "model = SingleLayerModel(\n",
-    "    AffineLayer(input_dim, output_dim, param_init, param_init)\n",
-    ")\n",
-    "\n",
-    "# Initialise the error object\n",
-    "error = CrossEntropySoftmaxError()\n",
-    "\n",
-    "# Use a basic gradient descent learning rule\n",
-    "learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n",
-    "\n",
-    "_ = train_model_and_plot_stats(\n",
-    "    model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<span style=\"color:red\">\n",
-    "This gives exactly the same training curves (and error / accuracy values over training) as the two runs with equivalent parameters above (second `init_scale` experiment and second `learning_rate` experiment).\n",
-    "</span>\n",
-    "\n",
-    "<span style=\"color:red\">\n",
-    "The times per epoch seems to be slightly lower on average (0.20s compared to 0.22s) suggesting the reformulation gives a small efficiency gain (though this will become less apparent in deeper architectures as the benefit only applies to the final layer).\n",
-    "</span>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercise 2: training deeper models on MNIST\n",
-    "\n",
-    "We are now going to investigate using deeper multiple-layer model archictures for the MNIST classification task. You should experiment with training models with two to five `AffineLayer` transformations interleaved with `SigmoidLayer` nonlinear transformations. Intermediate hidden layers between the input and output should have a dimension of 100. For example the `layers` definition of a model with two `AffineLayer` transformations would be\n",
-    "\n",
-    "```python\n",
-    "layers = [\n",
-    "    AffineLayer(input_dim, 100),\n",
-    "    SigmoidLayer(),\n",
-    "    AffineLayer(100, output_dim),\n",
-    "    SoftmaxLayer()\n",
-    "]\n",
-    "```\n",
-    "\n",
-    "If you read through the extension to the first exercise you may wish to use the `CrossEntropySoftmaxError` without the final `SoftmaxLayer`.\n",
-    "\n",
-    "Use the code from the first exercise as a starting point and start with training hyperparameters which gave reasonable performance for the shallow architecture trained previously.\n",
-    "\n",
-    "Some questions to investigate:\n",
-    "\n",
-    "  * How does increasing the number of layers affect the model's performance on the training data set? And on the validation data set?\n",
-    "  * Do deeper models seem to be harder or easier to train (e.g. in terms of ease of choosing training hyperparameters to give good final performance and/or quick convergence)?\n",
-    "  * Do the models seem to be sensitive to the choice of the parameter initialisation range? Can you think of any reasons for why setting individual parameter initialisation scales for each `AffineLayer` in a model might be useful? Can you come up with (or find) any heuristics for setting the parameter initialisation scales?\n",
-    "  \n",
-    "You do not need to come up with explanations for all of these (though if you can that's great!), they are meant as prompts to get you thinking about the various issues involved in training multiple-layer models. \n",
-    "\n",
-    "You may wish to start with shorter pilot training runs (by decreasing the number of training epochs) for each of the model architectures to get an initial idea of appropriate hyperparameter settings before doing one or two longer training runs to assess the final performance of the architectures."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# disable logging by setting handler to dummy object\n",
-    "logger.handlers = [logging.NullHandler()]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Models with two affine layers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--------------------------------------------------------------------------------\n",
-      "learning_rate=0.20 init_scale=0.10\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAENCAYAAAAi8D15AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XtgVPWd///nZ2Zyv8wwE0i4XybhEiIXCQqIQMgEEBAh\nyNa2fvvd1m8vtrtV2633trSsXdq1dtvu91u31CL9ueuyVlBEBTLhLqLIRQRUSAKESyCQkMn9Mjmf\n3x+DMVQggTPJTJL34y+HnJz55OWZvHPO+XzOW2mtNUIIIYQIe5ZQD0AIIYQQ7SNFWwghhOgipGgL\nIYQQXYQUbSGEEKKLkKIthBBCdBFStIUQQoguQoq2EEII0UVI0RZCCCG6CCnaQgghRBchRVsIIYTo\nImyhHsDVnD17Nqj7S0pK4uLFi0HdZ08jGZonGZonGZonGQZHsHPs169fu7aTM20hhBCii5CiLYQQ\nQnQRUrSFEEKILiIs72kLIYToHFpr6uvrMQwDpVSoh9NlnD9/noaGhhv6Hq01FouF6Ojom85airYQ\nQvRg9fX1REREYLNJObgRNpsNq9V6w9/n9/upr68nJibmpt5XLo8LIUQPZhiGFOxOZLPZMAzjpr9f\nirYQQvRgckm885nJvNsXbWPtSzQe+TDUwxBCCCFM69ZFW1dWoLe/zaWnHqR5+aPoA++hTVyWEEII\n0TWtWLGCV155BYDVq1dz7ty5G97HX/7yl5Z9XMvHH3/Mww8/fFNjbI9ufSNDJTqwLH+BuAPvUrX2\nPzH+7zPQdyBqdi7q9mkoW0SohyiEEKIdmpubr5j49bevr8Xv9wOBQr1hwwYAXnnlFUaOHElKSkqb\n79Pa1772tTbfb9SoUZSUlHDmzBn69+/f5vY3yrp06dKlQd+rSVVVVUHbl7LZsI+bSN1tMyClPxR9\nCts3ondtBq2h/yAp3u0QGxtLbW1tqIfRpUmG5kmG5v1thk1NTUREhP534KuvvsqPfvQjXnzxRQ4d\nOsTMmTMZMWIEly5d4p//+Z9JT08nNzeX0tJSnnnmGex2O36/n2984xv8+c9/Ztu2bUyfPp3o6Gju\nvfdeDh06xK9+9Stqa2upq6ujvLycuXPnsn79elatWsX27dt5+eWXWbJkCVOnTr1iv3v37uXxxx9n\n5cqV7Nixg5ycHCIiIvj1r3/NwYMHyczMZPHixRw7doxf/vKX/OEPf2D06NEMGDAAAJ/Px5EjR5g4\nceJVf9arZZ6QkNCunLr1mXZrymZDTZqBvn06HN6HsWEN+pU/o99cjZoxD5U9D5XYK9TDFEKIkDH+\newX61PGg7lMNHIrlvm9ed5tjx46xbt06XnvtNSIiInjiiSdYs2YNtbW1jB8/np/+9Kct2/bq1YuN\nGzcC4PF4WLZsGZMnT+Zf//Vfee655/j5z38OBArj22+/DcCzzz7LmDFjAJg/fz4vvvgiP/7xjxk7\nduxV91teXs5Xv/pVAH75y1/y8ssv841vfOML4/b7/bz55pvk5+fz3HPPsXr1agDGjh3Lv//7v/Pd\n7373pjK7nh5TtD+jlIKMCVgzJqCLPsXYuAb99ivovNdQU2aiZi1C9ekb6mEKIUSPsXPnTj766CPm\nzp0LBNaOJyUlYbVamTdv3hXbLliwAIDKykp8Ph+TJ08GYMmSJXz729/+wnYApaWlpKWlXXcMrbf/\n9NNP+dWvfkVlZSU1NTVMnz79qt/z2XjHjBnD6dOnW/7d5XJx/vz5Nn/um9HjinZratgIrA8+gT53\nGr3pNfQ7XvT2TagJU1BzFqMGu0M9RCGE6DRtnRF3FK01S5Ys4Yknnrji359//vkv3F+OjY1t1z5b\nbxcdHU19fX27t3/kkUd44YUXGD16NKtXr+bdd9+96vdERkYCYLVaW+6dAzQ0NBAdHd2ucd6obj17\nvL1UygAsX/sHLP+yAjVrIfrwPox/foTm536MPnIArXWohyiEEN3W1KlTWb9+fUury0uXLl1x5no1\niYmJ2O123nvvPSBwT3zSpElX3TY1NZUTJ060vI6Li6O6uvqa+66uriY5OZmmpibWrl17gz8NFBUV\nMWLEiBv+vvZo15n2gQMHWLlyJYZhkJ2dzcKFC6/4+vr168nPz8dqtZKYmMiDDz5I7969W75eW1vL\nD37wAyZOnMgDDzwQ3J8giJTDhbr379Fzl6C3bUDnr8P4zU9gkDtw5j1hMspy44+tE0IIcW3Dhw/n\n0Ucf5ctf/jJaa2w2G88880yb3/dv//ZvPP7449TX1zNo0CCee+65q243c+ZMvv/977e8/ru/+zse\nf/xxoqOjWbdu3Re2/9GPfsT8+fNxuVyMHz/+ugX+anbt2kV2dvYNfU97Kd3GaaRhGDz00EM8/fTT\nuFwunnjiCR566KGWWXIAhw4dIi0tjaioKDZt2sThw4d55JFHWr6+cuVKKisriY+Pb1fRPnv2rIkf\n6Ytutlm5bmpEv7sFvXEtlJ6F3imoWQtRU7JRkVFBHWO4C3bD955IMjRPMjTvbzOsra1t9yXnruyB\nBx7gqaeeYtiwYUHZn81mu+KS+GcaGhpYvHgxr7322jUfD3u1zPv169eu923z8nhBQQEpKSkkJydj\ns9mYMmUKe/bsuWKbjIwMoqICRSwtLY3y8vKWrxUVFeHz+a6YpddVqIhILNNmY1n2f7F853GIS0D/\n5/MYj/8fjDf/B11zY399CSGECI0nnniC0tLSDn+fM2fO8OSTT3bY89zb3Gt5eTkul6vltcvl4tix\nY9fcfvPmzYwbNw4InKX/5S9/4R//8R/56KOPrvk9Xq8Xr9cLwPLly0lKSmr3D9AeNpvN/D5nL0DP\nupumw/upWfMSja+9BBvWED1rAbF334c1qU9wBhumgpJhDycZmicZmve3GZ4/f75HNAwZOXJk0Pd5\ntdyGDx/O8OHDr/t9UVFRN30cB/X/1Pbt2ykqKuKz57Vs2rSJ8ePHX1H0r8bj8eDxeFpeB/vyV1Av\nqaUMgu8+ieXUcfSGNdSu/x9q3/wr6vbpqDm5qL4Dg/M+YUYuS5onGZonGZr3txk2NDTcVIvJnu5a\nl8fbo6Gh4QvHcXsvj7dZtJ1OJ2VlZS2vy8rKcDqdX9ju4MGDrF27lqVLl7Y86eXo0aN8/PHHbNq0\nifr6evx+P9HR0S2L1rsyNXAo6ps/RC+6//JysTz0rnwYexuWOYtRqaNCPUQhhBDdTJtF2+12U1JS\nQmlpKU6nk127dl0xCw/g+PHjrFixgieffBK73d7y762327p1K4WFhd2iYLemkpJRX/k2+u770Jvf\nRG95E+OXj0FqOpY5i+GWCSiLrKwTQghhXptF22q18o1vfINnnnkGwzDIyspi4MCBrF69GrfbTWZm\nJi+99BL19fUt0+2TkpJ47LHHOnzw4UQl2FH3fAU9Jxe9YxM673WMf18G/QYFGpTcNg3VA+4bCSGE\n6DhtLvkKhXBZ8mWG9vvRH+xAb1gDZ06CMwmVcw9q6ixUdEynjiUY5F6ieZKheZKheT11ydeKFStw\nOBwsWbLkhr/34YcfxuPxMH/+fP7pn/6Jb33rW6Snp19xT3v16tUcPHiQZ555hpUrVxITE8N99913\n1f2ZWfIlp34dJNCgJAt9+ww4tBdjw6vo1S+g31iNypqLyr4blWBvcz9CCCGC25rTjGeffbbNbe67\n7z7uueeeaxZtM+RmawdTSqFuycT6o3/B8vivYHgG+s3/wXjsAYz/fB594cYbsQshRHfz6quvMm/e\nPHJycnj00Udpbm4mLS2Nn/3sZ3g8Hvbu3cvtt9/OM888w+zZs1m/fj2HDh1i/vz5eDweHnjgASoq\nKgC49957+clPfsJdd93Fn/70J9555x0yMjKw2WwUFBRc0YTk1KlTLU8v+81vfsPcuXOZOXMmjz76\n6FUfYX3vvffy4YcfAoE/BKZOncq8efP44IMPWraJiYlh4MCB7N+/P+g5yZl2J1LukVi/9yS65DR6\n45rAve9tG1CZdwQekzooOE/qEUKIm/GnD85z/NL1G2vcqKG9ovk/mcnX3aYzW3OmpqbS2NhIcXEx\ngwYNYt26ddx9990A/P3f/33L0zz/8R//kby8PGbNmnXVMZ8/f55nn32WDRs2kJCQwJIlS8jIyGj5\n+pgxY3jvvfcYP378zcR2TXKmHQKq7wAsf//9QIOSnHvQH32Asexhmn/zU/THH0qDEiFEj9K6NWdO\nTg47d+6kuLj4hltzftY8pPV2EGjN2fp5IXfffXfLM8fXrVvXsu2uXbuYP38+2dnZ7Nq1i6NHj15z\nzHv37mXy5Mm4XC4iIyOveD8IzB3oiPaccqYdQqqXC7Xk6+h5S9Bb30bnv4Hx3I9hcCqWOblwqzQo\nEUJ0nrbOiDtKZ7fmXLBgAd/+9re56667UEoxbNgw6uvrefLJJ3nrrbfo378/v/71r2loaLjpn6mj\n2nPKmXYYULHxWOYuwbL8T6j7vwt1NRj/8SuMH38XY9sGdFNjqIcohBAdprNbcw4ZMgSr1cq//du/\ntZwhf1agnU4nNTU1vPnmm9d9/wkTJrB7927Ky8tpampi/fr1V3y9qKioYx6dGvQ9ipumIiJR0+eg\n78yBfe9ibFiDfun/odf9V2C2+Yy7ULHxoR6mEEIEVWe35oTA2fayZcvYvXs3AHa7na985StkZ2fT\nu3fvNptcJScn88Mf/pAFCxZgt9sZPXr0FV/fs2cPP/jBD9r8GW6UrNMOY1pr+OQgxoY1cGQ/RMeg\nps1BeRagel3/ee7B1lUzDCeSoXmSoXk9dZ12Z7XmhEC76v/4j//g97///VW/Luu0uymlFIwai3XU\nWHRxIXrDGnTe6+j8N1CTZgSetNZ3QNs7EkKIHu6z1pzBKtrXU15ezqOPPtoh+5Yz7S5GXzh3uUGJ\nF/xNMPZ2LHNyUe7g3ztprTtlGCqSoXmSoXk99Uw72Mx0+ZIz7R5E9U5BffU7lxuUrEdveQvjwG4Y\nPjrQoCRjQuAMXQgh2iEMz9u6PTOZS9HuolSiA7XwfvScxeidlxuU/O7n0H9woK935p3SoEQI0SaL\nxYLf78cmvy86hd/vx2Ki86P8X+riVHQMynMPesY89PvbA09ae+E36LUvBRqU3DkLFRX8tYJCiO7h\nszXMDQ0NcpXuBkRFRd3wOm6tNRaLxdT6bSna3YSy2VBTZqInzYCPPmtQ8if0+tWorHmomfNRCYmh\nHqYQIswopYiJ6XqdB0MtVPMrpGh3M8pigbETsY6diC74OFC81/83etMa1B05qFkLUUmheeqREEII\nc6Rod2MqdRTWf3gafbYYvXEtevtG9La3A/e75+SiBg4N9RCFEELcACnaPYDqNwj19YfQ93wV7X0d\nvX0T+v1tMHp8YMb5iFvkXpYQQnQBUrR7EOVMQv3dA+h5X0JvfSvQoOTXT8OQtEDxHn+7NCgRQogw\n1q6ifeDAAVauXIlhGGRnZ7Nw4cIrvr5+/Xry8/OxWq0kJiby4IMP0rt3by5cuMCzzz6LYRg0Nzcz\nZ86ca/YmFZ1HxcWj5v0dOuce9K589KbXMJ5fDsn9A/e8J89ERUSEephCCCH+RptPRDMMg4ceeoin\nn34al8vFE088wUMPPcSAAZ8/PvPQoUOkpaURFRXFpk2bOHz4MI888gh+vx+tNREREdTX1/PDH/6Q\nZcuW4XQ6rzsoeSJa59JGM3rvu+gNr0JxIdh7obIXoKbPQcXGAZJhMEiG5kmG5kmGwRHsHNv7RLQ2\nV3gXFBSQkpJCcnIyNpuNKVOmsGfPniu2ycjIICoqCoC0tDTKy8uBwGPeIi6fsTU1NWEYxg39EKJz\nKIsVy8SpWJ5+DssjP4d+g9BrVmE8/gDGX19EV5SHeohCCCFox+Xx8vJyXK7PO0q5XC6OHTt2ze03\nb97MuHHjWl5fvHiR5cuXc+7cOe6///42z7JF6CilIH0c1vRx6JMFgQYlm15D56+jcsZd6OlzUSn9\nQz1MIYTosYI6EW379u0UFRWxdOnSln9LSkri2Wefpby8nH/9139l0qRJOByOK77P6/Xi9XoBWL58\nOUlJScEcFjabLej77PaSkmDCJPwlp6l9/WXqNr8J+euJun06cYvuJ2J4eqhH2OXIcWieZGieZBgc\nocqxzaLtdDopKytreV1WVnbVs+WDBw+ydu1ali5d2nJJ/G/3M3DgQD755BMmTZp0xdc8Hg8ej6fl\ndTDvE2wu8jFtZH9sjdVB22ePEhEN936dpPseoOyVv9Cw9U0adm+FEbdgmZ0LGbfKcrF2knuJ5kmG\n5kmGwRG297TdbjclJSWUlpbi9/vZtWsXmZmZV2xz/PhxVqxYwaOPPordbm/597KyMhobGwGorq7m\n008/bffAgsFX7+f3u0u4d+UH/HTzKXaerKSpWe6r3wyrw4ll0f1YfvkCask34PxZjN/9DOPnD2Hs\n3opubg71EIUQottrVz/tffv2sWrVKgzDICsri9zcXFavXo3b7SYzM5Nly5ZRXFzcctk7KSmJxx57\njIMHD/KXv/wFpRRaa+bMmXPFGfW1BHP2eGl1E++ea+SNj0q4UOsnIcrKjKGJ5LgdDHZEBe19uru/\n/atS+5vQ7wUalFByClx9UDkLUVNzUFGS69XIGY55kqF5kmFwhOpMu11Fu7N1xJKv86UX+PBcDd5C\nH++drsJvwHBXNDmpDqYOTiA2Qh4qcj3XOkC1YcDBPRgbXoXCTyA+AZU1HzVzHipeGpS0Jr8szZMM\nzZMMg0OKdisdvU7bV+9n6/FKvIUVFPsaibIqpg5OJMdtZ2TvGLlHexXtOUD1sSOB4n1wD0RGBdqC\n5tyDcvXppFGGN/llaZ5kaJ5kGByhKto98jGm9mgb94xysmBkL46W1ZNXUMGOk1XkF/kYkBiJx20n\na5gdR3SPjOemqbR0rGnp6DMnA329t76F3vIm6rZpqNm5qAFDQj1EIYTo0nrkmfbV1DUZvFNcSV6B\nj08u1mFVcNuAeDxuB+P7xmG19Oyz75v5q1KXXQg0KNmxCRrqIWNC4Bnnw0f3yKsZcoZjnmRonmQY\nHHJ5vJVQP8b0lK8Bb6GPLUU+fA3NuGJszBxmx+O2k5IQGdSxdRVmDlBdU4Xe8hZ683qo8sHQ4YHi\nPe72QP/vHkJ+WZonGZonGQaHFO1WQl20P9PUrPngTDV5hRXsL6nB0DAmOZacVAeTBsYTaZWCcyN0\nQ8PlBiVr4eJ5SOmPmrUINSmrRzQokV+W5kmG5kmGwSFFu5VwKdqtXaxtYnOhj7xCH6U1TcRHWpg+\nJJGcVAdDe0UHaaThK5gHqG5uRu99J9Cg5NRxcDhRngWoaXNQMbFBeY9wJL8szZMMzZMMg0OKdivh\nWLQ/Y2jNR+drySuo4N1T1fgNjdsZTY7bzrQhicRFds+lYx3xQddaw5EDgRnnnxyEmDjUjDmBDmP2\nXkF9r3AgvyzNkwzNkwyDQ2aPdxEWpRibEsfYlDiqGprZdsJHXoGP5/ec58/7SpkyKIEct4PRfWTp\nWFuUUjB6PNbR49HHj2FsfDXQpCRvHWrKzMCl8+TOe4KeEEKEOynaJiREWZk/wsm84b0oKK8nr8DH\njpOVbD1eSb+ECLLdDmYOs+OMkZjbooamYf3O4+jzZwOdxXblB2ad3zoZy+zFqKFpoR6iEEKEnFwe\nD7IGv8E7xVXkFVRw5EIdFgWZ/ePxuO1k9ovvskvHOvuSmvZdQue/gd76NtTVBBqUzFkMo8d32SsY\nclnSPMnQPMkwOOTyeDcRZbMwc5idmcPsnKlsxFtYweYiH++frqZXjI2ZQxPxuB30S+yZS8faS9l7\noXK/hr7rXvT2jWjv6xi/XQoDhwYe1JI5FWXtnvMHhBDiWuRMuxP4Dc3eM9XkFfrYe7YaQ0NGnxg8\nbgdTBiUQZQv/pWOhzlA3NaHf2xpoUHLuDCQlo2YtRE3xdJkGJaHOsDuQDM2TDINDZo+30t2Kdmtl\ntU1sKaokr7CCc9VNxEVYmHZ56ZjbGb5Lx8IlQ20Y8OH7gRnnRZ9CfCIqez4qax4qLiHUw7uucMmw\nK5MMzZMMg0Muj/cQrtgI7s1wkTvayeHSWrwFPvKLfLx9rIKhvaLIcTuYPiSR+Ci59Hs1ymKB8ZOw\njLsdjh3G2LAG/fp/oTesCTQo8dyDcvUO9TCFEKJDSNEOEYtS3JIcxy3JcXyzsZntJwJdx/74wXlW\n7itl8qAEctx2MpJjsXTRiVcdSSkFwzOwDs9Anz4RaFCyeX2gQcnEaag5uaj+g0M9TCGECCq5PB5m\nisrrySusYNuJSmoaDVLiI8h228keZscVG7pHfXaFDHVZKTrvcoOSxga4JTMw4zwtPSxmnHeFDMOd\nZGieZBgcck+7lZ5ctD/T4Dd491QV3kIfH52vxaLg1r5xeFIdTOwfj62Tl451pQx1deXlBiVvQHUV\nuEdimZMLY24LaYOSrpRhuJIMzZMMg0OKditStK9UUtWIt9DH5iIf5XV+7NFWZg6140m1MyCxc2ZO\nd8UMdUMD+p089KbXoKwU+g5EzV6Eun06ytb5Vy26YobhRjI0TzIMDinarUjRvrpmQ7PvbA15hRV8\ncKaaZg2jeseQ47Zzx+BEojtw6VhXzlA3N6M/2BloUHL6BDhcqJwFqGmzUdGd16CkK2cYLiRD8yTD\n4Ajron3gwAFWrlyJYRhkZ2ezcOHCK76+fv168vPzsVqtJCYm8uCDD9K7d29OnDjBihUrqKurw2Kx\nkJuby5QpU9oclBTttl2q87OlKNB17GxVIzG2wNIxj9tOmis66Pdwu0OGWms4vA9jwxr49COIjUPN\nmBtYMpbY8Q1KukOGoSYZmicZBkfYFm3DMHjooYd4+umncblcPPHEEzz00EMMGDCgZZtDhw6RlpZG\nVFQUmzZt4vDhwzzyyCOcPXsWpRR9+/alvLycxx9/nN/85jfExcVdd1BStNtPa83HF+rIK6zgnZNV\nNDRrBtujyEm1M32oncQgLR3rbhnq40cDa7337warDTUlGzV7IapPxzUo6W4ZhoJkaJ5kGBxhu067\noKCAlJQUkpOTAZgyZQp79uy5omhnZGS0/HdaWho7duz4wiCcTid2u53Kyso2i7ZoP6UU6X1iSe8T\nyzczm9lxooq8wgr+tLeUF/df4PYB8cxKdTAmRZaOtaaGDsf64BPoc6cvNyjxondsQt06GXXXYtTg\n1FAPUQghvqDNol1eXo7L5Wp57XK5OHbs2DW337x5M+PGjfvCvxcUFOD3+1uKvwi+2Agrs9MczE5z\ncOJSPXmFPrYd9/FOcRV94mxkD3OQ7bbTOy50S8fCjUoZgPraP6AXfCXQoGTb2+i978CosYEZ56PG\nhcVyMSGEgCA/XGX79u0UFRWxdOnSK/790qVL/P73v+d73/selqssufF6vXi9XgCWL19OUlJSMIeF\nzWYL+j7DXVISZKYN4Ad+gx1FZbxx6Dwvf3SR//7oIrcNdjB/dApThzqJbOfktW6fYVISpP4Q4/5v\nU7fpNWrf+B+M3/wU27ARxC36KlGTZ6Cs5j4u3T7DTiAZmicZBkeocmzznvbRo0d55ZVXeOqppwBY\nu3YtAIsWLbpiu4MHD7Jy5UqWLl2K3W5v+ffa2lp+9rOfsWjRIiZNmtSuQck97Y5xvrqR/CIf3kIf\nZbV+EqOszBiaSI7bwSDH9ZeO9bQMdVMTevcW9Ma1cP4M9E653KAkGxV5c8vselqGHUEyNE8yDI6w\nvaftdrspKSmhtLQUp9PJrl27+P73v3/FNsePH2fFihU8+eSTVxRsv9/Ps88+y7Rp09pdsEXHSY6P\n5CtjevOljCQ+PFdDXqGPt45eYt0nlxiRFI3H7WDq4ARiI+S55yoiAnXnLPQd2XDgvcAzzv/zefS6\nl1HZdwdmncfFh3qYQogepl1Lvvbt28eqVaswDIOsrCxyc3NZvXo1brebzMxMli1bRnFxMQ6HAwj8\nBfLYY4+xfft2/vCHP1wxae173/seQ4YMue77yZl25/HV+9l6vJJNBRWcrmwk2qaYOjhw9j0i6fOl\nYz09Q601HD0UmHF+aB9ExaCmzUJ5FqCc7WtQ0tMzDAbJ0DzJMDjCdslXKEjR7nxaaz69GHju+c6T\nldT7NQMSI8lJtTNjqJ3UASmS4WX61PFAg5I9O0Ap1G3TAw1K+g267vfJcWieZGieZBgcUrRbkaId\nWrVNzbxzsoq8Qh+fXqzDquBOt4tpA2IY1zcOayc/9zxc6YvnAw1Kdm6CxkYYexuWObmo1PSrbi/H\noXmSoXmSYXBI0W5Finb4KPY14C2oYNvJKirq/LhibWQPs+Nx20mOjwz18MKCrqpEb1mP3vwm1FRB\n6qhAd7FbMq9oUCLHoXmSoXmSYXBI0W5Finb4sfdy8taHJ/AW+NhfUoMGxqbE4nE7mDQwnkhr6Lpn\nhQvdUI/eeblBSfmFQIOSObmo26ahbBFyHAaBZGieZBgcUrRbkaIdflpneKGmifwiH/mFFZTW+EmI\ntDB9qJ0ct50hvaJDPNLQ034/+oMd6A1r4MxJ6JWEyrmHpIVfprymNtTD69Lks2yeZBgcUrRbkaId\nfq6WoaE1B8/VkldYwe5T1fgNTaozmpxUO3cOTiQusmcvHdNaw6G9gRnnRw+j4hNg2l2XG5Q4Qj28\nLkk+y+ZJhsEhRbsVKdrhp60MKxua2XY80HXsZEUDkVbF1MEJeNwO0nvH9PhHgerCT4jYsp6G93eA\nLQJ1RzZq1iJU75RQD61Lkc+yeZJhcITtw1WEaI/EKCt3j3Qyf0QvCsrrySvwsf1EJZuLKumXEEmO\n207WMDu9YnrmIafcI3HcPpULHx0ILBfbkYfethGVeUfgvvcgd6iHKIToAuRMW7TLzWRY7zd452Ql\n3kIfRy7UYVEwsX88OW4Ht/breUvHWmeoK8rQ3nXobRugvg7SxwVmnI8c0+OvSlyPfJbNkwyDQy6P\ntyJFO/yYzfC0rwFvoY/Nx3346ptxxtiYeXnpWN+EnrF07GoZ6tpq9LYNaO86qKyAwamB7mK3TkZZ\nevacgKuRz7J5kmFwSNFuRYp2+AlWhn5Ds+dMNd6CCvaV1GBoyEiOJcdtZ/LABKLa2XWsK7pehrqp\nEf3u5kCPM/y7AAAgAElEQVSDktIS6NM3cM97ykxURM/4o6Y95LNsnmQYHFK0W5GiHX46IsOy2iY2\nX+46dq66ibhIC9MGJzIr1cEwZ/dbOtaeDLXRDPt3Y7z9KpwsgETH5QYld6FipUGJfJbNkwyDQ4p2\nK1K0w09HZmhozaHztXgLfewqrqLJ0AzrFUVOqoNpQxKJ7yZLx24kQ601fPpRYLnY4f2BBiXTZ6M8\n96B6uTp4pOFLPsvmSYbBIUW7FSna4aezMqxuaGbbiUryCis4fimwdGzywARyUu1k9Int0pO0bjZD\nXVx0uUHJTrBYUJOmo2bnovoO7IBRhjf5LJsnGQaHFO1WpGiHn1BkWFheT15BBdtPVFLTZJASH4HH\nbWfmMDuu2IhOHUswmM1QXziHznsNvdMLTY0w7nYscxaj3CODOMrwJp9l8yTD4JCi3YoU7fATygwb\n/Abvngp0HTt0vhaLggn94vC4HWT2j8fWRZaOBStDXeVDb77coKS2GtLSP29Q0oWvRLSHfJbNkwyD\nQx6uIsQ1RNkszBga6OtdUtWIt9BHfpGPPWfO4Ii2Xl465qB/Ys+YZa0S7Kh7voqenYveuQmd9zrG\n75dB/8GBy+YT70TZ5KMtRHckZ9qiXcItw2ZDs/dsNd5CH3vOVGNoSO8dQ06qgymDEogOw6VjHZWh\n9vvR729Hb1wDZ4vBGWhQoqbOQkXHBP39QincjsOuSDIMDrk83ooU7fATzhmW1/nZUuTDW1jB2aom\nYiMs3Dk4kZxUO6nO6LC5ZNzRGWrDgI8uNygpOAJxCaisuaiZ81EJ9g57384UzsdhVyEZBodcHhfi\nJjljbCwe7SI33cmR0jryCivYctzHxoIKhjii8LgDl9YTorrH0rFrURYLjJ2IdexEdMHHGBteRa9f\njd60FnWHB5WzUBqUCNHFtetM+8CBA6xcuRLDMMjOzmbhwoVXfH39+vXk5+djtVpJTEzkwQcfpHfv\n3gA888wzHDt2jJEjR/L444+3a1Byph1+ulqGNY3NbD8ReO55QXk9Noti8sB4PG4HY1JisYTg7DsU\nGeqSU4HlYru3gTZQmVNRcxajBg7t1HEES1c7DsORZBgcoTrTti5dunTp9TYwDINf/OIXPPXUUyxa\ntIiVK1eSnp5OYmJiyzaNjY186UtfYu7cuTQ0NJCfn8/kyZMB6NWrFxMmTKCoqIipU6e2a1BVVVXt\n2q69YmNjqa2tDeo+e5qulmGk1UKaK4bZaQ4mDYxHKcXuU1VsKvCxpaiSuiaD5PiITu35HYoMVYId\nNW4Sako2KNDvbUdvfgNd9AnK4QJXn7C5fdAeXe04DEeSYXAEO8eEhIR2bdfmbJ2CggJSUlJITk7G\nZrMxZcoU9uzZc8U2GRkZREVFAZCWlkZ5eXnL12655RZiYrrXZBjRtQztFc23MpNZmZvKD+/oR0pC\nBP918CLfer2Qn285xa7iSpqaw25qR1ApZxKWJd/A8ssXUAvvh+IijF8/jfGLf0Lv3RV4fKoQIuy1\neU+7vLwcl+vzxya6XC6OHTt2ze03b97MuHHjbmgQXq8Xr9cLwPLly0lKSrqh72+LzWYL+j57mu6S\nYW5yH3Iz4YyvnjePnOetI+f55Y6zOGIiuGtUH+aPTmaIM7ZD3jssMkxKgv/9XfR9D1C35S1qX/8v\nmp9fjrXvQGIXfoWYGXNQkVGhHeN1hEWGXZxkGByhyjGoE9G2b99OUVERbVxx/wKPx4PH42l5Hez7\nLXIPx7zulmEUkJsWxz3uoewvqcFbWMH/7D/Dy/vOMCIphlmpdu4YlEhMRPCWjoVdhpl3om+dgmXf\nuzRvWEPVH35J1X/9MdCgZPpdqNi4UI/wC8Iuwy5IMgyOsJ097nQ6KSsra3ldVlaG0+n8wnYHDx5k\n7dq1LF26lIiIrveISdEzWS2KzP7xZPaPp6L+s6VjPn6/+xwrPihl6uAEZqU6GO4Kn6VjwaQsVsic\nimXCHfDJwcCM8zV/Qb/1Cmr6HJRnQeDetxAiLLRZtN1uNyUlJZSWluJ0Otm1axff//73r9jm+PHj\nrFixgieffBK7vXusBxU9jyPaxqJ0FwtHOfnkYh15BT52XJ6BPtAeSY7bQdbQRBKju99KSaUUjBqL\nddRY9MnCwIzzTa+j899ATcpCzV6EShkQ6mEK0eO1a8nXvn37WLVqFYZhkJWVRW5uLqtXr8btdpOZ\nmcmyZcsoLi7G4XAAgcsGjz32GAA/+clPOHPmDPX19SQkJPCd73ynzXvesuQr/PTUDGubmtl5soq8\nggqOltVjs8BtAxLIcdsZmxKH9Qaee97VMtSlJYEGJe/kg7/p8wYlw0aEbExdLcNwJBkGhzwRrRUp\n2uFHMoSTFQ3kFVaw9XglVQ3NJMXayHbbyR5mJzm+7eeed9UMdWUFOn89euubUFsDwzMCDUoybu30\nWwZdNcNwIhkGhxTtVqRohx/J8HNNzQbvn65mU6GPD0tqABibEovHHVgTHmG9+uS1rp6hrq9Fbw80\nKKGiLNCgZE4uKrPzGpR09QzDgWQYHFK0W5GiHX4kw6srrW5i8+Xnnl+o9ZMQZWXGkEQ8bjtDekVf\nsW13yVD7mwIPadm4BkpOBR7QknMPamoOKiq67R2Y0F0yDCXJMDikaLciRTv8SIbX12xoDp6vJa+g\ngvdOV+E3IM0VTY7bwZ1DEoiNsHa7DLVhwME9gQYlhZ9AfAIqax4qaz4qIbHtHdyE7pZhKEiGwSFF\nuxUp2uFHMmy/yno/W09UkldQQbGvkSir4o7BiSyZMJi+EQ3dcumYLjiCsWENfPg+REYFzrpnLUS5\n+gT1feQ4NE8yDA4p2q1I0Q4/kuGN01pztKweb2EF209UUe836J8YicdtZ+ZQO46Y7rd0TJ8pDiwX\ne38baI2aeGfgvveA4DQokePQPMkwOKRotyJFO/xIhubUNRkcLNes/fA0H1+ow6pg4oB4ctwOxve9\nsaVjXYEuv4DOW4fesREa6iFjQmDG+fDRpq40yHFonmQYHFK0W5GiHX4kQ/M+y/CUrwFvoY8tRT58\nDc04Y2xkD7PjcdtJSWh76VhXomuq0FveQm9eD1U+GDo8ULzH3R7o/32D5Dg0TzIMDinarUjRDj+S\noXl/m2FTs+aDM9XkFVawv6QGQ8OY5Fg8bjuTByUQeY2lY12RbmxA78pHb3oNLpyDlP6oWYsCT1u7\ngccey3FonmQYHFK0W5GiHX4kQ/Oul+HF2iY2F/rwFvk4X91EXKTl8tIxB8OcHbuMqjPp5mb0vl3o\nDa9CcRHYnSjP3ahpc9rVoESOQ/Mkw+CQot2KFO3wIxma154MDa356Hwt3gIf756qosnQuJ3R5Ljt\n3DkkkfhIayeNtmNpreHjA4EZ5x9/CDGxgc5i2XejHF9sSPQZOQ7NkwyDI2y7fAkhOo9FKcamxDE2\nJY6qhma2nQh0HXt+z3n+vK+UKYMSyHE7GN0npksvHVNKQfp4rOnj0SeOoTesQW9ci/a+jpo8M3Dp\nPKV/qIcpRNiRoi1EmEqIsjJ/hJN5w3tRWB547vn2E5VsPV5J34QIPG4HM4fZcXbxpWNqSBrqO4+h\nS8+iN74WuPe9Mw/GTw40KBmaFuohChE25PK4aBfJ0LxgZNjgN9hVXEVeYQWHS+uwKJjQL56cVDuZ\n/eK7xdIx7buEzn8DvfVtqKuBEbdgmZMLo2+ld+/echyaJJ/l4JB72q1I0Q4/kqF5wc7wTGUj3sIK\nthT5uFTfTK9oKzOH2fG4HfRL7PpLx3RdLXr7RrT3dagohwFDSVzyv6keMRZl7R739kNBPsvBIUW7\nFSna4UcyNK+jMvQbmr1nq8kr8LH3bDWGhtF9YvC4HdwxKIEoW9deOqabmtDvbUVvXAvnTgcalMxa\niLojBxUVFerhdTnyWQ4OKdqtSNEOP5KheZ2RYVltE1uOV+ItrKCkqonYCAvThiSS43bgdkZ16clr\n2jBIOP4JvldevNygJBE1cz4qay4qvmMalHRH8lkODinarUjRDj+SoXmdmaHWmsOldeQVVLDrVBWN\nzZqhvaLwuO1MH2InIaprXl5OSkriwoULcOxIoLvYRx8EGpTcOQuVsxDl6h3qIYY9+SwHhyz5EkIE\njVKKjORYMpJj+WZjMztOVJJXWMGKD0p5cd8FJg9MICfVTkZyLJYudvatlILho7EOH40+fSKwVGzr\nW+itb6EmTgs0KOk/ONTDFKJDtOtM+8CBA6xcuRLDMMjOzmbhwoVXfH39+vXk5+djtVpJTEzkwQcf\npHfvwF+8W7duZc2aNQDk5uYyY8aMNgclZ9rhRzI0LxwyLCoPdB3beqKSmkaD5PgIPMPszHTbSYpt\n/+NEQ+VaGeqyC+i819A7NkFjA9ySGXjGeVp6l74l0BHC4TjsDkJ1pm1dunTp0uttYBgGv/jFL3jq\nqadYtGgRK1euJD09ncTEz+8hNTY28qUvfYm5c+fS0NBAfn4+kydPprq6mt/97nf8y7/8C9nZ2fzu\nd79j2rRpREZef2ZrVVVVuwbfXrGxsdTW1gZ1nz2NZGheOGTYK8bGhP7xzB/Ri4H2SM5XN+Et8rH+\n00scvVhHpNVC34TIsD37vlaGKjYOlTEBNX0OREXD/ncDZ99HDqDiE6BPPynel4XDcdgdBDvHhISE\ndm3XZtE+duwYxcXF3HXXXVgsFmpqajh79iyjRo1q2aZPnz7YbIEr7RaLhV27djFz5kzef/99LBYL\nkydPJjIyktOnT9Pc3MygQYOuOygp2uFHMjQvnDK0WRRDekUzc5idGUMTibZZ2FdSQ16hj40FFVTU\nN9M71kZidHjdQWsrQxUZhRqRgcqaBw4nHN6P3rYB/cFOiIyEvoN6/HKxcDoOu7JQFe02P5Hl5eW4\nXK6W1y6Xi2PHjl1z+82bNzNu3Lirfq/T6aS8vLxdAxNCdI6+CZHcP643Xx6TxP6SGvIKK3jjk3Je\n+7icUb1j8LjtTB0cKOxdhYqKQmXNQ0+bg/5gZ+Axqat+j379P1Gee1DTZqNiYkM9TCFuWFD/jN6+\nfTtFRUW0cfL+BV6vF6/XC8Dy5ctJSkoK5rCw2WxB32dPIxma1xUynNOnN3PGQnlNI29/Usr6w+f5\n/e5zvLDvAp7hScwfnUJ6cnzILjXfVIbzFqPn5tJ44D1q1rxE019XwluvEH1XLrHz/w7rdRqUdEdd\n4TjsCkKVY5tF2+l0UlZW1vK6rKwMp/OLB/nBgwdZu3YtS5cuJeJyf1yn08mRI0datikvLyc9Pf0L\n3+vxePB4PC2vgz1JQiZemCcZmtfVMpw9OJpZgwbx8YW6wGXzj0tZd+g8g+1ReFLtzBiS2OmXz01l\nODAVHlqK5fgxjA2vUrvm/6P29ZdRU7JRsxei+rRvIlBX19WOw3AVqolobV7vcrvdlJSUUFpait/v\nZ9euXWRmZl6xzfHjx1mxYgWPPvoodru95d/HjRvHhx9+SHV1NdXV1Xz44Yctl86FEOFPKUV6n1ge\nmtyXFxen8t3bUoi0KV7YW8rX1xbyqx1n2F9SgxF+j3u4JjU0DeuDj2P5+f9DTc5C7/JiPP0gzc8v\nR5+49q0/IcJBu5Z87du3j1WrVmEYBllZWeTm5rJ69WrcbjeZmZksW7aM4uJiHA4HEPgL5LHHHgMC\n97jXrl0LBJZ8ZWVltTkoWfIVfiRD87pThicu1eMt9LH1uI+qRoPesTY8bgfZbju94zpu6VhHZKgr\nygMNSra9DXW1MHJMYLlY+rhuOeO8Ox2HoSRPRGtFinb4kQzN644ZNjUb7D5Vjbewgg/PBWbSju0b\nxyy3ndsGxBNhDe7ktY7MUNfWoLdvQHvfAF85DBqGmp2LmnBHt5px3h2Pw1CQot2KFO3wIxma190z\nPF/dSH6Rj/xCHxdr/SREWckaGnju+SBHcBp7dEaGuqkJvXsLetNaOHcGeqcEHpF6RzYqsus3KOnu\nx2FnkaLdihTt8CMZmtdTMmw2NB+eC6z5fv90FX4DhruiyUl1MHVwArERN3/W2qnPbzcMOPBe4Bnn\nx49Cgv3zBiVx7VtTG456ynHY0aRotyJFO/xIhub1xAx99X62Hg889/yUr5Fom+KOQYnkpNoZmRRz\nw/eMQ5Gh1hqOHg4U70N7ISoadedsVM4ClLPrNSjpicdhR5CGIUKIbscebeOeUU4WjOzF0bJ6NhVU\nsPNkJflFPgYkRpKTamfGUDuOMHvyWmtKKRiRgXVEBvr08cCDWja/gd6yHnXb9ECDkn7Xf8qjEMEi\nZ9qiXSRD8yTDgLomg3eKK9lU4OPTi3VYFdw2IJ4ct4NxfeOwWq599h0uGeqL59F5r6N3boLGRhh7\nG5bZuai0Lz6HItyES4ZdnZxpCyF6hJgICx63A4/bQbGvgfxCH5uLfLx7qhpXrI3sYXY8bjvJ8ddv\nLBRKKikZ9eVvoeffh96yHr35TYwPHwf3yMBysTETUZau89hX0XXImbZoF8nQPMnw2pqaNXvOVJFX\n4GN/SQ0aGJMSS47bwaSB8UReXjoWrhnqhnr0Ti867zUoK4W+AwPLxW6fhrKFV8vTcM2wq5GJaK1I\n0Q4/kqF5kmH7XKhpYnORD29hBaU1fuIjLUwfaifHbWdi2oCwzlD7/ZcblLwKZ05CrySUZwFq2ixU\ndHg0KJHjMDikaLciRTv8SIbmSYY3xtCag+dq8RZW8O6pavyGZmSfeGYMjmPakETiIsP3gSdaazi0\nLzDj/OghiI1DzZiHyp6HSuwV0rHJcRgcUrRbkaIdfiRD8yTDm1fZ0My24z62nKimsKyWSKvijkEJ\n5LgdpPe58aVjnUkXfRoo3gfeA1sEaspM1KxFqD59QzIeOQ6DQ4p2K1K0w49kaJ5kaJ7L5WL30dPk\nFfjYfqKSOr9Bv4RIPG47M4fZ6RUTvnNr9bnT6I1r0bu3QLOBmjAFNWcxarC7U8chx2FwSNFuRYp2\n+JEMzZMMzWudYb3fYFdxFXkFFRy5UIdFwcT+8Xjcdib0i7/u0rFQ0hVlaO/lBiX1dTBqbGDG+aix\nnXLFQI7D4JAlX0IIcQOibRZmDgucYZ+u/Hzp2Hunq+kV8/nSsb4J4bV0TDlcqHv/Hj13CXrbBnT+\nOozf/AQGp15uUDIZZQnf+/UitORMW7SLZGieZGheWxn6Dc0HZwJdx/aercHQkJEcS47bzuSBCUTZ\nwm/ttG5qRL+7Bb1xLZSeDTQombUocO+7AxqUyHEYHHJ5vBUp2uFHMjRPMjTvRjIsq/1s6ZiPc9VN\nxEVYmDYkkZxUB25ndAeP9MZpoxn2X25QcuJYoEFJ9t2oGXNRcfFBex85DoNDinYrUrTDj2RonmRo\n3s1kaGjNofO1eAt9vHuqisZmzbBeUXjcDqYPSSQ+KrwuRWut4dOPAsX78H6IikFNn43y3IPq5TK9\nfzkOg0PuaQshRAewKMWYlDjGpMRR3dDMthOVeAsr+OMH53lxfymTBiaQ47aTkRyLJQyWjimlYOQY\nrCPHoIuL0BvXoPPWofPXoyZND9z37jsw1MMUISJn2qJdJEPzJEPzgplhYXk9eQUVbD9RSU2TQUp8\nRMvSMVdseD16VF84h857Df2O9/MGJXMWo1JH3fC+5DgMDrk83ooU7fAjGZonGZrXERk2+A3ePVVF\nXqGPQ+drsSi4tW8cOakOMvvHYwujpWO6yofeHGhQQm01pKYHlovdMqHdDUrkOAyOsC7aBw4cYOXK\nlRiGQXZ2NgsXLrzi60eOHGHVqlWcPHmShx9+mEmTJrV87aWXXmL//v0ALF68mClTprQ5KCna4Ucy\nNE8yNK+jMyypasRb6CO/yMelOj+OaCtZQ+14Uu0MSAz+TO6bpevr0DvzAg1Kyi9Cv0GBy+a3TUPZ\nrn/XU47D4AhV0bYuXbp06fU2MAyDX/ziFzz11FMsWrSIlStXkp6eTmJiYss2WmvGjx9PfX09/fr1\nY8CAAQDs27eP999/n5///OfMmDGDP/7xj0yZMoWIiOtfeqqqqmrX4NsrNjaW2traoO6zp5EMzZMM\nzevoDBOirIxNiePuEb1Ic8Xga2hm83Efb35awcFzNViUom9CZMjPvpUtAjVsBCprHiT3g8JPYPtG\n9Lv5oIB+g6/ZXUyOw+AIdo4JCQnt2q7NiWgFBQWkpKSQnJwMwJQpU9izZ09LYQbo06cPwBee5nP6\n9GlGjRqF1WrFarUyaNAgDhw40K6zbSGECBWrRTFxQDwTB8Rzqc7f0nXst++W8Mc95y8vHbOT6owO\n6XPPlc2GmpyFnjQDPvoAY8Or6NUvoN9YjcqaG1gylmAP2fhE8LVZtMvLy3G5Pl9m4HK5OHbsWLt2\nPnjwYP76179y991309DQwOHDh68o9kIIEe56xdhYPNpFbrqTIxfq8BZWsOW4j40FFQx2RJHjtjN9\nqJ3EEC4dU0rBmIlYx0xEF34SKN5v/g9602uoOzyoWQtRvVNCNj4RPB265Gvs2LEUFhby9NNPk5iY\nyPDhw7FcZbKE1+vF6/UCsHz5cpKSkoI6DpvNFvR99jSSoXmSoXmhznB6b5iePojqBj/eoxd449B5\n/rS3lFUHLjDN7eLu0SlMGGgP7dKxpKlw+1T8p05Q8/p/Ub9tA3r7BqKmzCQu935sKSlyHAZBqI7F\nNou20+mkrKys5XVZWRlOp7Pdb5Cbm0tubi4Av/3tb+nb94vt6DweDx6Pp+V1sCdJyMQL8yRD8yRD\n88Ipw6l9I5jadwAnLtWTV+hj6/Fy8o9epE9cBNluO9nD7PSOC+HSsZh4uO9bWGYvRnvX0bBtAw07\nvUSOuw3/zLth5Jiwbmka7kI1Ea3NNQJut5uSkhJKS0vx+/3s2rWLzMzMdu3cMIyWSWUnT56kuLiY\nsWPHtut7hRCiKxjSK5pvZiazMjeVH97Rj74JEbx88CLffK2Qn20+xTvFlTQ1h25lrerlwrLk61h+\n9QJq0f/Cf6IA47kfYzzzQ/TedwKPTxVdRruWfO3bt49Vq1ZhGAZZWVnk5uayevVq3G43mZmZFBQU\n8Oyzz1JTU0NERAQOh4PnnnuOxsZGHnvsMSAw0+6b3/wmQ4YMaXNQsuQr/EiG5kmG5nWVDM9Xf750\nrKzWT2KUlayhiXhSHQyyh3bpmCsxgQtvvILeuAYunIM+/VCzF6Imz0RFhFdHtHAW1uu0O5sU7fAj\nGZonGZrX1TJsNjQHSmrIK/Tx/ukqmjWMSIohx21n6uBEYiI6v+vYZxlqoxn2vYuxYQ2cLIBEB8qz\nADV9Dio2eA1Kuisp2q1I0Q4/kqF5kqF5XTnDino/W4/7yCvwcbqykWibYurgRHLcDkYkdd7Ssb/N\nUGsNnxwMNCg5cgCiYwKF27MA5TDfoKS7koYhQgjRjTmibSwc5eKekU4+uViHt9DHzpOVeAt9DLRH\nkuN2MGNoIvbozv21rJSCUWOxjhqLPlkYaFCy6XW09w3UpBmXG5TIUt1wIWfaol0kQ/MkQ/O6W4a1\nTc3sPFmFt7CCTy/WY7PAbQMCXcfGpsRh7YAnr7UnQ11acrlBST74m2Ds7Vjm5KLcI4M+nq5KzrSF\nEKKHiY2wMivVwaxUB8UVDeQVVrD1eCW7iqtIirW1LB1Lju/cCWKqT1/UVx9E3/3lQIOSLW9hHNgN\nw0cHGpRkTJDlYiEiZ9qiXSRD8yRD83pChk3NmvfPVJFX4ONASQ0AY1Ni8bgdTBoYT4TV3OS1m8lQ\n19eid+Sh816HSxeh/2DUnFxU5p1tNijprmQiWitStMOPZGieZGheT8vwQk0T+YU+8osqKK3xkxBp\nYcZQOx63nSG9om9qn2Yy1P4m9Pvb0RvWQMkpcPZG5dyDunMWKurmxtNVSdFuRYp2+JEMzZMMzeup\nGRpac/BcLZsKKnjvdDV+Q5PmiibH7eDOIQnERrT/uefByFAbRkuDEgo+hrgEVNY81Mz5qITEtnfQ\nDUjRbkWKdviRDM2TDM2TDKGy3s/WE5V4C3yc9DUQZVXcMTiBHLeDUb1j2rzXHOwMdcGRwFrvD9+H\nyEjUHTmBBiVJyUF7j3AkE9GEEEK0KTHaxoKRTu4e0YtjZfXkFVaw/UQVm4sq6Z8YiWeYnZnD7Dhi\nOufXu0pNx/oP6eizxeiNa9HbN6C3vR243z0nFzVwaKeMo6eQM23RLpKheZKheZLh1dX7Dd45WUle\noY+PL9RhVZDZP55ZqQ7G971y6VhHZ6jLL6K9r6O3b4KGOsi4NTDjfHhGt5pxLpfHW5GiHX4kQ/Mk\nQ/Mkw7ad9jXgLfSx+bgPX30zzhgbM4cFJq/1TYjstAx1TTV661vo/DegygdDh2OZkwvjbkdZQtd7\nPFikaLciRTv8SIbmSYbmSYbt5zc0e85Uk1dQwf6SGgwNtyTHsmjcADIcEGXrnOee68YG9K589KbX\nAg1KkvujZi9CTcpCRYSwdalJUrRbkaIdfiRD8yRD8yTDm3OxtonNRT7yC32cq24iLtLC9CGB554P\nc3bOUi3d3Izetwu94VUoLgJ7L1T2Zw1K4jplDMEkRbsVKdrhRzI0TzI0TzI0x9CaU/UR/HVfMe8W\nV9FkaNzOKDxuB9OGJBIf2fGXrbXW8PGBwIzzjz+EmFjUtM8alDg7/P2DRWaPCyGE6FAWpZgw0MHg\nGD9Vmc1sP1FJXmEF/7HnPCv3lTJlYAKeVDsZfWI7bNKYUgrSx2NNH48+WYB++1X0ptfQ+esCPb1n\nLUKl9O+Q9+4OpGgLIUQPlBBlZd6IXswd7qCwvAFvYQXbTlSy9UQlfRMi8AxzkDUsEVdsx913VoNT\nUd95DF16NlC438lH78yD8ZOwzFmMGjq8w967q5LL46JdJEPzJEPzJEPzrpdhg99gV3Gg69ih0jos\nCib0iyfHbWdC/3hsHdB1rDVdeQmdvx699S2orYERtwRmnI++NeyWi8nlcSGEECEVZbOQNcxO1jA7\nZysb8RZWsLnIx54z1fSKtpI1zI7H7aB/Ysd0HVOJvVCL/hf6rsXo7RvReeswfvszGDAk0Nd74p0o\nayS5z2UAABSpSURBVNdfLmaGnGmLdpEMzZMMzZMMzbvRDJsNzQdnq/EW+vjgTDWGhtF9YvC4Hdwx\nKKFDl45pfxP6vW2BBiXnToOrDypnIWpqDioqqsPetz3Cevb4gQMHWLlyJYZhkJ2dzcKFC6/4+pEj\nR1i1ahUnT57k4YcfZtKkSS1fe+mll9i3bx9aa2655Ra+/vWvt3mZQ4p2+JEMzZMMzZMMzTOTYXmd\nn81FPryFFZRUNREbYWHakEQ8bjupzugOu4StDQMO7gk0KCn8BOITUFnzUTPnoeJD06AkbC+PG4bB\nCy+8wNNPP43L5eKJJ54gMzOTAQMGtGyTlJTEd7/7Xd54440rvvfTTz/l008/5dlnnwXgxz/+MUeO\nHGH06NE38rMIIYQIA84YG/eOdrE43cnh0jryLl8+33CsgiGOKHJS7UwfYichKriXsJXFAuNuxzru\ndvSxIxgbXkW/8TJ645pAW9Cce1CuPkF9z3DVZtEuKCggJSWF5ORAx5YpU6awZ8+eK4p2nz6BsP72\nryylFI2Njfj9frTWNDc3Y7fbgzl+IYQQnUwpRUZyLBnJsXwzs5kdJwLPPV/xQSkv7rvA5MtLx25J\njsUS5LNvlZaONS0dfeYkeuOawKNSt7yJum1a4L73gCFBfb9w02bRLi8vx+Vytbx2uVwcO3asXTsf\nPnw4o0eP5lvf+hZaa+bMmXNFsf+M1+vF6/UCsHz5cpKSkto7/nax2WxB32dPIxmaJxmaJxmaF+wM\nk4Ah/ZL5X1Pg6IVq1h8+z6ZPStl+spJ+iVHMG53M3FHJ9EkI8j3opCQYO4HmC+eofWM1dXnrMHZv\nJXLCZOIW3U9E+rgOnXEeqmOxQ2ePnzt3jjNnzvD8888DsGzZMj7++GNGjRp1xXYejwePx9PyOtj3\nrOQ+mHmSoXmSoXmSoXkdmaFTwdcy7Nw3KoHdpwLPPV/xbjEv7C5mfN84ctwOMvvHE2ENYjFVNljw\nVVT2AtjyJo3562l8+nswbESgu9jY2wKX14MsbO9pO51OysrKWl6XlZXhdLbvUXPvv/8+aWlpREcH\nnm07fvx4jh49+oWiLYQQovuItAYmqE0bksi5qkbyLz/3fPmOM9ijPls6ZmegPXhn3youATX/Pv7/\n9u4+KKr73uP4ex9YQB52YZcHEczKiokmkZouStBoFMi9ibF6vQ1xkt7EG7yTCreTVuvY3GkzqdFU\nR9Q2iY6OjYY4t63MTXWqNbVCUBMxSkCNjw2g4hMReVoeBGF3z/2DuhPbGEl2w+Hg9zXjzK67e85n\nvzp8Ob9zfuenZP8bSlkxyu5teNe9DvHDeu+ypvEFSm66468fDoeDuro66uvrcbvdlJWV4XQ6+7Rx\nm83G6dOn8Xg8uN1uTp06xbBhcns6IYS4W8RHmHg2NYaNsxz84tFERseGsuNME/+98xyLd9dSXNNC\nZ483YPvTBQejnzod/bIN6OYtBKMJ5d238P7Pf+Hd/UeUzusB25ca+jTlq7KyksLCQrxeL1OnTmX2\n7Nls3boVh8OB0+mkurqagoICOjo6CAoKwmKxsHr1arxeL7/97W85ffo0AN/5znd4/vnn7xhKpnwN\nPFJD/0kN/Sc19N9AqGFLp5vScy721Li43NpNiFHPI/dEkD3SwihrYKeOKYoCJ4/0Thf723EIDUP3\n6L/2rjBmjvrG2x3Q87T7mzTtgUdq6D+pof+khv4bSDVUFIUz1zrZU+Pio9pWbngUhptNZDksTB0R\nSWRIYC+7Us5V4d39HlQeBIMRXcbfFyiJ61vD/CJp2l8gTXvgkRr6T2roP6mh/wZqDa/3ePioto2/\nVrdQ1diFUQ8TEiPIcphJjQ/DEMD7nitXr6D8dRtKWQl4PPDQw70LlNhT+ryNAXshmhBCCPFtGxJk\n4LGRFh4baaG25QZ7alrYe9bFgQttxAwxkukwk5lsITbc/4vJdHEJ6P4jH+V7z6CU/All7/t4K8r+\nvkDJv8P94wbcAiU3yZG26BOpof+khv6TGvpPSzXs8Xg5dKmdPTUujtV1AJA6NIxsh5kJieEEGQIz\nlUvpvI6y/y8oe/4EriZIGtF7oxbnpNsuUCLD418gTXvgkRr6T2roP6mh/7Raw/r2HkrOtlBc46Lh\nupuIYAOPjogk22HhHktgpo4pPT0oH5ei/HUbfH4ZbHHoHpuFLiPrnxYokab9BdK0Bx6pof+khv6T\nGvpP6zX0eBWOfd5BcY2LQ5facHthlDWE7JEWJt0TwZAg/+97rni9cOxw7xXnZ/8G4ZHoMp9EN3U6\nurAIQJr2LaRpDzxSQ/9JDf0nNfTfYKqhq8vN3nOtFNe0cMHVTbBBx6R7Isl2mLkvJtTv89KKokDV\nSbx/+SMc/wSCQ3wLlMSMGi0XogkhhBB9ZQ4xMnN0NN+7L4rPGrvYU93Ch7VtlJx1kRhpIsthZmqy\nGcs3nDqm0+lg1AMYRj2Acul87wIlH+xEKf0zXQuXQMqDAf5GfcgkR9qiL6SG/pMa+k9q6L/BXsPO\nHi8HLrSyp9rFmYZODDoYnxhOlsPCuKH+Tx1TGutRiv+E7Qcv0tTjCVBqOdIWQghxFwoN0pPlsJDl\nsHDRdYPiGhelZ10cvNiONbR36liWw0xcuOkbbV9njUX39Dz05ihQ4ZcfadpCCCEGpSRzMP/5UCw/\nSI2h/HIbxTUu/u9kI0UnGhkbN4TskRbSk8IxBWjqWH+Qpi2EEGJQCzLoyBgeScbwSK519PDBWRfF\nNS5WHbhCuEnPlBFmsh1mRkSFqB31jqRpCyGEuGvEhAXx9IM2nnrAyvGr19lT3cLuqhb+/LdmHNEh\nZDvMTLZHEmbyf+rYt0GathBCiLuOXqcjNT6M1PgwWm942Heu9+h7fflVNlXWkzE8gsccFsbE+j91\nLJCkaQshhLirRQYbmHFfNE/eG0V1Uxd7ql18WNvK3nOtJEQEkemwMC3ZTHSo+i1T/QRCCCHEAKDT\n6UixhpJiDSX3u7EcuNDGnuoWthy9xv8eu4ZzWDjZDjPfTQhXLaM0bSGEEOIfBBv1TEs2My3ZzOXW\nboprWvjgrIvDl9qJCjXyi38x4gjr/1zStIUQQoivMCzSxPPjYnk2NYaKy+3sqWkhITIEPB39nkWa\nthBCCNEHRr2OCUkRTEiKwBYVSkPDAG3aR48eZfPmzXi9XjIzM5k1a9Ytr586dYrCwkJqa2v58Y9/\nTHp6OgAnTpygsLDQ974rV67w0ksvMX78+AB+BSGEEOLucMem7fV6efvtt/n5z3+O1Wrl5Zdfxul0\nkpiY6HuPzWYjLy+PHTt23PLZBx54gJUrVwLQ3t7Oj370I1JTUwP8FYQQQoi7wx2bdnV1NfHx8cTF\nxQGQkZFBeXn5LU07NjYW4Cvnsn388ceMGzeO4ODALFYuhBBC3G3ueMPVpqYmrFar77nVaqWpqelr\n7+jAgQNMnDjxa39OCCGEEL365UK05uZmLly4cNuh8eLiYoqLiwFYvnw5NpstoPs3Go0B3+bdRmro\nP6mh/6SG/pMaBoZadbxj046OjqaxsdH3vLGxkejo6K+1k4MHDzJ+/HiMxi/fXVZWFllZWb7ngV7r\ndbCvH9sfpIb+kxr6T2roP6lhYAS6jn1dT/uOw+MOh4O6ujrq6+txu92UlZXhdDq/VhgZGhdCCCH8\nd8cjbYPBwAsvvMCyZcvwer1MnTqVpKQktm7disPhwOl0Ul1dTUFBAR0dHVRUVFBUVMTq1asBqK+v\np6GhgTFjxnzrX0YIIYQYzHSKoihqhxBCCCHEnd1xeHww+NnPfqZ2BM2TGvpPaug/qaH/pIaBoVYd\n74qmLYQQQgwG0rSFEEIIjTC8+uqrr6odoj8kJyerHUHzpIb+kxr6T2roP6lhYKhRR7kQTQghhNAI\nGR4XQgghNGJQr6edn59PSEgIer0eg8HA8uXL1Y6kOR0dHaxfv56LFy+i0+mYP38+o0aNUjuWply5\ncoU1a9b4ntfX15OTk8P06dNVTKU9O3fu5IMPPkCn05GUlEReXh4mk0ntWJqya9cuSkpKUBSFzMxM\n+T/YB+vWraOyshKz2cyqVauA3lUr16xZw7Vr14iJieEnP/kJ4eHh/RNIGcTy8vIUl8uldgxNe/PN\nN5Xi4mJFURSlp6dHaW9vVzmRtnk8HmXevHlKfX292lE0pbGxUcnLy1Nu3LihKIqirFq1SiktLVU3\nlMbU1tYqCxYsULq6uhS3260sWbJEqaurUzvWgHfy5EmlpqZGWbBgge/vtmzZomzbtk1RFEXZtm2b\nsmXLln7LI8Pj4rauX7/O6dOnmTZtGtB7g/ywsDCVU2nb8ePHiY+PJyYmRu0omuP1eunu7sbj8dDd\n3U1UVJTakTTl8uXLjBw5kuDgYAwGA6NHj+bQoUNqxxrwxowZ809H0eXl5UyZMgWAKVOmUF5e3m95\nBvXwOMCyZcsAyM7OvmVREnFn9fX1REZGsm7dOmpra0lOTmbu3LmEhISoHU2z5D7830x0dDQzZsxg\n/vz5mEwmUlNTb7tqoPhySUlJ/OEPf6CtrQ2TycSRI0dwOBxqx9Ikl8vl+6XRYrHgcrn6bd+Dumm/\n9tprREdH43K5WLp0KQkJCXIP9K/B4/Fw7tw5XnjhBVJSUti8eTPbt29nzpw5akfTJLfbTUVFBc88\n84zaUTSnvb2d8vJy1q5dy5AhQ1i9ejX79+9n8uTJakfTjMTERGbOnMnSpUsJCQnBbrej18tgq790\nOh06na7f9jeo/8VuLiFqNptJS0ujurpa5UTaYrVasVqtpKSkAJCens65c+dUTqVdR44cYcSIEVgs\nFrWjaM7x48eJjY0lMjISo9HIhAkT+Oyzz9SOpTnTpk1jxYoV/PKXvyQsLIyhQ4eqHUmTzGYzzc3N\nADQ3NxMZGdlv+x60Tburq4vOzk7f408//ZThw4ernEpbLBYLVquVK1euAL0/OBMTE1VOpV0yNP7N\n2Ww2qqqquHHjBoqicPz4cYYNG6Z2LM25OYzb0NDA4cOHmTRpksqJtMnpdLJv3z4A9u3bR1paWr/t\ne9DeXOXq1asUFBQAvcO8kyZNYvbs2Sqn0p7z58+zfv163G43sbGx5OXl9d/UhkGkq6uLvLw83nrr\nLYYMGaJ2HE0qKiqirKwMg8GA3W7nhz/8IUFBQWrH0pRXXnmFtrY2jEYjzz33HA8++KDakQa8X//6\n15w6dYq2tjbMZjM5OTmkpaWxZs0aGhoa+n3K16Bt2kIIIcRgM2iHx4UQQojBRpq2EEIIoRHStIUQ\nQgiNkKYthBBCaIQ0bSGEEEIjpGkLMQjl5OTw+eefqx3jnxQVFfHGG2+oHUMIzRrUtzEVYiDIz8+n\npaXllltGPvroo+Tm5qqYSgihRdK0hegHixcvZuzYsWrHGFQ8Hg8Gg0HtGEL0K2naQqho7969lJSU\nYLfb2b9/P1FRUeTm5vruVNXU1MTGjRs5c+YM4eHhzJw507dandfrZfv27ZSWluJyuRg6dCiLFi3C\nZrMB8Omnn/L666/T2trKpEmTyM3N/dKFDYqKirh06RImk4nDhw9js9nIz8/3rQCVk5PDG2+8QXx8\nPABr167FarUyZ84cTp48yZtvvsnjjz/Ojh070Ov1zJs3D6PRSGFhIa2trcyYMeOWuxH29PSwZs0a\njhw5wtChQ5k/fz52u933fTdt2sTp06cJCQlh+vTpPPHEE76cFy9eJCgoiIqKCp577jkyMzO/nX8Y\nIQYoOacthMqqqqqIi4vj7bffJicnh4KCAtrb2wH4zW9+g9VqZcOGDSxcuJDf//73nDhxAoCdO3dy\n4MABXn75ZQoLC5k/fz7BwcG+7VZWVvKrX/2KgoICDh48yLFjx26boaKigoyMDN555x2cTiebNm3q\nc/6WlhZ6enpYv349OTk5bNiwgQ8//JDly5ezZMkS3nvvPerr633v/+STT3j44YfZtGkTEydOZOXK\nlbjdbrxeLytWrMBut7NhwwZeeeUVdu3axdGjR2/5bHp6Ops3b+aRRx7pc0YhBgtp2kL0g5UrVzJ3\n7lzfn+LiYt9rZrOZ6dOnYzQaycjIICEhgcrKShoaGjhz5gzPPvssJpMJu91OZmamb6GCkpIS5syZ\nQ0JCAjqdDrvdTkREhG+7s2bNIiwsDJvNxv3338/58+dvm+++++7joYceQq/XM3ny5K987z8yGAzM\nnj0bo9HIxIkTaWtr44knniA0NJSkpCQSExNv2V5ycjLp6ekYjUaefPJJenp6qKqqoqamhtbWVr7/\n/e9jNBqJi4sjMzOTsrIy32dHjRrF+PHj0ev1mEymPmcUYrCQ4XEh+sGiRYtue047Ojr6lmHrmJgY\nmpqaaG5uJjw8nNDQUN9rNpuNmpoaABobG4mLi7vtPr+4BGhwcDBdXV23fa/ZbPY9NplM9PT09Pmc\ncUREhO8iu5uN9B+398V9W61W32O9Xo/Var1lmcO5c+f6Xvd6vYwePfpLPyvE3UiathAqa2pqQlEU\nX+NuaGjA6XQSFRVFe3s7nZ2dvsbd0NDgWyfearVy9erVb33J2eDgYG7cuOF73tLS4lfzbGxs9D32\ner00NjYSFRWFwWAgNjZWpoQJ8RVkeFwIlblcLt5//33cbjcHDx7k8uXLjBs3DpvNxr333svvfvc7\nuru7qa2tpbS01HcuNzMzk61bt1JXV4eiKNTW1tLW1hbwfHa7nY8++giv18vRo0c5deqUX9s7e/Ys\nhw4dwuPxsGvXLoKCgkhJSWHkyJGEhoayfft2uru78Xq9XLhwgerq6gB9EyG0T460hegHK1asuGWe\n9tixY1m0aBEAKSkp1NXVkZubi8ViYcGCBb5z0y+99BIbN27kxRdfJDw8nKeeeso3zH7zfPDSpUtp\na2tj2LBh/PSnPw149rlz57J27Vp2795NWloaaWlpfm3P6XRSVlbG2rVriY+PZ+HChRiNvT+KFi9e\nzLvvvkt+fj5ut5uEhASefvrpQHwNIQYFWU9bCBXdnPL12muvqR1FCKEBMjwuhBBCaIQ0bSGEEEIj\nZHhcCCGE0Ag50hZCCCE0Qpq2EEIIoRHStIUQQgiNkKYthBBCaIQ0bSGEEEIjpGkLIYQQGvH/mabC\neUwVoYwAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc4359d8780>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAAENCAYAAAAbl4wiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nzVwIIYTwcNLMhRBCCA/3/wMzjlojA5FJAwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc435999ef0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    final error(train) = 1.75e-01\n",
-      "    final error(valid) = 1.72e-01\n",
-      "    final acc(train)   = 9.50e-01\n",
-      "    final acc(valid)   = 9.53e-01\n",
-      "    run time per epoch = 9.98\n",
-      "--------------------------------------------------------------------------------\n",
-      "learning_rate=0.20 init_scale=0.20\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAENCAYAAAAi8D15AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nLYQQQpiENG0hhBDCJKRpCyGEECbxf+kLMXkn1E5rAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc43594c5f8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAAENCAYAAAAbl4wiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\naHuZcMLyOgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc435c3b828>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    final error(train) = 1.70e-01\n",
-      "    final error(valid) = 1.69e-01\n",
-      "    final acc(train)   = 9.52e-01\n",
-      "    final acc(valid)   = 9.55e-01\n",
-      "    run time per epoch = 10.36\n",
-      "--------------------------------------------------------------------------------\n",
-      "learning_rate=0.20 init_scale=0.50\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAENCAYAAAAi8D15AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc435cf9e10>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAENCAYAAADngqfoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n9EIIIYQf+3+DEMdHykKKUgAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc435a3feb8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    final error(train) = 1.69e-01\n",
-      "    final error(valid) = 1.71e-01\n",
-      "    final acc(train)   = 9.51e-01\n",
-      "    final acc(valid)   = 9.52e-01\n",
-      "    run time per epoch = 10.14\n",
-      "--------------------------------------------------------------------------------\n",
-      "learning_rate=0.20 init_scale=1.00\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAENCAYAAAAi8D15AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc435f2d9e8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAAENCAYAAAAbl4wiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc435de5588>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    final error(train) = 2.05e-01\n",
-      "    final error(valid) = 2.07e-01\n",
-      "    final acc(train)   = 9.40e-01\n",
-      "    final acc(valid)   = 9.39e-01\n",
-      "    run time per epoch = 10.99\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Set training run hyperparameters\n",
-    "batch_size = 100  # number of data points in a batch\n",
-    "num_epochs = 10  # number of training epochs to perform\n",
-    "stats_interval = 5  # epoch interval between recording and printing stats\n",
-    "learning_rate = 0.2  # learning rate for gradient descent\n",
-    "\n",
-    "init_scales = [0.1, 0.2, 0.5, 1.]  # scale for random parameter initialisation\n",
-    "final_errors_train = []\n",
-    "final_errors_valid = []\n",
-    "final_accs_train = []\n",
-    "final_accs_valid = []\n",
-    "\n",
-    "for init_scale in init_scales:\n",
-    "\n",
-    "    print('-' * 80)\n",
-    "    print('learning_rate={0:.2f} init_scale={1:.2f}'\n",
-    "          .format(learning_rate, init_scale))\n",
-    "    print('-' * 80)\n",
-    "    # Reset random number generator and data provider states on each run\n",
-    "    # to ensure reproducibility of results\n",
-    "    rng.seed(seed)\n",
-    "    train_data.reset()\n",
-    "    valid_data.reset()\n",
-    "\n",
-    "    # Alter data-provider batch size\n",
-    "    train_data.batch_size = batch_size \n",
-    "    valid_data.batch_size = batch_size\n",
-    "\n",
-    "    # Create a parameter initialiser which will sample random uniform values\n",
-    "    # from [-init_scale, init_scale]\n",
-    "    param_init = UniformInit(-init_scale, init_scale, rng=rng)\n",
-    "\n",
-    "    # Create a model with two affine layers\n",
-    "    hidden_dim = 100\n",
-    "    model = MultipleLayerModel([\n",
-    "        AffineLayer(input_dim, hidden_dim, param_init, param_init),\n",
-    "        SigmoidLayer(),\n",
-    "        AffineLayer(hidden_dim, output_dim, param_init, param_init)\n",
-    "    ])\n",
-    "\n",
-    "    # Initialise a cross entropy error object\n",
-    "    error = CrossEntropySoftmaxError()\n",
-    "\n",
-    "    # Use a basic gradient descent learning rule\n",
-    "    learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n",
-    "\n",
-    "    stats, keys, run_time, fig_1, ax_1, fig_2, ax_2 = train_model_and_plot_stats(\n",
-    "        model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)\n",
-    "\n",
-    "    plt.show()\n",
-    "\n",
-    "    print('    final error(train) = {0:.2e}'.format(stats[-1, keys['error(train)']]))\n",
-    "    print('    final error(valid) = {0:.2e}'.format(stats[-1, keys['error(valid)']]))\n",
-    "    print('    final acc(train)   = {0:.2e}'.format(stats[-1, keys['acc(train)']]))\n",
-    "    print('    final acc(valid)   = {0:.2e}'.format(stats[-1, keys['acc(valid)']]))\n",
-    "    print('    run time per epoch = {0:.2f}'.format(run_time * 1. / num_epochs))\n",
-    "\n",
-    "    final_errors_train.append(stats[-1, keys['error(train)']])\n",
-    "    final_errors_valid.append(stats[-1, keys['error(valid)']])\n",
-    "    final_accs_train.append(stats[-1, keys['acc(train)']])\n",
-    "    final_accs_valid.append(stats[-1, keys['acc(valid)']])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "| init_scale | final error(train) | final error(valid) | final acc(train) | final acc(valid) |\n",
-      "|------------|--------------------|--------------------|------------------|------------------|\n",
-      "| 0.1        | 1.75e-01           | 1.72e-01           |  0.95            | 0.95             |\n",
-      "| 0.2        | 1.70e-01           | 1.69e-01           |  0.95            | 0.95             |\n",
-      "| 0.5        | 1.69e-01           | 1.71e-01           |  0.95            | 0.95             |\n",
-      "| 1.0        | 2.05e-01           | 2.07e-01           |  0.94            | 0.94             |\n"
-     ]
-    }
-   ],
-   "source": [
-    "j = 0\n",
-    "print('| init_scale | final error(train) | final error(valid) | final acc(train) | final acc(valid) |')\n",
-    "print('|------------|--------------------|--------------------|------------------|------------------|')\n",
-    "for init_scale in init_scales:\n",
-    "    print('| {0:.1f}        | {1:.2e}           | {2:.2e}           |  {3:.2f}            | {4:.2f}             |'\n",
-    "          .format(init_scale, \n",
-    "                  final_errors_train[j], final_errors_valid[j],\n",
-    "                  final_accs_train[j], final_accs_valid[j]))\n",
-    "    j += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Models with three affine layers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--------------------------------------------------------------------------------\n",
-      "learning_rate=0.20 init_scale=0.10\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAENCAYAAAAi8D15AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc435b57b00>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAAENCAYAAAAbl4wiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xt4VOW5///3mpmczzOBBEg4JaDhDIaDiIGQECyIxVOt\nIliBqlWD31raS3vx6+63u7R0g1d7tZe6u93irnSnpd/WYLXahAARNEiCFgVBzYRjCJDjTCaHSTKz\nnt8fEwIRJECGzExyv/5ynMWaZ25C7tyzPnmWppRSCCGEECJgGXy9ACGEEEL0jjRzIYQQIsBJMxdC\nCCECnDRzIYQQIsBJMxdCCCECnDRzIYQQIsBJMxdCCCECnDRzIYQQIsBJMxdCCCECnDRzIYQQIsCZ\nfL2Aa1VVVeW1c8XHx1NbW+u18w1EUsPekxp6h9Sx96SGveftGg4dOvSqjpPJXAghhAhw0syFEEKI\nACfNXAghhAhwAXfN/KuUUjidTnRdR9O0a/qz586do62t7QatLPAopTAYDISGhl5zLYUQQvhOwDdz\np9NJUFAQJtO1vxWTyYTRaLwBqwpcLpcLp9NJWFiYr5cihBDiKgX8x+y6rl9XIxeXZzKZ0HXd18sQ\nQghxDQK+mcvHwd4nNRVCiMAiI60QQgjRSy0dbt4/4WBwvWKKue8HooCfzPsDpRT3338/DocDu93O\n//zP/1zXeZYvX47dbr/iMT/72c94//33r+v8QgghurPWOXlp31kefaOCF/edZeeXvtl0RyZzP7Bj\nxw7GjRtHVFQUp06d4vXXX+c73/nOJce5XK4r5gO2bNnS42utXLmSH/7wh8yZM6c3SxZCiAGrpcPN\ne8ca2V5ho6K+jWCjxu0jolk4JpbZNyVRV1fX52uSZu4FK1eupKqqira2NlatWsXDDz/Mrl272LBh\nA263G7PZzF/+8heam5tZt24dn376KZqm8f3vf5/FixeTn5/PsmXLAPjFL37BiRMnWLBgARkZGWRl\nZbFx40ZiYmKwWq28//77l309gJkzZ/Luu+/S3NzMww8/zIwZM9i/fz+JiYls3ryZsLAwkpKSaGho\noLq6msGDB/uybEIIETCUUpTXOSmw2thzvJE2t2JkbAiPT08gY2Q0kcGe34zyVeaoXzVz/c+voE4d\nu/rjNQ2l1BWP0ZJHYfj2d694zAsvvEBcXBytra0sXryYhQsX8sMf/pA33niD4cOH09DQAMBvfvMb\noqKi2LFjBwA2mw2AsrIyfvWrXwHw4x//mC+++ILt27cDUFJSwsGDB9m5cyfDhw+/7OstWrQIs9nc\nbU3Hjh3jxRdfZOPGjTz++OO888473HvvvQBMnDiRsrIyFi9efNW1EkKIgai53c17xxsptNo41tBG\nqEkjY2Q0OamxjLH4z54c/aqZ+8rmzZt59913Ac+NYP74xz8ya9asruYbFxcHwJ49e3jppZe6/lxs\nbCzgaeqRkZFfe/4pU6Z0netyr3fs2LFLmnlycjITJkwAYNKkSZw6darrOYvFwrlz5677/QohRH+m\nlOKLWs8U/v6JRtrditFxIXxvhmcKDw/yv/1J+lUz72mC/iqTyYTL5erVa5aUlLBnzx7eeustwsLC\nuO+++xg/fjwVFRXXtA5d1zEYLp9HDA8Pv+LrXW4Xu5CQkK7/NhqNOJ3OrsdtbW2EhoZe9fqEEGIg\naGpzU3zcTmG5nRP2NkJNBjJHxZCTGkuqxb+/Z0qavZccDgcxMTGEhYVhtVr5+OOPaWtr48MPP+Tk\nyZMAXR+zZ2RkdEuqn/+YffTo0Zw4cQKAiIgImpqarun1rtXRo0e56aabrvnPCSFEf6OU4nB1C78u\nqeLRfCuv7K8m2KTx1MxE/ueeVJ6cmej3jRz62WTuC/PmzWPLli3MnTuXlJQUpk2bhsVi4T/+4z9Y\nvXo1uq4THx/Pn//8Z5555hl+/OMfM3/+fAwGA88++yyLFi0iKyuLvXv3MmrUKMxmM9OnT2f+/Plk\nZmaSlZXV4+tdi46ODo4fP87kyZO9WQYhhAgojW1uio/ZKSi3UdnYTpjJQNZozxQ+2uz/zfurNNVT\nAszPVFVVdXvc0tLS7WPoa+GNj9m94dy5czzzzDP8+c9/vuGv9e6773Lw4EF+9KMffe0x11LT+Ph4\namt983uV/YXU0Dukjr3X32uolOKz6lYKrDb2nnTQoStuig8lJzWWOSOiCTX1/sNqb9dw6NChV3Wc\nTOZ+ICEhgYceegiHw0FUVNQNfS2Xy8Xjjz9+Q19DCCH8SaPTxc5jdgqtdk43thMRZCAn1TOFj4wL\nvCn8cqSZ+4m77rqrT15nyZIlffI6QgjhS7pSHDrXQoHVxoenmnDpirRBYdx36xBuGx5FiBemcH8i\nzVwIIUS/YXO62Flhp7DCxhlHB5HBBr4xJpac1FiGx4b0fIIAJc1cCCFEQNOV4tOzLRRabeyrdODS\nYfzgML49MZ5bk/tmCldKweef0h4TA0NH3vDX+6qrauYHDhzgtddeQ9d1srKyWLp0abfna2pqePnl\nl2lsbCQyMpLc3FwsFkvX8y0tLTz77LNMnz6dVatWAfDTn/6UhoYGgoODAVi3bh0xMTHeel9CCCH6\nuYZWFzs6p/BzTR1EhRhZPDaOBamxJMf0zRSuXC7URx+gCrfByQqaJ0+Hp/+/Pnnti/XYzHVd59VX\nX2XdunVYLBaef/550tPTSUpK6jpmy5YtZGRkMG/ePA4dOkReXh65ubldz2/dupW0tLRLzr1mzRpS\nUlK89FaEEEL0d7pSHDjTTKHVRmllE24FExLCeXjyIGYlRxJs7Jtr4crZgtqzHVX0d6ivgcRhaMuf\nIvbO+6hrdPTJGi7W47u2Wq0kJiaSkJCAyWRi9uzZlJWVdTumsrKya+vQ8ePHs3///q7njh49it1u\nl99rvoKLb4F6PcaMGQPA2bNn+e53L78L3n333ccnn3wCwAMPPNC1YY0QQgSCupYO/nKwlsffrOD/\n7qrks+pW7rrZzEtLRrM+ezgZI6P7pJErWx363/6A/qNVqL+8CpZBGJ5eh+H/voghYyFasG+uy/c4\nmdfX13f7yNxisVBeXt7tmBEjRlBaWsqiRYsoLS2ltbUVh8NBREQEr7/+Orm5uRw8ePCSc7/00ksY\nDAZmzpzJvffe6zcb1ve1i2+B2huJiYm88sorPR5377338oc//IFnnnmmV68nhBA3kltX/KtzCi87\n3YSuYFJiOI9MHczMpEiC+mgKB1CnT6AKt6H2vQe6DtNmYci5G220f+ym6ZUA3PLly9m8eTPFxcWk\npaVhNpsxGAwUFhYyderUbj8MnLdmzRrMZjOtra288MIL7N69m7lz515yXFFREUVFRQBs2LCB+Pj4\nbs+fO3fuivf47klv/ux5jzzySNctSVevXs2KFSvYuXMnv/jFL7pugfq3v/2N5uZmfvzjH3PgwAE0\nTWPt2rXceeedbNu2jeXLl2Mymfj3f/93hg0bxsqVKwHYuHEjERERPPLII6xYsQK73U5HRwfPPfcc\n3/jGN7q9j5MnT/Lwww+ze/duWltbeeaZZzh8+DCpqak4nU6MRiMmk4lFixZx11138YMf/OCy7yck\nJOSSOl+pfld7rLg8qaF3SB17z19qeM7Rxj8+O8fbn53jXFMbcWFBPHRLEkvGJ5AUG9Zn61BK0XHw\nI5rf/BPtH++F4BDCFi4l/M5vYRqSdNk/46sa9tjJzGZztxut19XVXXKHLrPZzNq1awFwOp3s27eP\niIgIvvzyS44cOUJhYSFOpxOXy0VoaCjLli3rOkdYWBhz5szBarVetplnZ2eTnZ3d9firO+u0tbVh\nNHruYPPf+89xrMHJ1dKu4haoo+JCWZ2ecMVjNm3a1O2WpAsWLODZZ5/tdgtUl8vFpk2biIiI6HYL\nVJfLRWlpKRs2bMDlcrFkyRL+7d/+jRUrVgDw5ptv8r//+78YjUb++7//m6ioKOrr61myZAnZ2dld\nn2a4XC7cbnfXf2/evJnQ0FCKi4s5fPgwd9xxB263G5fLRWRkJG1tbVRXV1/yd3m+ple7g1F/3zGq\nL0gNvUPq2Hu+rKFbV3xU1USh1cZHVc3oCqYMieA7Uy1MHxZFkFEDVzO1tc03fC3K7Ubtf78r1EZU\nDNo3l6HN+wbtkdG0A3xNnfx2B7iUlBTOnDnT9Y2/pKSENWvWdDvmfIrdYDCQn59PZmYmQLfjiouL\nqaioYNmyZbjdbpqbm4mOjsblcvHRRx8xceLEa3l/fsWbt0CdMGECtbW1nD17lrq6OmJiYhg2bBgd\nHR1s2LCBffv2oWkaZ8+epaamhsGDB192Tfv27eua7seNG3dJADE+Pp5z585dtpkLIURfqW7qYHuF\njR0VdupaXcSFGrlnnIWc1BgSIoP7dC3K2YJ6fztqe/dQm3ZrJlpQ367lWvXYzI1GIytXrmT9+vXo\nuk5mZibJycls3bqVlJQU0tPTOXz4MHl5eWiaRlpaWtevn32djo4O1q9fj9vtRtd1Jk6c2G36vl49\nTdBf5a+3QL3zzjv5xz/+QXV1ddfOcG+88QZ1dXW8++67BAUFMXPmzMve+vRqyW1QhRC+4tIV+097\npvCPqzyT9rShETw2PYH0YZGYDH2bn1K2OtTOt1Hv/RNammHMOAwPPgaTpqN9za2p/c1VXTCeNm3a\nJXfneuCBB7r+e9asWcyaNeuK55g3bx7z5s0DIDQ0lF/96lfXuFT/1NMtUM9/zB4XF9d1C9Sf/exn\ngGcij42N7boF6qhRowDP1q4//OEPqa+v529/+1vX68THxxMUFMQHH3xAZWXlFdc1c+ZMtm3bxpw5\nc/j88885cuRI13NKKWpqakhOTr5BVRFCiEuda2pnu9VO0VE7Da0uLGEm7p9gYUFKLIMjg/p8Per0\nSdT2fNSH/hlquxayA1wvefsWqAA33XQTzc3NXb8SCHDPPffwyCOPkJWVxaRJk0hNTb3iulasWMGz\nzz7L3LlzGTNmDJMmTep67tNPP2XatGleCf8JIcSVuHRFaaWDAqudT840o2lwy9AIclITuGVoJMa+\nnsKVgi8Oohfkw6GPIDgYLSMHLfubaIOH9OlavElugTrAboEK8JOf/IQFCxZw++23X/Z5uQVq35Ia\neofUsfe8WcMzjna2W23sOGrH5nRjCTeRkxJLVkoMgyJ8MIW73Z6d2gryL4Ta5i9Gm7cILTLaa6/j\ntwE4ceP15S1QwTP5f10jF0KI69XhVuyrdFBgtfHp2RYMGkwfFklOaixTh0T0+RQOF4Xait6CumpI\nGIa2/Em0WZk+2+DlRpBm7if66haoAMuWLeuz1xJC9H+nGz1T+M6jduxtbgZHmFg2KZ6slBgs4X0/\nhQMoWz1q51sXQm2p4zB8+7sBFWq7FgHfzAPsKkFAkJoKIXrS4dbZe8qTSD94zjOFz0zyTOGTE30z\nhcNXQ21umHorhpylaCk3+2Q9fSXgm7nBYMDlckmYy0tcLlfXr8gJIcRXVdrbKLTa2HmsEUebm4TI\nIJZPHsT8lBjMYb75PtwVaivcBgf395tQ27UI+A4YGhqK0+mkra3tmvd2DwkJ6dXvavc3SikMBoP8\n/rkQops2l87eUw4KrTY+q27FqMHM5CgWpsYyKTEcg4/uq9EVaivcBiesnTu1PYQ2dxFalPdCbYEg\n4Ju5pmmEhV3fXr2SfhVCiK930uaZwncds9PUrjMkKohHpgxi/ugYYn00hQMoZ2tnqO3v/TrUdi0C\nvpkLIYTwnjaXzgcnPVP4kZpWTAaY1TmFT0jw3RQO50Ntb6Pee/eiUNtqmDSjX4baroU0cyGEEBxv\ncPL6oQr+efgczR06Q6OCeXTaIDJHxRAT6ttWoapOdt5+tBjcAyfUdi2kmQshxADldOm8f6KRQquN\nL2qdBBk1bu2cwscPDrvmHJI3KaXgy0OendrOh9rm5KAtuAtt8NVtpDKQSDMXQogB5mi9k0KrjfeO\nN9LSoZMUHczKaYO5L30UHc12n65Nud2oj0s8O7WdD7Xd9ZBnp7YBFmq7FtLMhRBiAGjt0NnTOYWX\n1zkJNmrMHu6ZwtMGeabwmLAg+uB24ZelnK2oD4pQ29/0hNoGD0V7+EnP7UcHaKjtWkgzF0KIfsxa\nd2EKd7p0hscE8930wcwbGUNkiNHXy/OE2nb9A1X8LrQ0QWoahgdWw2QJtV0LaeZCCNHPtHS42X3c\nM4VX1LcRbNSYMyKahamx3BQf6tNr4eddGmrrvP2ohNquizRzIYToB5RSWOudFJTb2HOiEadLMTI2\nhMfSE5g7KprIYD+YwpWCLz9DL3jDE2oLCkabswBtwTcl1NZL0syFECKANbe7ea9zCj/W0EaIUeP2\nkZ4pfIzFT6bwr4baIqPRljyIlrkILSrG18vrF6SZCyFEgFFK8UWt51r4nhONtLsVo+NCeGK6ZwoP\nD/L9FA4SautL0syFECJANLW5KT5up7Dczgl7G6EmA5mjYshJjSXV4j/3VFD2Bs9ObedDbSk3d4ba\npqMZ/OMHjf5GmrkQQvgxpRSf17RSYLXxwUkH7W5FqjmUp2YmMmdElN9M4QDqzClPqO3DXRdCbQuW\noqWm+Xpp/Z40cyGE8EOONje7jtkpKLdR2dhOmMlA1mjPFD7a7EdT+PlQW2E+fFp2IdSW/U20BAm1\n9RVp5kII4SeUUhyu9kzhJScddOiKsZZQcmclMmdENKEm//m9a0+obS+qMB+Ol0uozceuqpkfOHCA\n1157DV3XycrKYunSpd2er6mp4eWXX6axsZHIyEhyc3OxWCxdz7e0tPDss88yffp0Vq1aBcDRo0d5\n8cUXaW9vZ+rUqTz66KN+kboUQoi+1uh0sfOYnUKrndON7UQEGchJ9UzhI+P8ZwqH86G2HaiiN6H2\nHAwegrbse2iz50uozYd6bOa6rvPqq6+ybt06LBYLzz//POnp6SQlJXUds2XLFjIyMpg3bx6HDh0i\nLy+P3Nzcrue3bt1KWlr3ayavvPIKjz/+OGPGjOGXv/wlBw4cYOrUqV58a0II4b+UUhw810Kh1cbe\nU024dMXN8WE8c+sQbhseRYgfTeHwNaG2+1fClBkSavMDPTZzq9VKYmIiCQkJAMyePZuysrJuzbyy\nspIVK1YAMH78eDZu3Nj13NGjR7Hb7UyZMoWKigoAGhoaaG1tZezYsQBkZGRQVlYmzVwI0e/ZnC52\nHrWz3WqjytFBRLCBb4yJJSc1luGx/jfZXhJqmzLTs1ObhNr8So/NvL6+vttH5haLhfLy8m7HjBgx\ngtLSUhYtWkRpaSmtra04HA4iIiJ4/fXXyc3N5eDBg1c8Z319/WVfv6ioiKKiIgA2bNhAfHz8tb3D\nKzCZTF4930AkNew9qaF3+HMddaX46JSdvx86y+6KOly6YvLQaFbPHsW8VAshJv+YbM/XUClFx2cH\naH4zj/b9H0BwMGFZSwi/69uYhib7epl+zVdfh14JwC1fvpzNmzdTXFxMWloaZrMZg8FAYWEhU6dO\n7da4r1V2djbZ2dldj2tra72xZADi4+O9er6BSGrYe1JD7/DHOja0utjROYWfbeogKtjAorGeKTw5\nxjOFO2wNOHy8zvMscbHUFL592VBbe1QM7QB+VmN/4+2vw6FDr+43Anps5mazmbq6uq7HdXV1mM3m\nS45Zu3YtAE6nk3379hEREcGXX37JkSNHKCwsxOl04nK5CA0NZdGiRT2eUwghApGuFAfONFNotVFa\n2YRbwYSEcJZNHsSs5EiCjf51LRxAtTlR7xdRt+tt1LmqC6G2W+ejhfjfR//iUj0285SUFM6cOUN1\ndTVms5mSkhLWrFnT7ZjzKXaDwUB+fj6ZmZkA3Y4rLi6moqKCZcuWARAWFsaXX37JmDFj2L17N3fc\ncYc335cQQvSpupaOzincTnVzB9EhRpbcbGZBagxJ0f7ZEFVjA2rHP1DF70BLE4abJqDueURCbQGo\nx2ZuNBpZuXIl69evR9d1MjMzSU5OZuvWraSkpJCens7hw4fJy8tD0zTS0tK6fv3sSlavXs1LL71E\ne3s7U6ZMkfCbECLguHXFvzqn8LLTTegKJiWG88jUQcxMiiTID6dwAHWmErV9G2rvLnC7ukJt5lm3\n+92lCnF1NKWU8vUirkVVVZXXzuWP19gCjdSw96SG3tGXdaxt6aCownMtvLbFRUyosWt3tiFRwX2y\nhmullILyz9ALt8EnpZ6d2mbP9+zUljgMkK9Fb/Dba+ZCCCE8U/hHVU0UWm18VNWMrmDKkAhW3jKY\nGcOiCDL656ZXSnfDx3s9TfzYlxAZhbbk22jzFqFFx/p6ecJLpJkLIcQV1DR3sL3CRpHVTl2ri7hQ\nI/eMs7AgJYZEP53CoTPUdv72o107tT2BdmuWhNr6IWnmQgjxFS5dsf+0Zwr/uKoZgGlDI/ju9ASm\nD4vEZPDPKRw6Q207/+HZqa3ZAaNvwnD/ozBlpoTa+jFp5kII0elcUzvbrXaKjtppaHVhDjNx/wQL\nC1JiGRwZ5OvlXdElobbJMzEsXIqWOs7XSxN9QJq5EGJAc+mK0koHhVY7B840o2kwbUgEOTMSSB8a\nidGfp3CloPyw5/ajn5SCKQhtdhbagrvQEpN6PoHoN6SZCyEGpDOOdrZbbew4asfmdGMJN/HtifFk\npcQwKMLPp3DdDf/6EL0g/0Ko7c5ve24/KqG2AUmauRBiwOhwe6bwAquNT862YNAgfVgkC1NjmTok\nwq+ncLgo1Fb0d6g5C4MS0R56wjONS6htQJNmLoTo96oa2ym02th51I69zc2gcBPLJnmmcEu4f0/h\n8DWhtvu+I6E20UWauRCiX+pw6+w95UmkHzznmcJnJHmm8MmJ/j+Fw+VCbTMwLLwbUtLQNP9fv+g7\n0syFEP1Kpb3NM4Ufa8TR5iYhMoiHJ8eTlRKLOcz/v+VJqE1cD///yhZCiB60u3UKPq/mb/86xWfV\nrRg1mJkcxcLUWCYlhmMIgClWQm2iN6SZCyEC1snOKXzXUTtN7TqJkUGsmDKIrNExxAbAFA6dobaS\nHZ6d2iTUJq5TYHy1CyFEpzaXzgcnHRRabRypacVkgFnJUXzrlhEkh3YExBQOnaG2Xe+gdr3jCbWN\nGovh3u/AVAm1iWsnzVwIERCONzgprLBTfMxOc7vO0KhgvjN1EPNHxxATaiI+PjYg7vilzlaitr+J\nKtl5IdSWczekSqhNXD9p5kIIv+V06bx/opFCq40vap2YDBqzh3uuhY8fHBYwzU8pBdYj6AVvXBRq\nm4+24JsSahNeIc1cCOF3jtY7KbTaeO94Iy0dOknRwaycNpjMUdFEhwbOty1PqG2fp4kf+xIiotDu\nfAAtc7GE2oRXBc6/CiFEv9baobOncwovr3MSZNC4bYRnCk8bFDhTOIBqa0OVFH0l1PZ4Z6gt1NfL\nE/2QNHMhhE9V1DspKPdM4U6XzvCYYFbfMph5o2KICgmsIJhqtKF2/QNV/A40SahN9B1p5kKIPtfS\n4Wb3cc8UXlHfRrBRY86IaHJSY7g5PrCmcAB19rRnp7aSneDq6Nyp7R4JtYk+I81cCNEnlFJYO6fw\nPScacboUI2JDeCw9gbmjookMDqzJVSkFFUc8m7x8UgpGk4TahM9IMxdC3FDN7W7e65zCjzW0EWLU\nuH1kNDmpsYy1hAbc5NoVaivMh6NfeEJti7/VuVNbnK+XJwaoq2rmBw4c4LXXXkPXdbKysli6dGm3\n52tqanj55ZdpbGwkMjKS3NxcLBYLNTU1bNq0CV3Xcbvd3HHHHeTk5ADw05/+lIaGBoKDgwFYt24d\nMTExXn57QghfUErxZZ1nCn//RCNtbsWouBCemO6ZwsODAmsKh/Ohth2o7dsk1Cb8To/NXNd1Xn31\nVdatW4fFYuH5558nPT2dpKQLHyNt2bKFjIwM5s2bx6FDh8jLyyM3N5e4uDh+/vOfExQUhNPp5Ac/\n+AHp6emYzWYA1qxZQ0pKyo17d0KIPtXU7ua9Y40UWG2csLURatKYO8ozhaeaA28Kh/OhtndQxf+4\nKNT2CEydJaE24Td6bOZWq5XExEQSEhIAmD17NmVlZd2aeWVlJStWrABg/PjxbNy40XNy04XTd3R0\noOu6VxcvhPA9pRSf17RSYLXxwUkH7W5FqjmUp2YmMmdEVEBO4XA+1PYmau9O6Gi/sFPbmHEB+UOJ\n6N96bOb19fVYLJauxxaLhfLy8m7HjBgxgtLSUhYtWkRpaSmtra04HA6ioqKora1lw4YNnD17locf\nfrhrKgd46aWXMBgMzJw5k3vvvfey/0CKioooKioCYMOGDcTHx1/3m/0qk8nk1fMNRFLD3gvUGjY6\nO/jnkWrePHSO4/UthAcbWTQugbsmJHLT4Mg+X4+36th+5FNa3syjrXQPmIIIm3cH4Xd9G1PSyN4v\n0s8F6teiP/FVDb0SgFu+fDmbN2+muLiYtLQ0zGYzBoMBgPj4eDZt2kR9fT0bN25k1qxZxMbGsmbN\nGsxmM62trbzwwgvs3r2buXPnXnLu7OxssrOzux57c+/l+Pj4gNjL2Z9JDXsvkGqolOJwtWcKLznp\noENXjLWE8vTMROaMiCYsyAA4qa119vnaelNHpbvhwD70wm1Q8bkn1LbofrT5i2mPjqMdIED+jnoj\nkL4W/ZW3azh06NCrOq7HZm42m6mrq+t6XFdX1226Pn/M2rVrAXA6nezbt4+IiIhLjklOTubzzz9n\n1qxZXecICwtjzpw5WK3WyzZzIYTvNTpd7Oq8Fn66sZ3wIAMLUmPISY1lVFzghr9UWxtqb+ftR6vP\nQHwC2oOPod2WLaE2EVB6bOYpKSmcOXOG6upqzGYzJSUlrFmzptsx51PsBoOB/Px8MjMzAU/jj4qK\nIjg4mKamJr744gvuvPNO3G43zc3NREdH43K5+Oijj5g4ceKNeYdCiOuilOLguRYKrTb2nmrCpStu\njg/jmVuHcNvwKEJMBl8v8boph92zU9uud6CpEUaOwfD4j2DarRJqEwGpx2ZuNBpZuXIl69evR9d1\nMjMzSU5OZuvWraSkpJCens7hw4fJy8tD0zTS0tJYtWoVAKdPn+b1119H0zSUUixZsoThw4fjdDpZ\nv349brdDDFYZAAAgAElEQVQbXdeZOHFit4/ShRC+Y3O62HnUznarjSpHBxHBBu4YE0tOaiwjYkN8\nvbxeuXyobSmMGS+hNhHQNKWU8vUirkVVVZXXziXXh3pPath7/lBDvXMKLyi3sa/SgUuHcYPCyEmN\nZXaATOFXqqOynt+pbR8YjWi3zkdbsBRtiOzUdjF/+FoMdH57zVwI0X81tLrY0TmFn23qICrYwKKx\ncSxIjWV4TIBP4V8NtYVHon3DE2rTYmSnNtG/SDMXYoDRleLAmWYKrXZKKx24FUwYHMZDk+K5dXgU\nwUb/n8KvREJtYiCSZi7EAFHX0tE5hdupbu4gOsTIkpvNLEiNISk6sKdwAN3egP73vEtDbVNvRTNK\nqE30b9LMhejH3LpnCi+w2ig73YSuYFJCOCumDGJWciRBAT6FA6hzVajt26jZuxPaJdQmBiZp5kL0\nQ7UtHRRV2Cmy2qhpcRETamRpmpmc1FiGRAX7enleoaxHPHcuO+AJtYXN+wZtGXegDUn29dKE6HPS\nzIXoJ9y64uMqzxT+UZVnCp+SGM6jtwxmxrAogoyBP6V6Qm2lnib+lVBbdMoYSWKLAUuauRABrqa5\ng+0VNooq7NS1uIgLNXLPOAsLUmJI7C9TeHsbqmRnZ6itCiyD0b79GNocCbUJAdLMhQhIbl2x/3QT\nBVYbH1c1AzB1SATfTU9g+rBITIbAn8LhMju1jUhFe+xHaNMk1CbExaSZCxFAzjW1e66FV9ipb3UR\nF2bi/gkWslNiSIjsH1M4XAi1qZLOndomTcew8G4JtQnxNaSZC+HnXLqirNIzhR8404ymwbQhETwx\nI4H0oZEY+8kUDpeG2rRZmWg5SyXUJkQPpJkL4afOOtrZXmGnqMKGzenGEm7igYkWslNiGRQR5Ovl\nec2VQm2yU5sQV0eauRB+pMOtKK10UGC18cnZFgwapA+LJCcllmlDI/rXFN7ehtq7C1W4rXuo7bYs\ntNAwXy9PiIAizVwIP1DV2M72Chs7KuzY29wMCjfx0KR4slJiiA/vP1M4gHI0doba/iGhNiG8RJq5\nED7S4dbZe6qJXe+d4eNKOwYNpg+LZGFqLFOG9K8pHEBVV3luP1qyw7NT26TpGHLuhrESahOit6SZ\nC9HHKhvb2G61s+OoHUebmyHRITw8OZ6slFjMYf3vn6Sq+NxzPfxfH14ItS34JtrQ4b5emhD9Rv/7\nziGEH2p365ScdLDdauNQdStGDWYkRbFwTCxZE4ZTX1fn6yV6ldJ1+KQz1GY90hlquw8tczFarNnX\nyxOi35FmLsQNdNLeRqHVRvFRO452ncTIIFZMGUTW6BhiO6dwQz/6iPnyobbvem4/KqE2IW4YaeZC\neFmbS+eDzin8cE0rJgPM7JzCJyaE96vmfZ5yNKKK3/GE2hz2zlDbD9GmzZZQmxB9QJq5EF5ywtZG\ngdVG8TE7ze06Q6OC+M7UQcwfHUNMaP/8p+YJtf0dVVLkCbVNTPfs1DZ2goTahOhD/fM7jBB9pM2l\n8/6JRgqsdr6obcVk0JidHEXOmBgmDA7vtw3tklDbzHloC5aiDZNQmxC+IM1ciOtwrMFJQbmN9443\n0tKhkxQdzMppg8kcFU10f53CdR0+LUUvOB9qi0C74160+XdKqE0IH7uq7zoHDhzgtddeQ9d1srKy\nWLp0abfna2pqePnll2lsbCQyMpLc3FwsFgs1NTVs2rQJXddxu93ccccd5OTkAHD06FFefPFF2tvb\nmTp1Ko8++mi/nWJE/9DacX4Kt1Fe5yTIoHHb8ChyxsQyblBYv/367Qq1bX8Tzp32hNoeWI02Z4GE\n2oTwEz02c13XefXVV1m3bh0Wi4Xnn3+e9PR0kpKSuo7ZsmULGRkZzJs3j0OHDpGXl0dubi5xcXH8\n/Oc/JygoCKfTyQ9+8APS09Mxm8288sorPP7444wZM4Zf/vKXHDhwgKlTp97QNyvE9aio90zhu483\n0urSSY4JZvUtg5k3KoaokP4b7pJQmxCBo8dmbrVaSUxMJCEhAYDZs2dTVlbWrZlXVlayYsUKAMaP\nH8/GjRs9JzddOH1HRwe6rgPQ0NBAa2srY8eOBSAjI4OysjJp5sJvtHS42XPcs0d6Rb2TYKPGnBFR\n5KTGcnN8/53CQUJtQgSiHpt5fX09Foul67HFYqG8vLzbMSNGjKC0tJRFixZRWlpKa2srDoeDqKgo\namtr2bBhA2fPnuXhhx/GbDZTUVFxyTnr6+sv+/pFRUUUFRUBsGHDBuLj46/rjV6OyWTy6vkGov5U\nQ6UUn59r4u+fnWX7FzW0duikWML5/rzR5Nw0+IZdC/eXGrZ/cYiWN/No+/A9MJoInbuQiLu+jWn4\naF8v7ar4Sx0DmdSw93xVQ698d1q+fDmbN2+muLiYtLQ0zGYzBoMBgPj4eDZt2kR9fT0bN25k1qxZ\n13Tu7OxssrOzux7X1tZ6Y8lda/Pm+Qai/lDD5nY3u497roUfa2gjxKhx+8hoclJjGWsJRdM02pts\n1DbdmNf3ZQ0vhNq2gfVwt1BbR6wZG0CA/P32h69FX5Ma9p63azh06NCrOq7HZm42m6m7aKvJuro6\nzGbzJcesXbsWAKfTyb59+4iIiLjkmOTkZD7//HNuuummHs8pxI2klOLLOieFVht7jjfS5laMigvh\niekJZIyMJiK4f18TVu1tqA93oQq/GmrLRgsN9/XyhBDXqMdmnpKSwpkzZ6iursZsNlNSUsKaNWu6\nHXM+xW4wGMjPzyczMxPwNOmoqCiCg4Npamriiy++4M477yQuLo6wsDC+/PJLxowZw+7du7njjjtu\nzDsU4iJN7W7eO+aZwk/Y2gg1acwd5ZnCU82h/f6asHI0ot57B7WzM9Q2PAXtu2vRbrlNQm1CBLAe\nm7nRaGTlypWsX78eXdfJzMwkOTmZrVu3kpKSQnp6OocPHyYvLw9N00hLS2PVqlUAnD59mtdffx1N\n01BKsWTJEoYP92wqsXr1al566SXa29uZMmWKhN/EDaOU4vOaVgorbLx/wkG7W5FiDuXJGYncPjKK\n8KD+38RU9RlU0ZuoDy4KteUshZsm9vsfYIQYCDSllPL1Iq5FVVWV184l14d6z59r6GhzU3zMToHV\nxil7O2EmQ9cUnmIO9fXyutzIGqqjX3g2efnXXjAY0WbNRVtwd7/cqc2fvxYDhdSw9/z2mrkQgUQp\nxeGaVgrLbXxw0kGHrhhjCeXpmYnMGRFNWJDB10u84TyhtrLOndoOQ1gE2h33dO7UZun5BEKIgCPN\nXPQLjU4Xu441Umi1UdnYTniQgQWpMeSkxjIqzn+m8BtJdbSj9u68EGozD0J7YFXnTm0SahOiP5Nm\nLgKWUopD1S0UltspOeXApStuig9jzaxEbhsRTaip/0/hAKqpc6c2CbUJMWBJMxcBx+Z0sfOone1W\nG1WODiKCDSwcE0tOSgwjB8gUDqBqzqK2b7sQaptwi2enNgm1CTHgSDMXAUFXioPnWigot7Gv0oFL\nh3GDwvjWhHhmD48iZIBM4QDq2JfoBW/Axx+CwdAZaluKNmyEr5cmhPARaebCr9laXew4aqfQauNs\nUwdRwQa+MTaOnNRYhseE+Hp5faYr1FaYD+XnQ213S6hNCAFIMxd+SFeKT856pvDSSgduBRMGh/HQ\npHhuHR5FsHEATeEd7Z23H90GZyXUJoS4PGnmwm/Ut7rYUWGj0GqnurmDqBAjS242syAlhqQBNIXD\n+VDbu6idb3eG2kajrf4BWvocCbUJIS4hzVz4lFtXHDjTTIHVRtnpJnQFkxLCWT5lELcmRxI0gKZw\nOB9qO79TW5sn1JazFG6eJKE2IcTXkmYufKKupYOiCk8ivabFRUyIkaVpZhakxDI0OtjXy+tz6tiX\nqIJ81Md7PaG2mXPRciTUJoS4OtLMRZ9x64qPqzxT+EdVnil8SmI4j04bzIykKIKMA2vyVLoOB/dT\nv/Nt9MMHPKG2hXejZUmoTQhxbaSZixuuprmDogob2yvs1LW4iA01cs84C9kpMQyJGoBTeEc76sNi\nVOE2OFsJ8Qlo31qFdruE2oQQ10eaubgh3Lpi/+kmCq02Pj7TjFIwZUgE370lgelJkZgMA2sKh85Q\n23v/9ITaGm2QPApt9Q+IX/hN6mw2Xy9PCBHApJkLrzrX1E5RhZ2iCjv1rS7iwkzcN94zhSdEDrwp\nHC4XapuGIefurlCbZpJ/hkKI3pHvIqLXXLqirNIzhf/rTDMA04ZG8MT0BNKHRWIcgFM4gDpWjip4\n40KobUaGJ9SWNNLXSxNC9DPSzMV1O+to569fHOetQ2ewOd1Ywkw8MNFCdkosgyKCfL08nzgfatML\n8+HLzyAs3NPAs5agxUmoTQhxY0gzF9ekw60oPe2gsNzGgbMtGDS4ZWgkC1NjmTY0YuBO4V8NtZnj\nPaG2OQvQwiTUJoS4saSZi6tyxtFOodXGjgo79jY3g8JNPDgpnm+lj8LQ5vD18nxGNTsu7NR2UahN\nu+U2uRYuhOgz8t1GfK0Ot86HpzzXwj8955nCpw/zTOFThnim8PioEGoHYDNXNWdRRX9Hvb/9sqE2\nIYToS9LMxSUqG9vYbrWz86idxjY3gyOCWDY5nqzRMVjCB+a18PPUsXJUYT7qoxIJtQkh/IY0cwFA\nu1tn70kHhVYbh6pbMWowIymKhWNimZwYjmEAT5ueUNtH6IVvSKhNCOGXrqqZHzhwgNdeew1d18nK\nymLp0qXdnq+pqeHll1+msbGRyMhIcnNzsVgsHD9+nFdeeYXW1lYMBgP33HMPs2fPBuDFF1/k8OHD\nhId7wkFPPfUUI0eO9O67Ez06aW+j0Gqj+KgdR7tOYmQQy6cMImt0DHFhA/tnva5Q2/Y34cwpT6jt\n/pVot+dIqE0I4Vd6/G6t6zqvvvoq69atw2Kx8Pzzz5Oenk5SUlLXMVu2bCEjI4N58+Zx6NAh8vLy\nyM3NJTg4mKeffpohQ4ZQX1/Pc889x+TJk4mIiABg+fLlzJo168a9O3FZbS6dks4p/HBNKyYDzOyc\nwicmDOwpHL4m1LbqWc/tRyXUJoTwQz1+Z7JarSQmJpKQkADA7NmzKSsr69bMKysrWbFiBQDjx49n\n48aNAAwdOrTrGLPZTExMDI2NjV3NXPStEzbPFL7rmJ3mdp0hUUE8MnUQ80fHEBsqTeqSUNv4qRgW\n3iOhNiGE3+vxO3h9fT0Wy4XrghaLhfLy8m7HjBgxgtLSUhYtWkRpaSmtra04HA6ioqK6jrFarbhc\nrq4fCgD+9Kc/8de//pUJEyawbNkygoIGdrjqRmhz6bx/opECq50valsxGTRmJ0eRMyaGCYPDpUkB\n6ni55/ajH5WAQbso1DbK10sTQoir4pVxbPny5WzevJni4mLS0tIwm80YDIau5xsaGvjd737HU089\n1fX/H3roIWJjY3G5XPz+97/nzTff5L777rvk3EVFRRQVFQGwYcMG4uPjvbFkAEwmk1fP50/Ka5r4\n+6FzFH5eTVO7m+FxYeTePoo70gYTG+a9H5oCtYZK12n/eC/N2/Lo+OxfaOERhC99kPBF92OMH9yn\nawnUGvobqWPvSQ17z1c17LGZm81m6urquh7X1dVhNpsvOWbt2rUAOJ1O9u3b1/VRektLCxs2bODB\nBx9k7NixXX8mLi4OgKCgIDIzM3nrrbcu+/rZ2dlkZ2d3Pa6trb3a99aj+Ph4r57P11o7zk/hNsrr\nnAQZNG4bHkVOaizjBoehaRquZju1zd57zUCroeroQO3r3KntK6G2trBw2gD6+P0EWg39ldSx96SG\nveftGl58ufpKemzmKSkpnDlzhurqasxmMyUlJaxZs6bbMedT7AaDgfz8fDIzMwFwuVxs2rSJjIyM\nS4JuDQ0NxMXFoZSirKyM5OTkq31v4isq6p0UWm28d6yRVpdOckwwq28ZzLxRMUSFGH29PL+gmptQ\nxe+gdv0D7A2QJKE2IUT/0eN3MaPRyMqVK1m/fj26rpOZmUlycjJbt24lJSWF9PR0Dh8+TF5eHpqm\nkZaWxqpVqwAoKSnhyJEjOBwOiouLgQu/gvbb3/6WxsZGwHPN/bHHHrtx77Ifaulws+e4gwKrjYp6\nJ8FGzxS+MDWWmweFybXwTqr23IVQW5vTE2pb+X1Imyw1EkL0G5pSSvl6EdeiqqrKa+cKtI+UlFJY\nO6fw3ccbcboUI2JCWDgmlrkjo4n0wRTurzVUx8tRhdtQ+z/w+1Cbv9Yw0Egde09q2Ht++zG78L3m\ndje7j3uuhR9raCPYqHH7iGgWjollrCVUJsxOStfh0Efohdvgi4OdO7V9E23+EjSzhHqEEP2XNHM/\npZTiyzrPFL7neCNtbsWouBCemJ5AxshoIoLlWvh5l4Ta4uLR7n8U7faFslObEGJAkGbuZ5ra3bx3\nrJFCq43jtjZCTRoZIz1TeKpZpvCLqeYm1HudO7V1hdq+j5Z+u4TahBADinzH8wNKKT6vbaXQauP9\nEw7a3YoUcyjfm+GZwsODZAq/2CWhtnFTMaz8P5A2RX7YEUIMSNLMfcjR5qb4mJ1Cq42T9nZCTQYy\nR8WQkxpLqiXU18vzO+qE1bNT2/lQ2/TOUFuy/4XahBCiL0kz72NKKQ7XtFJYbuODkw46dMUYSyhP\nz0xkzohowoIMPZ9kAFG6Dp99jF6Q7wm1hYahLfim5/ajEmoTQghAmnmfaWxzs+uoZwqvbGwnPMhA\ndopnCh9tlin8qy4JtcVa0O571HP70XC5UY8QQlxMmvkNpJTiUHULheV2Sk45cOmKm+JDyZ3lmcJD\nTTKFf9WlobaRnaG2OWgmuRGPEEJcjjTzG8DudLHzqJ1Cq50qRzsRQQYWjoklJyWGkXEyhV/OpaG2\nKRJqE0KIqyTN3Et0pTh4roWCchv7Kh24dEgbFMb9E4Zw2/AoQmQKv6yuUNtHH4AmoTYhhLge0sx7\nydbqYkfntfCzTR1EBhv4xtg4clJjGR4T4uvl+aXLhtqyv4mWdSeaeZCvlyeEEAFHmvl10JXi07Mt\nFFht7DvlwK1g/OAwHpwUz+zhUQQbZQq/HNXRgSp9D1WQL6E2IYTwImnm16C+1cWOChvbK+yca+og\nKsTIkpvNLEiJIUmm8K+lmptQu/+J2vE22Os9obaV30ebLqE2IYTwBmnmPXDrigNnmimssFFa2YSu\nYGJCOA9PHsStyZEEyRT+tVRdtSfUtqfwQqjt0WdgnITahBDCm6SZf426lg6KKuxst9qoaXERE2Lk\nmzebWZAay7DoYF8vz6+pExWogjcuCrXdjrZgKdrw0b5emhBC9EvSzC/i1hX/OtNMgdXG/tOeKXxy\nYjiPThvMjKQogowyTX4dpRQc+hi94I2LQm13de7UJqE2IYS4kaSZAzXNHRR1Xguva3ERG2rk7jTP\nFD4kSqbwK1Ed7egfFHl2aqs62Rlq+47n9qMSahNCiD4xYJu5W1fsOVrHXz86xcdnmlEKpgyJ4Lu3\nJDA9KRKTQabwK1EtTaj3/kntrndQDbUwbISE2oQQwkcGbDPf+P5p9p5qIi7MxL3jLCxIjSEhUqbw\nnlwItW2HtlaCJk/H9UiuhNqEEMKHBmwzXzQ2jrsmJzM2Spcp/CqoExWownzU/ve7hdrips2gtrbW\n18sTQogBbcA280mJEcTHW6QRXUFXqK0wHz7/VEJtQgjhp66qmR84cIDXXnsNXdfJyspi6dKl3Z6v\nqanh5ZdfprGxkcjISHJzc7FYLBw/fpxXXnmF1tZWDAYD99xzD7Nnzwagurqa3/zmNzgcDkaPHk1u\nbi4m04D92cKvKFcHat9u1PZtcPqEhNqEEMLP9dg9dV3n1VdfZd26dVgsFp5//nnS09NJSkrqOmbL\nli1kZGQwb948Dh06RF5eHrm5uQQHB/P0008zZMgQ6uvree6555g8eTIRERH88Y9/ZPHixdx22238\n13/9Fzt37iQnJ+eGvllxZZ5QWwFq51tgq/eE2h79P2gzbpdQmxBC+LEety+zWq0kJiaSkJCAyWRi\n9uzZlJWVdTumsrKSCRMmADB+/Hj2798PwNChQxkyZAgAZrOZmJgYGhsbUUrx2WefMWvWLADmzZt3\nyTlF31F11ehbX0X/0SrUG3+AIckYnvkphn/7LYbZ86WRCyGEn+txMq+vr8disXQ9tlgslJeXdztm\nxIgRlJaWsmjRIkpLS2ltbcXhcBAVFdV1jNVqxeVykZCQgMPhIDw8HKPRCHgafX19vbfek7hK6mQF\nqmAbav8eAE+oLWcp2vAUH69MCCHEtfDKRerly5ezefNmiouLSUtLw2w2YzBcGPobGhr43e9+x1NP\nPdXt/1+NoqIiioqKANiwYQPx8fHeWDIAJpPJq+cLBEop2v+1j5Y382j/dD9aaDjhd36L8Du/hXFQ\n4jWfbyDW0Nukht4hdew9qWHv+aqGPTZzs9lMXV1d1+O6ujrMZvMlx6xduxYAp9PJvn37iIjwBKVa\nWlrYsGEDDz74IGPHjgUgKiqKlpYW3G43RqOR+vr6S855XnZ2NtnZ2V2PvZk+j4+PHzBpduXqQJXu\n9uzUdvoExJrR7n0ELWMhbeGRtAFcRy0GUg1vFKmhd0gde09q2HveruHQoUOv6rgem3lKSgpnzpyh\nuroas9lMSUkJa9as6XbM+RS7wWAgPz+fzMxMAFwuF5s2bSIjI6Pr+jiApmmMHz+eDz/8kNtuu43i\n4mLS09Ov5f2Jq6RamlC7C1A7Lg61PYM2I0OuhQshRD/RYzM3Go2sXLmS9evXo+s6mZmZJCcns3Xr\nVlJSUkhPT+fw4cPk5eWhaRppaWmsWrUKgJKSEo4cOYLD4aC4uBiAp556ipEjR7Js2TJ+85vf8Oc/\n/5lRo0Yxf/78G/pGBxpVV3PR7UdbIW0yhkdyYfw02alNCCH6GU0ppXy9iGtRVVXltXP1x4+ULgm1\npd+OtvDGhdr6Yw37mtTQO6SOvSc17D2//Zhd+D+lFHz2MXrhNjjyCYSEoc1f4tmtzSI7tQkhRH8n\nzTyAXSnUpoVH+np5Qggh+og08wAkoTYhhBAXk2YeQFRdDWpHZ6jNKaE2IYQQHtLMA4A6WYEq3IYq\nuyjUlrMUbYTs1CaEEEKaud/yhNr+5bn9aLdQ2xI0y2BfL08IIYQfkWbuZzyhtj2ownxPqC3GjHbP\nI2hzJdQmhBDi8qSZ+wnV0oza/c8Lobahw9G+8wzaTAm1CSGEuDJp5j6m6i/aqc3ZCjdPklCbEEKI\nayLN3EfUyaOownzU/vdBKbT0OWg5d0uoTQghxDWTZt6HlFJw+AB6wRsXQm2Zd0qoTQghRK9IM+8D\nytWBKnvfE2qrPH4h1JaxEC1CQm1CCCF6R5r5DaRamlF7ClBFb4Gt7kKobUYGWpCE2oQQQniHNPMb\nQNXXoHa8hdpdcCHUtuJpmCChNiGEEN4nzdyL1KljnlBb2Z6LQm1L0Uak+nppQggh+jFp5r3UFWor\nzIfDByAkFC1zMVrWErT4BF8vTwghxAAgzfw6XRpqi0O7ZwVaxh0SahNCCNGnpJlfI0+orRBV9HdP\nqG1IMtp31qDNmCuhNiGEED4hzfwqeUJtb6N2/9MTartpIoYVT3l2ajMYfL08IYQQA5g08x5cEmq7\n5TZPqG3kGF8vTQghhACkmV+WUgqOHEAvuCjUNm8RWvZdEmoTQgjhd66qmR84cIDXXnsNXdfJyspi\n6dKl3Z6vqanh5ZdfprGxkcjISHJzc7FYLACsX7+e8vJybr75Zp577rmuP/Piiy9y+PBhwsPDAXjq\nqacYOXKkl97W9VEuF6psT/dQ293L0eZ+Q0JtQggh/FaPzVzXdV599VXWrVuHxWLh+eefJz09naSk\npK5jtmzZQkZGBvPmzePQoUPk5eWRm5sLwF133UVbWxtFRUWXnHv58uXMmjXLi2/n+qjWFtTuAs/t\nRxtqJdQmhBAioPTYzK1WK4mJiSQkeD5enj17NmVlZd2aeWVlJStWrABg/PjxbNy4seu5iRMn8tln\nn3l73V7hrq1G/39/QO0pgNYWT6ht+ZMSahNCCBFQemzm9fX1XR+ZA1gsFsrLy7sdM2LECEpLS1m0\naBGlpaW0trbicDiIioq64rn/9Kc/8de//pUJEyawbNkygvpwCtb/9gdqt28DXaGlS6hNCCFE4PJK\nAG758uVs3ryZ4uJi0tLSMJvNGHqYbB966CFiY2NxuVz8/ve/58033+S+++675LiioqKuj+g3bNhA\nfHy8N5ZMy4hR6IvvJ2zx/RgHD/HKOQcik8nktb+TgUpq6B1Sx96TGvaer2rYYzM3m83U1dV1Pa6r\nq8NsNl9yzNq1awFwOp3s27ePiIiIK543Li4OgKCgIDIzM3nrrbcue1x2djbZ2dldj2tra3ta8tVJ\nzyA+Pt5zPm+dcwDqqqG4blJD75A69p7UsPe8XcOhQ4de1XE9XhhOSUnhzJkzVFdX43K5KCkpIT09\nvdsxjY2N6LoOQH5+PpmZmT2+cENDA+D5NbCysjKSk5OvasFCCCGE6K7HydxoNLJy5UrWr1+Prutk\nZmaSnJzM1q1bSUlJIT09ncOHD5OXl4emaaSlpbFq1aquP/+Tn/yE06dP43Q6eeKJJ3jiiSeYMmUK\nv/3tb2lsbAQ819wfe+yxG/cuhRBCiH5MU0opXy/iWlRVVXntXPKRUu9JDXtPaugdUsfekxr2nt9+\nzC6EEEII/ybNXAghhAhw0syFEEKIACfNXAghhAhw0syFEEKIABdwaXYhhBBCdDegJ/OLb8kqro/U\nsPekht4hdew9qWHv+aqGA7qZCyGEEP2BNHMhhBAiwBl/+tOf/tTXi/Cl0aNH+3oJAU9q2HtSQ++Q\nOvae1LD3fFFDCcAJIYQQAU4+ZhdCCCECXI93TeuvnnrqKUJDQzEYDBiNRjZs2ODrJQWc5uZm/vM/\n/5NTp06haRrf+973GDt2rK+XFTCqqqr49a9/3fW4urqab33rWyxevNiHqwo8b7/9Njt37kTTNJKT\nk3ieykUAAAlWSURBVHnyyScJDg729bICyjvvvMOOHTtQSpGVlSVfg1fppZde4uOPPyYmJoYXXngB\ngKamJn79619TU1PDoEGD+P73v09kZOSNX4waoJ588kllt9t9vYyA9rvf/U4VFRUppZTq6OhQTU1N\nPl5R4HK73Wr16tWqurra10sJKHV1derJJ59UbW1tSimlXnjhBbVr1y7fLirAnDhxQj377LPK6XQq\nl8ulfvazn6kzZ874elkB4bPPPlMVFRXq2Wef7fp/W7ZsUfn5+UoppfLz89WWLVv6ZC3yMbu4Li0t\nLRw5coT58+cDYDKZiIiI8PGqAtfBgwdJTExk0KBBvl5KwNF1nfb2dtxuN+3t7cTFxfl6SQHl9OnT\npKamEhISgtFoJC0tjX379vl6WQFh3Lhxl0zdZWVlzJ07F4C5c+dSVlbWJ2sZsB+zA6xfvx6ABQsW\nkJ2d7ePVBJbq6mqio6N56aWXOHHiBP9/e/caEtX2xnH86ziOmtrozKRpepgsu1JSOGVpEvmqG4bU\nJAUiGZT5IqgkehNUUknSXUgkzV50EQKhKIKitDS6mKVlkV20ezFexhG0HMfzItocz/90iH8dt1uf\nDwxs2Xuv9dsMzDOzlnuv6OhoMjIy8PPzUzuaJlVVVZGQkKB2DM0xmUwsXbqUrKwsDAYDsbGxxMbG\nqh1LU6Kiojhz5gwulwuDwUBtbS3jxo1TO5ZmOZ1O5QtlcHAwTqdzQPodtsV8165dmEwmnE4nubm5\nREREMGXKFLVjaUZvby+vXr1izZo1xMTEUFJSQnl5OWlpaWpH0xy3201NTQ2rVq1SO4rmdHZ2cvfu\nXQoKChgxYgT79++nsrKSpKQktaNpRmRkJCkpKeTm5uLn54fVakWnk0Hb38HLywsvL68B6WvYvmMm\nkwkAo9GIzWbj+fPnKifSFrPZjNlsJiYmBoD4+HhevXqlciptqq2tZezYsQQHB6sdRXPq6+sJDQ1l\n5MiR6PV6Zs+ezbNnz9SOpTkLFiwgLy+PHTt2EBAQQHh4uNqRNMtoNNLW1gZAW1sbI0eOHJB+h2Ux\n7+7upqurS9muq6vjjz/+UDmVtgQHB2M2m3n//j3w7UM1MjJS5VTaJEPs/z+LxUJjYyNfvnyhr6+P\n+vp6xowZo3Yszfk+FOxwOLhz5w6JiYkqJ9KuuLg4KioqAKioqMBmsw1Iv8PyoTGfPn0iPz8f+DZc\nnJiYSGpqqsqptKepqYljx47hdrsJDQ1lw4YNA3MLxhDS3d3Nhg0bOHr0KCNGjFA7jiaVlZVRXV2N\nt7c3VquV9evX4+Pjo3YsTdm+fTsulwu9Xk96ejrTpk1TO5ImHDx4kIaGBlwuF0ajEbvdjs1m48CB\nAzgcjgG9NW1YFnMhhBBiKBmWw+xCCCHEUCLFXAghhNA4KeZCCCGExkkxF0IIITROirkQQgihcVLM\nhRhG7HY7Hz9+VDvG/ygrK+Pw4cNqxxBCs4bt41yFUFt2djbt7e39Hp05f/58MjMzVUwlhNAiKeZC\nqGjr1q1Mnz5d7RhDSm9vL97e3mrHEGJASTEXYhC6fv06V69exWq1UllZSUhICJmZmcqTuVpbWykq\nKuLp06cEBgaSkpKirPzn8XgoLy/n2rVrOJ1OwsPDycnJwWKxAFBXV8fu3bvp6OggMTGRzMzMf1wM\noqysjLdv32IwGLhz5w4Wi4Xs7GxlRS273c7hw4cZPXo0AAUFBZjNZtLS0nj8+DFHjhxh4cKFnD9/\nHp1Ox9q1a9Hr9ZSWltLR0cHSpUv7PXmxp6eHAwcOUFtbS3h4OFlZWVitVuV6i4uLefLkCX5+fixe\nvJhFixYpOd+8eYOPjw81NTWkp6eTnJz837wxQgxSMmcuxCDV2NhIWFgYx48fx263k5+fT2dnJwCH\nDh3CbDZTWFjI5s2bOX36NI8ePQLgwoULVFVVsW3bNkpLS8nKysLX11dp9/79++zZs4f8/Hxu3brF\nw4cPf5ihpqaGuXPncuLECeLi4iguLv7p/O3t7fT09HDs2DHsdjuFhYXcuHGDvXv3snPnTs6dO8fn\nz5+V4+/du8ecOXMoLi4mISGBffv24Xa78Xg85OXlYbVaKSwsZPv27Vy8eJEHDx70Ozc+Pp6SkhLm\nzZv30xmFGCqkmAuhon379pGRkaG8rly5ouwzGo0sXrwYvV7P3LlziYiI4P79+zgcDp4+fcrq1asx\nGAxYrVaSk5OVxR2uXr1KWloaEREReHl5YbVaCQoKUtpdtmwZAQEBWCwWpk6dSlNT0w/zTZo0iZkz\nZ6LT6UhKSvrXY//O29ub1NRU9Ho9CQkJuFwuFi1ahL+/P1FRUURGRvZrLzo6mvj4ePR6PUuWLKGn\np4fGxkZevHhBR0cHy5cvR6/XExYWRnJyMtXV1cq5EyZMYNasWeh0OgwGw09nFGKokGF2IVSUk5Pz\nwzlzk8nUb/h71KhRtLa20tbWRmBgIP7+/so+i8XCixcvAGhpaSEsLOyHff51qVVfX1+6u7t/eKzR\naFS2DQYDPT09Pz0nHRQUpPxz3/cC+/f2/tq32WxWtnU6HWazud9SkhkZGcp+j8fD5MmT//FcIYYj\nKeZCDFKtra309fUpBd3hcBAXF0dISAidnZ10dXUpBd3hcGAymYBvhe3Tp0//+bK+vr6+fPnyRfm7\nvb39l4pqS0uLsu3xeGhpaSEkJARvb29CQ0Pl1jUh/oUMswsxSDmdTi5duoTb7ebWrVu8e/eOGTNm\nYLFYmDhxIqdOneLr1680Nzdz7do1Za44OTmZs2fP8uHDB/r6+mhubsblcv32fFarlZs3b+LxeHjw\n4AENDQ2/1N7Lly+5ffs2vb29XLx4ER8fH2JiYhg/fjz+/v6Ul5fz9etXPB4Pr1+/5vnz57/pSoTQ\nPvllLoSK8vLy+t1nPn36dHJycgCIiYnhw4cPZGZmEhwczKZNm5S5740bN1JUVMS6desIDAxkxYoV\nynD99/nm3NxcXC4XY8aMYcuWLb89e0ZGBgUFBVy+fBmbzYbNZvul9uLi4qiurqagoIDRo0ezefNm\n9PpvH1Fbt27l5MmTZGdn43a7iYiIYOXKlb/jMoQYEmQ9cyEGoe+3pu3atUvtKEIIDZBhdiGEEELj\npJgLIYQQGifD7EIIIYTGyS9zIYQQQuOkmAshhBAaJ8VcCCGE0Dgp5kIIIYTGSTEXQgghNE6KuRBC\nCKFxfwIUz77RSc81WQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc435d50cc0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    final error(train) = 1.86e-01\n",
-      "    final error(valid) = 1.83e-01\n",
-      "    final acc(train)   = 9.46e-01\n",
-      "    final acc(valid)   = 9.48e-01\n",
-      "    run time per epoch = 13.08\n",
-      "--------------------------------------------------------------------------------\n",
-      "learning_rate=0.20 init_scale=0.20\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAENCAYAAAAi8D15AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc435c54358>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAAENCAYAAAAbl4wiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nw+dCEwAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc43577cc88>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    final error(train) = 1.74e-01\n",
-      "    final error(valid) = 1.75e-01\n",
-      "    final acc(train)   = 9.49e-01\n",
-      "    final acc(valid)   = 9.51e-01\n",
-      "    run time per epoch = 15.16\n",
-      "--------------------------------------------------------------------------------\n",
-      "learning_rate=0.20 init_scale=0.50\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAENCAYAAAAi8D15AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3XtcVNe9///XmhnuMIMMIArijHi/gmJUooKKd02Midq0\n/ban7WnTtKf5Jr3k3h570qS2NTk9bb+/0zax1p70tESN1uA1aAAN0ZioMRc1GgfwjoIyXOS61++P\naSg2KugeYNDP8/Ho49GBPXvWfmfJZ/bea6+ltNYaIYQQQgQ8S1c3QAghhBDtI0VbCCGE6CakaAsh\nhBDdhBRtIYQQopuQoi2EEEJ0E1K0hRBCiG5CirYQQgjRTUjRFkIIIboJKdpCCCFENyFFWwghhOgm\nbF3dgKs5ffq0X/cXGxvLhQsX/LrP241kaJ5kaJ5kaJ5k6B/+zrF3797t2k7OtIUQQohuQoq2EEII\n0U206/L4gQMHWLlyJYZhMG3aNBYsWHDF73Nzc9m+fTtWqxW73c6DDz5IXFwc58+fZ/ny5RiGQXNz\nM7NmzWLGjBkdciBCCCHEra7Nom0YBitWrODpp5/G6XTyxBNPkJ6eTlJSUss2LpeLZcuWERISwrZt\n23j55Zd55JFH6NGjBz/5yU8ICgqirq6O733ve6SnpxMTE9OhByWEEKJ9tNbU1dVhGAZKqa5uTrdx\n7tw56uvrb+g9WmssFguhoaE3nXWbRfvYsWMkJCTQs2dPADIyMti7d+8VRXv48OEt/3/AgAHs3LnT\nt3PbP3bf2NiIYRg31UghhBAdo66ujqCgoCv+Xou22Ww2rFbrDb+vqamJuro6wsLCbu5z29qgoqIC\np9PZ8trpdHL06NFrbr9jxw5SU1NbXl+4cIFly5Zx9uxZvvjFL8pZthBCBBDDMKRgdyKbzXbDZ+hX\nvN+PbaGwsJDjx4+zdOnSlp/FxsayfPlyKioq+MUvfsH48eOJjo6+4n15eXnk5eUBsGzZMmJjY/3Z\nLGw2m9/3ebuRDM2TDM2TDM375wybm5ulaN+km80tNDT0pvtxm58YExNDeXl5y+vy8vKrni0fPHiQ\ndevWsXTpUoKCgq66nz59+nD48GHGjx9/xe+ys7PJzs5uee3PZ9+M9S8TPXEa3theftvn7Uie7TRP\nMjRPMjTvnzOsr6+/qcu8tzubzUZTU9NNvbe+vv4z/dhvz2mnpKRw5swZysrKaGpqoqioiPT09Cu2\n8Xg8vPjiizz66KM4HI6Wn5eXl9PQ0ABAdXU1R44caXfD/EF7L6ILtnDxiQdofuGH6CMfdNpnCyGE\nCBwvvvgiq1evBiAnJ4ezZ8/e8D7+9Kc/tezjWg4dOsTDDz98U21sjzbPtK1WK1/96ld59tlnMQyD\nKVOm0KdPH3JyckhJSSE9PZ2XX36Zuro6XnjhBcD3Te6xxx7j1KlT/OlPf0Iphdaa+fPnk5yc3GEH\n88+UvQeWZS8R/k4h1a++jLH8SRgwFMvcJTA0VUZKCiFEN9Hc3HzFFYF/fn0tn54N5+TksGXLFgBW\nr17N4MGDSUhIaPNzWvvSl77U5ucNGTKEM2fOcOrUKRITE9vc/kYprbX2+15N6ohpTM+fPoXe9Tp6\ny6tw8QK4B/qK98h0Kd7tIJclzZMMzZMMzfvnDGtrawkPD+/CFvmsXbuWP/zhDzQ0NJCWlsZPf/pT\nBg8ezBe/+EV27tzJc889x3e+8x3uuusuCgsL+da3vkVKSgqPP/44dXV19O3bl+eff57o6Gjuu+8+\nhg4dyt69e7n77rsZMmQI69at45e//CW5ubk88sgjJCQkEBoayoYNG8jKyrpiv9XV1fz5z3+moaEB\nt9vNr371K8LCwnj++eeJiIjgm9/8JosWLSI1NZWioiIqKyt5/vnnGTduHAAvvfQSDQ0NfOtb37rq\nsV4t8/Zehb5tRh+o4BDU1HnoSTPRb21Hb1qD8ZtnoI/bV7zTxqMsMkGcEOL2Zfz1RfQJj1/3qfq4\nsXzu69fd5ujRo2zYsIH169cTFBTEE088wauvvkptbS1paWn8+7//e8u2PXr0YOvWrYBvPNQzzzzD\nhAkT+MUvfsELL7zAf/zHfwC+x4w3b94MwPLlyxk5ciQA8+bN449//CM//OEPGTVq1FX3W1FRwRe+\n8AUAfvazn/GXv/yFr371q59pd1NTExs3bmT79u288MIL5OTkADBq1Ch+85vfXLNom3HbFO1PqaAg\n1ORZ6Ixs9NsFvuL922XQqw9q7mLU2IkoiwzKEEKIzrJr1y7ef/995syZA/ieHY+NjcVqtTJ37twr\ntr3rrrsA8Hq9VFZWMmHCBAAWLVrEAw888JntAMrKyhgwYMB129B6+yNHjvDzn/8cr9dLTU0NmZmZ\nV33Pp+0dOXIkJ0+ebPm50+nk3LlzbR73zbjtivanlM2GypiGHp+FfudN9MZX0C89j97wF9ScRahx\nmSh5DEIIcRtp64y4o2itWbRoEU888cQVP//tb3/7mfvL7b2U33q70NBQ6urq2r39I488wooVKxg2\nbBg5OTm89dZbV31PcHAw4Bv71XokeX19PaGhoe1q54267a8HK4sVyx2Tsfz7r7A8+DiEhqL/+F8Y\nT38To2ALurGxq5sohBC3tIkTJ5Kbm9tyr/3ixYtXnLlejd1ux+FwsGfPHsB3T/yfHyf+VP/+/Sku\nLm55HRERQXV19TX3XV1dTc+ePWlsbGTdunU3eDRw/PhxBg0adMPvaw85lfw7ZbHA6AwsaRPg/Xcw\ncnPQL/9/6Nwc1KyFqEkzUMEhXd1MIYS45QwcOJBHH32U+++/H601NpuNZ599ts33/fKXv2wZiJac\nnNzyBNM/mzp1Kg899FDL68WLF/P444+3DET7Zz/4wQ+YN28eTqeTtLS06xb4qykqKmLatGk39J72\num1Gj9/oiFOtNRx6D2NjDnz8IdijUTPuQWXOQoXe3Jyx3ZmM2jVPMjRPMjQvUEePd7Svfe1rPPXU\nU/Tr188v+7vW5Cr19fXce++9rF+//pozpsno8Q6glIKhqViHpqI//sB35r1mJXrLGlT23agpc1Hh\nEV3dTCGEEO3wxBNPUFZW5reifS2nTp3iySef7LCpYaVot4MaOBzrd4ejPzmMsfEV9PqX0dvWoabO\nR2XPR0VEdXUThRBCXEf//v3p379/h39Ov379OvSLgRTtG6BSBmN96Efokk8wNuagc/+Kfv1vqClz\nUNPvRtmj296JEEIIcZOkaN8E1TcF67eeRJ8q8T0qtvVV9I7XUJNnoWbeg4p2tr0TIYQQ4gZJ0TZB\nJfZFfeMH6LvuR29ag96Ri87fjJo43Tfi3Bnf1U0UQghxC5Gi7QcqIQn11YfR8z+H3rwGvXMbeudW\n1ISpqNn3oeJlWVAhhBDm3faTq/iTikvA8qV/w/Lc73xTpe7Ox/jhgxgr/hN95voTBQghhOg4rZfm\nvFEPP/wwubm5AHz/+9/n448//sw2OTk5PPXUUwCsXLmSv/71rzff2OuQM+0OoGLiUJ9/AD1nEfr1\n9ej8zeg9+agxd/rmN09ydXUThRCiW/Hn0pxmLF++vM1tPve5z3H33Xfzuc99zvTn/TM50+5AKjoG\ny6KvYln2EmrWvegP3sX48UM0/79n0SXHurp5QggRMNauXcvcuXOZPn06jz76KM3NzQwYMIAf//jH\nZGdn8+677zJu3DieffZZZs6cSW5uLh988AHz5s0jOzubr33ta1y6dAmA++67jx/96EfMnj2bl156\niTfffJPhw4djs9k4duzYFYuQnDhxomX2sv/8z/9kzpw5TJ06lUcffZSrzT1233338d577wG+LwIT\nJ05k7ty5vPPOOy3bhIWF0adPH/bv3+/3nORMuxOoKAdq4ZfQMxeit7+G3r4B4yd7YPgYLHMXo/oP\n6eomCiEEL71zDs/F6y+scaPcPUL51/Se192mM5fm7N+/Pw0NDZSWlpKcnMyGDRuYP38+AP/yL//C\nI488AsB3vvMdXn/9dWbMmHHVNp87d47ly5ezZcsWoqKiWLRoEcOHD2/5/ciRI9mzZw9paWk3E9s1\nyZl2J1IRkVjuuh/LshWohV+C4qMYP3uM5uefRh95/6rf6oQQ4lbXemnO6dOns2vXLkpLS294ac5P\nFw9pvR34luZ0Ov/xKO78+fNb5hzfsGFDy7ZFRUXMmzePadOmUVRUdNV715969913mTBhAk6nk+Dg\n4Cs+D3zTxXbE8pxypt0FVFg4avZ96Knz0AVb0NvWYSx/CvoPxTJ3MQxL802jKoQQnaitM+KO0tlL\nc95111088MADzJ49G6UU/fr1o66ujieffJJNmzaRmJjI888/T319/U0fU0ctzyln2l1IhYRimbEA\ny3O/R33+Aagow/ivpRjPfR/93tty5i2EuC109tKcLpcLq9XKL3/5y5Yz5E8LdExMDDU1NWzcuPG6\nnz9mzBh2795NRUUFjY2NLaPLP3X8+HEGDx583X3cDDnTDgAqOAQ1ZS560gx00Q705jUYv/kJJLmx\nzFsMaRN8S4cKIcQtqLOX5gTf2fYzzzzD7t27AXA4HHz+859n2rRpxMXFMWrUqOt+ds+ePfne977H\nXXfdhcPhYNiwYVf8fu/evXz3u99t8xhulCzNGYB0czN6TwF602o4dwp69UHNWYQaOwnVjkccOkJ3\nyzAQSYbmSYbmydKcHbs0J8AHH3zA7373O379619f9fdmluaU07cApKxWLBlTsfzHb1Df+AFYLOgV\nL2D86FsYb+ahr9FRhBBCXN2nS3N2hoqKCh599NEO2bdcHg9gymJFjZ2EHnMnHNjjW1nsj79Cv/ZX\n1Kx7UXdmo4KCurqZQggR8DpraU6AyZMnd9i+pWh3A8pigdETsKSNhw/excjNQf/5v9Ebc1AzF6Im\nzUSFhHR1M4UQ3VAA3iG95ZnJvF1F+8CBA6xcuRLDMJg2bRoLFiy44ve5ubls374dq9WK3W7nwQcf\nJC4ujuLiYl588UUuX76MxWJh4cKFZGRk3HRjb3dKKRiRjmX4GDj0nu/MO+cl9KbVviVBM2ejQsO6\nuplCiG7EYrHQ1NSEzSbncJ2hqakJi4mBxW3+VzIMgxUrVvD000/jdDp54oknSE9PJykpqWUbl8vF\nsmXLCAkJYdu2bbz88ss88sgjBAcH82//9m/06tWLiooKHn/8cUaNGkVERMRNN1j8vXgPTcU6NBX9\n8QcYG19Br/kjevNaVPZdqKnzUOGSsRCibZ8+w1xfXy/zQ9yAkJCQG36OW2uNxWIx9fx2m0X72LFj\nJCQk0LOn76H7jIwM9u7de0XRbj1124ABA9i5cydw5Wi4mJgYHA4HXq+3U4t2XZPRaZ/VFdTA4VgH\nDkcfP+Ir3n/7M3rbetS0eahp81GR9q5uohAigCmlCAuTK3Q3qqueZGizaFdUVFwx/ZvT6eTo0aPX\n3H7Hjh2kpqZ+5ufHjh2jqamppfi3lpeXR15eHgDLli0jNja2XY1vS2OzwZdX7GVg/HmmD4wls7+T\niOBb9BJQbCzccSeNx49Qs3oV9bk5kPcaobPvIeKu+7FEx5javc1m89t/l9uVZGieZGieZOgfXZWj\nXytYYWEhx48fZ+nSpVf8/OLFi/z617/m29/+9lWv5WdnZ5Odnd3y2l/fXmobm5mRYmdnaTXPvn6U\nX+w4xrikSLLcDlJ7RWCz3IKXguxO+Np3scy6D73pFWrX/4XajatRk2ehZtyD6uFsex9XIc/HmicZ\nmicZmicZ+oe/c2zvc9ptFu2YmBjKy8tbXpeXlxMT89mztoMHD7Ju3TqWLl1KUKvHkGpra1m2bBn3\n338/AwcObFej/CU8yMoXRsXx0NTB7Dp8ggKPl10lXnaWVGEPsTKxbxRZbgcDnaG33L0clZiM+vr3\n0fPvR29eg96Ri87fhJo43fe4mDO+q5sohBDiBrVZtFNSUjhz5gxlZWXExMRQVFT0mengPB4PL774\nIk8++SQOh6Pl501NTSxfvpzJkydfc07YzqCUYkhcOEPiwvnamJ7sP1NNvsdL3ieVbPr4EgmRQWS6\n7WS6HCTag7usnR1BJSSivvJ/0fOWoLesRe98Hb1zG2r8FNSc+1Dx7ft2J4QQouu1axrTffv2sWrV\nKgzDYMqUKSxcuJCcnBxSUlJIT0/nmWeeobS0lOjoaMB32eCxxx6jsLCQ//7v/75i0Nq3v/1tXC7X\ndT+vs6YxrWlo5q0TVRR4vLx/rhYNDHCGkuW2M7GvnejQW+/+t664gN76KnrnNmhqQo2b7JsitVef\n675PLqmZJxmaJxmaJxn6R1ddHpe5x/+uvLaRwmIvBcVePBfrsShI6xVBpsvOuD5RhNpurRlfdeVF\n9Lb16PxN0NiAGp2BmrcYleS+6vbyD908ydA8ydA8ydA/pGi30tULhpRcqqfAU0lBsZcLtU2E2hTj\nk6LIdNsZlRCB9RYawKarvOi8v6F35ELdZUgdh2XuYpRrwBXbyT908yRD8yRD8yRD/5Ci3UpXF+1P\nGVrzUdllCoorebOkippGg+hQK5NcdjJddvrH3DoD2HRNtW+wWt4GqK2G4aOxzF2C6j8EkH/o/iAZ\nmicZmicZ+ocU7VYCpWi31tBs8O6pGvKLK3nnVA1NhibRHkyWy06m207PyFtjAJu+XIvO34Teth6q\nvTBoBJZ5S4i9c8oVTxGIGyd/LM2TDM2TDP1DinYrgVi0W6uub6boRBX5nko+LLsMwJC4MDJddu7s\na8ce0jVrXvuTrq9DF25Fb10HlRUEDR5J88x7YNjoW+bqQmeTP5bmSYbmSYb+IUW7lUAv2q2VVfsG\nsOUXV3KisgGbBUb3jiTTZWdsYiQh3XwAm25sQO/KQ21bh3HhHPTtj2XeEhh1hxTvGyR/LM2TDM2T\nDP1DinYr3alof0prjediPQV/H4F+8XITYTYLGclRZLntDIsP79YD2JwOB+dzV6M3r4HzZyHJhWXu\nYhid4Vs6VLRJ/liaJxmaJxn6R8DOiCbaRylFv5hQ+sWE8qXUOD4oqyXf46WotIrtxytxhtmY5LKT\n5bbjig7pdmepKigIy6QZ6Ixp6LcL0Ztewfjdz6FXH98kLWMno6zd/7aAEEIEMjnT7mD1TQZvn6ym\noLiSfadraNbQ1xFCptvOZJeduIigtncSAP45Q200o999C70xB06VQHwv1Oz7UOOzULbucUydTc5w\nzJMMzZMM/UMuj7dyKxXt1rx1TewqrSLf4+XIBd8AtuHxYWS6HWQkRxEZHLhnqtfKUBsGvPc2Rm4O\nlH4CMXGo2fei7pyOCpLi3Vqg9MPuTDI0TzL0DynardyqRbu1M1UNvgFsHi+nqxqwWRRjEyPIdDtI\n7x1BkDWw7hO3laHWGj7Yh7ExBz45DNExqJkLUZNmokJCOrGlgSsQ+2F3IxmaJxn6h9zTvs30igpm\nyYhYFg93cqyijgKPl8ISL2+dqCYi2MLEZN8ELkPiw7B0g/vfSikYMQbL8NFw+CBGbg465yX0ptWo\nGQtQWbNRoeFd3UwhhOjWpGh3MaUUA5xhDHCG8ZXR8bx3toZ8j5d8TyVbj10iLtxGpttBpttOsiPw\nz1iVUjBkFNYho9Aff4ix8RX02lXoLa+isu9CTZ2LCo/s6mYKIUS3JEU7gFgtitG9IxndO5LLjQZ7\nTvpWIHv1o3LWfFhOvx6+AWyT+tpxhgf+/WI1cBjWgT9Gez72Fe+//Rm9bR1q6jxfAY+0d3UThRCi\nW5F72t3AxctN7CrxPf99tLwOBYxMCCfTZWdCchThQR0/gM0fGerS4xgbX4F9RRAS6rtkPmMByt7D\nT60MbN29HwYCydA8ydA/ZCBaK1K0r+2kt54Cj6+An6tuJNiquCMpkiyXg7TeEdg6aAIXf2aoT5Wi\nN61G790JQTbfYLWZC1E9nH7Zf6C6lfphV5EMzZMM/UOKditStNumtebIhTryPZXsKq2iqr6ZqBAr\nE5OjyHI7GBTr3xXIOiJDffYUessa9O58UAp1Z7bvWW9nvF8/J1Dciv2ws0mG5kmG/iFFuxUp2jem\nsVmz/0w1BcVe3j5ZTUOzJiEyiEy3nUyXg0S7+RXIOjJDfeEcevNadFEeaO2boGXOIlR8+zpxd3Gr\n98POIBmaJxn6hxTtVqRo37zaxmbeKq0iv9jL+2dr0cAAZyiZLt8Atuiwmxt72Cnzt1dcQG9bhy7c\nCk1NqDsmoeYuRvXq06Gf21lup37YUSRD8yRD/5Ci3YoUbf8or21kZ4lvAhfPxXosClITIsh02xnf\nJ4rQG1iBrDMz1JUX0dvWows2Q0M9jJ6AZe4SVB93p3x+R7ld+6E/SYbmSYb+IUW7FSna/ld66e8r\nkHkqOV/bRKhNMT4piky3nVEJEW2uQNYVGeoqLzpvA/qNXLhcC6Pu8BVv94BObYe/SD80TzI0TzL0\nDynarUjR7jiG1hwqu0x+cSVvllZR02DgCLUyua+dTLed/jFXH8DWlRnq2mr0jlz06xugthqGpWGZ\ntwTVf2iXtOdmST80TzI0TzL0DynarUjR7hyNzQbvnK6hwFPJ3lM1NBma3lHBZLl9U6gmRP1jAFsg\nZKjratFvbEa/vh6qKmHQCN+a3oNHdoulTgMhw+5OMjRPMvQPKdqtSNHufNX1zRSdqKLAU8kHZb4V\nyAbHhpHptjMxOYp+SQkBk6Gur0Pv3Iresg4qKyBlMJa5S2D46IAu3tIPzZMMzZMM/SOgi/aBAwdY\nuXIlhmEwbdo0FixYcMXvc3Nz2b59O1arFbvdzoMPPkhcXBwAzz77LEePHmXw4ME8/vjj7WqUFO2u\ndb6mseX+d2llA1YF410xZCSGMjYxkpAbGMDWkXRjA/rNPPTmtVBxHvr2xzJvMYy8A2UJjDa2Jv3Q\nPMnQPMnQP7qqaFuXLl269HobGIbBc889x1NPPcU999zDypUrGTp0KHb7P+aNbmhoYMmSJcyZM4f6\n+nq2b9/OhAkTAOjRowdjxozh+PHjTJw4sV2Nqqqqatd27RUeHk5tba1f93kriwi2MjQ+nNkDohnf\nJ4oQm4X9p6vJ++QSuUcucqqqgbAgC3HhQV26ApmyWlGuAagpc8AZD4cOoPM3ow/shogo6JWIUoFT\nvKUfmicZmicZ+oe/c4yKimrXdm0+tHvs2DESEhLo2bMnABkZGezdu5ekpKSWbYYPH97y/wcMGMDO\nnTtbXo8YMYIPP/yw3Q0XgUMphbtHKO4eoXw3ewj5H5VS4PFSVFrFjuOVxITZmOzy3f929wjpskvT\nyhaEmjQDnTENvbcQvXE1+vc/RyckoeYuQo2djLJ2/PzsQgjR0dos2hUVFTid/5gT2ul0cvTo0Wtu\nv2PHDlJTU/3TOhEwrBbFqIQIRiVE8MDYnuw95ZuB7bXDFaw/VEGyI9i3hKjLTlxE16xApqxW1Pgp\n6Dsmo999C70xB73iP9Eb/uKbHnXCFJQt8FdHE0KIa/Hr0pyFhYUcP36cNq64f0ZeXh55eXkALFu2\njNjYWH82C5vN5vd93m7+OcPEhHgWjIFLlxt54+gFth4u438OnOd/DpwnNdHOzMHxZPWPxR7aRau/\nzl6AnnkX9Xt3UbP6jzT96TeozasJv+eLhE2bhwru/LXJpR+aJxmaJxn6R1fl2OZf1JiYGMrLy1te\nl5eXExMT85ntDh48yLp161i6dClBQTd2NpOdnU12dnbLa38PkpCBF+ZdL8NJvYOY1DuRs1UNFBZ7\necPj5Wfbj/H8G58wNjGCTJeD9MQIgqxdcH85ZSj6sZ9h+WAfxsYcqn7/PFWvrETNvAc1aRYqpPOK\nt/RD8yRD8yRD/+iqgWhtFu2UlBTOnDlDWVkZMTExFBUV8dBDD12xjcfj4cUXX+TJJ5/E4XDcXItF\nt5cQFcziEbEsGu7kWEUdBR4vO0u8vHWimohgC3cmR5HlcjAkPqxTB7AppWDEGCzDR8PhgxgbX0Hn\nrEBvWoOavgA1ZTYqNLzT2iOEEDerXY987du3j1WrVmEYBlOmTGHhwoXk5OSQkpJCeno6zzzzDKWl\npURHRwO+byCPPfYYAD/60Y84deoUdXV1REVF8c1vfrPNe97yyFfgudkMmw3Ne2drKPB42X2yirom\nTVy4bwBblttBcnTnX6YG0Ec/wtiYAx/uh4goVPZ81NR5qPDIDvtM6YfmSYbmSYb+EdDPaXc2KdqB\nxx8Z1jUZ7DlRRUGxl/1najA0uHuEkOmyM9llxxne+YPEtOdjjI2vwHtvQ1g4aso8VPZdqCh722++\nQdIPzZMMzZMM/UOKditStAOPvzO8dLmJnSVeCoq9HC2vQwEjEsLJctmZkBxFeFDnPqKlS49jbHoF\n9r0FwSGozNmoGQtQjh5++wzph+ZJhuZJhv4hRbsVKdqBpyMzPOVtoKC4kgKPl7PVjQRbFWMTI8ly\n20nrFUmQtfPuf+vTpehNq9Fv7wSbDTV5JmrGPagY86NEpR+aJxmaJxn6hxTtVqRoB57OyFBrzZEL\ndRQUV7KzpIqq+maiQqxMTPYtITo4NqzTJnDR506jN69B734DlEJlZKNm34uK7XnT+5R+aJ5kaJ5k\n6B9StFuRoh14OjvDJkOz/3QNBcWV7DlZTUOzJiEyyDcDm9tOkr1zBrDpC+fQW9ai38wDrVHjs1Cz\nF6F6tu8fWGvSD82TDM2TDP1DinYrUrQDT1dmWNvYzO4T1eR7Kjl4thYN9I8JJcttZ1JfO9FhHT+B\ni664gN62Dl24FZqaUGMn+aZI7Z3c7n1IPzRPMjRPMvQPKdqtSNEOPIGSYXltI7tKqsj3VHL8Yj0W\nBakJEWS67YxLiiIsqGMncNHei+ht69H5m6GhHtImYJm7GJXcr833BkqG3ZlkaJ5k6B9StFuRoh14\nAjHD0kv1FBR7KSyupKymiRCrYnyfKDJddlJ7RWC1dNz9b13tRedtQO/Ihcu1MOoOX/F2D7zmewIx\nw+5GMjRPMvQPKdqtSNEOPIGcoaE1h85fpsDj5c1SL9UNBo5QK5P62sly2+kfE9phA9h0bTV6x0Z0\n3gaoqYKhaVjmLUENGPqZbQM5w+5CMjRPMvQPKdqtSNEOPN0lw8Zmg3dP15Dv8bL3VDVNhqZ3VFDL\nCmS9ooKIMMuHAAAgAElEQVQ75HN1Xa1vLe9t66GqEgYOxzJvCQwe2fKFobtkGMgkQ/MkQ/+Qot2K\nFO3A0x0zrG5opqjUNwPbB+d8i9UPig0jy21nYnJUh6xApuvr0Tu3ore+CpcqIGUwlrmLYfgY4uLi\nul2GgaY79sNAIxn6hxTtVqRoB57unuH5mkYKi70UeLyUVNZjVTC6t28FsjuSIgmx+XcAm25sQL+Z\nh968FirOQ9/+OO7/GlXuIShLF6x2dovo7v0wEEiG/iFFuxUp2oHnVsqw+GId+R4vhcVeyi83EWqz\nkJEcSabLwYie4X4dwKabGtG789GbVsP5s5DYFzV3MWpMBsrSuVO13gpupX7YVSRD/5Ci3YoU7cBz\nK2bYbGg+LKuloNhLUWkVtY0GPcJsZLrsZLrsuHuE+G0Am25uJvLwAbw5f4AzJyAhCTVnEeqOySir\nFO/2uhX7YWeTDP1DinYrUrQDz62eYX2TwTunqskv9vLuqWqaNfRxBJPlcjDZZSc+0vwKZLGxsZwv\nK4N9Rb6VxU4WQ1wCavZ9qAlTULbOX+Wsu7nV+2FnkAz9Q4p2K1K0A8/tlKG3vpk3/74C2aHzlwEY\nGhdGltvBnclRRIbc3Jlx6wy1YcDBvRi5OVByDGLiULPuRU3MRgV1zAj3W8Ht1A87imToH1K0W5Gi\nHXhu1wzPVTdQUOwl3+PllLcBm0WRnhhBpstOemIkwdb2Dyq7WoZaa/hwn694f3IYHDGomff4VhcL\nCfX34XR7t2s/9CfJ0D+6qmh3/KTNQnRjPSODWTw8lkXDnHxSUU9+cSU7i73sPlFNRJCFjOQostwO\nhsaHYbmJ+99KKRg+Bsuw0XDkfYzcHPQrK9Cb16Cm342aMgcVGt4BRyaE6I6kaAvRDkop+jtD6e8M\n5Stp8Rw8V0u+p5KdJV5e/6SS2HAbk112stwO+kbf+ApkSikYPBLr4JHoYx9hbHwF/eqf0FteRWXf\nhZo6DxUR2QFHJoToTuTyuGgXyfDq6poM9pzwTeCy/0wNhgZ3jxAmu+xMdtmJDf/H4LIbzVB7jmJs\negUO7IGwcNSUuajsu1FR9o44lG5B+qF5kqF/yD3tVqRoBx7JsG2X6prYVeKbwOXj8joUMKJnOJlu\nOxP6RNG3d8+bylCf8KA3voLeVwRBwais2agZ96AcPfx/EAFO+qF5kqF/SNFuRYp24JEMb8xpbwMF\nxZXke7ycrW4kyKKYlOJkfO8QRveKJMh64/e/9ZkT6E2r0XsKwWZDTZqBmrkQFRPbAUcQmKQfmicZ\n+ocU7VakaAceyfDmaK35uLyOAk8lb56o5tLlJqKCLUzs65vAZXBc2A1P4KLLTqM3rUHvfgNQqDun\n+R4Xi0vomIMIINIPzZMM/UOKditStAOPZGhedI8Y8j4oJd9TyZ6T1TQ0a3pGBrXMwJbkuLEBbLq8\nDL1lLXrX62AYqPFTfBO1JCR20BF0PemH5kmG/hHQRfvAgQOsXLkSwzCYNm0aCxYsuOL3ubm5bN++\nHavVit1u58EHHyQuLg6A/Px8Xn31VQAWLlxIVlZWm42Soh14JEPzWmdY29jM7hPVFHgqOXiuFkND\nSkwoWW47k/ra6RHW/gc79MVy9LZ16MIt0NiEGjsRNWcxKjG5ow6ly0g/NE8y9I+AfU7bMAxWrFjB\n008/jdPp5IknniA9PZ2kpKSWbVwuF8uWLSMkJIRt27bx8ssv88gjj1BdXc2aNWtYtmwZAI8//jjp\n6elERsqjK+L2Fh5kZWo/B1P7Oai43MTOYi8FxZWseLeMlfvKGJkQQZbLzvg+UYQFXX8CF9XDiVry\nr+jZ96K3/Q2dvwn9diGMnoBl7mJUckonHZUQoqNZly5duvR6Gxw9epTS0lJmz56NxWKhpqaG06dP\nM2TIkJZt4uPjsdl89d9isVBUVMTUqVN5++23sVgsTJgwgeDgYE6ePElzczPJydc/A6iqqjJ/ZK2E\nh4dTW1vr133ebiRD866VYViQhcFxYcwc0IM7+0YRHmTlg3M1bD/u5bXDFZRWNhBiVfSMDLruBC4q\nJAw1NBU1eSYEBcHbO9HbX0OXHEPFJaB6dP8Ba9IPzZMM/cPfOUZFRbVruzbPtCsqKnA6nS2vnU4n\nR48eveb2O3bsIDU19arvjYmJoaKiol0NE+J2lOwI4f+kxvGFUbEcPn+ZfI+XN0t9y4g6QqxMdNnJ\nctkZ4Ay95gA2FWlH3f0F9PS70Ts2ovM2YPz0BzA0FcvcJaiBwzr5qIQQ/uLXGdEKCws5fvw4bZy8\nf0ZeXh55eXkALFu2jNhY/54R2Gw2v+/zdiMZmnejGcbHweSh0NBksLvkItsOl/H6sQo2HrlIn+hQ\nZgyKZ8bgOJKiw66xh1j4l29jLPkKl7eso/Zv/4vxiycIGppKxOKvEDwy3W9Lj3YW6YfmSYb+0VU5\ntlm0Y2JiKC8vb3ldXl5OTEzMZ7Y7ePAg69atY+nSpQQFBbW896OPPmrZpqKigqFDh37mvdnZ2WRn\nZ7e89vcgCRl4YZ5kaJ6ZDIc6YOi4OP41LYa3Sn0zsP1hTykr9pQyKDaUTJeDiX2jcIRe45/0pJlw\nRxZq1zYat7zKpaX/F/oNwjJvCQwf022Kt/RD8yRD/+iqgWhtLlGUkpLCmTNnKCsro6mpiaKiItLT\n06/YxuPx8OKLL/Loo4/icDhafp6amsp7771HdXU11dXVvPfeey2XzoUQNy4y2Mr0/tH8JDuZFxek\n8OXUOOqaNL9/5xxfefUYz7xxgsJiL/VNxmfeq0JCsEybj+W536O+8CBUXsT41X9g/OS76H1v+ZYL\nFUIEtHY98rVv3z5WrVqFYRhMmTKFhQsXkpOTQ0pKCunp6TzzzDOUlpYSHR0N+L6BPPbYY4DvHve6\ndesA3yNfU6ZMabNR8shX4JEMzevIDIsv1lFQ7JtCtfxyE6E2CxP6RJLldjCiZzhWy2fPpHVTE3pP\nPnrTaig7A4l9UXMXo8ZkoCw3t2Z4R5N+aJ5k6B8B/Zx2Z5OiHXgkQ/M6I8NmQ/NhWS0FxV6KSquo\nbTToEWZjct8oMt0O+vUI+cylcN3cjN6701e8z5yAhETU7EWocZkoa2AVb+mH5kmG/iFFuxUp2oFH\nMjSvszNsaDbYe6qaAo+Xd09X02RAkj2YLLdvBbKekcFXbK8NA/a/hZH7Cpz0QGxP3wxrGVNRtqBr\nfErnkn5onmToH1K0W5GiHXgkQ/O6MsOq+mbeLPVdPv/o/GUAhsaFkem2c2eynaiQf5xRa63h4F6M\n3BwoPgoxsb65zSdORwUFX+sjOoX0Q/MkQ/+Qot2KFO3AIxmaFygZnqtuaLn/fdLbgM0CY3pHkuW2\nk54YSbDVNz5Vaw0f7sfYmAPHDoGjh29J0MxZqJDQLml7oGTYnUmG/hGw05gKIW4tPSODWTw8lkXD\nnBy/WE++p5KdxV72nKwmIsjChOQostx2hsWHYxk+GsuwNPj4A4zcHPTqP6A3r0FNvxs1ZS4qLLyr\nD0eI24oUbSFuU0opUmJCSYkJ5V/S4nn/XC35nkp2lVSR90klznBbywpkrkEjsA4agT52CGPjK+h1\n/4Peug41bb7vfxGynoAQnUEuj4t2kQzN6y4Z1jUZvH3StwLZvjM1GBpc0SFk/n0AW2x4ELr4KMbG\n1XBgN4SG+c66p9+NinK0/QEmdJcMA5lk6B9yeVwIERBCbRYmu3wF+lJdE2+WVJHvqWTV/vP8af95\nhvcMJ8sdx4SvP0Z4WSl642rfut7bX0NlzUZNX4CK/uysiUII8+RMW7SLZGhed8/wtLeBwmIv+cWV\nnKlqJMiiGJsUSZbLTprlIrYta9FvF4DFipo0AzVrISomzq9t6O4ZBgLJ0D/kTFsIEdB624P53MhY\nloxwcrS8jvxiL7v+PolLVLCFjOH3kznxPgbtXg+FW9CFW33PeM++DxWX0NXNF+KWIEVbCHFDlFIM\njA1jYGwYXx0dz4EzNRR4vLzhqWRrsybeMYfML89n0vFCknatR7+ZhxqXhZpzHyohqaubL0S3JkVb\nCHHTbBZFemIk6YmR1DY2s+dENfnFXtZ6aljNHfSbk0Fm9VHu3JNDzO58VPqdvvnNE/t2ddOF6Jak\naAsh/CI8yMqUfg6m9HNQcbmJXSVe8j1eVhpuVo19nJGWS0w+sp1xz3yfsJFpWOYtQSWndHWzhehW\npGgLIfwuJszGXYNjuGtwDCcq6ynweCkoDuJXA+4leMA9jLvwIZP/3+8YlRhF8LzFqH6DurrJQnQL\nUrSFEB2qjyOEL6bG8YVRsRw+f9k3gC1oFDtjR2BvrOHO1bvIDNvMoJnZWAYN7+rmChHQpGgLITqF\nUooh8eEMiQ/nX8f0ZN+ZavKPXSQvaAKbsdCr8AKTdvyVrHGD6Z026jNLiAohpGgLIbpAkFUxLimK\ncUlR1DQ0U+S5SP7BWlbXj+KVQ4oBB3aS5bIzccIwosMCY1lQIQKBFG0hRJeKCLYyfVAs0wfFcr7y\nMoU791NwFl48E8qKtUdJi2wic2Qy45PtXd1UIbqcFG0hRMCIc4Rx77wMFjY1UbzzTQoOllJYP4B3\n3zpL6O7TZA6sYEJiGCN7hmO1yOVzcfuRoi2ECDjKZsM9JRNXZjNffHsXH77xKgVBSexqGsXWIyH0\nCLUyyWUny+2gX48Quf8tbhtStIUQAUtZrNjGZzLyjkmM3L+bB7f+mT1VNgqSJ7Cprj8bDl8kyR5M\nptu3hGjPyOCubrIQHUqKthAi4CmLBcZkkDBjPnfu2MKEjTlUHXyZt/pmUJgymT+/18Cf37vA0Lgw\nJrvs3NnXjj3E2tXNFsLvpGgLIboNpRRq1FgsI9NxfHSAGbk5zNj275TFudg59j4K6hL57d5zvPTu\nOcb0jiTTZSc9MZIQm6Wrmy6EX0jRFkJ0O0opGJaGdVga+sgHxG/M4d5Ny1kYaad4yhIKeo5m58la\n9pysJjzIQkZyFJkuO8N7hmOR+9+iG5OiLYTo1tSg4VgHDUd/chhj4yu4X3sRd3gEX5o6nw8zZlBw\ntoFdJVXkfVKJM9zG5L52stx2XD1Cu7rpQtwwpbXWbW104MABVq5ciWEYTJs2jQULFlzx+48++ohV\nq1ZRUlLCww8/zPjx41t+9/LLL7N//34A7r33XjIyMtps1OnTp2/0OK5LFn03TzI0TzI0rz0Z6pJj\nGLmvwIHdEBqGmjKHhil383alhcLiSvadrqFZQ9/oELJcdia57MRF3D4TuEg/9A9/59i7d+92bdfm\nmbZhGKxYsYKnn34ap9PJE088QXp6OklJ/1gXNzY2lm9961u89tprV7x33759eDwefv7zn9PY2MiP\nf/xjUlNTCQ8Pv8HDEUKI9lF9+2P99pPok8XoTavRW14laHsuEzNnMWnGPXjH92JXSRUFxZWsOnCe\nPx04z7Ce4WS57ExIjiIyWAawicDVZtE+duwYCQkJ9OzZE4CMjAz27t17RdGOj48H+MyzkidPnmTI\nkCFYrVasVivJyckcOHCgXWfbQghhhkpyob7xA/T8+9GbV6O3v4Z+YxNRk6YzZ9a9zB3k4kxVAwXF\nXgo8lfxmz1l+t/cc6YmRZLntjOkdQZBVBrCJwNJm0a6oqMDpdLa8djqdHD16tF0779u3L2vWrGH+\n/PnU19fz4YcfXlHsP5WXl0deXh4Ay5YtIzY2tr3tbxebzeb3fd5uJEPzJEPzbirD2FgYkUrTmZPU\nvvo/XM7fjN65jbCs2Qy590uMmDKYb2dpDp2rZuvhMvI+vsBbJ6qICrExdUAsMwfHMaK3/ZYZwCb9\n0D+6KscOHYg2atQoPvnkE55++mnsdjsDBw7EYvnsN9fs7Gyys7NbXvv7fovcwzFPMjRPMjTPVIZB\nobDk61iyF6C3ruVy/hYu79iIGpeJmrOI+IQk/s9wB/cPtfPemRryi71sOXSOv31wlvgIG5NdDjLd\ndpIdIf49qE4m/dA/AvaedkxMDOXl5S2vy8vLiYmJaXdDFi5cyMKFCwH4r//6L3r16tXu9wohhL8p\nZxzq899Ez1mE3roeXbgZvTsflT4RNWcRtiQXYxIjGZMYyeVGgz0nq8j3eHn1o3LWfFhOvx4hZLkd\nTHLZiQmTB3BE52qzx6WkpHDmzBnKysqIiYmhqKiIhx56qF07NwyDmpoaoqKiKCkpobS0lFGjRplu\ntBBCmKWinaglX0PPvhed9zf0jk3ovTshdTyWeUtQfVMIC7KQ5XaQ5XZw8XITu0q85Hu8/GFfGX/c\nX8bInuFkuh2M7xNJeJAMYBMdr12PfO3bt49Vq1ZhGAZTpkxh4cKF5OTkkJKSQnp6OseOHWP58uXU\n1NQQFBREdHQ0L7zwAg0NDTz22GMAhIeH8/Wvfx2Xy9Vmo+SRr8AjGZonGZrXkRnqmirfYLXtr0Ft\nDYxIxzJ3MSpl8Ge2PVlZT0Gxr4CX1TQSbFWMS4oky+0gtVcEtgBegUz6oX901eXxdhXtziZFO/BI\nhuZJhuZ1Roa6tgb9xkZ03t+gugqGjMIydwlq0PDPbqs1hy9cpsDjZVeJl6oGA3uIlYl9o8hyOxjo\nDA24FcikH/qHFO1WpGgHHsnQPMnQvM7MUNddRhduQW9dB95LMGAolnlLYEjqVQtxY7Nm/5lq8j1e\n9p6qpqFZkxAZ9PcVyBwk2gNjBTLph/4hRbsVKdqBRzI0TzI0rysy1A316J2vo7eshUvl4B6IZe4S\nGJl+zbPomoZm3jpRRYHHy/vnatHAAGcoWW47E/vaiQ7tugFs0g/9Q4p2K1K0A49kaJ5kaF5XZqgb\nG9FvbUdvWgPlZdDH7SveaeN9S4deQ3ltI4XFXgqKvXgu1mNRkNYrgkyXnXF9ogjt5BXIpB/6hxTt\nVqRoBx7J0DzJ0LxAyFA3NaHfLkBvXA1lp6F3MmrOItTYiSjL9UeQl1yqp8BTSUGxlwu1TYTaFOP7\n+FYgG5UQgbUTBrAFQoa3AinarUjRDjySoXmSoXmBlKE2mtF7d6E3rYbTpRDf21e8x2WibNe//G1o\nzUdll8n3VFJUWkVNo0F0qJVJLjuZLjv9YzpuAFsgZdidSdFuRYp24JEMzZMMzQvEDLVhwIHdGBtf\ngdLj4IxHzb4PlTENFdT26mENzQbvnqohv7iSd07V0GRoEu3BZLnsZLrt9Iz07wC2QMywO5Ki3YoU\n7cAjGZonGZoXyBlqreH9dzByc8DzMfSIRc1ciJo0HRXcvqlPq+ubKTpRRb6nkg/LLgMwJC6MTJed\nO/vasYeYn8AlkDPsTqRotyJFO/BIhuZJhuZ1hwy11nDogK94H/0I7NGoGfegMmehQsPavZ+yat8A\ntvziSk5UNmCzwOjekWS57KQnRhJykwPYukOG3UHAzj0uhBCi/ZRSMDQN69A09McfYOTmoNesRG9Z\ng8q+GzVlLio8os39xEcGcd9wJ/cOi8Fz0TcDW0Gxl7dPVhMeZGFCnyiy3HaGxYd3ygA2ERikaAsh\nRAdRA4dj/e5w9CeHMTa+gl7/MnrbOtTU+ajs+aiIqLb3oRT9YkLpFxPKl1Lj+KCslnyPl6LSKrYf\nr8QZZmOSy06W244rOiTgZmAT/iWXx0W7SIbmSYbmdfcMdcknGBtzYP9uCAlDTZmDmn43yh59w/uq\nbzJ4+2Q1BcWV7DtdQ7OGvo4QMt12JrvsxEVcfRBcd88wUMg97VakaAceydA8ydC8WyVDfbIYvWk1\n+p1dEBSEmjwbNfMeVHT7lz1uzVvXxK5S3xKiRy74BrANjw8j0+0gIzmKyOB/DGC7VTLsalK0W5Gi\nHXgkQ/MkQ/NutQz12ZO+4r2nACxW1MTpqFn3opxxN73PM1UNvgFsHi+nqxqwWRRjEyPIdDtI7x1B\nr57xt1SGXUWKditStAOPZGieZGjerZqhPn8WvXkNumgHACpjqq94x/e6+X1qzbGKOgo8XgpLvFTW\nNRMRbCF7YDzjEoIZEh+GRe5/3zQp2q1I0Q48kqF5kqF5t3qGuvw8euur6J3bwGhG3ZHpm2WtV5Kp\n/TYbmvfO1pDv8bLnZDV1TQZx4TYy3Q4y3XaSHe17jlz8gxTtVqRoBx7J0DzJ0LzbJUN9qQK9bR26\nYAs0NqDG3ImauxiV5DK973B7Dza9V0yBx8uBszUYGvr18A1gm9TXjjO87VnchBTtK0jRDjySoXmS\noXm3W4a6qhL9+t/Qb2yEusuQOh7LvMWovv1vep+tM7x4uYldJb7nv4+W16GAkQnhZLrsTEiOIjzI\n/Axstyop2q1I0Q48kqF5kqF5t2uGuqYKvf019PbXoLYGho/BMm8JKmXwDe/rWhme9NZT4PEV8HPV\njQRbFXckRZLlcpDWOwKbTOByBSnarUjRDjySoXmSoXm3e4b6ci36jY3o1/8G1V4YPBLLvCUwcHi7\nJ1VpK0OtNUcu1JHvqWRXaRVV9c1EhViZmBxFltvBoNiOW4GsO5Gi3YoU7cAjGZonGZonGfro+jp0\nwWb01nXgvQT9h/qK99DUNgvqjWTY2KzZf6a6ZfrUhmZNQmQQmW47mS4HiXb/rkDWnUjRbkWKduCR\nDM2TDM2TDK+kG+rRu15Hb3kVLl4A90AscxfDyLHXLN43m2FtYzNvlVaRX+zl/bO1aGCAM5RMl28A\nW3TY7TUrthTtVqRoBx7J0DzJ0DzJ8Op0UyO6aAd68xq4cA6S3FjmLYa0CSjLlauB+SPD8tpGdpb4\nJnDxXKzHoiA1IYJMt53xfaIIvckVyLoTKdqtSNEOPJKheZKheZLh9emmJvTbBehNa+DcKejVx/eo\n2NiJKItvJLi/Myy99PcVyDyVnK9tItSmGJ8URabbzqiEiFt2BbKALtoHDhxg5cqVGIbBtGnTWLBg\nwRW//+ijj1i1ahUlJSU8/PDDjB8/vuV3L7/8Mvv27UNrzYgRI/jKV77S5j0XKdqBRzI0TzI0TzJs\nH200o995E73xFThdCvG9UXPuQ43LIi4hoUMyNLTmUNll8osrebO0ipoGA0eolcl97WS67fSPubUG\nsHVV0bYuXbp06fU2MAyD5557jqeeeop77rmHlStXMnToUOx2e8s2WmvS0tKoq6ujd+/eJCX5Zu85\ncuQIb7zxBj/96U+ZOXMma9euJSEhgfj4+Os2qqqqql2Nb6/w8HBqa2v9us/bjWRonmRonmTYPkpZ\nUIl9UZmzUH3caM8RKNiC3v0GlpBQGuJ6oaz+fQZbKUV8ZBB3JEVx1+Ae9IsJpbahmYLiKrYcvcTO\nkiqqG5qJDbcRGdL9n//2d1+Mimp7mVZox3rax44dIyEhgZ49ewKQkZHB3r17Wwoz0FKE//lblFKK\nhoYGmpqa0FrT3NyMw+Fo90EIIYS4ecpigdETsKSNh/ffwcjNoeq3P4doJ2rWQtSkGahg/09hGmS1\nMKFPFBP6RFFd30zRiSoKPJX878EL/O/BCwyODSPTbWdichT20NtrAJtZbaZVUVGB0+lsee10Ojl6\n9Gi7dj5w4ECGDRvGN77xDbTWzJo164piL4QQouMppWDkWCwj0rGfLubS//4e/dcX0ZtWo2YsQGXO\nRoWGdchnR4ZYmdE/mhn9ozlf09hy//t3e8/x0jvnGN07kiy3nbGJkYTcBgPYzOrQrzhnz57l1KlT\n/Pa3vwXgmWee4dChQwwZMuSK7fLy8sjLywNg2bJlxMbG+rUdNpvN7/u83UiG5kmG5kmG5tl69SJk\n1FgaPtxPzeo/0rDmj7B1HWHzlxA+5z4sEZEd9tmxsTCkby8emKw5dqGGrYfP8/qR8+zddZrwYCtZ\nKU5mDo4nLckR8APYuqovtlm0Y2JiKC8vb3ldXl5OTEz7Fmp/++23GTBgAKGhoQCkpaXx8ccff6Zo\nZ2dnk52d3fLa34MkZPCKeZKheZKheZKheS0Z9uwD//ZDLJ8cxti0mpr//T016/6MmjYPlX0XKqJ9\n91hvVg8FnxsSxaJBkXxQVkuBx8sbRy+w6VAZMWE2JrvsZLrsuHuEBOQAtq4aiNZm0U5JSeHMmTOU\nlZURExNDUVERDz30ULt2Hhsby/bt22lubkZrzUcffcScOXPa9V4hhBAdT6UMxvqdH6JLP8HY+Ao6\nNwf9+gbUlDmo6Xej7NEd+vlWi2JUQgSjEiJ4YGxP9p6qJt/j5bXDFaw/VEGyI9i3hKjLTlyErEDW\nrke+9u3bx6pVqzAMgylTprBw4UJycnJISUkhPT2dY8eOsXz5cmpqaggKCiI6OpoXXngBwzB46aWX\nOHToEACpqal8+ctfbrNR8shX4JEMzZMMzZMMzWtz7vFTJehNq9F7d0GQDTV5FmrmPaho5zXf0xG8\ndU28WVpFvsfL4QuXARgWH0aW20FGn6guH4Ee0M9pdzYp2oFHMjRPMjRPMjSvvRnqs6d8xXtPPlgs\nqInTUbPuRTmv/8huRzhb1UBhsZc3PF5OVzVgsyjGJkaQ6XKQnhhBkLXzB7BJ0W5FinbgkQzNkwzN\nkwzNu9EM9fmz6C1r0W9uBzRqwlTU7HtR8e0rMv6kteZYRR0FHi+FJV4q65qJCLZwZ3IUWS4HQ+LD\nsHTS/W8p2q1I0Q48kqF5kqF5kqF5N5uhrjiP3vIqeuc2aG5GjZuMmrMI1atPB7Sybc2G5r2zNRR4\nvOw+WUVdkyYu3DeALcvtIDna/8+ftxawA9GEEEIIFROH+vwD6DmL0K+vR+dvRu8pQI3OQM1bjEpy\nd2p7rBbF6N6RjO4dSV2TwZ4TVRQUe1l3qIK1H1Xg7hFCpsvOZJcdZ/itM4BNzrRFu0iG5kmG5kmG\n5vkrQ11Vic7bgN6RC3WXIXUclrmLUa4B5htpwqXLTews8VJQ7OVoeR0KGJEQTpbLzoTkKMKD/DOA\nTS6PtyJFO/BIhuZJhuZJhub5O0NdU43e/hp6+waorYHho7HMXYLqP6TtN3ewU94GCoorKfB4OVvd\nSLBVMTbRNwNbWq9Igqw3f/9binYrUrQDj2RonmRonmRoXkdlqC/XovM3obeth2ovDB6JZe5iGDSi\ny0DMwIIAABF+SURBVCdH0Vpz5EIdBcWV7Cypoqq+magQKxOTfUuIDo4Nu+E2StFuRYp24JEMzZMM\nzZMMzevoDHV9HbpgC3rbOqi8CP2HYJm7BIaldXnxBmgyNPtP11BQXMmek9U0NGsSIoN8M7C57STZ\n2zeATYp2K1K0A49kaJ5kaJ5kaF5nZagbG9C7XkdvWQsVF8A1wHfmPeqOgCjeALWNzew+UU2+p5KD\nZ2vRQP+YULLcdib1tRMddu2x2lK0W5GiHXgkQ/MkQ/MkQ/M6O0Pd1Ih+6w305jVw/iwkubHMXQSj\nM3xLhwaI8tpGdpVUke+p5PjFeiwKUhMiyHTbGZcURVjQlW2Vot2KFO3AIxmaJxmaJxma11UZ6uZm\n9J4C9P/f3t0HR1Ueehz/nt0lCSEkJLuEJBAbgaCAErEJBMKLEqyFQKEUA61ThoptTRiHCuWq91rq\nC1oYIlQRL4wXaHT6QqyFXiCoDSIooUIDyHsNCMECGvNCXiiQl33uH7luRQWxiTk54feZYWaT3T3n\nt0+Y/LLP2fOcTS/Dh6cgNr7pPO+U4Vhue5ck/ayTZy+y9UQ1205UUXqugWC3RWp8Z0YmhHNLbCfc\nLkul/Wkq7bZHY9h8GsPm0xg2n91jaPyNmKJCzMY8OFUC0bFYYyZjpd6O5WlbS4f4jeHwx+fZerya\n7Serqa3zExHiZvg3wrl7cE9CG8+12L60uIqIiLQ5lsuNlTIc8800eHcn/g1rMLlLMRvWNK1tnjYa\nq0PbWAzFZVn0jw6lf3QoP06Opuj0Od48Xs2rxWcZ3e8i14e2fiaVtoiItDrL5YKBqbhuGQwHiprK\n+7f/jdm4BuvOSVjD78QK/nqXIv0qOrhdpMZ3JjW+M7V1jcTHRFBZUd7qOVTaIiJiG8uy4OZkXDd9\nE47sayrvNf+DyX8Z61sTsW4bgxViw1vaKwgLcuN22fMJeJW2iIjYzrIs6JuEu28S5r2D+DfmYV7J\nxbz6J6zR38EaNQ4rtJPdMW2n0hYRkTbF6tMfd5/HMO//vam8//xbzOvrsEZlNBV4WLjdEW2j0hYR\nkTbJ6nkD7vt/gTl5DP/GlzEb8zAF65umzL81ASs80u6IrU6lLSIibZp1XS/cWQ9hTp3E5L+MeX0d\nZsuGpg+r3TkJK9Jrd8RWo9IWERFHsLpfh/XjOZjxUzGb/ojZshGzdVPTaWJjJmN5o+2O+LVTaYuI\niKNYMd2xfjQLM24K5tU/Yd4uwLz9l6YFWsZOxoq+uoVKnEilLSIijmR1jcH6YTYmIxPz2p8wb72O\nKXwDa9BwrIxMrNh4uyO2OJW2iIg4mhXlw/r+TzBj72o63r11E2bnNrh1CK6MKVjx19sdscWotEVE\npF2wIiKx7voR5tvfwxT8GfPGBvxFhZA0qKm8r0+0O2KzqbRFRKRdsTqHY333h5hvfRfzxgZMwf/i\nf2oO9B+Ia9wUrN797I74b7uq0t67dy+rV6/G7/eTnp7OxIkTL7n/0KFD5ObmUlJSws9+9jNSU1MB\nOHDgALm5uYHHnT59mlmzZjFo0KAWfAkiIiKfZ3UKwxo/FTP6O5g3N2H+sg7/wofghptxZWTCjQOa\nVmJzkC8tbb/fz8qVK3nkkUfwer08/PDDJCcn06NHj8BjfD4f2dnZrF+//pLn3nTTTSxatAiA2tpa\n7r//fpKSklr4JYiIiFye1TEUa8z3MKMyMNtew7y2Fv/iX0CvG3GNmwL9b3VMeX9paR89epSYmBi6\ndesGwNChQ9m1a9clpR0d3XRu3JVe9F//+lcGDhxIcBu6aouIiFw7rOAQrDsmYG4b03Sa2Kt/xP/M\nY/CN3rjGZULS4DZf3l9a2hUVFXi9/1ptxuv1Ulxc/JV3tH37dsaNG/eF9xUUFFBQUADAggUL8Pl8\nX3n7V+LxeFp8m9cajWHzaQybT2PYfBrD/3fXNMzE73Nh66uc+2MujcuewpPQm06TpxM85LamS4de\ngV3j2CofRKusrOTkyZOXnRofPXo0o0ePDnxdVlbWovv3+Xwtvs1rjcaw+TSGzacxbD6N4WfcMgRz\n8yCsndtoyM+jKucRiI1vWqQlZQSW2/2FT2vpcYyLu7oFYa78pwQQFRVFefm/LvRdXl5OVFTUVwqz\nY8cOBg0ahMejD6uLiEjbYrnduIbcjuux57B+8h/gcmFWLsH/iyz8b72Oaai3O2LAl5Z2r169OHPm\nDKWlpTQ0NFBYWEhycvJX2sn27dtJS0v7t0OKiIh83SyXG1fKMFzznsGV/Z8QGoZ58Tn8/3Uf/jfz\nMfV1dkf88ulxt9vNPffcw5NPPonf7+f2228nPj6eNWvW0KtXL5KTkzl69Cg5OTmcO3eOoqIi8vLy\nWLx4MQClpaWUlZXRr59zz4sTEZFrh+VywcBUXLcMhgO78W9cg/ntcszGPKw7v4s1/Nv2ZTPGGNv2\nfhmnT59u0e3pGE7zaQybT2PYfBrD5tMYfnXGGDiyD//GPPj7fugcQZcHfklNfO8W28fVHtPWQWYR\nEZErsCwL+ibh7puEKT6EP/9lPHHX2ZLlS49pi4iISBMrsR/uWb/E3c2ey3+qtEVERBxCpS0iIuIQ\nKm0RERGHUGmLiIg4hEpbRETEIVTaIiIiDqHSFhERcQiVtoiIiEO0yWVMRURE5POuiXfaDz30kN0R\nHE9j2Hwaw+bTGDafxrBl2DWO10Rpi4iItAcqbREREYdwP/roo4/aHaI19OzZ0+4IjqcxbD6NYfNp\nDJtPY9gy7BhHfRBNRETEITQ9LiIi4hAeuwN8nWbOnElISAgulwu3282CBQvsjuQ4586dY/ny5Xzw\nwQdYlkVWVhZ9+vSxO5ajnD59miVLlgS+Li0tJTMzk4yMDBtTOc+GDRt44403sCyL+Ph4srOzCQoK\nsjuWo+Tn57N582aMMaSnp+v/4FV4/vnn2b17NxERETz99NMA1NbWsmTJEj7++GO6du3KAw88QFhY\nWOsEMu1Ydna2qaqqsjuGoy1dutQUFBQYY4ypr683tbW1NidytsbGRnPvvfea0tJSu6M4Snl5ucnO\nzjYXL140xhjz9NNPmy1bttgbymFKSkrM7NmzzYULF0xDQ4N5/PHHzZkzZ+yO1eYdPHjQHDt2zMye\nPTvwvZdeesmsXbvWGGPM2rVrzUsvvdRqeTQ9Lpf1z3/+k8OHDzNq1CgAPB4PnTp1sjmVs+3fv5+Y\nmBi6du1qdxTH8fv91NXV0djYSF1dHZGRkXZHcpRTp07Ru3dvgoODcbvd9O3bl3feecfuWG1ev379\nPvcueteuXYwcORKAkSNHsmvXrlbL066nxwGefPJJAO644w5Gjx5tcxpnKS0tJTw8nOeff56SkhJ6\n9uzJ9OnTCQkJsTuaY23fvp20tDS7YzhOVFQU48ePJysri6CgIJKSkkhKSrI7lqPEx8fzhz/8gZqa\nGoKCgtizZw+9evWyO5YjVVVVBf5o7NKlC1VVVa2273Zd2k888QRRUVFUVVUxf/584uLi6Nevn92x\nHKOxsZHjx49zzz33kJiYyOrVq1m3bh1Tp061O5ojNTQ0UFRUxA9+8AO7ozhObW0tu3btYtmyZYSG\nhrJ48WK2bdvGiBEj7I7mGD169GDChAnMnz+fkJAQEhIScLk02dpclmVhWVar7a9d/8SioqIAiIiI\nICUlhaNHj9qcyFm8Xi9er5fExEQAUlNTOX78uM2pnGvPnj1cf/31dOnSxe4ojrN//36io6MJDw/H\n4/EwePBg3nvvPbtjOc6oUaNYuHAhjz32GJ06dSI2NtbuSI4UERFBZWUlAJWVlYSHh7favtttaV+4\ncIHz588Hbu/bt4/rrrvO5lTO0qVLF7xeL6dPnwaafnH26NHD5lTOpanxf5/P56O4uJiLFy9ijGH/\n/v10797d7liO88k0bllZGTt37mTYsGE2J3Km5ORktm7dCsDWrVtJSUlptX2328VVPvroI3JycoCm\nad5hw4YxadIkm1M5z4kTJ1i+fDkNDQ1ER0eTnZ3deqc2tCMXLlwgOzub5557jtDQULvjOFJeXh6F\nhYW43W4SEhK477776NChg92xHGXevHnU1NTg8XiYNm0aN998s92R2rxf//rXHDp0iJqaGiIiIsjM\nzCQlJYUlS5ZQVlbW6qd8tdvSFhERaW/a7fS4iIhIe6PSFhERcQiVtoiIiEOotEVERBxCpS0iIuIQ\nKm2RdigzM5MPP/zQ7hifk5eXx7PPPmt3DBHHatfLmIq0BTNnzuTs2bOXLBl52223MWPGDBtTiYgT\nqbRFWsGDDz7IgAED7I7RrjQ2NuJ2u+2OIdKqVNoiNnrzzTfZvHkzCQkJbNu2jcjISGbMmBFYqaqi\nooIXXniBI0eOEBYWxoQJEwJXq/P7/axbt44tW7ZQVVVFbGwsc+fOxefzAbBv3z6eeuopqqurGTZs\nGDNmzPjCCxvk5eXxj3/8g6CgIHbu3InP52PmzJmBK0BlZmby7LPPEhMTA8CyZcvwer1MnTqVgwcP\nsnTpUsaMGcP69etxuVzce++9eDwecnNzqa6uZvz48ZesRlhfX8+SJUvYs2cPsbGxZGVlkZCQEHi9\nq1at4vDhw4SEhJCRkcHYsWMDOT/44AM6dOhAUVER06ZNIz09/ev5wYi0UTqmLWKz4uJiunXrxsqV\nK8nMzCQnJ4fa2loAnnnmGbxeLytWrGDOnDn8/ve/58CBAwBs2LCB7du38/DDD5Obm0tWVhbBwcGB\n7e7evZtf/epX5OTksGPHDt59993LZigqKmLo0KH85je/ITk5mVWrVl11/rNnz1JfX8/y5cvJzMxk\nxYoVvPXWWyxYsIDHH3+cV155hdLS0sDj//a3vzFkyBBWrVpFWloaixYtoqGhAb/fz8KFC0lISGDF\nihXMmzeP/Px89u7de8lzU1NTWb16NcOHD7/qjCLthUpbpBUsWrSI6dOnB/4VFBQE7ouIiCAjIwOP\nx8PQoUOJi4tj9+7dlJWVceTIEe6++26CgoJISEggPT09cKGCzZs3M3XqVOLi4rAsi4SEBDp37hzY\n7sSJE+nUqRM+n4/+/ftz4sSJy+a78cYbufXWW3G5XIwYMeKKj/0st9vNpEmT8Hg8pKWlUVNTw9ix\nY+nYsSPx8fH06NHjku317NmT1NRUPB4P48aNo76+nuLiYo4dO0Z1dTWTJ0/G4/HQrVs30tPTKSws\nDDy3T58+DBo0CJfLRVBQ0FVnFGkvND0u0grmzp172WPaUVFRl0xbd+3alYqKCiorKwkLC6Njx46B\n+3w+H8eOHQOgvLycbt26XXafn74EaHBwMBcuXLjsYyMiIgK3g4KCqK+vv+pjxp07dw58yO6TIv3s\n9j69b6/XG7jtcrnwer2XXOZw+vTpgfv9fj99+/b9wueKXItU2iI2q6iowBgTKO6ysjKSk5OJjIyk\ntraW8+fPB4q7rKwscJ14r9fLRx999LVfcjY4OJiLFy8Gvj579myzyrO8vDxw2+/3U15eTmRkJG63\nm+joaJ0SJnIFmh4XsVlVVRWbNm2ioaGBHTt2cOrUKQYOHIjP5+OGG27gd7/7HXV1dZSUlLBly5bA\nsdz09HTWrFnDmTNnMMZQUlJCTU1Ni+dLSEjg7bffxu/3s3fvXg4dOtSs7b3//vu88847NDY2kp+f\nT4cOHUhMTKR379507NiRdevWUVdXh9/v5+TJkxw9erSFXomI8+mdtkgrWLhw4SXnaQ8YMIC5c+cC\nkJiYyJkzZ5gxYwZdunRh9uzZgWPTs2bN4oUXXuCnP/0pYWFh3HXXXYFp9k+OB8+fP5+amhq6d+/O\nz3/+8xbPPn36dJYtW8Zrr71GSkoKKSkpzdpecnIyhYWFLFu2jJiYGObMmYPH0/Sr6MEHH+TFF19k\n5syZNDQ0EBcXx5QpU1riZYi0C7qetoiNPjnl64knnrA7iog4gKbHRUREHEKlLSIi4hCaHhcREXEI\nvdMWERFxCJW2iIiIQ6i0RUREHEKlLSIi4hAqbREREYdQaYuIiDjE/wGEDk/X3CSHMwAAAABJRU5E\nrkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc435b3abe0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfoAAAENCAYAAADngqfoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQgjhw/4/zKYGnsCH5MEAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc435b957f0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    final error(train) = 1.68e-01\n",
-      "    final error(valid) = 1.75e-01\n",
-      "    final acc(train)   = 9.50e-01\n",
-      "    final acc(valid)   = 9.50e-01\n",
-      "    run time per epoch = 14.17\n",
-      "--------------------------------------------------------------------------------\n",
-      "learning_rate=0.20 init_scale=1.00\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAENCAYAAAAi8D15AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nwQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc435ca8908>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAfMAAAENCAYAAAAbl4wiAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc435fae828>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    final error(train) = 1.98e-01\n",
-      "    final error(valid) = 2.10e-01\n",
-      "    final acc(train)   = 9.41e-01\n",
-      "    final acc(valid)   = 9.38e-01\n",
-      "    run time per epoch = 16.21\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Set training run hyperparameters\n",
-    "batch_size = 100  # number of data points in a batch\n",
-    "num_epochs = 10  # number of training epochs to perform\n",
-    "stats_interval = 5  # epoch interval between recording and printing stats\n",
-    "learning_rate = 0.2  # learning rate for gradient descent\n",
-    "\n",
-    "init_scales = [0.1, 0.2, 0.5, 1.]  # scale for random parameter initialisation\n",
-    "final_errors_train = []\n",
-    "final_errors_valid = []\n",
-    "final_accs_train = []\n",
-    "final_accs_valid = []\n",
-    "\n",
-    "for init_scale in init_scales:\n",
-    "\n",
-    "    print('-' * 80)\n",
-    "    print('learning_rate={0:.2f} init_scale={1:.2f}'\n",
-    "          .format(learning_rate, init_scale))\n",
-    "    print('-' * 80)\n",
-    "    # Reset random number generator and data provider states on each run\n",
-    "    # to ensure reproducibility of results\n",
-    "    rng.seed(seed)\n",
-    "    train_data.reset()\n",
-    "    valid_data.reset()\n",
-    "\n",
-    "    # Alter data-provider batch size\n",
-    "    train_data.batch_size = batch_size \n",
-    "    valid_data.batch_size = batch_size\n",
-    "\n",
-    "    # Create a parameter initialiser which will sample random uniform values\n",
-    "    # from [-init_scale, init_scale]\n",
-    "    param_init = UniformInit(-init_scale, init_scale, rng=rng)\n",
-    "\n",
-    "    # Create a model with three affine layers\n",
-    "    hidden_dim = 100\n",
-    "    model = MultipleLayerModel([\n",
-    "        AffineLayer(input_dim, hidden_dim, param_init, param_init),\n",
-    "        SigmoidLayer(),\n",
-    "        AffineLayer(hidden_dim, hidden_dim, param_init, param_init),\n",
-    "        SigmoidLayer(),\n",
-    "        AffineLayer(hidden_dim, output_dim, param_init, param_init)\n",
-    "    ])\n",
-    "\n",
-    "    # Initialise a cross entropy error object\n",
-    "    error = CrossEntropySoftmaxError()\n",
-    "\n",
-    "    # Use a basic gradient descent learning rule\n",
-    "    learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n",
-    "\n",
-    "    stats, keys, run_time, fig_1, ax_1, fig_2, ax_2 = train_model_and_plot_stats(\n",
-    "        model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)\n",
-    "\n",
-    "    plt.show()\n",
-    "\n",
-    "    print('    final error(train) = {0:.2e}'.format(stats[-1, keys['error(train)']]))\n",
-    "    print('    final error(valid) = {0:.2e}'.format(stats[-1, keys['error(valid)']]))\n",
-    "    print('    final acc(train)   = {0:.2e}'.format(stats[-1, keys['acc(train)']]))\n",
-    "    print('    final acc(valid)   = {0:.2e}'.format(stats[-1, keys['acc(valid)']]))\n",
-    "    print('    run time per epoch = {0:.2f}'.format(run_time * 1. / num_epochs))\n",
-    "    \n",
-    "    final_errors_train.append(stats[-1, keys['error(train)']])\n",
-    "    final_errors_valid.append(stats[-1, keys['error(valid)']])\n",
-    "    final_accs_train.append(stats[-1, keys['acc(train)']])\n",
-    "    final_accs_valid.append(stats[-1, keys['acc(valid)']])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "| init_scale | final error(train) | final error(valid) | final acc(train) | final acc(valid) |\n",
-      "|------------|--------------------|--------------------|------------------|------------------|\n",
-      "| 0.1        | 1.86e-01           | 1.83e-01           |  0.95            | 0.95             |\n",
-      "| 0.2        | 1.74e-01           | 1.75e-01           |  0.95            | 0.95             |\n",
-      "| 0.5        | 1.68e-01           | 1.75e-01           |  0.95            | 0.95             |\n",
-      "| 1.0        | 1.98e-01           | 2.10e-01           |  0.94            | 0.94             |\n"
-     ]
-    }
-   ],
-   "source": [
-    "j = 0\n",
-    "print('| init_scale | final error(train) | final error(valid) | final acc(train) | final acc(valid) |')\n",
-    "print('|------------|--------------------|--------------------|------------------|------------------|')\n",
-    "for init_scale in init_scales:\n",
-    "    print('| {0:.1f}        | {1:.2e}           | {2:.2e}           |  {3:.2f}            | {4:.2f}             |'\n",
-    "          .format(init_scale, \n",
-    "                  final_errors_train[j], final_errors_valid[j],\n",
-    "                  final_accs_train[j], final_accs_valid[j]))\n",
-    "    j += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Models with four affine layers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--------------------------------------------------------------------------------\n",
-      "learning_rate=0.20 init_scale=0.10\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAecAAAENCAYAAAA10q2wAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc437ffeeb8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAENCAYAAAAi8D15AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc435cf5080>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    final error(train) = 2.03e-03\n",
-      "    final error(valid) = 1.35e-01\n",
-      "    final acc(train)   = 1.00e+00\n",
-      "    final acc(valid)   = 9.73e-01\n",
-      "    run time per epoch = 19.51\n",
-      "--------------------------------------------------------------------------------\n",
-      "learning_rate=0.20 init_scale=0.20\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAENCAYAAAAi8D15AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc42c324278>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAENCAYAAAAi8D15AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nIiIiUUKhLSIiEiX+H2dunxAXe5sDAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc43596be80>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    final error(train) = 1.99e-03\n",
-      "    final error(valid) = 1.17e-01\n",
-      "    final acc(train)   = 1.00e+00\n",
-      "    final acc(valid)   = 9.75e-01\n",
-      "    run time per epoch = 20.58\n",
-      "--------------------------------------------------------------------------------\n",
-      "learning_rate=0.20 init_scale=0.50\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAENCAYAAAAi8D15AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nlLKJsUBG72gAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc435c0f518>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAENCAYAAAAi8D15AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc4355f6198>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    final error(train) = 3.07e-03\n",
-      "    final error(valid) = 1.34e-01\n",
-      "    final acc(train)   = 1.00e+00\n",
-      "    final acc(valid)   = 9.71e-01\n",
-      "    run time per epoch = 20.54\n",
-      "--------------------------------------------------------------------------------\n",
-      "learning_rate=0.20 init_scale=1.00\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAENCAYAAAAi8D15AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc435674e80>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAENCAYAAAAi8D15AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7fc435c960f0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    final error(train) = 7.72e-03\n",
-      "    final error(valid) = 1.62e-01\n",
-      "    final acc(train)   = 1.00e+00\n",
-      "    final acc(valid)   = 9.62e-01\n",
-      "    run time per epoch = 21.37\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Set training run hyperparameters\n",
-    "batch_size = 100  # number of data points in a batch\n",
-    "num_epochs = 100  # number of training epochs to perform\n",
-    "stats_interval = 5  # epoch interval between recording and printing stats\n",
-    "learning_rate = 0.2  # learning rate for gradient descent\n",
-    "\n",
-    "init_scales = [0.1, 0.2, 0.5, 1.]  # scale for random parameter initialisation\n",
-    "final_errors_train = []\n",
-    "final_errors_valid = []\n",
-    "final_accs_train = []\n",
-    "final_accs_valid = []\n",
-    "\n",
-    "for init_scale in init_scales:\n",
-    "\n",
-    "    print('-' * 80)\n",
-    "    print('learning_rate={0:.2f} init_scale={1:.2f}'\n",
-    "          .format(learning_rate, init_scale))\n",
-    "    print('-' * 80)\n",
-    "    # Reset random number generator and data provider states on each run\n",
-    "    # to ensure reproducibility of results\n",
-    "    rng.seed(seed)\n",
-    "    train_data.reset()\n",
-    "    valid_data.reset()\n",
-    "\n",
-    "    # Alter data-provider batch size\n",
-    "    train_data.batch_size = batch_size \n",
-    "    valid_data.batch_size = batch_size\n",
-    "\n",
-    "    # Create a parameter initialiser which will sample random uniform values\n",
-    "    # from [-init_scale, init_scale]\n",
-    "    param_init = UniformInit(-init_scale, init_scale, rng=rng)\n",
-    "\n",
-    "    # Create a model with four affine layers\n",
-    "    hidden_dim = 100\n",
-    "    model = MultipleLayerModel([\n",
-    "        AffineLayer(input_dim, hidden_dim, param_init, param_init),\n",
-    "        SigmoidLayer(),\n",
-    "        AffineLayer(hidden_dim, hidden_dim, param_init, param_init),\n",
-    "        SigmoidLayer(),\n",
-    "        AffineLayer(hidden_dim, hidden_dim, param_init, param_init),\n",
-    "        SigmoidLayer(),\n",
-    "        AffineLayer(hidden_dim, output_dim, param_init, param_init)\n",
-    "    ])\n",
-    "\n",
-    "    # Initialise a cross entropy error object\n",
-    "    error = CrossEntropySoftmaxError()\n",
-    "\n",
-    "    # Use a basic gradient descent learning rule\n",
-    "    learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n",
-    "\n",
-    "    stats, keys, run_time, fig_1, ax_1, fig_2, ax_2 = train_model_and_plot_stats(\n",
-    "        model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)\n",
-    "\n",
-    "    plt.show()\n",
-    "\n",
-    "    print('    final error(train) = {0:.2e}'.format(stats[-1, keys['error(train)']]))\n",
-    "    print('    final error(valid) = {0:.2e}'.format(stats[-1, keys['error(valid)']]))\n",
-    "    print('    final acc(train)   = {0:.2e}'.format(stats[-1, keys['acc(train)']]))\n",
-    "    print('    final acc(valid)   = {0:.2e}'.format(stats[-1, keys['acc(valid)']]))\n",
-    "    print('    run time per epoch = {0:.2f}'.format(run_time * 1. / num_epochs))\n",
-    "\n",
-    "    final_errors_train.append(stats[-1, keys['error(train)']])\n",
-    "    final_errors_valid.append(stats[-1, keys['error(valid)']])\n",
-    "    final_accs_train.append(stats[-1, keys['acc(train)']])\n",
-    "    final_accs_valid.append(stats[-1, keys['acc(valid)']])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "| init_scale | final error(train) | final error(valid) | final acc(train) | final acc(valid) |\n",
-      "|------------|--------------------|--------------------|------------------|------------------|\n",
-      "| 0.1        | 2.03e-03           | 1.35e-01           |  1.00            | 0.97             |\n",
-      "| 0.2        | 1.99e-03           | 1.17e-01           |  1.00            | 0.97             |\n",
-      "| 0.5        | 3.07e-03           | 1.34e-01           |  1.00            | 0.97             |\n",
-      "| 1.0        | 7.72e-03           | 1.62e-01           |  1.00            | 0.96             |\n"
-     ]
-    }
-   ],
-   "source": [
-    "j = 0\n",
-    "print('| init_scale | final error(train) | final error(valid) | final acc(train) | final acc(valid) |')\n",
-    "print('|------------|--------------------|--------------------|------------------|------------------|')\n",
-    "for init_scale in init_scales:\n",
-    "    print('| {0:.1f}        | {1:.2e}           | {2:.2e}           |  {3:.2f}            | {4:.2f}             |'\n",
-    "          .format(init_scale, \n",
-    "                  final_errors_train[j], final_errors_valid[j],\n",
-    "                  final_accs_train[j], final_accs_valid[j]))\n",
-    "    j += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Models with five affine layers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set training run hyperparameters\n",
-    "batch_size = 100  # number of data points in a batch\n",
-    "num_epochs = 100  # number of training epochs to perform\n",
-    "stats_interval = 5  # epoch interval between recording and printing stats\n",
-    "learning_rate = 0.2  # learning rate for gradient descent\n",
-    "\n",
-    "init_scales = [0.1, 0.2, 0.5, 1.]  # scale for random parameter initialisation\n",
-    "final_errors_train = []\n",
-    "final_errors_valid = []\n",
-    "final_accs_train = []\n",
-    "final_accs_valid = []\n",
-    "\n",
-    "for init_scale in init_scales:\n",
-    "\n",
-    "    print('-' * 80)\n",
-    "    print('learning_rate={0:.2f} init_scale={1:.2f}'\n",
-    "          .format(learning_rate, init_scale))\n",
-    "    print('-' * 80)\n",
-    "    # Reset random number generator and data provider states on each run\n",
-    "    # to ensure reproducibility of results\n",
-    "    rng.seed(seed)\n",
-    "    train_data.reset()\n",
-    "    valid_data.reset()\n",
-    "\n",
-    "    # Alter data-provider batch size\n",
-    "    train_data.batch_size = batch_size \n",
-    "    valid_data.batch_size = batch_size\n",
-    "\n",
-    "    # Create a parameter initialiser which will sample random uniform values\n",
-    "    # from [-init_scale, init_scale]\n",
-    "    param_init = UniformInit(-init_scale, init_scale, rng=rng)\n",
-    "\n",
-    "    # Create a model with five affine layers\n",
-    "    hidden_dim = 100\n",
-    "    model = MultipleLayerModel([\n",
-    "        AffineLayer(input_dim, hidden_dim, param_init, param_init),\n",
-    "        SigmoidLayer(),\n",
-    "        AffineLayer(hidden_dim, hidden_dim, param_init, param_init),\n",
-    "        SigmoidLayer(),\n",
-    "        AffineLayer(hidden_dim, hidden_dim, param_init, param_init),\n",
-    "        SigmoidLayer(),\n",
-    "        AffineLayer(hidden_dim, hidden_dim, param_init, param_init),\n",
-    "        SigmoidLayer(),\n",
-    "        AffineLayer(hidden_dim, output_dim, param_init, param_init)\n",
-    "    ])\n",
-    "\n",
-    "    # Initialise a cross entropy error object\n",
-    "    error = CrossEntropySoftmaxError()\n",
-    "\n",
-    "    # Use a basic gradient descent learning rule\n",
-    "    learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n",
-    "\n",
-    "    stats, keys, run_time, fig_1, ax_1, fig_2, ax_2 = train_model_and_plot_stats(\n",
-    "        model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval)\n",
-    "\n",
-    "    plt.show()\n",
-    "\n",
-    "    print('    final error(train) = {0:.2e}'.format(stats[-1, keys['error(train)']]))\n",
-    "    print('    final error(valid) = {0:.2e}'.format(stats[-1, keys['error(valid)']]))\n",
-    "    print('    final acc(train)   = {0:.2e}'.format(stats[-1, keys['acc(train)']]))\n",
-    "    print('    final acc(valid)   = {0:.2e}'.format(stats[-1, keys['acc(valid)']]))\n",
-    "    print('    run time per epoch = {0:.2f}'.format(run_time * 1. / num_epochs))\n",
-    "\n",
-    "    final_errors_train.append(stats[-1, keys['error(train)']])\n",
-    "    final_errors_valid.append(stats[-1, keys['error(valid)']])\n",
-    "    final_accs_train.append(stats[-1, keys['acc(train)']])\n",
-    "    final_accs_valid.append(stats[-1, keys['acc(valid)']])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "j = 0\n",
-    "print('| init_scale | final error(train) | final error(valid) | final acc(train) | final acc(valid) |')\n",
-    "print('|------------|--------------------|--------------------|------------------|------------------|')\n",
-    "for init_scale in init_scales:\n",
-    "    print('| {0:.1f}        | {1:.2e}           | {2:.2e}           |  {3:.2f}            | {4:.2f}             |'\n",
-    "          .format(init_scale, \n",
-    "                  final_errors_train[j], final_errors_valid[j],\n",
-    "                  final_accs_train[j], final_accs_valid[j]))\n",
-    "    j += 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "> How does increasing the number of layers affect the model's performance on the training data set? And on the validation data set?\n",
-    "\n",
-    "<span style='color: red;'>\n",
-    "The best final training set error across the four initialisation scales used above for each model architecture, consistently decreases as we increase the number of layers.\n",
-    "</span>\n",
-    "\n",
-    "| Number of affine layers | Best final training set error |\n",
-    "|-------------------------|-------------------------------|\n",
-    "| 2                       | $1.85 \\times 10^{-2}$         |\n",
-    "| 3                       | $5.21 \\times 10^{-3}$         |\n",
-    "| 4                       | $1.99 \\times 10^{-3}$         |\n",
-    "| 5                       | $1.14 \\times 10^{-3}$         |\n",
-    "\n",
-    "<span style='color: red;'>\n",
-    "This makes sense as because the number of layers increase, for a fixed hidden layer width, the total number of free parameters in the model increases and so we would expect the model to be able to fit too the training data better.\n",
-    "</span>\n",
-    "\n",
-    "<span style='color: red;'>\n",
-    "If we look at the validation set however we see the opposite trend; as the number of layers increases the best final validation set error increases.\n",
-    "</span>\n",
-    "\n",
-    "| Number of affine layers | Best final validation set error |\n",
-    "|-------------------------|---------------------------------|\n",
-    "| 2                       | $7.47 \\times 10^{-2}$           |\n",
-    "| 3                       | $8.77 \\times 10^{-2}$           |\n",
-    "| 4                       | $1.17 \\times 10^{-1}$           |\n",
-    "| 5                       | $1.47 \\times 10^{-1}$           |\n",
-    "\n",
-    "<span style='color: red;'>\n",
-    "If we look more closely at the training curves for the models with more layers we can see what is happening here. For the models with three or more layers, after a certain number of epochs the validation set error begins to *increase* even as the training set error continues to decrease. This indicates that these models have begun *overfitting* to the training data. We could get a better validation set error in these cases by stopping the training early. *Early stopping* like this is one way of trying to overcome overfitting, in later labs we will consider other methods for improving generalisation by reducing overfitting.\n",
-    "</span>\n",
-    "\n",
-    "> Do deeper models seem to be harder or easier to train (e.g. in terms of ease of choosing training hyperparameters to give good final performance and/or quick convergence)?\n",
-    "\n",
-    "> Do the models seem to be sensitive to the choice of the parameter initialisation range? Can you think of any reasons for why setting individual parameter initialisation scales for each AffineLayer in a model might be useful? Can you come up with (or find) any heuristics for setting the parameter initialisation scales?\n",
-    "\n",
-    "<span style='color: red;'>\n",
-    "The final performance of the deeper models becomes increasingly sensitive to the choice of parameter initialisation. For the models with two affine layers, the final training errors for initialisation scales 0.1, 0.2 and 0.5 are all within approximately 10% of each other, while for the models with five affine layers there is an approximately 400% increase in final training error if moving from an initialisation scale of 0.2 to 0.1 and a 50% increase in final training error when moving from 0.2 to 0.5. The smaller parameter initialisation scales for the deeper models in particular seem to give poorer initial performance (error curves start from higher values) and for the five affine layer model the smallest parameter initialisation scale run shows a pronounced flatter section at the start of training with around 15 epochs before the error starts significantly decreasing.\n",
-    "</span>\n",
-    "\n",
-    "<span style='color: red;'>\n",
-    "In general the models with more layers also take longer to train per epoch, so on top of issues of potential overfitting and difficulty of choosing parameter initilisations we also need to factor in the potentially slower training of deeper models if computational time is a key constraint.\n",
-    "</span>\n",
-    "\n",
-    "<span style='color: red;'>\n",
-    "We might expect the appropriate initialisation scale for a given affine layer to depend on its input and output dimensionalities. Each output is calculated as the weighted sum of all the inputs, and so for a larger number of inputs the typical magnitude of the output activations will become larger as each will be calculate from a sum over more values. Similarly the backpropagated gradient at each input is calculated as a weighted sum over the gradients at each output, and so for a larger number outputs the typical magnitude of backpropagated gradients will become larger.\n",
-    "</span>\n",
-    "\n",
-    "<span style='color: red;'>\n",
-    "If we wish to keep some measure of the typical magnitude of the activations and backpropagated gradients at a given layer roughly constant through the network then we may therefore wish to set the parameter initialisation in a layer dimensionality dependent way. One heuristic based on trying to achieve a roughly constant variance in activations and backpropagated gradients through the network is to initialise the weights for a layer from a distribution with variance inversely proportional to the sum of the input and output dimensions of the layer. This is sometimes known as the Glorot or Xavier initialisation, after the name of the author of [the paper](http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf) in which this scheme was proposed. This is discussed in [lecture 4](http://www.inf.ed.ac.uk/teaching/courses/mlp/2017-18/mlp04-learn.pdf).\n",
-    "</span>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercise 3: Hyperbolic tangent and rectified linear layers\n",
-    "\n",
-    "In the models we have been investigating so far we have been applying elementwise logistic sigmoid transformations to the outputs of intermediate (affine) layers. The logistic sigmoid is just one particular choice of an elementwise non-linearity we can use. \n",
-    "\n",
-    "As mentioned in [lecture 3](http://www.inf.ed.ac.uk/teaching/courses/mlp/2017-18/mlp03-mlp.pdf), although logistic sigmoid has some favourable properties in terms of interpretability, there are also disadvantages from a computational perspective. In particular that the gradients of the sigmoid become very close to zero (and may actually become exactly zero to a finite numerical precision) for very positive or negative inputs, and that the outputs are non-centred - they cover the interval $[0,\\,1]$ so negative outputs are never produced.\n",
-    "\n",
-    "Two alternative elementwise non-linearities which are often used in multiple layer models are the hyperbolic tangent and the rectified linear function.\n",
-    "\n",
-    "For a hyperbolic tangent (`Tanh`) layer the forward propagation corresponds to\n",
-    "\n",
-    "\\begin{equation}\n",
-    "    y^{(b)}_k = \n",
-    "    \\tanh\\left(x^{(b)}_k\\right) = \n",
-    "    \\frac{\\exp\\left(x^{(b)}_k\\right) - \\exp\\left(-x^{(b)}_k\\right)}{\\exp\\left(x^{(b)}_k\\right) + \\exp\\left(-x^{(b)}_k\\right)}\n",
-    "\\end{equation}\n",
-    "\n",
-    "which has corresponding partial derivatives\n",
-    "\n",
-    "\\begin{equation}\n",
-    "    \\frac{\\partial y^{(b)}_k}{\\partial x^{(b)}_d} = \n",
-    "    \\begin{cases} \n",
-    "      1 - \\left(y^{(b)}_k\\right)^2 & \\quad k = d \\\\\n",
-    "      0 & \\quad k \\neq d\n",
-    "    \\end{cases}.\n",
-    "\\end{equation}\n",
-    "\n",
-    "For a rectified linear (`Relu`) layer the forward propagation corresponds to\n",
-    "\n",
-    "\\begin{equation}\n",
-    "    y^{(b)}_k = \n",
-    "    \\max\\left(0,\\,x^{(b)}_k\\right)\n",
-    "\\end{equation}\n",
-    "\n",
-    "which has corresponding partial derivatives\n",
-    "\n",
-    "\\begin{equation}\n",
-    "    \\frac{\\partial y^{(b)}_k}{\\partial x^{(b)}_d} = \n",
-    "    \\begin{cases} \n",
-    "      1 & \\quad k = d \\quad\\textrm{and} &x^{(b)}_d > 0 \\\\\n",
-    "      0 & \\quad k \\neq d \\quad\\textrm{or} &x^{(b)}_d < 0\n",
-    "    \\end{cases}.\n",
-    "\\end{equation}\n",
-    "\n",
-    "Using these definitions implement the `fprop` and `bprop` methods for the skeleton `TanhLayer` and `ReluLayer` class definitions below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from mlp.layers import Layer\n",
-    "\n",
-    "class TanhLayer(Layer):\n",
-    "    \"\"\"Layer implementing an element-wise hyperbolic tangent transformation.\"\"\"\n",
-    "\n",
-    "    def fprop(self, inputs):\n",
-    "        \"\"\"Forward propagates activations through the layer transformation.\n",
-    "\n",
-    "        For inputs `x` and outputs `y` this corresponds to `y = tanh(x)`.\n",
-    "        \"\"\"\n",
-    "        return np.tanh(inputs)\n",
-    "\n",
-    "    def bprop(self, inputs, outputs, grads_wrt_outputs):\n",
-    "        \"\"\"Back propagates gradients through a layer.\n",
-    "\n",
-    "        Given gradients with respect to the outputs of the layer calculates the\n",
-    "        gradients with respect to the layer inputs.\n",
-    "        \"\"\"\n",
-    "        return (1. - outputs**2) * grads_wrt_outputs\n",
-    "\n",
-    "    def __repr__(self):\n",
-    "        return 'TanhLayer'\n",
-    "    \n",
-    "\n",
-    "class ReluLayer(Layer):\n",
-    "    \"\"\"Layer implementing an element-wise rectified linear transformation.\"\"\"\n",
-    "\n",
-    "    def fprop(self, inputs):\n",
-    "        \"\"\"Forward propagates activations through the layer transformation.\n",
-    "\n",
-    "        For inputs `x` and outputs `y` this corresponds to `y = max(0, x)`.\n",
-    "        \"\"\"\n",
-    "        return np.maximum(inputs, 0.)\n",
-    "\n",
-    "    def bprop(self, inputs, outputs, grads_wrt_outputs):\n",
-    "        \"\"\"Back propagates gradients through a layer.\n",
-    "\n",
-    "        Given gradients with respect to the outputs of the layer calculates the\n",
-    "        gradients with respect to the layer inputs.\n",
-    "        \"\"\"\n",
-    "        return (outputs > 0) * grads_wrt_outputs\n",
-    "\n",
-    "    def __repr__(self):\n",
-    "        return 'ReluLayer'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your implementations by running the cells below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Outputs and gradients calculated correctly for TanhLayer.\n"
-     ]
-    }
-   ],
-   "source": [
-    "test_inputs = np.array([[0.1, -0.2, 0.3], [-0.4, 0.5, -0.6]])\n",
-    "test_grads_wrt_outputs = np.array([[5., 10., -10.], [-5., 0., 10.]])\n",
-    "test_tanh_outputs = np.array(\n",
-    "    [[ 0.09966799, -0.19737532,  0.29131261],\n",
-    "     [-0.37994896,  0.46211716, -0.53704957]])\n",
-    "test_tanh_grads_wrt_inputs = np.array(\n",
-    "    [[ 4.95033145,  9.61042983, -9.15136962],\n",
-    "     [-4.27819393,  0.,          7.11577763]])\n",
-    "tanh_layer = TanhLayer()\n",
-    "tanh_outputs = tanh_layer.fprop(test_inputs)\n",
-    "all_correct = True\n",
-    "if not tanh_outputs.shape == test_tanh_outputs.shape:\n",
-    "    print('TanhLayer.fprop returned array with wrong shape.')\n",
-    "    all_correct = False\n",
-    "elif not np.allclose(test_tanh_outputs, tanh_outputs):\n",
-    "    print('TanhLayer.fprop calculated incorrect outputs.')\n",
-    "    all_correct = False\n",
-    "tanh_grads_wrt_inputs = tanh_layer.bprop(\n",
-    "    test_inputs, tanh_outputs, test_grads_wrt_outputs)\n",
-    "if not tanh_grads_wrt_inputs.shape == test_tanh_grads_wrt_inputs.shape:\n",
-    "    print('TanhLayer.bprop returned array with wrong shape.')\n",
-    "    all_correct = False\n",
-    "elif not np.allclose(tanh_grads_wrt_inputs, test_tanh_grads_wrt_inputs):\n",
-    "    print('TanhLayer.bprop calculated incorrect gradients with respect to inputs.')\n",
-    "    all_correct = False\n",
-    "if all_correct:\n",
-    "    print('Outputs and gradients calculated correctly for TanhLayer.')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Outputs and gradients calculated correctly for ReluLayer.\n"
-     ]
-    }
-   ],
-   "source": [
-    "test_inputs = np.array([[0.1, -0.2, 0.3], [-0.4, 0.5, -0.6]])\n",
-    "test_grads_wrt_outputs = np.array([[5., 10., -10.], [-5., 0., 10.]])\n",
-    "test_relu_outputs = np.array([[0.1, 0., 0.3], [0., 0.5, 0.]])\n",
-    "test_relu_grads_wrt_inputs = np.array([[5., 0., -10.], [-0., 0., 0.]])\n",
-    "relu_layer = ReluLayer()\n",
-    "relu_outputs = relu_layer.fprop(test_inputs)\n",
-    "all_correct = True\n",
-    "if not relu_outputs.shape == test_relu_outputs.shape:\n",
-    "    print('ReluLayer.fprop returned array with wrong shape.')\n",
-    "    all_correct = False\n",
-    "elif not np.allclose(test_relu_outputs, relu_outputs):\n",
-    "    print('ReluLayer.fprop calculated incorrect outputs.')\n",
-    "    all_correct = False\n",
-    "relu_grads_wrt_inputs = relu_layer.bprop(\n",
-    "    test_inputs, relu_outputs, test_grads_wrt_outputs)\n",
-    "if not relu_grads_wrt_inputs.shape == test_relu_grads_wrt_inputs.shape:\n",
-    "    print('ReluLayer.bprop returned array with wrong shape.')\n",
-    "    all_correct = False\n",
-    "elif not np.allclose(relu_grads_wrt_inputs, test_relu_grads_wrt_inputs):\n",
-    "    print('ReluLayer.bprop calculated incorrect gradients with respect to inputs.')\n",
-    "    all_correct = False\n",
-    "if all_correct:\n",
-    "    print('Outputs and gradients calculated correctly for ReluLayer.')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "anaconda-cloud": {},
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/notebooks/05_Non-linearities_and_regularisation.ipynb b/notebooks/05_Non-linearities_and_regularisation.ipynb
deleted file mode 100644
index 83e4b82..0000000
--- a/notebooks/05_Non-linearities_and_regularisation.ipynb
+++ /dev/null
@@ -1,1219 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Regularisation\n",
-    "\n",
-    "In this lab we will explore different methods for regularising networks to reduce overfitting and improve generalisation. This uses the material covered in the [fifth lecture slides](http://www.inf.ed.ac.uk/teaching/courses/mlp/2016/mlp05-hid.pdf)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Exercise 1: L1 and L2 penalties\n",
-    "\n",
-    "In the previous lab notebook we explored the issue of overfitting. There we saw that this arises when the model is 'too complex' ($\\sim$ has too many degrees of freedom / parameters) for the amount of data we have available.\n",
-    "\n",
-    "One method for trying to reduce overfitting is therefore to try to decrease the flexibility of the model. We can do this by simply reducing the number of free parameters in the model (e.g. by using a shallower model with fewer layers or layers with smaller dimensionality). More generally however we might want some way of more continuously varying the effective flexibility of a model with a fixed architecture.\n",
-    "\n",
-    "A common method for doing this is to add an additional term to the objective function being minimised during training which penalises some measure of the complexity of a model as a function of the model parameters. The aim of training is then to minimise with respect to the model parameters the sum $E^\\star$ of the data-driven error function term $\\bar{E}$ and a model complexity term $C$.\n",
-    "\n",
-    "\\begin{equation}\n",
-    "   E^\\star =\n",
-    "   \\underbrace{\\bar{E}}_{\\textrm{data term}} + \\underbrace{C}_{\\textrm{complexity term}}\n",
-    "\\end{equation}\n",
-    "\n",
-    "We need the complexity term $C$ to be easy to compute and differentiable with respect to the model parameters. A common choice is to use terms involving the *norms* ($\\sim$ a measure of size) of the parameters. This penalises models with large parameter values. Two commonly used norms are the L1 and L2 norms. \n",
-    "\n",
-    "For a $D$ dimensional vector $\\mathbf{v}$ the L1 norm is defined as\n",
-    "\n",
-    "\\begin{equation}\n",
-    "\\| \\boldsymbol{v} \\|_1 = \\sum_{d=1}^D \\left| v_d \\right|,\n",
-    "\\end{equation}\n",
-    "\n",
-    "and the L2 norm is defined as\n",
-    "\n",
-    "\\begin{equation}\n",
-    "\\| \\boldsymbol{v} \\|_2 = \\left[ \\sum_{d=1}^D \\left( v_d^2 \\right) \\right]^{\\frac{1}{2}}.\n",
-    "\\end{equation}\n",
-    "\n",
-    "For a $K \\times D$ matrix $\\mathbf{M}$, we will define norms by collapsing the matrix to a vector $\\boldsymbol{m} = \\mathrm{vec}\\left[\\mathbf{M}\\right] = \\left[ M_{1,1} \\dots M_{1,D} ~ M_{2,1} \\dots M_{K,D} \\right]^{\\rm T}$ and then taking the norm as defined above of this resulting vector (practically this just results in summing over two sets of indices rather than one).\n",
-    "\n",
-    "The overall complexity penalty term $C$ is defined as a sum over individual complexity terms for each of the $P$ parameters of the model \n",
-    "\n",
-    "\\begin{equation}\n",
-    "    C = \\sum_{i=1}^P \\left[ C^{(i)} \\right]\n",
-    "\\end{equation}\n",
-    "\n",
-    "Some of these per-parameter penalty terms $C^{(i)}$ may be set to zero if we do not wish to penalise the size of the corresponding parameter.\n",
-    "\n",
-    "To enable us to tradeoff between the model complexity and data error terms, it is typical to introduce positive scalar coefficients $\\beta_i$ to scale the penalty term on the $i$th parameter. A *L1 penalty* on the $i$th vector parameter $\\boldsymbol{p}^{(i)}$ (or matrix parameter collapsed to a vector) is then commonly defined as\n",
-    "\n",
-    "\\begin{equation}\n",
-    "  C^{(i)}_{\\textrm{L1}} = \n",
-    "  \\beta_i \\left\\| \\boldsymbol{p}^{(i)} \\right\\|_1 = \n",
-    "  \\beta_i  \\sum_{d=1}^D \\left| p^{(i)}_d \\right|.\n",
-    "\\end{equation}\n",
-    "\n",
-    "This has a gradient with respect to the parameter vector\n",
-    "\n",
-    "\\begin{equation}\n",
-    "  \\frac{\\partial C^{(i)}_{\\textrm{L1}}}{\\partial p^{(i)}_d} = \\beta_i \\, \\textrm{sgn}\\left( p^{(i)}_d \\right)\n",
-    "\\end{equation}\n",
-    "\n",
-    "where $\\textrm{sgn}(u) = +1$ if $u > 0$, $\\textrm{sgn}(u) = -1$ if $u < 0$ (and is not well defined for $u=0$ though a common convention is to have $\\textrm{sgn}(0) = 0$).\n",
-    "\n",
-    "Similarly a *L2 penalty* on the $i$th vector parameter $\\boldsymbol{p}^{(i)}$ (or matrix parameter collapsed to a vector) is commonly defined as\n",
-    "\n",
-    "\\begin{equation}\n",
-    "  C^{(i)}_{\\textrm{L2}} = \n",
-    "  \\frac{1}{2} \\beta_i \\left\\| \\boldsymbol{p}^{(i)} \\right\\|_2^2 =\n",
-    "  \\frac{1}{2} \\beta_i \\sum_{d=1}^D \\left[ \\left( p^{(i)}_d \\right)^2 \\right].\n",
-    "\\end{equation}\n",
-    "\n",
-    "Somewhat confusingly this is proportional to the square of the L2 norm rather than the L2 norm itself, however it is an almost universal convention to call this an L2 penalty so we will stick with this nomenclature here. The $\\frac{1}{2}$ term is less universal and is sometimes not included; we include it here for consistency with how we defined the sum of squared errors cost. Similarly to that case, the $\\frac{1}{2}$ cancels when calculating the gradient with respect to the parameter\n",
-    "\n",
-    "\\begin{equation}\n",
-    "  \\frac{\\partial C^{(i)}_{\\textrm{L2}}}{\\partial p^{(i)}_d} = \\beta_i p^{(i)}_d\n",
-    "\\end{equation}\n",
-    "\n",
-    "Use the above definitions for the L1 and L2 penalties for a parameter and corresponding gradients to implement the `__call__` and `grad` methods respectively for the skeleton `L1Penalty` and `L2Penalty` class definitions below. The `coefficient` propert of these classes should be used as the $\\beta_i$ value in the equations above. The parameter the penalty term (or gradient) is being evaluated for will be either a one or two-dimensional NumPy array (corresponding to a vector or matrix parameter respectively) and your implementations should be able to cope with both cases."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "seed = 22102017 \n",
-    "rng = np.random.RandomState(seed)\n",
-    "class L1Penalty(object):\n",
-    "    \"\"\"L1 parameter penalty.\n",
-    "    \n",
-    "    Term to add to the objective function penalising parameters\n",
-    "    based on their L1 norm.\n",
-    "    \"\"\"\n",
-    "    \n",
-    "    def __init__(self, coefficient):\n",
-    "        \"\"\"Create a new L1 penalty object.\n",
-    "        \n",
-    "        Args:\n",
-    "            coefficient: Positive constant to scale penalty term by.\n",
-    "        \"\"\"\n",
-    "        assert coefficient > 0., 'Penalty coefficient must be positive.'\n",
-    "        self.coefficient = coefficient\n",
-    "        \n",
-    "    def __call__(self, parameter):\n",
-    "        \"\"\"Calculate L1 penalty value for a parameter.\n",
-    "        \n",
-    "        Args:\n",
-    "            parameter: Array corresponding to a model parameter.\n",
-    "            \n",
-    "        Returns:\n",
-    "            Value of penalty term.\n",
-    "        \"\"\"\n",
-    "        return self.coefficient * abs(parameter).sum()\n",
-    "        \n",
-    "    def grad(self, parameter):\n",
-    "        \"\"\"Calculate the penalty gradient with respect to the parameter.\n",
-    "        \n",
-    "        Args:\n",
-    "            parameter: Array corresponding to a model parameter.\n",
-    "            \n",
-    "        Returns:\n",
-    "            Value of penalty gradient with respect to parameter. This\n",
-    "            should be an array of the same shape as the parameter.\n",
-    "        \"\"\"\n",
-    "        return self.coefficient * np.sign(parameter)\n",
-    "    \n",
-    "    def __repr__(self):\n",
-    "        return 'L1Penalty({0})'.format(self.coefficient)\n",
-    "        \n",
-    "\n",
-    "class L2Penalty(object):\n",
-    "    \"\"\"L1 parameter penalty.\n",
-    "    \n",
-    "    Term to add to the objective function penalising parameters\n",
-    "    based on their L2 norm.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    def __init__(self, coefficient):\n",
-    "        \"\"\"Create a new L2 penalty object.\n",
-    "        \n",
-    "        Args:\n",
-    "            coefficient: Positive constant to scale penalty term by.\n",
-    "        \"\"\"\n",
-    "        assert coefficient > 0., 'Penalty coefficient must be positive.'\n",
-    "        self.coefficient = coefficient\n",
-    "        \n",
-    "    def __call__(self, parameter):\n",
-    "        \"\"\"Calculate L2 penalty value for a parameter.\n",
-    "        \n",
-    "        Args:\n",
-    "            parameter: Array corresponding to a model parameter.\n",
-    "            \n",
-    "        Returns:\n",
-    "            Value of penalty term.\n",
-    "        \"\"\"\n",
-    "        return 0.5 * self.coefficient * (parameter**2).sum()\n",
-    "        \n",
-    "    def grad(self, parameter):\n",
-    "        \"\"\"Calculate the penalty gradient with respect to the parameter.\n",
-    "        \n",
-    "        Args:\n",
-    "            parameter: Array corresponding to a model parameter.\n",
-    "            \n",
-    "        Returns:\n",
-    "            Value of penalty gradient with respect to parameter. This\n",
-    "            should be an array of the same shape as the parameter.\n",
-    "        \"\"\"\n",
-    "        return self.coefficient * parameter\n",
-    "    \n",
-    "    def __repr__(self):\n",
-    "        return 'L2Penalty({0})'.format(self.coefficient)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Test your implementations by running the cells below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "All test values calculated correctly for L1Penalty\n"
-     ]
-    }
-   ],
-   "source": [
-    "test_params_1 = np.array([[0.5, 0.3, -1.2, 5.8], [0.2, -3.1, 4.9, -5.0]])\n",
-    "test_params_2 = np.array([0.8, -0.6, -0.3, 1.5, 2.8])\n",
-    "true_l1_cost_1 = 10.5\n",
-    "true_l1_grad_1 = np.array([[0.5, 0.5, -0.5, 0.5], [0.5, -0.5, 0.5, -0.5]])\n",
-    "true_l1_cost_2 = 3.\n",
-    "true_l1_grad_2 = np.array([0.5, -0.5, -0.5, 0.5, 0.5])\n",
-    "l1 = L1Penalty(0.5)\n",
-    "if (not np.allclose(l1(test_params_1), true_l1_cost_1) or\n",
-    "        not np.allclose(l1(test_params_2), true_l1_cost_2)):\n",
-    "    print('L1Penalty.__call__ giving incorrect value(s).')\n",
-    "elif (not np.allclose(l1.grad(test_params_1), true_l1_grad_1) or \n",
-    "          not np.allclose(l1.grad(test_params_2), true_l1_grad_2)):\n",
-    "    print('L1Penalty.grad giving incorrect value(s).')\n",
-    "else:\n",
-    "    print('All test values calculated correctly for L1Penalty')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "All test values calculated correctly for L2Penalty\n"
-     ]
-    }
-   ],
-   "source": [
-    "test_params_1 = np.array([[0.5, 0.3, -1.2, 5.8], [0.2, -3.1, 4.9, -5.0]])\n",
-    "test_params_2 = np.array([0.8, -0.6, -0.3, 1.5, 2.8])\n",
-    "true_l2_cost_1 = 23.52\n",
-    "true_l2_grad_1 = np.array([[0.25, 0.15, -0.6, 2.9], [0.1, -1.55, 2.45, -2.5]])\n",
-    "true_l2_cost_2 = 2.795\n",
-    "true_l2_grad_2 = np.array([0.4, -0.3, -0.15, 0.75, 1.4])\n",
-    "l2 = L2Penalty(0.5)\n",
-    "if (not np.allclose(l2(test_params_1), true_l2_cost_1) or\n",
-    "        not np.allclose(l2(test_params_2), true_l2_cost_2)):\n",
-    "    print('L2Penalty.__call__ giving incorrect value(s).')\n",
-    "elif (not np.allclose(l2.grad(test_params_1), true_l2_grad_1) or \n",
-    "          not np.allclose(l2.grad(test_params_2), true_l2_grad_2)):\n",
-    "    print('L2Penalty.grad giving incorrect value(s).')\n",
-    "else:\n",
-    "    print('All test values calculated correctly for L2Penalty')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercise 2: Training with regularisation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Previously in the second laboratory you implemented a function `grads_wrt_params`  to calculate the gradient of an error function with respect to the parameters of an affine model (layer), given gradients of the error function with respect to the model (layer) outputs.\n",
-    "\n",
-    "If we are training a model using a regularised objective function, we need to additionally calculate the gradients of the regularisation penalty terms with respect to the parameters and add these to the error function gradient terms. Following from the definition of the regularised objective $E^\\star$ above we have that the gradient of the overall objective with respect to the $d$th element of the $i$th model parameter is\n",
-    "\n",
-    "\\begin{equation}\n",
-    "    \\frac{\\partial E^\\star}{\\partial p^{(i)}_d} =\n",
-    "    \\frac{\\partial \\bar{E}}{\\partial p^{(i)}_d} + \n",
-    "    \\frac{\\partial C}{\\partial p^{(i)}_d}\n",
-    "\\end{equation}\n",
-    "\n",
-    "We have already discussed in the second lab notebook how to calculate the error function gradient term $\\frac{\\partial \\bar{E}}{\\partial p^{(i)}_d}$. As the model complexity term is composed of a sum of per parameter terms and only one of these will depend on the $i$th parameter we can write\n",
-    "\n",
-    "\\begin{equation}\n",
-    "    \\frac{\\partial C}{\\partial p^{(i)}_d} = \\frac{\\partial C^{(i)}}{\\partial p^{(i)}_d}\n",
-    "\\end{equation}\n",
-    "\n",
-    "which corresponds to the penalty term gradients you implemented above. To enable us to use the same `Optimiser` implementation that we have previously used to train models without regularisation, we have altered the implementation of the `AffineLayer` class (this being the only layer we currently have defined with parameters) to allow us to specify penalty terms on the weight matrix and bias vector when creating an instance of the class and to add the corresponding penalty gradients to the returned value from the `grads_wrt_params` method. \n",
-    "\n",
-    "The penalty terms need to be specified as a class matching the interface of the `L1Penalty` and `L2Penalty` classes you implemented above, defining both a `__call__` method to calculate the penalty value for a parameter and a `grad` method to calculate the gradient of the penalty with respect to the parameter. Separate penalties can be specified for the weight and bias parameters, with it common to only regularise the weight parameters. \n",
-    "\n",
-    "The penalty terms for a layer are specifed using the `weights_penalty` and `biases_penalty` arguments to the `__init__` method of the `AffineLayer` class. If either (or both) ofthese are set to `None` (the default) no regularisation is applied to the corresponding parameter.\n",
-    "\n",
-    "Using the `L1Penalty` and `L2Penalty` classes you implemented in the previous exercise, train models to classify MNIST digit images with\n",
-    "\n",
-    "  * no regularisation\n",
-    "  * an L1 penalty with coefficient $10^{-5}$ on the all of the weight matrix parameters\n",
-    "  * an L1 penalty with coefficient $10^{-3}$ on the all of the weight matrix parameters\n",
-    "  * an L2 penalty with coefficient $10^{-4}$ on the all of the weight matrix parameters\n",
-    "  * an L2 penalty with coefficient $10^{-2}$ on the all of the weight matrix parameters\n",
-    "  \n",
-    "The models should all have three affine layers interspersed with rectified linear layers (as implemented in the first exercise) and intermediate layers between the input and output should have dimensionalities of 100. The final output layer should be an `AffineLayer` (the model outputting the logarithms of the non-normalised class probabilties) and you should use the `CrossEntropySoftmaxError` as the error function (which calculates the softmax of the model outputs to convert to normalised class probabilities before calculating the corresponding multi-class cross entropy error). \n",
-    "\n",
-    "Use the `GlorotInit` class introduced in the first coursework to initialise the weights in all layers, using a gain of 0.5 (this adjusts for the fact that the rectified linear sets zeros all negative inputs), and initialises the biases to zero with a `ConstantInit` object. \n",
-    "\n",
-    "As an example the necessary parameter initialisers, model and error can be defined using\n",
-    "\n",
-    "```python\n",
-    "weights_init = GlorotUniformInit(0.5, rng)\n",
-    "biases_init = ConstantInit(0.)\n",
-    "input_dim, output_dim, hidden_dim = 784, 10, 100\n",
-    "model = MultipleLayerModel([\n",
-    "    AffineLayer(input_dim, hidden_dim, weights_init, \n",
-    "                biases_init, weights_penalty=weights_penalty),\n",
-    "    ReluLayer(),\n",
-    "    AffineLayer(hidden_dim, hidden_dim, weights_init, \n",
-    "                biases_init, weights_penalty=weights_penalty),\n",
-    "    ReluLayer(),\n",
-    "    AffineLayer(hidden_dim, output_dim, weights_init, \n",
-    "                biases_init, weights_penalty=weights_penalty)\n",
-    "])\n",
-    "error = CrossEntropySoftmaxError()\n",
-    "```\n",
-    "\n",
-    "This assumes all the relevant classes have been imported from their modules, a penalty object has been assigned to `weights_penalty` and a seeded random number generator assigned to `rng`.\n",
-    "\n",
-    "For each regularisation scheme, train the model for 100 epochs with a batch size of 50 and using a gradient descent with momentum learning rule with learning rate 0.01 and momentum coefficient 0.9. For each regularisation scheme you should store the run statistics (output of `Optimiser.train`) and the final values of the first layer weights for each of the trained models."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "from collections import OrderedDict\n",
-    "import logging\n",
-    "from mlp.layers import AffineLayer, ReluLayer\n",
-    "from mlp.errors import CrossEntropySoftmaxError\n",
-    "from mlp.models import MultipleLayerModel\n",
-    "from mlp.initialisers import GlorotUniformInit, ConstantInit\n",
-    "from mlp.learning_rules import MomentumLearningRule\n",
-    "from mlp.data_providers import MNISTDataProvider\n",
-    "from mlp.optimisers import Optimiser\n",
-    "\n",
-    "# Seed a random number generator\n",
-    "seed = 24102016 \n",
-    "rng = np.random.RandomState(seed)\n",
-    "\n",
-    "num_epochs = 100\n",
-    "stats_interval = 5\n",
-    "batch_size = 50\n",
-    "learning_rate = 0.01\n",
-    "mom_coeff = 0.9\n",
-    "weights_init_gain = 0.5\n",
-    "biases_init = 0.\n",
-    "\n",
-    "# Set up a logger object to print info about the training run to stdout\n",
-    "logger = logging.getLogger()\n",
-    "logger.setLevel(logging.INFO)\n",
-    "logger.handlers = [logging.StreamHandler()]\n",
-    "\n",
-    "# Create data provider objects for the MNIST data set\n",
-    "train_data = MNISTDataProvider('train', batch_size, rng=rng)\n",
-    "valid_data = MNISTDataProvider('valid', batch_size, rng=rng)\n",
-    "input_dim, output_dim, hidden_dim = 784, 10, 100\n",
-    "\n",
-    "weights_init = GlorotUniformInit(weights_init_gain, rng)\n",
-    "biases_init = ConstantInit(biases_init)\n",
-    "error = CrossEntropySoftmaxError()\n",
-    "learning_rule = MomentumLearningRule(learning_rate, mom_coeff)\n",
-    "data_monitors = {'acc': lambda y, t: (y.argmax(-1) == t.argmax(-1)).mean()}\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Regularisation: None\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 5: 10.9s to complete\n",
-      "    error(train)=6.34e-02, acc(train)=9.82e-01, error(valid)=9.67e-02, acc(valid)=9.72e-01\n",
-      "Epoch 10: 10.1s to complete\n",
-      "    error(train)=2.36e-02, acc(train)=9.94e-01, error(valid)=8.34e-02, acc(valid)=9.77e-01\n",
-      "Epoch 15: 11.7s to complete\n",
-      "    error(train)=8.49e-03, acc(train)=9.98e-01, error(valid)=8.34e-02, acc(valid)=9.78e-01\n",
-      "Epoch 20: 10.5s to complete\n",
-      "    error(train)=3.42e-03, acc(train)=1.00e+00, error(valid)=9.58e-02, acc(valid)=9.78e-01\n",
-      "Epoch 25: 10.7s to complete\n",
-      "    error(train)=1.42e-03, acc(train)=1.00e+00, error(valid)=9.48e-02, acc(valid)=9.79e-01\n",
-      "Epoch 30: 11.4s to complete\n",
-      "    error(train)=9.06e-04, acc(train)=1.00e+00, error(valid)=1.01e-01, acc(valid)=9.79e-01\n",
-      "Epoch 35: 12.0s to complete\n",
-      "    error(train)=6.40e-04, acc(train)=1.00e+00, error(valid)=1.04e-01, acc(valid)=9.79e-01\n",
-      "Epoch 40: 10.9s to complete\n",
-      "    error(train)=5.06e-04, acc(train)=1.00e+00, error(valid)=1.06e-01, acc(valid)=9.79e-01\n",
-      "Epoch 45: 15.1s to complete\n",
-      "    error(train)=4.23e-04, acc(train)=1.00e+00, error(valid)=1.08e-01, acc(valid)=9.79e-01\n",
-      "Epoch 50: 13.2s to complete\n",
-      "    error(train)=3.59e-04, acc(train)=1.00e+00, error(valid)=1.10e-01, acc(valid)=9.79e-01\n",
-      "Epoch 55: 10.1s to complete\n",
-      "    error(train)=3.07e-04, acc(train)=1.00e+00, error(valid)=1.11e-01, acc(valid)=9.79e-01\n",
-      "Epoch 60: 10.3s to complete\n",
-      "    error(train)=2.71e-04, acc(train)=1.00e+00, error(valid)=1.13e-01, acc(valid)=9.79e-01\n",
-      "Epoch 65: 11.4s to complete\n",
-      "    error(train)=2.42e-04, acc(train)=1.00e+00, error(valid)=1.15e-01, acc(valid)=9.79e-01\n",
-      "Epoch 70: 10.8s to complete\n",
-      "    error(train)=2.19e-04, acc(train)=1.00e+00, error(valid)=1.16e-01, acc(valid)=9.79e-01\n",
-      "Epoch 75: 13.8s to complete\n",
-      "    error(train)=1.97e-04, acc(train)=1.00e+00, error(valid)=1.17e-01, acc(valid)=9.79e-01\n",
-      "Epoch 80: 11.4s to complete\n",
-      "    error(train)=1.82e-04, acc(train)=1.00e+00, error(valid)=1.18e-01, acc(valid)=9.79e-01\n",
-      "Epoch 85: 15.5s to complete\n",
-      "    error(train)=1.67e-04, acc(train)=1.00e+00, error(valid)=1.19e-01, acc(valid)=9.79e-01\n",
-      "Epoch 90: 15.0s to complete\n",
-      "    error(train)=1.54e-04, acc(train)=1.00e+00, error(valid)=1.20e-01, acc(valid)=9.79e-01\n",
-      "Epoch 95: 11.7s to complete\n",
-      "    error(train)=1.44e-04, acc(train)=1.00e+00, error(valid)=1.22e-01, acc(valid)=9.79e-01\n",
-      "Epoch 100: 12.6s to complete\n",
-      "    error(train)=1.34e-04, acc(train)=1.00e+00, error(valid)=1.22e-01, acc(valid)=9.79e-01\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Regularisation: L1Penalty(1e-05)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 5: 25.2s to complete\n",
-      "    error(train)=6.46e-02, acc(train)=9.82e-01, error(valid)=9.39e-02, acc(valid)=9.73e-01\n",
-      "Epoch 10: 15.4s to complete\n",
-      "    error(train)=2.68e-02, acc(train)=9.93e-01, error(valid)=8.04e-02, acc(valid)=9.78e-01\n",
-      "Epoch 15: 17.1s to complete\n",
-      "    error(train)=1.42e-02, acc(train)=9.97e-01, error(valid)=8.12e-02, acc(valid)=9.78e-01\n",
-      "Epoch 20: 15.4s to complete\n",
-      "    error(train)=6.73e-03, acc(train)=9.99e-01, error(valid)=8.81e-02, acc(valid)=9.77e-01\n",
-      "Epoch 25: 43.1s to complete\n",
-      "    error(train)=3.55e-03, acc(train)=1.00e+00, error(valid)=8.79e-02, acc(valid)=9.78e-01\n",
-      "Epoch 30: 18.9s to complete\n",
-      "    error(train)=2.77e-03, acc(train)=1.00e+00, error(valid)=9.02e-02, acc(valid)=9.78e-01\n",
-      "Epoch 35: 23.9s to complete\n",
-      "    error(train)=2.19e-03, acc(train)=1.00e+00, error(valid)=8.69e-02, acc(valid)=9.79e-01\n",
-      "Epoch 40: 22.3s to complete\n",
-      "    error(train)=2.16e-03, acc(train)=1.00e+00, error(valid)=8.94e-02, acc(valid)=9.79e-01\n",
-      "Epoch 45: 17.4s to complete\n",
-      "    error(train)=1.91e-03, acc(train)=1.00e+00, error(valid)=8.66e-02, acc(valid)=9.78e-01\n",
-      "Epoch 50: 22.0s to complete\n",
-      "    error(train)=1.76e-03, acc(train)=1.00e+00, error(valid)=8.73e-02, acc(valid)=9.79e-01\n",
-      "Epoch 55: 16.9s to complete\n",
-      "    error(train)=2.14e-03, acc(train)=1.00e+00, error(valid)=9.00e-02, acc(valid)=9.79e-01\n",
-      "Epoch 60: 21.0s to complete\n",
-      "    error(train)=1.98e-03, acc(train)=1.00e+00, error(valid)=8.77e-02, acc(valid)=9.80e-01\n",
-      "Epoch 65: 15.8s to complete\n",
-      "    error(train)=1.09e-02, acc(train)=9.96e-01, error(valid)=1.07e-01, acc(valid)=9.75e-01\n",
-      "Epoch 70: 14.3s to complete\n",
-      "    error(train)=9.57e-04, acc(train)=1.00e+00, error(valid)=8.86e-02, acc(valid)=9.80e-01\n",
-      "Epoch 75: 18.5s to complete\n",
-      "    error(train)=9.22e-04, acc(train)=1.00e+00, error(valid)=8.57e-02, acc(valid)=9.80e-01\n",
-      "Epoch 80: 20.6s to complete\n",
-      "    error(train)=1.03e-03, acc(train)=1.00e+00, error(valid)=8.58e-02, acc(valid)=9.81e-01\n",
-      "Epoch 85: 15.3s to complete\n",
-      "    error(train)=1.20e-03, acc(train)=1.00e+00, error(valid)=8.28e-02, acc(valid)=9.80e-01\n",
-      "Epoch 90: 14.0s to complete\n",
-      "    error(train)=1.24e-03, acc(train)=1.00e+00, error(valid)=8.32e-02, acc(valid)=9.81e-01\n",
-      "Epoch 95: 18.4s to complete\n",
-      "    error(train)=1.33e-03, acc(train)=1.00e+00, error(valid)=8.41e-02, acc(valid)=9.80e-01\n",
-      "Epoch 100: 14.4s to complete\n",
-      "    error(train)=1.70e-03, acc(train)=1.00e+00, error(valid)=8.21e-02, acc(valid)=9.80e-01\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Regularisation: L1Penalty(0.001)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 5: 18.9s to complete\n",
-      "    error(train)=2.53e-01, acc(train)=9.31e-01, error(valid)=2.40e-01, acc(valid)=9.35e-01\n",
-      "Epoch 10: 17.4s to complete\n",
-      "    error(train)=2.20e-01, acc(train)=9.36e-01, error(valid)=2.13e-01, acc(valid)=9.38e-01\n",
-      "Epoch 15: 22.7s to complete\n",
-      "    error(train)=2.26e-01, acc(train)=9.34e-01, error(valid)=2.14e-01, acc(valid)=9.37e-01\n",
-      "Epoch 20: 14.2s to complete\n",
-      "    error(train)=2.75e-01, acc(train)=9.15e-01, error(valid)=2.58e-01, acc(valid)=9.22e-01\n",
-      "Epoch 25: 17.6s to complete\n",
-      "    error(train)=2.31e-01, acc(train)=9.30e-01, error(valid)=2.29e-01, acc(valid)=9.30e-01\n",
-      "Epoch 30: 21.7s to complete\n",
-      "    error(train)=2.20e-01, acc(train)=9.36e-01, error(valid)=2.12e-01, acc(valid)=9.40e-01\n",
-      "Epoch 35: 14.0s to complete\n",
-      "    error(train)=2.11e-01, acc(train)=9.37e-01, error(valid)=2.03e-01, acc(valid)=9.40e-01\n",
-      "Epoch 40: 15.9s to complete\n",
-      "    error(train)=2.06e-01, acc(train)=9.37e-01, error(valid)=2.03e-01, acc(valid)=9.39e-01\n",
-      "Epoch 45: 17.8s to complete\n",
-      "    error(train)=2.02e-01, acc(train)=9.40e-01, error(valid)=1.99e-01, acc(valid)=9.42e-01\n",
-      "Epoch 50: 15.7s to complete\n",
-      "    error(train)=2.01e-01, acc(train)=9.40e-01, error(valid)=1.95e-01, acc(valid)=9.45e-01\n",
-      "Epoch 55: 14.7s to complete\n",
-      "    error(train)=2.07e-01, acc(train)=9.36e-01, error(valid)=2.00e-01, acc(valid)=9.39e-01\n",
-      "Epoch 60: 21.3s to complete\n",
-      "    error(train)=2.38e-01, acc(train)=9.28e-01, error(valid)=2.34e-01, acc(valid)=9.31e-01\n",
-      "Epoch 65: 17.6s to complete\n",
-      "    error(train)=2.07e-01, acc(train)=9.38e-01, error(valid)=2.02e-01, acc(valid)=9.41e-01\n",
-      "Epoch 70: 21.5s to complete\n",
-      "    error(train)=1.98e-01, acc(train)=9.40e-01, error(valid)=1.92e-01, acc(valid)=9.44e-01\n",
-      "Epoch 75: 14.2s to complete\n",
-      "    error(train)=2.09e-01, acc(train)=9.37e-01, error(valid)=2.08e-01, acc(valid)=9.39e-01\n",
-      "Epoch 80: 16.7s to complete\n",
-      "    error(train)=2.03e-01, acc(train)=9.40e-01, error(valid)=1.98e-01, acc(valid)=9.42e-01\n",
-      "Epoch 85: 16.8s to complete\n",
-      "    error(train)=1.98e-01, acc(train)=9.42e-01, error(valid)=1.93e-01, acc(valid)=9.43e-01\n",
-      "Epoch 90: 23.6s to complete\n",
-      "    error(train)=2.00e-01, acc(train)=9.38e-01, error(valid)=1.97e-01, acc(valid)=9.40e-01\n",
-      "Epoch 95: 15.5s to complete\n",
-      "    error(train)=1.88e-01, acc(train)=9.44e-01, error(valid)=1.86e-01, acc(valid)=9.46e-01\n",
-      "Epoch 100: 17.7s to complete\n",
-      "    error(train)=1.97e-01, acc(train)=9.42e-01, error(valid)=1.93e-01, acc(valid)=9.46e-01\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Regularisation: L2Penalty(0.0001)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 5: 10.8s to complete\n",
-      "    error(train)=6.49e-02, acc(train)=9.81e-01, error(valid)=9.48e-02, acc(valid)=9.72e-01\n",
-      "Epoch 10: 15.5s to complete\n",
-      "    error(train)=2.65e-02, acc(train)=9.93e-01, error(valid)=8.14e-02, acc(valid)=9.78e-01\n",
-      "Epoch 15: 13.5s to complete\n",
-      "    error(train)=1.41e-02, acc(train)=9.97e-01, error(valid)=8.20e-02, acc(valid)=9.78e-01\n",
-      "Epoch 20: 14.2s to complete\n",
-      "    error(train)=6.66e-03, acc(train)=9.99e-01, error(valid)=8.53e-02, acc(valid)=9.78e-01\n",
-      "Epoch 25: 11.3s to complete\n",
-      "    error(train)=3.56e-03, acc(train)=1.00e+00, error(valid)=8.34e-02, acc(valid)=9.79e-01\n",
-      "Epoch 30: 12.6s to complete\n",
-      "    error(train)=2.80e-03, acc(train)=1.00e+00, error(valid)=8.27e-02, acc(valid)=9.79e-01\n",
-      "Epoch 35: 10.2s to complete\n",
-      "    error(train)=2.53e-03, acc(train)=1.00e+00, error(valid)=8.15e-02, acc(valid)=9.80e-01\n",
-      "Epoch 40: 13.2s to complete\n",
-      "    error(train)=2.44e-03, acc(train)=1.00e+00, error(valid)=8.21e-02, acc(valid)=9.79e-01\n",
-      "Epoch 45: 17.8s to complete\n",
-      "    error(train)=2.29e-03, acc(train)=1.00e+00, error(valid)=7.91e-02, acc(valid)=9.80e-01\n",
-      "Epoch 50: 13.7s to complete\n",
-      "    error(train)=2.24e-03, acc(train)=1.00e+00, error(valid)=8.01e-02, acc(valid)=9.79e-01\n",
-      "Epoch 55: 12.4s to complete\n",
-      "    error(train)=2.20e-03, acc(train)=1.00e+00, error(valid)=7.90e-02, acc(valid)=9.80e-01\n",
-      "Epoch 60: 13.7s to complete\n",
-      "    error(train)=2.39e-03, acc(train)=1.00e+00, error(valid)=7.88e-02, acc(valid)=9.80e-01\n",
-      "Epoch 65: 11.1s to complete\n",
-      "    error(train)=2.12e-03, acc(train)=1.00e+00, error(valid)=7.79e-02, acc(valid)=9.80e-01\n",
-      "Epoch 70: 11.4s to complete\n",
-      "    error(train)=2.22e-03, acc(train)=1.00e+00, error(valid)=7.74e-02, acc(valid)=9.80e-01\n",
-      "Epoch 75: 14.5s to complete\n",
-      "    error(train)=2.16e-03, acc(train)=1.00e+00, error(valid)=7.53e-02, acc(valid)=9.80e-01\n",
-      "Epoch 80: 12.6s to complete\n",
-      "    error(train)=2.19e-03, acc(train)=1.00e+00, error(valid)=7.74e-02, acc(valid)=9.81e-01\n",
-      "Epoch 85: 11.5s to complete\n",
-      "    error(train)=2.21e-03, acc(train)=1.00e+00, error(valid)=7.49e-02, acc(valid)=9.81e-01\n",
-      "Epoch 90: 11.4s to complete\n",
-      "    error(train)=2.11e-03, acc(train)=1.00e+00, error(valid)=7.49e-02, acc(valid)=9.81e-01\n",
-      "Epoch 95: 11.2s to complete\n",
-      "    error(train)=2.12e-03, acc(train)=1.00e+00, error(valid)=7.54e-02, acc(valid)=9.80e-01\n",
-      "Epoch 100: 15.5s to complete\n",
-      "    error(train)=2.32e-03, acc(train)=1.00e+00, error(valid)=7.57e-02, acc(valid)=9.81e-01\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Regularisation: L2Penalty(0.01)\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 5: 10.6s to complete\n",
-      "    error(train)=2.36e-01, acc(train)=9.41e-01, error(valid)=2.21e-01, acc(valid)=9.45e-01\n",
-      "Epoch 10: 10.9s to complete\n",
-      "    error(train)=2.13e-01, acc(train)=9.45e-01, error(valid)=1.99e-01, acc(valid)=9.52e-01\n",
-      "Epoch 15: 11.8s to complete\n",
-      "    error(train)=2.22e-01, acc(train)=9.44e-01, error(valid)=2.08e-01, acc(valid)=9.48e-01\n",
-      "Epoch 20: 11.0s to complete\n",
-      "    error(train)=2.15e-01, acc(train)=9.46e-01, error(valid)=2.03e-01, acc(valid)=9.49e-01\n",
-      "Epoch 25: 15.5s to complete\n",
-      "    error(train)=2.14e-01, acc(train)=9.47e-01, error(valid)=2.03e-01, acc(valid)=9.50e-01\n",
-      "Epoch 30: 11.8s to complete\n",
-      "    error(train)=2.02e-01, acc(train)=9.51e-01, error(valid)=1.91e-01, acc(valid)=9.55e-01\n",
-      "Epoch 35: 14.7s to complete\n",
-      "    error(train)=2.11e-01, acc(train)=9.47e-01, error(valid)=2.00e-01, acc(valid)=9.50e-01\n",
-      "Epoch 40: 12.3s to complete\n",
-      "    error(train)=2.01e-01, acc(train)=9.50e-01, error(valid)=1.92e-01, acc(valid)=9.53e-01\n",
-      "Epoch 45: 11.9s to complete\n",
-      "    error(train)=2.01e-01, acc(train)=9.52e-01, error(valid)=1.91e-01, acc(valid)=9.55e-01\n",
-      "Epoch 50: 12.1s to complete\n",
-      "    error(train)=2.05e-01, acc(train)=9.50e-01, error(valid)=1.93e-01, acc(valid)=9.55e-01\n",
-      "Epoch 55: 11.4s to complete\n",
-      "    error(train)=2.03e-01, acc(train)=9.50e-01, error(valid)=1.92e-01, acc(valid)=9.54e-01\n",
-      "Epoch 60: 12.8s to complete\n",
-      "    error(train)=2.07e-01, acc(train)=9.49e-01, error(valid)=1.95e-01, acc(valid)=9.54e-01\n",
-      "Epoch 65: 11.7s to complete\n",
-      "    error(train)=2.10e-01, acc(train)=9.46e-01, error(valid)=1.98e-01, acc(valid)=9.49e-01\n",
-      "Epoch 70: 12.8s to complete\n",
-      "    error(train)=2.03e-01, acc(train)=9.50e-01, error(valid)=1.91e-01, acc(valid)=9.55e-01\n",
-      "Epoch 75: 15.1s to complete\n",
-      "    error(train)=2.06e-01, acc(train)=9.50e-01, error(valid)=1.95e-01, acc(valid)=9.54e-01\n",
-      "Epoch 80: 11.1s to complete\n",
-      "    error(train)=1.98e-01, acc(train)=9.52e-01, error(valid)=1.86e-01, acc(valid)=9.56e-01\n",
-      "Epoch 85: 12.4s to complete\n",
-      "    error(train)=2.02e-01, acc(train)=9.51e-01, error(valid)=1.91e-01, acc(valid)=9.55e-01\n",
-      "Epoch 90: 13.2s to complete\n",
-      "    error(train)=2.02e-01, acc(train)=9.51e-01, error(valid)=1.91e-01, acc(valid)=9.55e-01\n",
-      "Epoch 95: 10.6s to complete\n",
-      "    error(train)=2.00e-01, acc(train)=9.52e-01, error(valid)=1.87e-01, acc(valid)=9.57e-01\n",
-      "Epoch 100: 11.3s to complete\n",
-      "    error(train)=1.98e-01, acc(train)=9.53e-01, error(valid)=1.86e-01, acc(valid)=9.58e-01\n"
-     ]
-    }
-   ],
-   "source": [
-    "weights_penalties = [\n",
-    "    None,\n",
-    "    L1Penalty(1e-5),\n",
-    "    L1Penalty(1e-3),\n",
-    "    L2Penalty(1e-4),\n",
-    "    L2Penalty(1e-2)\n",
-    "]\n",
-    "run_info = OrderedDict()\n",
-    "models = OrderedDict()\n",
-    "for weights_penalty in weights_penalties:\n",
-    "    # Reset random number generator and data provider states on each run\n",
-    "    # to ensure reproducibility of results\n",
-    "    rng.seed(seed)\n",
-    "    train_data.reset()\n",
-    "    valid_data.reset()\n",
-    "    print('Regularisation: {0}'.format(weights_penalty))\n",
-    "    model = MultipleLayerModel([\n",
-    "        AffineLayer(input_dim, hidden_dim, weights_init, \n",
-    "                    biases_init, weights_penalty),\n",
-    "        ReluLayer(),\n",
-    "        AffineLayer(hidden_dim, hidden_dim, weights_init, \n",
-    "                    biases_init, weights_penalty),\n",
-    "        ReluLayer(),\n",
-    "        AffineLayer(hidden_dim, output_dim, weights_init, \n",
-    "                    biases_init, weights_penalty)\n",
-    "    ])\n",
-    "    optimiser = Optimiser(model, error, learning_rule, train_data, valid_data, data_monitors)\n",
-    "    run_info[weights_penalty] = optimiser.train(num_epochs, stats_interval)\n",
-    "    models[weights_penalty] = model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Plot the training set error against epoch number for all different regularisation schemes on the same axis. On a second axis plot the validation set error against epoch number for all the different regularisation schemes. Interpret and comment on what you see."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAuAAAAF6CAYAAABYy+2xAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQoheJAm4EEIIIYQQvej/AeKNgoTC9s87AAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f1dac8bef98>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "plt.style.use('ggplot')\n",
-    "\n",
-    "fig = plt.figure(figsize=(12, 6))\n",
-    "ax1 = fig.add_subplot(1, 2, 1)\n",
-    "ax2 = fig.add_subplot(1, 2, 2)\n",
-    "for weight_penalty, run in run_info.items():\n",
-    "    stats, keys, run_time = run\n",
-    "    ax1.plot(np.arange(1, stats.shape[0]) * stats_interval, \n",
-    "             stats[1:, keys['error(train)']], label=str(weight_penalty))\n",
-    "    ax2.plot(np.arange(1, stats.shape[0]) * stats_interval, \n",
-    "             stats[1:, keys['error(valid)']], label=str(weight_penalty))\n",
-    "ax1.legend(loc=0)\n",
-    "ax1.set_xlabel('Epoch number')\n",
-    "ax1.set_ylabel('Training set error')\n",
-    "ax1.set_yscale('log')\n",
-    "ax2.legend(loc=0)\n",
-    "ax2.set_xlabel('Epoch number')\n",
-    "ax2.set_ylabel('Validation set error')\n",
-    "ax2.set_yscale('log')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The cell below defines two functions for visualising the first layer weights of the models trained above. The first plots a histogram of the weight values and the second plots the first layer weights as feature maps, i.e. each row of the first layer weight matrix (corresponding to the weights going from the input MNIST image to a particular first layer output) is visualised as a $28\\times 28$ image. In these feature maps white corresponds to negative weights, black to positive weights and grey to weights close to zero. \n",
-    "\n",
-    "Use these functions to plot a histogram and feature map visualisation for the first layer weights of each model trained above. You should try to interpret the plots in the context of what you were told in the lecture about the behaviour of L1 versus L2 regularisation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def plot_param_histogram(param, fig_size=(6, 3), interval=[-1.5, 1.5]):\n",
-    "    \"\"\"Plots a normalised histogram of an array of parameter values.\"\"\"\n",
-    "    fig = plt.figure(figsize=fig_size)\n",
-    "    ax = fig.add_subplot(111)\n",
-    "    ax.hist(param.flatten(), 50, interval, normed=True)\n",
-    "    ax.set_xlabel('Parameter value')\n",
-    "    ax.set_ylabel('Normalised frequency density')\n",
-    "    return fig, ax\n",
-    "\n",
-    "def visualise_first_layer_weights(weights, fig_size=(5, 5)):\n",
-    "    \"\"\"Plots a grid of first layer weights as feature maps.\"\"\"\n",
-    "    fig = plt.figure(figsize=fig_size)\n",
-    "    num_feature_maps = weights.shape[0]\n",
-    "    grid_size = int(num_feature_maps**0.5)\n",
-    "    max_abs = np.abs(model.params[0]).max()\n",
-    "    tiled = -np.ones((30 * grid_size, \n",
-    "                      30 * num_feature_maps // grid_size)) * max_abs\n",
-    "    for i, fm in enumerate(model.params[0]):\n",
-    "        r, c = i % grid_size, i // grid_size\n",
-    "        tiled[1 + r * 30:(r + 1) * 30 - 1, \n",
-    "              1 + c * 30:(c + 1) * 30 - 1] = fm.reshape((28, 28))\n",
-    "    ax = fig.add_subplot(111)\n",
-    "    max_abs = np.abs(tiled).max()\n",
-    "    ax.imshow(tiled, cmap='Greys', vmin=-max_abs, vmax=max_abs)\n",
-    "    ax.axis('off')\n",
-    "    fig.tight_layout()\n",
-    "    plt.show()\n",
-    "    return fig, ax"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Regularisation: None\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAADcCAYAAACYu6GTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngVj+REQtEMufiKgFYvkTEbVA/w+EGH/YSBp9NAAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f1dac51d278>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWEAAAFYCAYAAACCik2yAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f1daa9b5710>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Regularisation: L1Penalty(1e-05)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAADcCAYAAACf1b3DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n652WAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f1daa9b5f28>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWEAAAFYCAYAAACCik2yAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSP8bceV+ZgtIZjIAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f1daa889cf8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Regularisation: L1Penalty(0.001)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAADcCAYAAACf1b3DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCkREJPhfB6DxTLL63U4AAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f1daa843358>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWEAAAFYCAYAAACCik2yAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n3lpa4/98LroAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f1daa7fa8d0>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Regularisation: L2Penalty(0.0001)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY8AAADcCAYAAACI9y0KAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f1daa6fed68>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWEAAAFYCAYAAACCik2yAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f1daa5ef780>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Regularisation: L2Penalty(0.01)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAADcCAYAAACf1b3DAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nICIiAYsCEREJWBSIiEjAokBERIL/A1VJygJyEU4bAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f1daa680b00>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAWEAAAFYCAYAAACCik2yAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkBoh3FBDDTX0hPQ/PfuCS6vVinYAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f1daa236278>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "for weight_penalty, model in models.items():\n",
-    "    print('Regularisation: {0}'.format(weight_penalty))\n",
-    "    _ = plot_param_histogram(model.params[0], interval=[-0.5, 0.5])\n",
-    "    _ = visualise_first_layer_weights(model.params[0])\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercise 4: Random data augmentation\n",
-    "\n",
-    "Another technique mentioned in the lectures for trying to reduce overfitting is to artificially augment the training data set by performing random transformations to the original training data inputs. The idea is to produce further artificial inputs corresponding to the same target class as the original input. The closer the artificially generated inputs are to the appearing like the true inputs the better as they provide more realistic additional examples for the model to learn from.\n",
-    "\n",
-    "For the handwritten image inputs in the MNIST dataset, an obvious way to considering augmenting the dataset is to apply small rotations to the original images. Providing the rotations are small we would generally expect that what we would identify as the class of a digit image will remain the same.\n",
-    "\n",
-    "Implement a function which given a batch of MNIST images as 784-dimensional vectors, i.e. an array of shape `(batch_size, 784)`\n",
-    "\n",
-    "  * chooses 25% of the images in the batch at random\n",
-    "  * for each image in the 25% chosen, rotates the image by a random angle in $\\left[-30^\\circ,\\,30^\\circ\\right]$\n",
-    "  * returns a new array of size `(batch_size, 784)` in which the rows corresponding to the 25% chosen images are the vectors corresponding to the new randomly rotated images, while the remaining rows correspond to the original images.\n",
-    "  \n",
-    "You will need to make use of the [`scipy.ndimage.interpolation.rotate`](https://docs.scipy.org/doc/scipy-0.16.0/reference/generated/scipy.ndimage.interpolation.rotate.html#scipy.ndimage.interpolation.rotate) function which is imported below for you. For computational efficiency you should use bilinear interpolation by setting `order=1` as a keyword argument to this function rather than using the default of bicubic interpolation. Additionally you should make sure the original shape of the images is maintained by passing a `reshape=False` keyword argument."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from scipy.ndimage.interpolation import rotate\n",
-    "\n",
-    "def random_rotation(inputs, rng):\n",
-    "    \"\"\"Randomly rotates a subset of images in a batch.\n",
-    "    \n",
-    "    Args:\n",
-    "        inputs: Input image batch, an array of shape (batch_size, 784).\n",
-    "        rng: A seeded random number generator.\n",
-    "        \n",
-    "    Returns:\n",
-    "        An array of shape (batch_size, 784) corresponding to a copy\n",
-    "        of the original `inputs` array with the randomly selected\n",
-    "        images rotated by a random angle. The original `inputs`\n",
-    "        array should not be modified.\n",
-    "    \"\"\"\n",
-    "    orig_ims = inputs.reshape((-1, 28, 28))\n",
-    "    new_ims = orig_ims.copy()\n",
-    "    indices = rng.choice(orig_ims.shape[0], orig_ims.shape[0] // 4, False)\n",
-    "    angles = rng.uniform(-1., 1., size=indices.shape[0]) * 30.\n",
-    "    for i, j in enumerate(indices):\n",
-    "        new_ims[j] = rotate(orig_ims[j], angles[i], order=1, reshape=False)\n",
-    "    return new_ims.reshape((-1, 784))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Use the cell below to test your implementation. This uses the `show_batch_of_images` function we implemented in the first lab notebook to visualise the images in a batch before and after application of the random rotation transformation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAANUAAADMCAYAAADkmebQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f1db1636470>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAANUAAADMCAYAAADkmebQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nkXEwsqhkZByMLCoZGQfzfyHNI6b77aCeAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f1daa2aa7b8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from mlp.data_providers import MNISTDataProvider\n",
-    "import matplotlib.pyplot as plt\n",
-    "def show_batch_of_images(img_batch, fig_size=(3, 3)):\n",
-    "    fig = plt.figure(figsize=fig_size)\n",
-    "    batch_size, im_height, im_width = img_batch.shape\n",
-    "    # calculate no. columns per grid row to give square grid\n",
-    "    grid_size = int(batch_size**0.5)\n",
-    "    # intialise empty array to tile image grid into\n",
-    "    tiled = np.empty((im_height * grid_size, \n",
-    "                      im_width * batch_size // grid_size))\n",
-    "    # iterate over images in batch + indexes within batch\n",
-    "    for i, img in enumerate(img_batch):\n",
-    "        # calculate grid row and column indices\n",
-    "        r, c = i % grid_size, i // grid_size\n",
-    "        tiled[r * im_height:(r + 1) * im_height, \n",
-    "              c * im_height:(c + 1) * im_height] = img\n",
-    "    ax = fig.add_subplot(111)\n",
-    "    ax.imshow(tiled, cmap='Greys') #, vmin=0., vmax=1.)\n",
-    "    ax.axis('off')\n",
-    "    fig.tight_layout()\n",
-    "    plt.show()\n",
-    "    return fig, ax\n",
-    "\n",
-    "test_data = MNISTDataProvider('test', 100, rng=rng)\n",
-    "inputs, targets = test_data.next()\n",
-    "_ = show_batch_of_images(inputs.reshape((-1, 28, 28)))\n",
-    "transformed_inputs = random_rotation(inputs, rng)\n",
-    "_ = show_batch_of_images(transformed_inputs.reshape((-1, 28, 28)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercise 5: Training with data augmentation\n",
-    "\n",
-    "One simple way to use data augmentation is to just statically augment the training data set - for example we could iterate through the training dataset applying a transformation function like that implemented above to generate new artificial inputs, and use both the original and newly generated data in a new data provider object. We are quite limited however in how far we can augment the dataset by with a static method like this however - if we wanted to apply 9 random rotations to each image in the original datase, we would end up with a dataset with 10 times the memory requirements and that would take 10 times as long to run through each epoch.\n",
-    "\n",
-    "An alternative is to randomly augment the data on the fly as we iterate through the data provider in each epoch. In this method a new data provider class can be defined that inherits from the original data provider to be augmented, and provides a new `next` method which applies a random transformation function like that implemented in the previous exercise to each input batch before returning it. This method means that on every epoch a different set of training examples are provided to the model and so in some ways corresponds to an 'infinite' data set (although the amount of variability in the dataset will still be significantly less than the variability in all possible digit images). Compared to static augmentation, this dynamic augmentation scheme comes at the computational cost of having to apply the random transformation each time a new batch is provided. We can vary this overhead by changing the proportion of images in a batch randomly transformed.\n",
-    "\n",
-    "An implementation of this scheme has been provided for the MNIST data set in the `AugmentedMNISTDataProvider` object in the `mlp.data_providers` module. In addition to the arguments of the original `MNISTDataProvider.__init__` method, this additional takes a `transformer` argument, which should be a function which takes as arguments an inputs batch array and a random number generator object, and returns an array corresponding to a random transformation of the inputs. \n",
-    "\n",
-    "Train a model with the same architecture as in exercise 3 and with no L1 / L2 regularisation using a training data provider which randomly augments the training images using your `random_rotation` transformer function. Plot the training and validation set errors over the training epochs and compare this plot to your previous results from exercise 3. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from mlp.data_providers import AugmentedMNISTDataProvider\n",
-    "\n",
-    "aug_train_data = AugmentedMNISTDataProvider('train', rng=rng, transformer=random_rotation)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 5: 18.6s to complete\n",
-      "    error(train)=1.45e-01, acc(train)=9.57e-01, error(valid)=1.23e-01, acc(valid)=9.65e-01\n",
-      "Epoch 10: 19.2s to complete\n",
-      "    error(train)=8.53e-02, acc(train)=9.74e-01, error(valid)=8.48e-02, acc(valid)=9.74e-01\n",
-      "Epoch 15: 19.3s to complete\n",
-      "    error(train)=6.26e-02, acc(train)=9.81e-01, error(valid)=7.69e-02, acc(valid)=9.77e-01\n",
-      "Epoch 20: 22.9s to complete\n",
-      "    error(train)=5.26e-02, acc(train)=9.85e-01, error(valid)=7.79e-02, acc(valid)=9.76e-01\n",
-      "Epoch 25: 16.4s to complete\n",
-      "    error(train)=4.33e-02, acc(train)=9.88e-01, error(valid)=7.29e-02, acc(valid)=9.78e-01\n",
-      "Epoch 30: 19.1s to complete\n",
-      "    error(train)=3.72e-02, acc(train)=9.89e-01, error(valid)=7.23e-02, acc(valid)=9.78e-01\n",
-      "Epoch 35: 19.4s to complete\n",
-      "    error(train)=3.56e-02, acc(train)=9.90e-01, error(valid)=7.50e-02, acc(valid)=9.79e-01\n",
-      "Epoch 40: 18.6s to complete\n",
-      "    error(train)=2.90e-02, acc(train)=9.93e-01, error(valid)=7.00e-02, acc(valid)=9.79e-01\n",
-      "Epoch 45: 18.8s to complete\n",
-      "    error(train)=2.77e-02, acc(train)=9.93e-01, error(valid)=6.72e-02, acc(valid)=9.80e-01\n",
-      "Epoch 50: 19.0s to complete\n",
-      "    error(train)=3.12e-02, acc(train)=9.91e-01, error(valid)=7.63e-02, acc(valid)=9.80e-01\n",
-      "Epoch 55: 20.1s to complete\n",
-      "    error(train)=2.23e-02, acc(train)=9.94e-01, error(valid)=6.63e-02, acc(valid)=9.83e-01\n",
-      "Epoch 60: 25.8s to complete\n",
-      "    error(train)=2.10e-02, acc(train)=9.94e-01, error(valid)=6.99e-02, acc(valid)=9.81e-01\n",
-      "Epoch 65: 20.6s to complete\n",
-      "    error(train)=1.95e-02, acc(train)=9.95e-01, error(valid)=6.53e-02, acc(valid)=9.83e-01\n",
-      "Epoch 70: 17.1s to complete\n",
-      "    error(train)=2.11e-02, acc(train)=9.94e-01, error(valid)=6.88e-02, acc(valid)=9.81e-01\n",
-      "Epoch 75: 24.2s to complete\n",
-      "    error(train)=1.93e-02, acc(train)=9.95e-01, error(valid)=6.44e-02, acc(valid)=9.83e-01\n",
-      "Epoch 80: 20.2s to complete\n",
-      "    error(train)=1.78e-02, acc(train)=9.95e-01, error(valid)=6.52e-02, acc(valid)=9.83e-01\n",
-      "Epoch 85: 20.1s to complete\n",
-      "    error(train)=1.68e-02, acc(train)=9.96e-01, error(valid)=6.30e-02, acc(valid)=9.84e-01\n",
-      "Epoch 90: 19.9s to complete\n",
-      "    error(train)=2.17e-02, acc(train)=9.94e-01, error(valid)=7.03e-02, acc(valid)=9.82e-01\n",
-      "Epoch 95: 18.5s to complete\n",
-      "    error(train)=1.72e-02, acc(train)=9.96e-01, error(valid)=6.59e-02, acc(valid)=9.82e-01\n",
-      "Epoch 100: 20.6s to complete\n",
-      "    error(train)=2.21e-02, acc(train)=9.93e-01, error(valid)=8.22e-02, acc(valid)=9.80e-01\n"
-     ]
-    }
-   ],
-   "source": [
-    "batch_size = 100\n",
-    "num_epochs = 100\n",
-    "learning_rate = 0.01\n",
-    "mom_coeff = 0.9\n",
-    "stats_interval = 5\n",
-    "\n",
-    "rng.seed(seed)\n",
-    "aug_train_data.reset()\n",
-    "valid_data.reset()\n",
-    "aug_train_data.batch_size = batch_size \n",
-    "valid_data.batch_size = batch_size\n",
-    "\n",
-    "weights_init = GlorotUniformInit(0.5, rng=rng)\n",
-    "biases_init = ConstantInit(0.)\n",
-    "\n",
-    "model = MultipleLayerModel([\n",
-    "    AffineLayer(input_dim, hidden_dim, weights_init, biases_init),\n",
-    "    ReluLayer(),\n",
-    "    AffineLayer(hidden_dim, hidden_dim, weights_init, biases_init),\n",
-    "    ReluLayer(),\n",
-    "    AffineLayer(hidden_dim, output_dim, weights_init, biases_init)\n",
-    "])\n",
-    "\n",
-    "error = CrossEntropySoftmaxError()\n",
-    "learning_rule = MomentumLearningRule(learning_rate=learning_rate, mom_coeff=mom_coeff)\n",
-    "data_monitors={'acc': lambda y, t: (y.argmax(-1) == t.argmax(-1)).mean()}\n",
-    "optimiser = Optimiser(\n",
-    "    model, error, learning_rule, aug_train_data, valid_data, data_monitors)\n",
-    "\n",
-    "aug_stats, aug_keys, aug_run_time = optimiser.train(\n",
-    "    num_epochs=num_epochs, stats_interval=stats_interval)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAvEAAALACAYAAAAaHi3pAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nERHJDEM8EREREZHM/Al4ZxDqWa1dNQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x7f1daa8943c8>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "fig = plt.figure(figsize=(12, 12))\n",
-    "ax1 = fig.add_subplot(2, 1, 1)\n",
-    "ax2 = fig.add_subplot(2, 1, 2)\n",
-    "for weight_penalty, run in run_info.items():\n",
-    "    stats, keys, run_time = run\n",
-    "    ax1.plot(np.arange(1, stats.shape[0]) * stats_interval, \n",
-    "             stats[1:, keys['error(train)']], label=str(weight_penalty))\n",
-    "    ax2.plot(np.arange(1, stats.shape[0]) * stats_interval, \n",
-    "             stats[1:, keys['error(valid)']], label=str(weight_penalty))\n",
-    "ax1.plot(np.arange(1, aug_stats.shape[0]) * stats_interval, \n",
-    "         aug_stats[1:, aug_keys['error(train)']], label='Data augmentation')\n",
-    "ax2.plot(np.arange(1, aug_stats.shape[0]) * stats_interval, \n",
-    "         aug_stats[1:, aug_keys['error(valid)']], label='Data augmentation')\n",
-    "ax1.legend(loc=0)\n",
-    "ax1.set_xlabel('Epoch number')\n",
-    "ax1.set_ylabel('Training set error')\n",
-    "ax1.set_yscale('log')\n",
-    "ax2.legend(loc=0)\n",
-    "ax2.set_xlabel('Epoch number')\n",
-    "ax2.set_ylabel('Validation set error')\n",
-    "ax2.set_yscale('log')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "anaconda-cloud": {},
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.2"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/notebooks/06_Dropout_and_maxout.ipynb b/notebooks/06_Dropout_and_maxout.ipynb
index 9a21615..05c2a1f 100644
--- a/notebooks/06_Dropout_and_maxout.ipynb
+++ b/notebooks/06_Dropout_and_maxout.ipynb
@@ -21,10 +21,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 1,
+   "metadata": {},
    "outputs": [],
    "source": [
     "def random_binary_mask(prob_1, shape, rng):\n",
@@ -43,7 +41,7 @@
     "    Returns:\n",
     "        Random binary mask array of specified shape.\n",
     "    \"\"\"\n",
-    "    raise NotImplementedError()"
+    "    return rng.uniform(size=shape) < prob_1"
    ]
   },
   {
@@ -55,10 +53,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -116,10 +112,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 3,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from mlp.layers import StochasticLayer\n",
@@ -127,17 +121,20 @@
     "class DropoutLayer(StochasticLayer):\n",
     "    \"\"\"Layer which stochastically drops input dimensions in its output.\"\"\"\n",
     "    \n",
-    "    def __init__(self, rng=None, incl_prob=0.5):\n",
+    "    def __init__(self, rng=None, incl_prob=0.5, share_across_batch=True):\n",
     "        \"\"\"Construct a new dropout layer.\n",
     "        \n",
     "        Args:\n",
     "            rng (RandomState): Seeded random number generator.\n",
     "            incl_prob: Scalar value in (0, 1] specifying the probability of\n",
     "                each input dimension being included in the output.\n",
+    "            share_across_batch: Whether to use same dropout mask across\n",
+    "                all inputs in a batch or use per input masks.\n",
     "        \"\"\"\n",
     "        super(DropoutLayer, self).__init__(rng)\n",
     "        assert incl_prob > 0. and incl_prob <= 1.\n",
     "        self.incl_prob = incl_prob\n",
+    "        self.share_across_batch = share_across_batch\n",
     "        \n",
     "    def fprop(self, inputs, stochastic=True):\n",
     "        \"\"\"Forward propagates activations through the layer transformation.\n",
@@ -154,7 +151,12 @@
     "        Returns:\n",
     "            outputs: Array of layer outputs of shape (batch_size, output_dim).\n",
     "        \"\"\"\n",
-    "        raise NotImplementedError()\n",
+    "        if stochastic:\n",
+    "            mask_shape = (1,) + inputs.shape[1:] if self.share_across_batch else inputs.shape\n",
+    "            self._mask = (rng.uniform(size=mask_shape) < self.incl_prob)\n",
+    "            return inputs * self._mask\n",
+    "        else:\n",
+    "            return inputs * self.incl_prob\n",
     "    \n",
     "    def bprop(self, inputs, outputs, grads_wrt_outputs):\n",
     "        \"\"\"Back propagates gradients through a layer.\n",
@@ -174,7 +176,7 @@
     "            Array of gradients with respect to the layer inputs of shape\n",
     "            (batch_size, input_dim).\n",
     "        \"\"\"\n",
-    "        raise NotImplementedError()\n",
+    "        return grads_wrt_outputs * self._mask \n",
     "\n",
     "    def __repr__(self):\n",
     "        return 'DropoutLayer(incl_prob={0:.1f})'.format(self.incl_prob)"
@@ -189,10 +191,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 4,
+   "metadata": {},
    "outputs": [],
    "source": [
     "seed = 31102016 \n",
@@ -249,10 +249,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 5,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -283,11 +281,3136 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1570e8d049a44c52b2608f768bf7431f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0ba46a7ab1344643b0918cdc4be9cca2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a07d519729341d3830025aae432be59",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1c257f5e31344710b8b70d079779f5f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dce6a1b493d1458ba621933e0b142790",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "16cb7154e175437abe3442279be3495d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 5: 5.0s to complete\n",
+      "    error(train)=4.50e-01, acc(train)=8.63e-01, error(valid)=3.95e-01, acc(valid)=8.75e-01\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fef614f668e047adb56e9b46072b5313",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6dbffb51864e49b482d2d3cb19614d32",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "95eb02808ab64475a6f7df48e18ced07",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "aad36029708246879ae1115fbbe28467",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d1c268026f0d4553837075abfd269951",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 10: 5.1s to complete\n",
+      "    error(train)=3.72e-01, acc(train)=8.86e-01, error(valid)=3.35e-01, acc(valid)=9.00e-01\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d27023b34df34004aa0c88b8859c7286",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2ba31ddcc89243358ecb5af656ac5da1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "acb54a7df3ca4d178d95bc6e80e21af2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "adf7558500464c81b76b9a873dcdfd3a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e834c74b669d4f9c9bc7a24cb02ef55d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 15: 6.3s to complete\n",
+      "    error(train)=3.33e-01, acc(train)=8.99e-01, error(valid)=3.23e-01, acc(valid)=9.04e-01\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8df1ec88360243dcbc5ae9fa2c936c95",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a3ab83357cf14be587cd3513ebe2d1f1",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ca9850b800d4175b32fb521aefd0efe",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "30d0300afce0437a88d50726255b7de4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "209cdfede4f64daca753d20ea94fbb74",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 20: 7.8s to complete\n",
+      "    error(train)=3.09e-01, acc(train)=9.06e-01, error(valid)=2.85e-01, acc(valid)=9.15e-01\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1daa05b3785c486daf4f1c5392ad6aa9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c453707b73094c18921585d433db7c0d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ea96921abe4241c894c630d7bb3411a0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "475ab1b604ce4303a3e8ee6a9eb853bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "278bb04b0b1d4022a1b9dc3e7c4cf94f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 25: 4.4s to complete\n",
+      "    error(train)=2.98e-01, acc(train)=9.10e-01, error(valid)=2.87e-01, acc(valid)=9.13e-01\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3d550f1782be4ef2b3c292ada8270585",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d9d958a3603841e4986535355f051346",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b6d9aabde2f7431999bee9512044e122",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ba3f1f442317416a992f5089047a630c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "75550efdb5074490a4638a365e777421",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 30: 4.7s to complete\n",
+      "    error(train)=2.92e-01, acc(train)=9.10e-01, error(valid)=2.71e-01, acc(valid)=9.20e-01\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "17d66dc35b014d4b9ee81ac16f295099",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2256aef45a814d83ad8109385a0857a2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "64641729794b42d4948e0102b8390375",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d8e1fb104f094c1dbf0a4144d2ec39f2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2f466aa9dd104da7a1b222b707fcfc95",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 35: 7.4s to complete\n",
+      "    error(train)=2.83e-01, acc(train)=9.14e-01, error(valid)=2.87e-01, acc(valid)=9.15e-01\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0884d3d86ec94e658497215e65566d80",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e820d07bc4b144bb8d3675a12992be70",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "00bbd01802ca4896985099582e4dae63",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd33d19947ba446081f8cfc96f9aab55",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e44bc5dd043f4ee19ca28044fb440410",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 40: 4.9s to complete\n",
+      "    error(train)=2.74e-01, acc(train)=9.17e-01, error(valid)=2.56e-01, acc(valid)=9.24e-01\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "43358e22a2f2416795e4808e118ef138",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e2c9235d716442a3974242d3b69077f2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6e1607a023d24a50b375b13c92fccd35",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a3dee9b0f99247aa93ad6cdc0b16cf4c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0bf336981b6b4761af29fb783d5037d9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 45: 9.7s to complete\n",
+      "    error(train)=2.73e-01, acc(train)=9.18e-01, error(valid)=2.64e-01, acc(valid)=9.22e-01\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "eee9189d58274a1289aac7e09ee0b4bc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5429d270e0da42e9becefe000112eaa4",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8885af6289624c99b6337d13e41fbd63",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ede51727693c4c23863475afd864631e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a73a7b5dc4114240896160fd5e987447",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 50: 9.0s to complete\n",
+      "    error(train)=2.72e-01, acc(train)=9.18e-01, error(valid)=2.62e-01, acc(valid)=9.26e-01\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "18d11f77d5e44ecc9d395c63c06c9317",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ef9359b9222646ef89544e40d4da8076",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "05836a0d5e5a44b7846c09f57a4c56ca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "35014e8b5c284f80bc6b210ee40910fa",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d9c0357cffbf4edd8573ecda737cbe28",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 55: 10.8s to complete\n",
+      "    error(train)=2.62e-01, acc(train)=9.21e-01, error(valid)=2.50e-01, acc(valid)=9.26e-01\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "095e101ea8414d2f8e21f03a3fcdbfff",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ed9da8a4db5643e289c1b16cf26d170d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fccf93993b5242df9b976c11fb3846ae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b3985082d8f24531a5dd7b0dccd074ae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ac075e7579d84afbac8bf5108ba49aca",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 60: 7.0s to complete\n",
+      "    error(train)=2.61e-01, acc(train)=9.21e-01, error(valid)=2.48e-01, acc(valid)=9.27e-01\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4ab6620b5100414c9af5fc6465c99bc7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d84a2a69eab41af86bea91820aa9d09",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b444569827ea47f787d6399871ff8acc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5d5ba3c9163647ed98f41d6b74f7c5a8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a285e64ceaaa4b8d8a7abb6424f15d90",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 65: 8.5s to complete\n",
+      "    error(train)=2.58e-01, acc(train)=9.22e-01, error(valid)=2.62e-01, acc(valid)=9.24e-01\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6eaa64732a9d4cfe8cba94b7ad519182",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6d8ca76d20c145a3947ca7f9d7f68bbb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ad2a274f76a448099a41fc9b743aa68c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a119af1dc2924ea3a844ece31f4de437",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "668f3f9c541c4baea191481db86260cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 70: 6.5s to complete\n",
+      "    error(train)=2.53e-01, acc(train)=9.24e-01, error(valid)=2.39e-01, acc(valid)=9.29e-01\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "15b5beb781ad4dc2bbb6a8a0a740378b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4d9680a210ca4139aead1d91635eb483",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "47a5a602989c4e40bd8e0c9eb2c726c3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "70f7e81581364cbfa33bc05983857b17",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bfe4a1bc800145d4862158d2ff6bd148",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 75: 4.8s to complete\n",
+      "    error(train)=2.55e-01, acc(train)=9.23e-01, error(valid)=2.52e-01, acc(valid)=9.28e-01\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1adf3ded095347a7a32dd514a4a8d44e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "46216a3cdb774ccdab27bdfaad7491b3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "fd851493e6a241e5b0ad402785d9e3bf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a3c918d4fe0b44039421c8f50106490e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e3ac9dc85ac64c1db29a15cb0aa53e9d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 80: 5.5s to complete\n",
+      "    error(train)=2.49e-01, acc(train)=9.25e-01, error(valid)=2.52e-01, acc(valid)=9.27e-01\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "abfad44544a843858505c3f1388c38e0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "32f89d02068d487c9c2fafb1462fb4eb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4485b100a7724386ad2bf6ea14241cef",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8a599deab8f14eeaaabc45e97e263e54",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b414cae29a10405ead343f58858d6d8a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 85: 5.4s to complete\n",
+      "    error(train)=2.51e-01, acc(train)=9.26e-01, error(valid)=2.53e-01, acc(valid)=9.29e-01\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "70042b7a5cd64378a8b51b11f3f1f385",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "10b961c8c6254297ba70df418afe67bb",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2c36eb7fb8844be09ba83b979a3afd0c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "50c5549d709e47a7a6b6709c7e7cb74d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7ce364d66cf742108a468ec40c4e3913",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 90: 5.0s to complete\n",
+      "    error(train)=2.45e-01, acc(train)=9.26e-01, error(valid)=2.48e-01, acc(valid)=9.27e-01\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5cdcaa8fc3354cc1be2227d1d0b2ec85",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ceb8c00fc9844678cecb81604620ee8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "c2f182aeeaa546e7a544425843bbba96",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1419f1fea31c48a5a765a087e9ca47d0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f8b87965d23476a9d1dcba586b7209f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 95: 5.2s to complete\n",
+      "    error(train)=2.47e-01, acc(train)=9.25e-01, error(valid)=2.40e-01, acc(valid)=9.29e-01\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3f100ae52a704bec8a696fbae64f6114",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "dc4c1d2a13d04ca78bbabf3ac96986d8",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "3a9592147abd412fa7a06e23b700d0cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2b26af7df3a64327adeb2c285fc57d4d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "50e49d26598747c9bd4f3b511370ba09",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/html": [
+       "<p>Failed to display Jupyter Widget of type <code>HBox</code>.</p>\n",
+       "<p>\n",
+       "  If you're reading this message in Jupyter Notebook or JupyterLab, it may mean\n",
+       "  that the widgets JavaScript is still loading. If this message persists, it\n",
+       "  likely means that the widgets JavaScript library is either not installed or\n",
+       "  not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
+       "  Widgets Documentation</a> for setup instructions.\n",
+       "</p>\n",
+       "<p>\n",
+       "  If you're reading this message in another notebook frontend (for example, a static\n",
+       "  rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
+       "  it may mean that your frontend doesn't currently support widgets.\n",
+       "</p>\n"
+      ],
+      "text/plain": [
+       "HBox(children=(IntProgress(value=0, max=1000), HTML(value='')))"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Epoch 100: 5.3s to complete\n",
+      "    error(train)=2.50e-01, acc(train)=9.25e-01, error(valid)=2.30e-01, acc(valid)=9.31e-01\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.text.Text at 0x7f214a6de710>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAEKCAYAAAA/9Q3BAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nvXoAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f214d28e080>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAe0AAAEKCAYAAAA/9Q3BAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x7f214a722f98>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Probability of input being included in output in dropout layer\n",
     "incl_prob = 0.5\n",
@@ -322,7 +3445,7 @@
     "data_monitors={'acc': lambda y, t: (y.argmax(-1) == t.argmax(-1)).mean()}\n",
     "\n",
     "optimiser = Optimiser(\n",
-    "    model, error, learning_rule, train_data, valid_data, data_monitors)\n",
+    "    model, error, learning_rule, train_data, valid_data, data_monitors, notebook=True)\n",
     "\n",
     "num_epochs = 100\n",
     "stats_interval = 5\n",
@@ -400,10 +3523,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [],
    "source": [
     "from mlp.layers import Layer\n",
@@ -420,7 +3541,6 @@
     "               is a multiple of this pool size such that the outputs\n",
     "               can be split in to pools with no dimensions left over.\n",
     "        \"\"\"\n",
-    "        assert pool_size > 0\n",
     "        self.pool_size = pool_size\n",
     "    \n",
     "    def fprop(self, inputs):\n",
@@ -437,7 +3557,12 @@
     "        \"\"\"\n",
     "        assert inputs.shape[-1] % self.pool_size == 0, (\n",
     "            'Last dimension of inputs must be multiple of pool size')\n",
-    "        raise NotImplementedError()\n",
+    "        pooled_inputs = inputs.reshape(\n",
+    "            inputs.shape[:-1] + \n",
+    "            (inputs.shape[-1] // self.pool_size, self.pool_size))\n",
+    "        pool_maxes = pooled_inputs.max(-1)\n",
+    "        self._mask = pooled_inputs == pool_maxes[..., None]\n",
+    "        return pool_maxes\n",
     "\n",
     "    def bprop(self, inputs, outputs, grads_wrt_outputs):\n",
     "        \"\"\"Back propagates gradients through a layer.\n",
@@ -456,7 +3581,7 @@
     "            Array of gradients with respect to the layer inputs of shape\n",
     "            (batch_size, input_dim).\n",
     "        \"\"\"\n",
-    "        raise NotImplementedError()\n",
+    "        return (self._mask * grads_wrt_outputs[..., None]).reshape(inputs.shape)\n",
     "\n",
     "    def __repr__(self):\n",
     "        return 'MaxPoolingLayer(pool_size={0})'.format(self.pool_size)"
@@ -471,10 +3596,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 8,
+   "metadata": {},
    "outputs": [],
    "source": [
     "test_inputs = np.array([[-3, -4, 5, 8], [0, -2, 3, -8], [1, 5, 3, 2]])\n",
@@ -515,10 +3638,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "execution_count": 9,
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
@@ -549,11 +3670,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "train() got an unexpected keyword argument 'notebook'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-11-f63f26d97836>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     33\u001b[0m \u001b[0mstats_interval\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     34\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 35\u001b[0;31m \u001b[0mstats\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrun_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0moptimiser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_epochs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstats_interval\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mstats_interval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnotebook\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     36\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     37\u001b[0m \u001b[0;31m# Plot the change in the validation and training set error over training.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: train() got an unexpected keyword argument 'notebook'"
+     ]
+    }
+   ],
    "source": [
     "# Size of pools to take maximum over\n",
     "pool_size = 2\n",
@@ -584,7 +3715,7 @@
     "data_monitors={'acc': lambda y, t: (y.argmax(-1) == t.argmax(-1)).mean()}\n",
     "\n",
     "optimiser = Optimiser(\n",
-    "    model, error, learning_rule, train_data, valid_data, data_monitors)\n",
+    "    model, error, learning_rule, train_data, valid_data, data_monitors, notebook=True)\n",
     "\n",
     "num_epochs = 100\n",
     "stats_interval = 5\n",
@@ -607,30 +3738,37 @@
     "    ax_2.plot(np.arange(1, stats.shape[0]) * stats_interval, \n",
     "              stats[1:, keys[k]], label=k)\n",
     "ax_2.legend(loc=0)\n",
-    "ax_2.set_xlabel('Epoch number')"
+    "ax_2.set_xlabel('Epoch number')\n"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
   "anaconda-cloud": {},
   "kernelspec": {
-   "display_name": "Python [conda env:mlp]",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "conda-env-mlp-py"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2.0
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.12"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
-}
\ No newline at end of file
+ "nbformat_minor": 1
+}
diff --git a/notebooks/BatchNormalizationLayer_tests.ipynb b/notebooks/BatchNormalizationLayer_tests.ipynb
new file mode 100644
index 0000000..38b9d46
--- /dev/null
+++ b/notebooks/BatchNormalizationLayer_tests.ipynb
@@ -0,0 +1,155 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from mlp.layers import BatchNormalizationLayer\n",
+    "test_inputs = np.array([[-1.38066782, -0.94725498, -3.05585424,  2.28644454,  0.85520889,\n",
+    "         0.10575624,  0.23618609,  0.84723205,  1.06569909, -2.21704034],\n",
+    "       [ 0.11060968, -0.0747448 ,  0.56809029,  2.45926149, -2.28677816,\n",
+    "        -0.9964566 ,  2.7356007 ,  1.98002308, -0.39032315,  1.46515481]])\n",
+    "test_grads_wrt_outputs = np.array([[-0.43857052,  1.00380109, -1.18425494,  0.00486091,  0.21470207,\n",
+    "        -0.12179054, -0.11508482,  0.738482  , -1.17249238,  0.69188295],\n",
+    "       [ 1.07802015,  0.69901145,  0.81603688, -1.76743026, -1.24418692,\n",
+    "        -0.65729963, -0.50834305, -0.49016145,  1.63749743, -0.71123104]])\n",
+    "\n",
+    "#produce BatchNorm fprop and bprop\n",
+    "activation_layer = BatchNormalizationLayer(input_dim=10)\n",
+    "\n",
+    "beta = np.array(10*[0.3])\n",
+    "gamma = np.array(10*[0.5])\n",
+    "\n",
+    "activation_layer.params = [gamma, beta]\n",
+    "BN_fprop = activation_layer.fprop(test_inputs)\n",
+    "BN_bprop = activation_layer.bprop(\n",
+    "    test_inputs, BN_fprop, test_grads_wrt_outputs)\n",
+    "BN_grads_wrt_params = activation_layer.grads_wrt_params(\n",
+    "    test_inputs, test_grads_wrt_outputs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "true_fprop_outputs = np.array([[-0.1999955 , -0.19998686, -0.19999924, -0.1996655 ,  0.79999899,\n",
+    "         0.79999177, -0.1999984 , -0.19999221,  0.79999528, -0.19999926],\n",
+    "       [ 0.7999955 ,  0.79998686,  0.79999924,  0.7996655 , -0.19999899,\n",
+    "        -0.19999177,  0.7999984 ,  0.79999221, -0.19999528,  0.79999926]])\n",
+    "shape_test=BN_fprop.shape == true_fprop_outputs.shape, (\n",
+    "    'Layer bprop returns incorrect shaped array. '\n",
+    "    'Correct shape is \\n\\n{0}\\n\\n but returned shape is \\n\\n{1}.'\n",
+    "    .format(true_fprop_outputs.shape, BN_fprop.shape)\n",
+    ")\n",
+    "numerical_test=np.allclose(np.round(BN_fprop, decimals=2), np.round(true_fprop_outputs, decimals=2)), (\n",
+    "'Layer bprop does not return correct values. '\n",
+    "'Correct output is \\n\\n{0}\\n\\n but returned output is \\n\\n{1}\\n\\n difference is \\n\\n{2}'\n",
+    ".format(true_fprop_outputs, BN_fprop, BN_fprop-true_fprop_outputs)\n",
+    ")\n",
+    "\n",
+    "if shape_test and numerical_test:\n",
+    "    print(\"Batch Normalization F-prop test passed\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "true_bprop_outputs = np.array([[ -9.14558020e-06,   9.17665617e-06,  -8.40575535e-07,\n",
+    "          6.85384297e-03,   9.40668131e-07,   7.99795574e-06,\n",
+    "          5.03719464e-07,   1.69038704e-05,  -1.82061629e-05,\n",
+    "          5.62083224e-07],\n",
+    "       [  9.14558020e-06,  -9.17665617e-06,   8.40575535e-07,\n",
+    "         -6.85384297e-03,  -9.40668131e-07,  -7.99795574e-06,\n",
+    "         -5.03719464e-07,  -1.69038704e-05,   1.82061629e-05,\n",
+    "         -5.62083224e-07]])\n",
+    "shape_test=BN_bprop.shape == true_bprop_outputs.shape, (\n",
+    "    'Layer bprop returns incorrect shaped array. '\n",
+    "    'Correct shape is \\n\\n{0}\\n\\n but returned shape is \\n\\n{1}.'\n",
+    "    .format(true_bprop_outputs.shape, BN_bprop.shape)\n",
+    ")\n",
+    "numerical_test=np.allclose(np.round(BN_bprop, decimals=2), np.round(true_bprop_outputs, decimals=2)), (\n",
+    "'Layer bprop does not return correct values. '\n",
+    "'Correct output is \\n\\n{0}\\n\\n but returned output is \\n\\n{1}\\n\\n difference is \\n\\n{2}'\n",
+    ".format(true_bprop_outputs, BN_bprop, BN_bprop-true_bprop_outputs)\n",
+    ")\n",
+    "\n",
+    "if shape_test and numerical_test:\n",
+    "    print(\"Batch Normalization B-prop test passed\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "grads_wrt_gamma, grads_wrt_beta = BN_grads_wrt_params\n",
+    "true_grads_wrt_gamma = np.array(([ 1.51657703, -0.30478163,  2.00028878, -1.77110552,  1.45888603,\n",
+    "        0.53550028, -0.39325697, -1.2286243 , -2.8099633 , -1.40311192]))\n",
+    "true_grads_wrt_beta = np.array([ 0.63944963,  1.70281254, -0.36821806, -1.76256935, -1.02948485,\n",
+    "       -0.77909018, -0.62342786,  0.24832055,  0.46500505, -0.01934809])\n",
+    "\n",
+    "grads_gamma_shape_test=grads_wrt_gamma.shape == true_grads_wrt_gamma.shape, (\n",
+    "    'Layer bprop returns incorrect shaped array. '\n",
+    "    'Correct shape is \\n\\n{0}\\n\\n but returned shape is \\n\\n{1}.'\n",
+    "    .format(true_grads_wrt_gamma.shape, grads_wrt_gamma.shape)\n",
+    ")\n",
+    "grads_gamma_numerical_test=np.allclose(np.round(grads_wrt_gamma, decimals=2), np.round(true_grads_wrt_gamma, decimals=2)), (\n",
+    "'Layer bprop does not return correct values. '\n",
+    "'Correct output is \\n\\n{0}\\n\\n but returned output is \\n\\n{1}\\n\\n difference is \\n\\n{2}'\n",
+    ".format(true_grads_wrt_gamma, grads_wrt_gamma, grads_wrt_gamma-true_grads_wrt_gamma)\n",
+    ")\n",
+    "\n",
+    "grads_beta_shape_test=grads_wrt_beta.shape == true_grads_wrt_beta.shape, (\n",
+    "    'Layer bprop returns incorrect shaped array. '\n",
+    "    'Correct shape is \\n\\n{0}\\n\\n but returned shape is \\n\\n{1}.'\n",
+    "    .format(true_grads_wrt_beta.shape, grads_wrt_beta.shape)\n",
+    ")\n",
+    "grads_beta_numerical_test=np.allclose(np.round(grads_wrt_beta, decimals=2), np.round(true_grads_wrt_beta, decimals=2)), (\n",
+    "'Layer bprop does not return correct values. '\n",
+    "'Correct output is \\n\\n{0}\\n\\n but returned output is \\n\\n{1}\\n\\n difference is \\n\\n{2}'\n",
+    ".format(true_grads_wrt_beta, grads_wrt_beta, grads_wrt_beta-true_grads_wrt_beta)\n",
+    ")\n",
+    "\n",
+    "if grads_gamma_shape_test and grads_gamma_numerical_test and grads_beta_shape_test and grads_beta_numerical_test:\n",
+    "    print(\"Batch Normalization grads wrt to params test passed\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/Convolutional layer tests.ipynb b/notebooks/Convolutional layer tests.ipynb
new file mode 100644
index 0000000..8b04ae1
--- /dev/null
+++ b/notebooks/Convolutional layer tests.ipynb	
@@ -0,0 +1,307 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Below a skeleton class and associated test functions for the `fprop`, `bprop` and `grads_wrt_params` methods of the ConvolutionalLayer class are included.\n",
+    "\n",
+    "The test functions assume that in your implementation of `fprop` for the convolutional layer, outputs are calculated only for 'valid' overlaps of the kernel filters with the input - i.e. without any padding.\n",
+    "\n",
+    "It is also assumed that if convolutions with non-unit strides are implemented the default behaviour is to take unit-strides, with the test cases only correct for unit strides in both directions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The three test functions are defined in the cell below. All the functions take as first argument the *class* corresponding to the convolutional layer implementation to be tested (**not** an instance of the class). It is assumed the class being tested has an `__init__` method with at least all of the arguments defined in the skeleton definition above. A boolean second argument to each function can be used to specify if the layer implements a cross-correlation or convolution based operation (see note in [seventh lecture slides](http://www.inf.ed.ac.uk/teaching/courses/mlp/2016/mlp07-cnn.pdf))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "def test_conv_layer_fprop(layer_class, do_cross_correlation=False):\n",
+    "    \"\"\"Tests `fprop` method of a convolutional layer.\n",
+    "    \n",
+    "    Checks the outputs of `fprop` method for a fixed input against known\n",
+    "    reference values for the outputs and raises an AssertionError if\n",
+    "    the outputted values are not consistent with the reference values. If\n",
+    "    tests are all passed returns True.\n",
+    "    \n",
+    "    Args:\n",
+    "        layer_class: Convolutional layer implementation following the \n",
+    "            interface defined in the provided skeleton class.\n",
+    "        do_cross_correlation: Whether the layer implements an operation\n",
+    "            corresponding to cross-correlation (True) i.e kernels are\n",
+    "            not flipped before sliding over inputs, or convolution\n",
+    "            (False) with filters being flipped.\n",
+    "\n",
+    "    Raises:\n",
+    "        AssertionError: Raised if output of `layer.fprop` is inconsistent \n",
+    "            with reference values either in shape or values.\n",
+    "    \"\"\"\n",
+    "    inputs = np.arange(96).reshape((2, 3, 4, 4))\n",
+    "    kernels = np.arange(-12, 12).reshape((2, 3, 2, 2))\n",
+    "    if do_cross_correlation:\n",
+    "        kernels = kernels[:, :, ::-1, ::-1]\n",
+    "    biases = np.arange(2)\n",
+    "    true_output = np.array(\n",
+    "        [[[[ -958., -1036., -1114.],\n",
+    "           [-1270., -1348., -1426.],\n",
+    "           [-1582., -1660., -1738.]],\n",
+    "          [[ 1707.,  1773.,  1839.],\n",
+    "           [ 1971.,  2037.,  2103.],\n",
+    "           [ 2235.,  2301.,  2367.]]],\n",
+    "         [[[-4702., -4780., -4858.],\n",
+    "           [-5014., -5092., -5170.],\n",
+    "           [-5326., -5404., -5482.]],\n",
+    "          [[ 4875.,  4941.,  5007.],\n",
+    "           [ 5139.,  5205.,  5271.],\n",
+    "           [ 5403.,  5469.,  5535.]]]]\n",
+    "    )\n",
+    "    \n",
+    "    layer = layer_class(\n",
+    "        num_input_channels=kernels.shape[1], \n",
+    "        num_output_channels=kernels.shape[0], \n",
+    "        input_dim_1=inputs.shape[2], \n",
+    "        input_dim_2=inputs.shape[3],\n",
+    "        kernel_dim_1=kernels.shape[2],\n",
+    "        kernel_dim_2=kernels.shape[3]\n",
+    "    )\n",
+    "    layer.params = [kernels, biases]\n",
+    "    layer_output = layer.fprop(inputs)\n",
+    "    \n",
+    "    assert layer_output.shape == true_output.shape, (\n",
+    "        'Layer fprop gives incorrect shaped output. '\n",
+    "        'Correct shape is \\n\\n{0}\\n\\n but returned shape is \\n\\n{1}.'\n",
+    "        .format(true_output.shape, layer_output.shape)\n",
+    "    )\n",
+    "    assert np.allclose(layer_output, true_output), (\n",
+    "        'Layer fprop does not give correct output. '\n",
+    "        'Correct output is \\n\\n{0}\\n\\n but returned output is \\n\\n{1}\\n\\n difference is \\n\\n{2}.'\n",
+    "        .format(true_output, layer_output, true_output-layer_output)\n",
+    "    )\n",
+    "    return True\n",
+    "\n",
+    "def test_conv_layer_bprop(layer_class, do_cross_correlation=False):\n",
+    "    \"\"\"Tests `bprop` method of a convolutional layer.\n",
+    "    \n",
+    "    Checks the outputs of `bprop` method for a fixed input against known\n",
+    "    reference values for the gradients with respect to inputs and raises \n",
+    "    an AssertionError if the returned values are not consistent with the\n",
+    "    reference values. If tests are all passed returns True.\n",
+    "    \n",
+    "    Args:\n",
+    "        layer_class: Convolutional layer implementation following the \n",
+    "            interface defined in the provided skeleton class.\n",
+    "        do_cross_correlation: Whether the layer implements an operation\n",
+    "            corresponding to cross-correlation (True) i.e kernels are\n",
+    "            not flipped before sliding over inputs, or convolution\n",
+    "            (False) with filters being flipped.\n",
+    "\n",
+    "    Raises:\n",
+    "        AssertionError: Raised if output of `layer.bprop` is inconsistent \n",
+    "            with reference values either in shape or values.\n",
+    "    \"\"\"\n",
+    "    inputs = np.arange(96).reshape((2, 3, 4, 4))\n",
+    "    kernels = np.arange(-12, 12).reshape((2, 3, 2, 2))\n",
+    "    if do_cross_correlation:\n",
+    "        kernels = kernels[:, :, ::-1, ::-1]\n",
+    "    biases = np.arange(2)\n",
+    "    grads_wrt_outputs = np.arange(-20, 16).reshape((2, 2, 3, 3))\n",
+    "    outputs = np.array(\n",
+    "        [[[[ -958., -1036., -1114.],\n",
+    "           [-1270., -1348., -1426.],\n",
+    "           [-1582., -1660., -1738.]],\n",
+    "          [[ 1707.,  1773.,  1839.],\n",
+    "           [ 1971.,  2037.,  2103.],\n",
+    "           [ 2235.,  2301.,  2367.]]],\n",
+    "         [[[-4702., -4780., -4858.],\n",
+    "           [-5014., -5092., -5170.],\n",
+    "           [-5326., -5404., -5482.]],\n",
+    "          [[ 4875.,  4941.,  5007.],\n",
+    "           [ 5139.,  5205.,  5271.],\n",
+    "           [ 5403.,  5469.,  5535.]]]]\n",
+    "    )\n",
+    "    true_grads_wrt_inputs = np.array(\n",
+    "      [[[[ 147.,  319.,  305.,  162.],\n",
+    "         [ 338.,  716.,  680.,  354.],\n",
+    "         [ 290.,  608.,  572.,  294.],\n",
+    "         [ 149.,  307.,  285.,  144.]],\n",
+    "        [[  23.,   79.,   81.,   54.],\n",
+    "         [ 114.,  284.,  280.,  162.],\n",
+    "         [ 114.,  272.,  268.,  150.],\n",
+    "         [  73.,  163.,  157.,   84.]],\n",
+    "        [[-101., -161., -143.,  -54.],\n",
+    "         [-110., -148., -120.,  -30.],\n",
+    "         [ -62.,  -64.,  -36.,    6.],\n",
+    "         [  -3.,   19.,   29.,   24.]]],\n",
+    "       [[[  39.,   67.,   53.,   18.],\n",
+    "         [  50.,   68.,   32.,   -6.],\n",
+    "         [   2.,  -40.,  -76.,  -66.],\n",
+    "         [ -31.,  -89., -111.,  -72.]],\n",
+    "        [[  59.,  115.,  117.,   54.],\n",
+    "         [ 114.,  212.,  208.,   90.],\n",
+    "         [ 114.,  200.,  196.,   78.],\n",
+    "         [  37.,   55.,   49.,   12.]],\n",
+    "        [[  79.,  163.,  181.,   90.],\n",
+    "         [ 178.,  356.,  384.,  186.],\n",
+    "         [ 226.,  440.,  468.,  222.],\n",
+    "         [ 105.,  199.,  209.,   96.]]]])\n",
+    "    layer = layer_class(\n",
+    "        num_input_channels=kernels.shape[1], \n",
+    "        num_output_channels=kernels.shape[0], \n",
+    "        input_dim_1=inputs.shape[2], \n",
+    "        input_dim_2=inputs.shape[3],\n",
+    "        kernel_dim_1=kernels.shape[2],\n",
+    "        kernel_dim_2=kernels.shape[3]\n",
+    "    )\n",
+    "    layer.params = [kernels, biases]\n",
+    "    layer_grads_wrt_inputs = layer.bprop(inputs, outputs, grads_wrt_outputs)\n",
+    "    assert layer_grads_wrt_inputs.shape == true_grads_wrt_inputs.shape, (\n",
+    "        'Layer bprop returns incorrect shaped array. '\n",
+    "        'Correct shape is \\n\\n{0}\\n\\n but returned shape is \\n\\n{1}.'\n",
+    "        .format(true_grads_wrt_inputs.shape, layer_grads_wrt_inputs.shape)\n",
+    "    )\n",
+    "    assert np.allclose(layer_grads_wrt_inputs, true_grads_wrt_inputs), (\n",
+    "        'Layer bprop does not return correct values. '\n",
+    "        'Correct output is \\n\\n{0}\\n\\n but returned output is \\n\\n{1}\\n\\n difference is \\n\\n{2}'\n",
+    "        .format(true_grads_wrt_inputs, layer_grads_wrt_inputs, layer_grads_wrt_inputs-true_grads_wrt_inputs)\n",
+    "    )\n",
+    "    return True\n",
+    "\n",
+    "def test_conv_layer_grad_wrt_params(\n",
+    "        layer_class, do_cross_correlation=False):\n",
+    "    \"\"\"Tests `grad_wrt_params` method of a convolutional layer.\n",
+    "    \n",
+    "    Checks the outputs of `grad_wrt_params` method for fixed inputs \n",
+    "    against known reference values for the gradients with respect to \n",
+    "    kernels and biases, and raises an AssertionError if the returned\n",
+    "    values are not consistent with the reference values. If tests\n",
+    "    are all passed returns True.\n",
+    "    \n",
+    "    Args:\n",
+    "        layer_class: Convolutional layer implementation following the \n",
+    "            interface defined in the provided skeleton class.\n",
+    "        do_cross_correlation: Whether the layer implements an operation\n",
+    "            corresponding to cross-correlation (True) i.e kernels are\n",
+    "            not flipped before sliding over inputs, or convolution\n",
+    "            (False) with filters being flipped.\n",
+    "\n",
+    "    Raises:\n",
+    "        AssertionError: Raised if output of `layer.bprop` is inconsistent \n",
+    "            with reference values either in shape or values.\n",
+    "    \"\"\"\n",
+    "    inputs = np.arange(96).reshape((2, 3, 4, 4))\n",
+    "    kernels = np.arange(-12, 12).reshape((2, 3, 2, 2))\n",
+    "    biases = np.arange(2)\n",
+    "    grads_wrt_outputs = np.arange(-20, 16).reshape((2, 2, 3, 3))\n",
+    "    true_kernel_grads = np.array(\n",
+    "        [[[[ -240.,  -114.],\n",
+    "         [  264.,   390.]],\n",
+    "        [[-2256., -2130.],\n",
+    "         [-1752., -1626.]],\n",
+    "        [[-4272., -4146.],\n",
+    "         [-3768., -3642.]]],\n",
+    "       [[[ 5268.,  5232.],\n",
+    "         [ 5124.,  5088.]],\n",
+    "        [[ 5844.,  5808.],\n",
+    "         [ 5700.,  5664.]],\n",
+    "        [[ 6420.,  6384.],\n",
+    "         [ 6276.,  6240.]]]])\n",
+    "    if do_cross_correlation:\n",
+    "        kernels = kernels[:, :, ::-1, ::-1]\n",
+    "        true_kernel_grads = true_kernel_grads[:, :, ::-1, ::-1]\n",
+    "    true_bias_grads = np.array([-126.,   36.])\n",
+    "    layer = layer_class(\n",
+    "        num_input_channels=kernels.shape[1], \n",
+    "        num_output_channels=kernels.shape[0], \n",
+    "        input_dim_1=inputs.shape[2], \n",
+    "        input_dim_2=inputs.shape[3],\n",
+    "        kernel_dim_1=kernels.shape[2],\n",
+    "        kernel_dim_2=kernels.shape[3]\n",
+    "    )\n",
+    "    layer.params = [kernels, biases]\n",
+    "    layer_kernel_grads, layer_bias_grads = (\n",
+    "        layer.grads_wrt_params(inputs, grads_wrt_outputs))\n",
+    "    assert layer_kernel_grads.shape == true_kernel_grads.shape, (\n",
+    "        'grads_wrt_params gives incorrect shaped kernel gradients output. '\n",
+    "        'Correct shape is \\n\\n{0}\\n\\n but returned shape is \\n\\n{1}.'\n",
+    "        .format(true_kernel_grads.shape, layer_kernel_grads.shape)\n",
+    "    )\n",
+    "    assert np.allclose(layer_kernel_grads, true_kernel_grads), (\n",
+    "        'grads_wrt_params does not give correct kernel gradients output. '\n",
+    "        'Correct output is \\n\\n{0}\\n\\n but returned output is \\n\\n{1}.'\n",
+    "        .format(true_kernel_grads, layer_kernel_grads)\n",
+    "    )\n",
+    "    assert layer_bias_grads.shape == true_bias_grads.shape, (\n",
+    "        'grads_wrt_params gives incorrect shaped bias gradients output. '\n",
+    "        'Correct shape is \\n\\n{0}\\n\\n but returned shape is \\n\\n{1}.'\n",
+    "        .format(true_bias_grads.shape, layer_bias_grads.shape)\n",
+    "    )\n",
+    "    assert np.allclose(layer_bias_grads, true_bias_grads), (\n",
+    "        'grads_wrt_params does not give correct bias gradients output. '\n",
+    "        'Correct output is \\n\\n{0}\\n\\n but returned output is \\n\\n{1}.'\n",
+    "        .format(true_bias_grads, layer_bias_grads)\n",
+    "    )\n",
+    "    return True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An example of using the test functions if given in the cell below. This assumes you implement a convolution (rather than cross-correlation) operation. If the implementation is correct "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mlp.layers import ConvolutionalLayer\n",
+    "fprop_correct = test_conv_layer_fprop(ConvolutionalLayer, False)\n",
+    "bprop_correct = test_conv_layer_bprop(ConvolutionalLayer, False)\n",
+    "grads_wrt_param_correct = test_conv_layer_grad_wrt_params(ConvolutionalLayer, False)\n",
+    "if fprop_correct and grads_wrt_param_correct and bprop_correct:\n",
+    "    print('All tests passed.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/notebooks/Coursework_2.ipynb b/notebooks/Coursework_2.ipynb
new file mode 100644
index 0000000..3f4e522
--- /dev/null
+++ b/notebooks/Coursework_2.ipynb
@@ -0,0 +1,147 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Coursework 2\n",
+    "\n",
+    "This notebook is intended to be used as a starting point for your experiments. The instructions can be found in the instructions file located under spec/coursework2.pdf. The methods provided here are just helper functions. If you want more complex graphs such as side by side comparisons of different experiments you should learn more about matplotlib and implement them. Before each experiment remember to re-initialize neural network weights and reset the data providers so you get a properly initialized experiment. For each experiment try to keep most hyperparameters the same except the one under investigation so you can understand what the effects of each are."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "plt.style.use('ggplot')\n",
+    "\n",
+    "def train_model_and_plot_stats(\n",
+    "        model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval, notebook=True):\n",
+    "    \n",
+    "    # As well as monitoring the error over training also monitor classification\n",
+    "    # accuracy i.e. proportion of most-probable predicted classes being equal to targets\n",
+    "    data_monitors={'acc': lambda y, t: (y.argmax(-1) == t.argmax(-1)).mean()}\n",
+    "\n",
+    "    # Use the created objects to initialise a new Optimiser instance.\n",
+    "    optimiser = Optimiser(\n",
+    "        model, error, learning_rule, train_data, valid_data, data_monitors, notebook=notebook)\n",
+    "\n",
+    "    # Run the optimiser for 5 epochs (full passes through the training set)\n",
+    "    # printing statistics every epoch.\n",
+    "    stats, keys, run_time = optimiser.train(num_epochs=num_epochs, stats_interval=stats_interval)\n",
+    "\n",
+    "    # Plot the change in the validation and training set error over training.\n",
+    "    fig_1 = plt.figure(figsize=(8, 4))\n",
+    "    ax_1 = fig_1.add_subplot(111)\n",
+    "    for k in ['error(train)', 'error(valid)']:\n",
+    "        ax_1.plot(np.arange(1, stats.shape[0]) * stats_interval, \n",
+    "                  stats[1:, keys[k]], label=k)\n",
+    "    ax_1.legend(loc=0)\n",
+    "    ax_1.set_xlabel('Epoch number')\n",
+    "\n",
+    "    # Plot the change in the validation and training set accuracy over training.\n",
+    "    fig_2 = plt.figure(figsize=(8, 4))\n",
+    "    ax_2 = fig_2.add_subplot(111)\n",
+    "    for k in ['acc(train)', 'acc(valid)']:\n",
+    "        ax_2.plot(np.arange(1, stats.shape[0]) * stats_interval, \n",
+    "                  stats[1:, keys[k]], label=k)\n",
+    "    ax_2.legend(loc=0)\n",
+    "    ax_2.set_xlabel('Epoch number')\n",
+    "    \n",
+    "    return stats, keys, run_time, fig_1, ax_1, fig_2, ax_2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The below code will set up the data providers, random number\n",
+    "# generator and logger objects needed for training runs. As\n",
+    "# loading the data from file take a little while you generally\n",
+    "# will probably not want to reload the data providers on\n",
+    "# every training run. If you wish to reset their state you\n",
+    "# should instead use the .reset() method of the data providers.\n",
+    "import numpy as np\n",
+    "import logging\n",
+    "from mlp.data_providers import MNISTDataProvider, EMNISTDataProvider\n",
+    "\n",
+    "# Seed a random number generator\n",
+    "seed = 10102016 \n",
+    "rng = np.random.RandomState(seed)\n",
+    "batch_size = 100\n",
+    "# Set up a logger object to print info about the training run to stdout\n",
+    "logger = logging.getLogger()\n",
+    "logger.setLevel(logging.INFO)\n",
+    "logger.handlers = [logging.StreamHandler()]\n",
+    "\n",
+    "# Create data provider objects for the MNIST data set\n",
+    "train_data = EMNISTDataProvider('train', batch_size=batch_size, rng=rng)\n",
+    "valid_data = EMNISTDataProvider('valid', batch_size=batch_size, rng=rng)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The model set up code below is provided as a starting point.\n",
+    "# You will probably want to add further code cells for the\n",
+    "# different experiments you run.\n",
+    "\n",
+    "from mlp.layers import AffineLayer, SoftmaxLayer, SigmoidLayer, ReluLayer, LeakyReluLayer, ELULayer, SELULayer\n",
+    "from mlp.errors import CrossEntropySoftmaxError\n",
+    "from mlp.models import MultipleLayerModel\n",
+    "from mlp.initialisers import ConstantInit, GlorotUniformInit\n",
+    "from mlp.learning_rules import GradientDescentLearningRule\n",
+    "from mlp.optimisers import Optimiser\n",
+    "\n",
+    "#setup hyperparameters\n",
+    "learning_rate = 0.1\n",
+    "num_epochs = 100\n",
+    "stats_interval = 1\n",
+    "input_dim, output_dim, hidden_dim = 784, 47, 100\n",
+    "\n",
+    "weights_init = GlorotUniformInit(rng=rng)\n",
+    "biases_init = ConstantInit(0.)\n",
+    "model = MultipleLayerModel([\n",
+    "    AffineLayer(input_dim, hidden_dim, weights_init, biases_init), \n",
+    "    ReluLayer(),\n",
+    "    AffineLayer(hidden_dim, hidden_dim, weights_init, biases_init), \n",
+    "    ReluLayer(),\n",
+    "    AffineLayer(hidden_dim, output_dim, weights_init, biases_init)\n",
+    "])\n",
+    "\n",
+    "error = CrossEntropySoftmaxError()\n",
+    "# Use a basic gradient descent learning rule\n",
+    "learning_rule = GradientDescentLearningRule(learning_rate=learning_rate)\n",
+    "\n",
+    "#Remember to use notebook=False when you write a script to be run in a terminal\n",
+    "_ = train_model_and_plot_stats(\n",
+    "    model, error, learning_rule, train_data, valid_data, num_epochs, stats_interval, notebook=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/report/algorithm.sty b/report/algorithm.sty
new file mode 100644
index 0000000..a723c1c
--- /dev/null
+++ b/report/algorithm.sty
@@ -0,0 +1,79 @@
+% ALGORITHM STYLE -- Released 8 April 1996
+%    for LaTeX-2e
+% Copyright -- 1994 Peter Williams
+% E-mail Peter.Williams@dsto.defence.gov.au
+\NeedsTeXFormat{LaTeX2e}
+\ProvidesPackage{algorithm}
+\typeout{Document Style `algorithm' - floating environment}
+
+\RequirePackage{float}
+\RequirePackage{ifthen}
+\newcommand{\ALG@within}{nothing}
+\newboolean{ALG@within}
+\setboolean{ALG@within}{false}
+\newcommand{\ALG@floatstyle}{ruled}
+\newcommand{\ALG@name}{Algorithm}
+\newcommand{\listalgorithmname}{List of \ALG@name s}
+
+% Declare Options
+% first appearance
+\DeclareOption{plain}{
+  \renewcommand{\ALG@floatstyle}{plain}
+}
+\DeclareOption{ruled}{
+  \renewcommand{\ALG@floatstyle}{ruled}
+}
+\DeclareOption{boxed}{
+  \renewcommand{\ALG@floatstyle}{boxed}
+}
+% then numbering convention
+\DeclareOption{part}{
+  \renewcommand{\ALG@within}{part}
+  \setboolean{ALG@within}{true}
+}
+\DeclareOption{chapter}{
+  \renewcommand{\ALG@within}{chapter}
+  \setboolean{ALG@within}{true}
+}
+\DeclareOption{section}{
+  \renewcommand{\ALG@within}{section}
+  \setboolean{ALG@within}{true}
+}
+\DeclareOption{subsection}{
+  \renewcommand{\ALG@within}{subsection}
+  \setboolean{ALG@within}{true}
+}
+\DeclareOption{subsubsection}{
+  \renewcommand{\ALG@within}{subsubsection}
+  \setboolean{ALG@within}{true}
+}
+\DeclareOption{nothing}{
+  \renewcommand{\ALG@within}{nothing}
+  \setboolean{ALG@within}{true}
+}
+\DeclareOption*{\edef\ALG@name{\CurrentOption}}
+
+% ALGORITHM
+%
+\ProcessOptions
+\floatstyle{\ALG@floatstyle}
+\ifthenelse{\boolean{ALG@within}}{
+  \ifthenelse{\equal{\ALG@within}{part}}
+     {\newfloat{algorithm}{htbp}{loa}[part]}{}
+  \ifthenelse{\equal{\ALG@within}{chapter}}
+     {\newfloat{algorithm}{htbp}{loa}[chapter]}{}
+  \ifthenelse{\equal{\ALG@within}{section}}
+     {\newfloat{algorithm}{htbp}{loa}[section]}{}
+  \ifthenelse{\equal{\ALG@within}{subsection}}
+     {\newfloat{algorithm}{htbp}{loa}[subsection]}{}
+  \ifthenelse{\equal{\ALG@within}{subsubsection}}
+     {\newfloat{algorithm}{htbp}{loa}[subsubsection]}{}
+  \ifthenelse{\equal{\ALG@within}{nothing}}
+     {\newfloat{algorithm}{htbp}{loa}}{}
+}{
+  \newfloat{algorithm}{htbp}{loa}
+}
+\floatname{algorithm}{\ALG@name}
+
+\newcommand{\listofalgorithms}{\listof{algorithm}{\listalgorithmname}}
+
diff --git a/report/algorithmic.sty b/report/algorithmic.sty
new file mode 100644
index 0000000..e2502a6
--- /dev/null
+++ b/report/algorithmic.sty
@@ -0,0 +1,201 @@
+% ALGORITHMIC STYLE -- Released 8 APRIL 1996
+%    for LaTeX version 2e
+% Copyright -- 1994 Peter Williams
+% E-mail PeterWilliams@dsto.defence.gov.au
+%
+% Modified by Alex Smola (08/2000)
+% E-mail Alex.Smola@anu.edu.au
+%
+\NeedsTeXFormat{LaTeX2e}
+\ProvidesPackage{algorithmic}
+\typeout{Document Style `algorithmic' - environment}
+%
+\RequirePackage{ifthen}
+\RequirePackage{calc}
+\newboolean{ALC@noend}
+\setboolean{ALC@noend}{false}
+\newcounter{ALC@line}
+\newcounter{ALC@rem}
+\newlength{\ALC@tlm}
+%
+\DeclareOption{noend}{\setboolean{ALC@noend}{true}}
+%
+\ProcessOptions
+%
+% ALGORITHMIC
+\newcommand{\algorithmicrequire}{\textbf{Require:}}
+\newcommand{\algorithmicensure}{\textbf{Ensure:}}
+\newcommand{\algorithmiccomment}[1]{\{#1\}}
+\newcommand{\algorithmicend}{\textbf{end}}
+\newcommand{\algorithmicif}{\textbf{if}}
+\newcommand{\algorithmicthen}{\textbf{then}}
+\newcommand{\algorithmicelse}{\textbf{else}}
+\newcommand{\algorithmicelsif}{\algorithmicelse\ \algorithmicif}
+\newcommand{\algorithmicendif}{\algorithmicend\ \algorithmicif}
+\newcommand{\algorithmicfor}{\textbf{for}}
+\newcommand{\algorithmicforall}{\textbf{for all}}
+\newcommand{\algorithmicdo}{\textbf{do}}
+\newcommand{\algorithmicendfor}{\algorithmicend\ \algorithmicfor}
+\newcommand{\algorithmicwhile}{\textbf{while}}
+\newcommand{\algorithmicendwhile}{\algorithmicend\ \algorithmicwhile}
+\newcommand{\algorithmicloop}{\textbf{loop}}
+\newcommand{\algorithmicendloop}{\algorithmicend\ \algorithmicloop}
+\newcommand{\algorithmicrepeat}{\textbf{repeat}}
+\newcommand{\algorithmicuntil}{\textbf{until}}
+
+%changed by alex smola
+\newcommand{\algorithmicinput}{\textbf{input}}
+\newcommand{\algorithmicoutput}{\textbf{output}}
+\newcommand{\algorithmicset}{\textbf{set}}
+\newcommand{\algorithmictrue}{\textbf{true}}
+\newcommand{\algorithmicfalse}{\textbf{false}}
+\newcommand{\algorithmicand}{\textbf{and\ }}
+\newcommand{\algorithmicor}{\textbf{or\ }}
+\newcommand{\algorithmicfunction}{\textbf{function}}
+\newcommand{\algorithmicendfunction}{\algorithmicend\ \algorithmicfunction}
+\newcommand{\algorithmicmain}{\textbf{main}}
+\newcommand{\algorithmicendmain}{\algorithmicend\ \algorithmicmain}
+%end changed by alex smola
+
+\def\ALC@item[#1]{%
+\if@noparitem \@donoparitem
+  \else \if@inlabel \indent \par \fi
+         \ifhmode \unskip\unskip \par \fi
+         \if@newlist \if@nobreak \@nbitem \else
+                        \addpenalty\@beginparpenalty
+                        \addvspace\@topsep \addvspace{-\parskip}\fi
+           \else \addpenalty\@itempenalty \addvspace\itemsep
+          \fi
+    \global\@inlabeltrue
+\fi
+\everypar{\global\@minipagefalse\global\@newlistfalse
+          \if@inlabel\global\@inlabelfalse \hskip -\parindent \box\@labels
+             \penalty\z@ \fi
+          \everypar{}}\global\@nobreakfalse
+\if@noitemarg \@noitemargfalse \if@nmbrlist \refstepcounter{\@listctr}\fi \fi
+\sbox\@tempboxa{\makelabel{#1}}%
+\global\setbox\@labels
+ \hbox{\unhbox\@labels \hskip \itemindent
+       \hskip -\labelwidth \hskip -\ALC@tlm
+       \ifdim \wd\@tempboxa >\labelwidth
+                \box\@tempboxa
+          \else \hbox to\labelwidth {\unhbox\@tempboxa}\fi
+       \hskip \ALC@tlm}\ignorespaces}
+%
+\newenvironment{algorithmic}[1][0]{
+\let\@item\ALC@item
+  \newcommand{\ALC@lno}{%
+\ifthenelse{\equal{\arabic{ALC@rem}}{0}}
+{{\footnotesize \arabic{ALC@line}:}}{}%
+}
+\let\@listii\@listi
+\let\@listiii\@listi
+\let\@listiv\@listi
+\let\@listv\@listi
+\let\@listvi\@listi
+\let\@listvii\@listi
+  \newenvironment{ALC@g}{
+    \begin{list}{\ALC@lno}{ \itemsep\z@ \itemindent\z@
+    \listparindent\z@ \rightmargin\z@ 
+    \topsep\z@ \partopsep\z@ \parskip\z@\parsep\z@
+    \leftmargin 1em
+    \addtolength{\ALC@tlm}{\leftmargin}
+    }
+  }
+  {\end{list}}
+  \newcommand{\ALC@it}{\addtocounter{ALC@line}{1}\addtocounter{ALC@rem}{1}\ifthenelse{\equal{\arabic{ALC@rem}}{#1}}{\setcounter{ALC@rem}{0}}{}\item}
+  \newcommand{\ALC@com}[1]{\ifthenelse{\equal{##1}{default}}%
+{}{\ \algorithmiccomment{##1}}}
+  \newcommand{\REQUIRE}{\item[\algorithmicrequire]}
+  \newcommand{\ENSURE}{\item[\algorithmicensure]}
+  \newcommand{\STATE}{\ALC@it}
+  \newcommand{\COMMENT}[1]{\algorithmiccomment{##1}}
+%changes by alex smola
+  \newcommand{\INPUT}{\item[\algorithmicinput]}
+  \newcommand{\OUTPUT}{\item[\algorithmicoutput]}
+  \newcommand{\SET}{\item[\algorithmicset]}
+%  \newcommand{\TRUE}{\algorithmictrue}
+%  \newcommand{\FALSE}{\algorithmicfalse}
+  \newcommand{\AND}{\algorithmicand}
+  \newcommand{\OR}{\algorithmicor}
+  \newenvironment{ALC@func}{\begin{ALC@g}}{\end{ALC@g}}
+  \newenvironment{ALC@main}{\begin{ALC@g}}{\end{ALC@g}}
+%end changes by alex smola
+  \newenvironment{ALC@if}{\begin{ALC@g}}{\end{ALC@g}}
+  \newenvironment{ALC@for}{\begin{ALC@g}}{\end{ALC@g}}
+  \newenvironment{ALC@whl}{\begin{ALC@g}}{\end{ALC@g}}
+  \newenvironment{ALC@loop}{\begin{ALC@g}}{\end{ALC@g}}
+  \newenvironment{ALC@rpt}{\begin{ALC@g}}{\end{ALC@g}}
+  \renewcommand{\\}{\@centercr}
+  \newcommand{\IF}[2][default]{\ALC@it\algorithmicif\ ##2\ \algorithmicthen%
+\ALC@com{##1}\begin{ALC@if}}
+  \newcommand{\SHORTIF}[2]{\ALC@it\algorithmicif\ ##1\
+    \algorithmicthen\ {##2}}
+  \newcommand{\ELSE}[1][default]{\end{ALC@if}\ALC@it\algorithmicelse%
+\ALC@com{##1}\begin{ALC@if}}
+  \newcommand{\ELSIF}[2][default]%
+{\end{ALC@if}\ALC@it\algorithmicelsif\ ##2\ \algorithmicthen%
+\ALC@com{##1}\begin{ALC@if}}
+  \newcommand{\FOR}[2][default]{\ALC@it\algorithmicfor\ ##2\ \algorithmicdo%
+\ALC@com{##1}\begin{ALC@for}}
+  \newcommand{\FORALL}[2][default]{\ALC@it\algorithmicforall\ ##2\ %
+\algorithmicdo%
+\ALC@com{##1}\begin{ALC@for}}
+  \newcommand{\SHORTFORALL}[2]{\ALC@it\algorithmicforall\ ##1\ %
+    \algorithmicdo\ {##2}}
+  \newcommand{\WHILE}[2][default]{\ALC@it\algorithmicwhile\ ##2\ %
+\algorithmicdo%
+\ALC@com{##1}\begin{ALC@whl}}
+  \newcommand{\LOOP}[1][default]{\ALC@it\algorithmicloop%
+\ALC@com{##1}\begin{ALC@loop}}
+%changed by alex smola
+  \newcommand{\FUNCTION}[2][default]{\ALC@it\algorithmicfunction\ ##2\ %
+    \ALC@com{##1}\begin{ALC@func}}
+  \newcommand{\MAIN}[2][default]{\ALC@it\algorithmicmain\ ##2\ %
+    \ALC@com{##1}\begin{ALC@main}}
+%end changed by alex smola
+  \newcommand{\REPEAT}[1][default]{\ALC@it\algorithmicrepeat%
+    \ALC@com{##1}\begin{ALC@rpt}}
+    \newcommand{\UNTIL}[1]{\end{ALC@rpt}\ALC@it\algorithmicuntil\ ##1}
+  \ifthenelse{\boolean{ALC@noend}}{
+    \newcommand{\ENDIF}{\end{ALC@if}}
+    \newcommand{\ENDFOR}{\end{ALC@for}}
+    \newcommand{\ENDWHILE}{\end{ALC@whl}}
+    \newcommand{\ENDLOOP}{\end{ALC@loop}}
+    \newcommand{\ENDFUNCTION}{\end{ALC@func}}
+    \newcommand{\ENDMAIN}{\end{ALC@main}}
+  }{
+    \newcommand{\ENDIF}{\end{ALC@if}\ALC@it\algorithmicendif}
+    \newcommand{\ENDFOR}{\end{ALC@for}\ALC@it\algorithmicendfor}
+    \newcommand{\ENDWHILE}{\end{ALC@whl}\ALC@it\algorithmicendwhile}
+    \newcommand{\ENDLOOP}{\end{ALC@loop}\ALC@it\algorithmicendloop}
+    \newcommand{\ENDFUNCTION}{\end{ALC@func}\ALC@it\algorithmicendfunction}
+    \newcommand{\ENDMAIN}{\end{ALC@main}\ALC@it\algorithmicendmain}
+  } 
+  \renewcommand{\@toodeep}{}
+  \begin{list}{\ALC@lno}{\setcounter{ALC@line}{0}\setcounter{ALC@rem}{0}%
+      \itemsep\z@ \itemindent\z@ \listparindent\z@%
+      \partopsep\z@ \parskip\z@ \parsep\z@%
+      \labelsep 0.5em \topsep 0.2em%
+      \ifthenelse{\equal{#1}{0}}
+      {\labelwidth 0.5em }
+      {\labelwidth  1.2em }
+      \leftmargin\labelwidth \addtolength{\leftmargin}{\labelsep}
+      \ALC@tlm\labelsep
+      }
+    }
+  {\end{list}}
+
+
+
+
+
+
+
+
+
+
+
+
+
+
diff --git a/report/example-refs.bib b/report/example-refs.bib
new file mode 100644
index 0000000..a9565fe
--- /dev/null
+++ b/report/example-refs.bib
@@ -0,0 +1,75 @@
+@inproceedings{langley00,
+ author    = {P. Langley},
+ title     = {Crafting Papers on Machine Learning},
+ year      = {2000},
+ pages     = {1207--1216},
+ editor    = {Pat Langley},
+ booktitle     = {Proceedings of the 17th International Conference
+              on Machine Learning (ICML 2000)},
+ address   = {Stanford, CA},
+ publisher = {Morgan Kaufmann}
+}
+
+@TechReport{mitchell80,
+  author = 	 "T. M. Mitchell",
+  title = 	 "The Need for Biases in Learning Generalizations",
+  institution =  "Computer Science Department, Rutgers University",
+  year = 	 "1980",
+  address =	 "New Brunswick, MA",
+}
+
+@phdthesis{kearns89,
+  author = {M. J. Kearns},
+  title =  {Computational Complexity of Machine Learning},
+  school = {Department of Computer Science, Harvard University},
+  year =   {1989}
+}
+
+@Book{MachineLearningI,
+  editor = 	 "R. S. Michalski and J. G. Carbonell and T.
+		  M. Mitchell",
+  title = 	 "Machine Learning: An Artificial Intelligence
+		  Approach, Vol. I",
+  publisher = 	 "Tioga",
+  year = 	 "1983",
+  address =	 "Palo Alto, CA"
+}
+
+@Book{DudaHart2nd,
+  author =       "R. O. Duda and P. E. Hart and D. G. Stork",
+  title =        "Pattern Classification",
+  publisher =    "John Wiley and Sons",
+  edition =      "2nd",
+  year =         "2000"
+}
+
+@misc{anonymous,
+  title= {Suppressed for Anonymity},
+  author= {Author, N. N.},
+  year= {2011},
+}
+
+@InCollection{Newell81,
+  author =       "A. Newell and P. S. Rosenbloom",
+  title =        "Mechanisms of Skill Acquisition and the Law of
+                  Practice", 
+  booktitle =    "Cognitive Skills and Their Acquisition",
+  pages =        "1--51",
+  publisher =    "Lawrence Erlbaum Associates, Inc.",
+  year =         "1981",
+  editor =       "J. R. Anderson",
+  chapter =      "1",
+  address =      "Hillsdale, NJ"
+}
+
+
+@Article{Samuel59,
+  author = 	 "A. L. Samuel",
+  title = 	 "Some Studies in Machine Learning Using the Game of
+		  Checkers",
+  journal =	 "IBM Journal of Research and Development",
+  year =	 "1959",
+  volume =	 "3",
+  number =	 "3",
+  pages =	 "211--229"
+}
\ No newline at end of file
diff --git a/report/fancyhdr.sty b/report/fancyhdr.sty
new file mode 100644
index 0000000..5a4d897
--- /dev/null
+++ b/report/fancyhdr.sty
@@ -0,0 +1,485 @@
+% fancyhdr.sty version 3.2
+% Fancy headers and footers for LaTeX.
+% Piet van Oostrum, 
+% Dept of Computer and Information Sciences, University of Utrecht,
+% Padualaan 14, P.O. Box 80.089, 3508 TB Utrecht, The Netherlands
+% Telephone: +31 30 2532180. Email: piet@cs.uu.nl
+% ========================================================================
+% LICENCE:
+% This file may be distributed under the terms of the LaTeX Project Public
+% License, as described in lppl.txt in the base LaTeX distribution.
+% Either version 1 or, at your option, any later version.
+% ========================================================================
+% MODIFICATION HISTORY:
+% Sep 16, 1994
+% version 1.4: Correction for use with \reversemargin
+% Sep 29, 1994:
+% version 1.5: Added the \iftopfloat, \ifbotfloat and \iffloatpage commands
+% Oct 4, 1994:
+% version 1.6: Reset single spacing in headers/footers for use with
+% setspace.sty or doublespace.sty
+% Oct 4, 1994:
+% version 1.7: changed \let\@mkboth\markboth to
+% \def\@mkboth{\protect\markboth} to make it more robust
+% Dec 5, 1994:
+% version 1.8: corrections for amsbook/amsart: define \@chapapp and (more
+% importantly) use the \chapter/sectionmark definitions from ps@headings if
+% they exist (which should be true for all standard classes).
+% May 31, 1995:
+% version 1.9: The proposed \renewcommand{\headrulewidth}{\iffloatpage...
+% construction in the doc did not work properly with the fancyplain style. 
+% June 1, 1995:
+% version 1.91: The definition of \@mkboth wasn't restored on subsequent
+% \pagestyle{fancy}'s.
+% June 1, 1995:
+% version 1.92: The sequence \pagestyle{fancyplain} \pagestyle{plain}
+% \pagestyle{fancy} would erroneously select the plain version.
+% June 1, 1995:
+% version 1.93: \fancypagestyle command added.
+% Dec 11, 1995:
+% version 1.94: suggested by Conrad Hughes <chughes@maths.tcd.ie>
+% CJCH, Dec 11, 1995: added \footruleskip to allow control over footrule
+% position (old hardcoded value of .3\normalbaselineskip is far too high
+% when used with very small footer fonts).
+% Jan 31, 1996:
+% version 1.95: call \@normalsize in the reset code if that is defined,
+% otherwise \normalsize.
+% this is to solve a problem with ucthesis.cls, as this doesn't
+% define \@currsize. Unfortunately for latex209 calling \normalsize doesn't
+% work as this is optimized to do very little, so there \@normalsize should
+% be called. Hopefully this code works for all versions of LaTeX known to
+% mankind.  
+% April 25, 1996:
+% version 1.96: initialize \headwidth to a magic (negative) value to catch
+% most common cases that people change it before calling \pagestyle{fancy}.
+% Note it can't be initialized when reading in this file, because
+% \textwidth could be changed afterwards. This is quite probable.
+% We also switch to \MakeUppercase rather than \uppercase and introduce a
+% \nouppercase command for use in headers. and footers.
+% May 3, 1996:
+% version 1.97: Two changes:
+% 1. Undo the change in version 1.8 (using the pagestyle{headings} defaults
+% for the chapter and section marks. The current version of amsbook and
+% amsart classes don't seem to need them anymore. Moreover the standard
+% latex classes don't use \markboth if twoside isn't selected, and this is
+% confusing as \leftmark doesn't work as expected.
+% 2. include a call to \ps@empty in ps@@fancy. This is to solve a problem
+% in the amsbook and amsart classes, that make global changes to \topskip,
+% which are reset in \ps@empty. Hopefully this doesn't break other things.
+% May 7, 1996:
+% version 1.98:
+% Added % after the line  \def\nouppercase
+% May 7, 1996:
+% version 1.99: This is the alpha version of fancyhdr 2.0
+% Introduced the new commands \fancyhead, \fancyfoot, and \fancyhf.
+% Changed \headrulewidth, \footrulewidth, \footruleskip to
+% macros rather than length parameters, In this way they can be
+% conditionalized and they don't consume length registers. There is no need
+% to have them as length registers unless you want to do calculations with
+% them, which is unlikely. Note that this may make some uses of them
+% incompatible (i.e. if you have a file that uses \setlength or \xxxx=)
+% May 10, 1996:
+% version 1.99a:
+% Added a few more % signs
+% May 10, 1996:
+% version 1.99b:
+% Changed the syntax of \f@nfor to be resistent to catcode changes of :=
+% Removed the [1] from the defs of \lhead etc. because the parameter is
+% consumed by the \@[xy]lhead etc. macros.
+% June 24, 1997:
+% version 1.99c:
+% corrected \nouppercase to also include the protected form of \MakeUppercase
+% \global added to manipulation of \headwidth.
+% \iffootnote command added.
+% Some comments added about \@fancyhead and \@fancyfoot.
+% Aug 24, 1998
+% version 1.99d
+% Changed the default \ps@empty to \ps@@empty in order to allow
+% \fancypagestyle{empty} redefinition.
+% Oct 11, 2000
+% version 2.0
+% Added LPPL license clause.
+%
+% A check for \headheight is added. An errormessage is given (once) if the
+% header is too large. Empty headers don't generate the error even if
+% \headheight is very small or even 0pt. 
+% Warning added for the use of 'E' option when twoside option is not used.
+% In this case the 'E' fields will never be used.
+%
+% Mar 10, 2002
+% version 2.1beta
+% New command: \fancyhfoffset[place]{length}
+% defines offsets to be applied to the header/footer to let it stick into
+% the margins (if length > 0).
+% place is like in fancyhead, except that only E,O,L,R can be used.
+% This replaces the old calculation based on \headwidth and the marginpar
+% area.
+% \headwidth will be dynamically calculated in the headers/footers when
+% this is used.
+%
+% Mar 26, 2002
+% version 2.1beta2
+% \fancyhfoffset now also takes h,f as possible letters in the argument to
+% allow the header and footer widths to be different.
+% New commands \fancyheadoffset and \fancyfootoffset added comparable to
+% \fancyhead and \fancyfoot.
+% Errormessages and warnings have been made more informative.
+%
+% Dec 9, 2002
+% version 2.1
+% The defaults for \footrulewidth, \plainheadrulewidth and
+% \plainfootrulewidth are changed from \z@skip to 0pt. In this way when
+% someone inadvertantly uses \setlength to change any of these, the value
+% of \z@skip will not be changed, rather an errormessage will be given.
+
+% March 3, 2004
+% Release of version 3.0
+
+% Oct 7, 2004
+% version 3.1
+% Added '\endlinechar=13' to \fancy@reset to prevent problems with
+% includegraphics in header when verbatiminput is active.
+
+% March 22, 2005
+% version 3.2
+% reset \everypar (the real one) in \fancy@reset because spanish.ldf does
+% strange things with \everypar between << and >>.
+
+\def\ifancy@mpty#1{\def\temp@a{#1}\ifx\temp@a\@empty}
+
+\def\fancy@def#1#2{\ifancy@mpty{#2}\fancy@gbl\def#1{\leavevmode}\else
+                                   \fancy@gbl\def#1{#2\strut}\fi}
+
+\let\fancy@gbl\global
+
+\def\@fancyerrmsg#1{%
+        \ifx\PackageError\undefined
+        \errmessage{#1}\else
+        \PackageError{Fancyhdr}{#1}{}\fi}
+\def\@fancywarning#1{%
+        \ifx\PackageWarning\undefined
+        \errmessage{#1}\else
+        \PackageWarning{Fancyhdr}{#1}{}\fi}
+
+% Usage: \@forc \var{charstring}{command to be executed for each char}
+% This is similar to LaTeX's \@tfor, but expands the charstring.
+
+\def\@forc#1#2#3{\expandafter\f@rc\expandafter#1\expandafter{#2}{#3}}
+\def\f@rc#1#2#3{\def\temp@ty{#2}\ifx\@empty\temp@ty\else
+                                    \f@@rc#1#2\f@@rc{#3}\fi}
+\def\f@@rc#1#2#3\f@@rc#4{\def#1{#2}#4\f@rc#1{#3}{#4}}
+
+% Usage: \f@nfor\name:=list\do{body}
+% Like LaTeX's \@for but an empty list is treated as a list with an empty
+% element
+
+\newcommand{\f@nfor}[3]{\edef\@fortmp{#2}%
+    \expandafter\@forloop#2,\@nil,\@nil\@@#1{#3}}
+
+% Usage: \def@ult \cs{defaults}{argument}
+% sets \cs to the characters from defaults appearing in argument
+% or defaults if it would be empty. All characters are lowercased.
+
+\newcommand\def@ult[3]{%
+    \edef\temp@a{\lowercase{\edef\noexpand\temp@a{#3}}}\temp@a
+    \def#1{}%
+    \@forc\tmpf@ra{#2}%
+        {\expandafter\if@in\tmpf@ra\temp@a{\edef#1{#1\tmpf@ra}}{}}%
+    \ifx\@empty#1\def#1{#2}\fi}
+% 
+% \if@in <char><set><truecase><falsecase>
+%
+\newcommand{\if@in}[4]{%
+    \edef\temp@a{#2}\def\temp@b##1#1##2\temp@b{\def\temp@b{##1}}%
+    \expandafter\temp@b#2#1\temp@b\ifx\temp@a\temp@b #4\else #3\fi}
+
+\newcommand{\fancyhead}{\@ifnextchar[{\f@ncyhf\fancyhead h}%
+                                     {\f@ncyhf\fancyhead h[]}}
+\newcommand{\fancyfoot}{\@ifnextchar[{\f@ncyhf\fancyfoot f}%
+                                     {\f@ncyhf\fancyfoot f[]}}
+\newcommand{\fancyhf}{\@ifnextchar[{\f@ncyhf\fancyhf{}}%
+                                   {\f@ncyhf\fancyhf{}[]}}
+
+% New commands for offsets added
+
+\newcommand{\fancyheadoffset}{\@ifnextchar[{\f@ncyhfoffs\fancyheadoffset h}%
+                                           {\f@ncyhfoffs\fancyheadoffset h[]}}
+\newcommand{\fancyfootoffset}{\@ifnextchar[{\f@ncyhfoffs\fancyfootoffset f}%
+                                           {\f@ncyhfoffs\fancyfootoffset f[]}}
+\newcommand{\fancyhfoffset}{\@ifnextchar[{\f@ncyhfoffs\fancyhfoffset{}}%
+                                         {\f@ncyhfoffs\fancyhfoffset{}[]}}
+
+% The header and footer fields are stored in command sequences with
+% names of the form: \f@ncy<x><y><z> with <x> for [eo], <y> from [lcr]
+% and <z> from [hf].
+
+\def\f@ncyhf#1#2[#3]#4{%
+    \def\temp@c{}%
+    \@forc\tmpf@ra{#3}%
+        {\expandafter\if@in\tmpf@ra{eolcrhf,EOLCRHF}%
+            {}{\edef\temp@c{\temp@c\tmpf@ra}}}%
+    \ifx\@empty\temp@c\else
+        \@fancyerrmsg{Illegal char `\temp@c' in \string#1 argument:
+          [#3]}%
+    \fi
+    \f@nfor\temp@c{#3}%
+        {\def@ult\f@@@eo{eo}\temp@c
+         \if@twoside\else
+           \if\f@@@eo e\@fancywarning
+             {\string#1's `E' option without twoside option is useless}\fi\fi
+         \def@ult\f@@@lcr{lcr}\temp@c
+         \def@ult\f@@@hf{hf}{#2\temp@c}%
+         \@forc\f@@eo\f@@@eo
+             {\@forc\f@@lcr\f@@@lcr
+                 {\@forc\f@@hf\f@@@hf
+                     {\expandafter\fancy@def\csname
+                      f@ncy\f@@eo\f@@lcr\f@@hf\endcsname
+                      {#4}}}}}}
+
+\def\f@ncyhfoffs#1#2[#3]#4{%
+    \def\temp@c{}%
+    \@forc\tmpf@ra{#3}%
+        {\expandafter\if@in\tmpf@ra{eolrhf,EOLRHF}%
+            {}{\edef\temp@c{\temp@c\tmpf@ra}}}%
+    \ifx\@empty\temp@c\else
+        \@fancyerrmsg{Illegal char `\temp@c' in \string#1 argument:
+          [#3]}%
+    \fi
+    \f@nfor\temp@c{#3}%
+        {\def@ult\f@@@eo{eo}\temp@c
+         \if@twoside\else
+           \if\f@@@eo e\@fancywarning
+             {\string#1's `E' option without twoside option is useless}\fi\fi
+         \def@ult\f@@@lcr{lr}\temp@c
+         \def@ult\f@@@hf{hf}{#2\temp@c}%
+         \@forc\f@@eo\f@@@eo
+             {\@forc\f@@lcr\f@@@lcr
+                 {\@forc\f@@hf\f@@@hf
+                     {\expandafter\setlength\csname
+                      f@ncyO@\f@@eo\f@@lcr\f@@hf\endcsname
+                      {#4}}}}}%
+     \fancy@setoffs}
+
+% Fancyheadings version 1 commands. These are more or less deprecated,
+% but they continue to work.
+
+\newcommand{\lhead}{\@ifnextchar[{\@xlhead}{\@ylhead}}
+\def\@xlhead[#1]#2{\fancy@def\f@ncyelh{#1}\fancy@def\f@ncyolh{#2}}
+\def\@ylhead#1{\fancy@def\f@ncyelh{#1}\fancy@def\f@ncyolh{#1}}
+
+\newcommand{\chead}{\@ifnextchar[{\@xchead}{\@ychead}}
+\def\@xchead[#1]#2{\fancy@def\f@ncyech{#1}\fancy@def\f@ncyoch{#2}}
+\def\@ychead#1{\fancy@def\f@ncyech{#1}\fancy@def\f@ncyoch{#1}}
+
+\newcommand{\rhead}{\@ifnextchar[{\@xrhead}{\@yrhead}}
+\def\@xrhead[#1]#2{\fancy@def\f@ncyerh{#1}\fancy@def\f@ncyorh{#2}}
+\def\@yrhead#1{\fancy@def\f@ncyerh{#1}\fancy@def\f@ncyorh{#1}}
+
+\newcommand{\lfoot}{\@ifnextchar[{\@xlfoot}{\@ylfoot}}
+\def\@xlfoot[#1]#2{\fancy@def\f@ncyelf{#1}\fancy@def\f@ncyolf{#2}}
+\def\@ylfoot#1{\fancy@def\f@ncyelf{#1}\fancy@def\f@ncyolf{#1}}
+
+\newcommand{\cfoot}{\@ifnextchar[{\@xcfoot}{\@ycfoot}}
+\def\@xcfoot[#1]#2{\fancy@def\f@ncyecf{#1}\fancy@def\f@ncyocf{#2}}
+\def\@ycfoot#1{\fancy@def\f@ncyecf{#1}\fancy@def\f@ncyocf{#1}}
+
+\newcommand{\rfoot}{\@ifnextchar[{\@xrfoot}{\@yrfoot}}
+\def\@xrfoot[#1]#2{\fancy@def\f@ncyerf{#1}\fancy@def\f@ncyorf{#2}}
+\def\@yrfoot#1{\fancy@def\f@ncyerf{#1}\fancy@def\f@ncyorf{#1}}
+
+\newlength{\fancy@headwidth}
+\let\headwidth\fancy@headwidth
+\newlength{\f@ncyO@elh}
+\newlength{\f@ncyO@erh}
+\newlength{\f@ncyO@olh}
+\newlength{\f@ncyO@orh}
+\newlength{\f@ncyO@elf}
+\newlength{\f@ncyO@erf}
+\newlength{\f@ncyO@olf}
+\newlength{\f@ncyO@orf}
+\newcommand{\headrulewidth}{0.4pt}
+\newcommand{\footrulewidth}{0pt}
+\newcommand{\footruleskip}{.3\normalbaselineskip}
+
+% Fancyplain stuff shouldn't be used anymore (rather
+% \fancypagestyle{plain} should be used), but it must be present for
+% compatibility reasons.
+
+\newcommand{\plainheadrulewidth}{0pt}
+\newcommand{\plainfootrulewidth}{0pt}
+\newif\if@fancyplain \@fancyplainfalse
+\def\fancyplain#1#2{\if@fancyplain#1\else#2\fi}
+
+\headwidth=-123456789sp %magic constant
+
+% Command to reset various things in the headers:
+% a.o.  single spacing (taken from setspace.sty)
+% and the catcode of ^^M (so that epsf files in the header work if a
+% verbatim crosses a page boundary)
+% It also defines a \nouppercase command that disables \uppercase and
+% \Makeuppercase. It can only be used in the headers and footers.
+\let\fnch@everypar\everypar% save real \everypar because of spanish.ldf
+\def\fancy@reset{\fnch@everypar{}\restorecr\endlinechar=13
+ \def\baselinestretch{1}%
+ \def\nouppercase##1{{\let\uppercase\relax\let\MakeUppercase\relax
+     \expandafter\let\csname MakeUppercase \endcsname\relax##1}}%
+ \ifx\undefined\@newbaseline% NFSS not present; 2.09 or 2e
+   \ifx\@normalsize\undefined \normalsize % for ucthesis.cls
+   \else \@normalsize \fi
+ \else% NFSS (2.09) present
+  \@newbaseline%
+ \fi}
+
+% Initialization of the head and foot text.
+
+% The default values still contain \fancyplain for compatibility.
+\fancyhf{} % clear all
+% lefthead empty on ``plain'' pages, \rightmark on even, \leftmark on odd pages
+% evenhead empty on ``plain'' pages, \leftmark on even, \rightmark on odd pages
+\if@twoside
+  \fancyhead[el,or]{\fancyplain{}{\sl\rightmark}}
+  \fancyhead[er,ol]{\fancyplain{}{\sl\leftmark}}
+\else
+  \fancyhead[l]{\fancyplain{}{\sl\rightmark}}
+  \fancyhead[r]{\fancyplain{}{\sl\leftmark}}
+\fi
+\fancyfoot[c]{\rm\thepage} % page number
+
+% Use box 0 as a temp box and dimen 0 as temp dimen. 
+% This can be done, because this code will always
+% be used inside another box, and therefore the changes are local.
+
+\def\@fancyvbox#1#2{\setbox0\vbox{#2}\ifdim\ht0>#1\@fancywarning
+  {\string#1 is too small (\the#1): ^^J Make it at least \the\ht0.^^J
+    We now make it that large for the rest of the document.^^J
+    This may cause the page layout to be inconsistent, however\@gobble}%
+  \dimen0=#1\global\setlength{#1}{\ht0}\ht0=\dimen0\fi
+  \box0}
+
+% Put together a header or footer given the left, center and
+% right text, fillers at left and right and a rule.
+% The \lap commands put the text into an hbox of zero size,
+% so overlapping text does not generate an errormessage.
+% These macros have 5 parameters:
+% 1. LEFTSIDE BEARING % This determines at which side the header will stick
+%    out. When \fancyhfoffset is used this calculates \headwidth, otherwise
+%    it is \hss or \relax (after expansion).
+% 2. \f@ncyolh, \f@ncyelh, \f@ncyolf or \f@ncyelf. This is the left component.
+% 3. \f@ncyoch, \f@ncyech, \f@ncyocf or \f@ncyecf. This is the middle comp.
+% 4. \f@ncyorh, \f@ncyerh, \f@ncyorf or \f@ncyerf. This is the right component.
+% 5. RIGHTSIDE BEARING. This is always \relax or \hss (after expansion).
+
+\def\@fancyhead#1#2#3#4#5{#1\hbox to\headwidth{\fancy@reset
+  \@fancyvbox\headheight{\hbox
+    {\rlap{\parbox[b]{\headwidth}{\raggedright#2}}\hfill
+      \parbox[b]{\headwidth}{\centering#3}\hfill
+      \llap{\parbox[b]{\headwidth}{\raggedleft#4}}}\headrule}}#5}
+
+\def\@fancyfoot#1#2#3#4#5{#1\hbox to\headwidth{\fancy@reset
+    \@fancyvbox\footskip{\footrule
+      \hbox{\rlap{\parbox[t]{\headwidth}{\raggedright#2}}\hfill
+        \parbox[t]{\headwidth}{\centering#3}\hfill
+        \llap{\parbox[t]{\headwidth}{\raggedleft#4}}}}}#5}
+
+\def\headrule{{\if@fancyplain\let\headrulewidth\plainheadrulewidth\fi
+    \hrule\@height\headrulewidth\@width\headwidth \vskip-\headrulewidth}}
+
+\def\footrule{{\if@fancyplain\let\footrulewidth\plainfootrulewidth\fi
+    \vskip-\footruleskip\vskip-\footrulewidth
+    \hrule\@width\headwidth\@height\footrulewidth\vskip\footruleskip}}
+
+\def\ps@fancy{%
+\@ifundefined{@chapapp}{\let\@chapapp\chaptername}{}%for amsbook
+%
+% Define \MakeUppercase for old LaTeXen.
+% Note: we used \def rather than \let, so that \let\uppercase\relax (from
+% the version 1 documentation) will still work.
+%
+\@ifundefined{MakeUppercase}{\def\MakeUppercase{\uppercase}}{}%
+\@ifundefined{chapter}{\def\sectionmark##1{\markboth
+{\MakeUppercase{\ifnum \c@secnumdepth>\z@
+ \thesection\hskip 1em\relax \fi ##1}}{}}%
+\def\subsectionmark##1{\markright {\ifnum \c@secnumdepth >\@ne
+ \thesubsection\hskip 1em\relax \fi ##1}}}%
+{\def\chaptermark##1{\markboth {\MakeUppercase{\ifnum \c@secnumdepth>\m@ne
+ \@chapapp\ \thechapter. \ \fi ##1}}{}}%
+\def\sectionmark##1{\markright{\MakeUppercase{\ifnum \c@secnumdepth >\z@
+ \thesection. \ \fi ##1}}}}%
+%\csname ps@headings\endcsname % use \ps@headings defaults if they exist
+\ps@@fancy
+\gdef\ps@fancy{\@fancyplainfalse\ps@@fancy}%
+% Initialize \headwidth if the user didn't
+%
+\ifdim\headwidth<0sp
+%
+% This catches the case that \headwidth hasn't been initialized and the
+% case that the user added something to \headwidth in the expectation that
+% it was initialized to \textwidth. We compensate this now. This loses if
+% the user intended to multiply it by a factor. But that case is more
+% likely done by saying something like \headwidth=1.2\textwidth. 
+% The doc says you have to change \headwidth after the first call to
+% \pagestyle{fancy}. This code is just to catch the most common cases were
+% that requirement is violated.
+%
+    \global\advance\headwidth123456789sp\global\advance\headwidth\textwidth
+\fi}
+\def\ps@fancyplain{\ps@fancy \let\ps@plain\ps@plain@fancy}
+\def\ps@plain@fancy{\@fancyplaintrue\ps@@fancy}
+\let\ps@@empty\ps@empty
+\def\ps@@fancy{%
+\ps@@empty % This is for amsbook/amsart, which do strange things with \topskip
+\def\@mkboth{\protect\markboth}%
+\def\@oddhead{\@fancyhead\fancy@Oolh\f@ncyolh\f@ncyoch\f@ncyorh\fancy@Oorh}%
+\def\@oddfoot{\@fancyfoot\fancy@Oolf\f@ncyolf\f@ncyocf\f@ncyorf\fancy@Oorf}%
+\def\@evenhead{\@fancyhead\fancy@Oelh\f@ncyelh\f@ncyech\f@ncyerh\fancy@Oerh}%
+\def\@evenfoot{\@fancyfoot\fancy@Oelf\f@ncyelf\f@ncyecf\f@ncyerf\fancy@Oerf}%
+}
+% Default definitions for compatibility mode:
+% These cause the header/footer to take the defined \headwidth as width
+% And to shift in the direction of the marginpar area
+
+\def\fancy@Oolh{\if@reversemargin\hss\else\relax\fi}
+\def\fancy@Oorh{\if@reversemargin\relax\else\hss\fi}
+\let\fancy@Oelh\fancy@Oorh
+\let\fancy@Oerh\fancy@Oolh
+
+\let\fancy@Oolf\fancy@Oolh
+\let\fancy@Oorf\fancy@Oorh
+\let\fancy@Oelf\fancy@Oelh
+\let\fancy@Oerf\fancy@Oerh
+
+% New definitions for the use of \fancyhfoffset
+% These calculate the \headwidth from \textwidth and the specified offsets.
+
+\def\fancy@offsolh{\headwidth=\textwidth\advance\headwidth\f@ncyO@olh
+                   \advance\headwidth\f@ncyO@orh\hskip-\f@ncyO@olh}
+\def\fancy@offselh{\headwidth=\textwidth\advance\headwidth\f@ncyO@elh
+                   \advance\headwidth\f@ncyO@erh\hskip-\f@ncyO@elh}
+
+\def\fancy@offsolf{\headwidth=\textwidth\advance\headwidth\f@ncyO@olf
+                   \advance\headwidth\f@ncyO@orf\hskip-\f@ncyO@olf}
+\def\fancy@offself{\headwidth=\textwidth\advance\headwidth\f@ncyO@elf
+                   \advance\headwidth\f@ncyO@erf\hskip-\f@ncyO@elf}
+
+\def\fancy@setoffs{%
+% Just in case \let\headwidth\textwidth was used
+  \fancy@gbl\let\headwidth\fancy@headwidth
+  \fancy@gbl\let\fancy@Oolh\fancy@offsolh
+  \fancy@gbl\let\fancy@Oelh\fancy@offselh
+  \fancy@gbl\let\fancy@Oorh\hss
+  \fancy@gbl\let\fancy@Oerh\hss
+  \fancy@gbl\let\fancy@Oolf\fancy@offsolf
+  \fancy@gbl\let\fancy@Oelf\fancy@offself
+  \fancy@gbl\let\fancy@Oorf\hss
+  \fancy@gbl\let\fancy@Oerf\hss}
+
+\newif\iffootnote
+\let\latex@makecol\@makecol
+\def\@makecol{\ifvoid\footins\footnotetrue\else\footnotefalse\fi
+\let\topfloat\@toplist\let\botfloat\@botlist\latex@makecol}
+\def\iftopfloat#1#2{\ifx\topfloat\empty #2\else #1\fi}
+\def\ifbotfloat#1#2{\ifx\botfloat\empty #2\else #1\fi}
+\def\iffloatpage#1#2{\if@fcolmade #1\else #2\fi}
+
+\newcommand{\fancypagestyle}[2]{%
+  \@namedef{ps@#1}{\let\fancy@gbl\relax#2\relax\ps@fancy}}
diff --git a/report/icml2017.bst b/report/icml2017.bst
new file mode 100644
index 0000000..c147918
--- /dev/null
+++ b/report/icml2017.bst
@@ -0,0 +1,1441 @@
+%% File: `icml2017.bst'
+%% A modification of `plainnl.bst' for use with natbib package 
+%%
+%% Copyright 2010 Hal Daum\'e III
+%% Modified by J. Fürnkranz
+%% - Changed labels from (X and Y, 2000) to (X & Y, 2000)
+%% - Changed References to last name first and abbreviated first names.
+%%
+%% Copyright 1993-2007 Patrick W Daly
+%% Max-Planck-Institut f\"ur Sonnensystemforschung
+%% Max-Planck-Str. 2
+%% D-37191 Katlenburg-Lindau
+%% Germany
+%% E-mail: daly@mps.mpg.de
+%%
+%% This program can be redistributed and/or modified under the terms
+%% of the LaTeX Project Public License Distributed from CTAN
+%% archives in directory macros/latex/base/lppl.txt; either
+%% version 1 of the License, or any later version.
+%%
+ % Version and source file information:
+ % \ProvidesFile{icml2010.mbs}[2007/11/26 1.93 (PWD)]
+ %
+ % BibTeX `plainnat' family
+ %   version 0.99b for BibTeX versions 0.99a or later,
+ %   for LaTeX versions 2.09 and 2e.
+ %
+ % For use with the `natbib.sty' package; emulates the corresponding
+ %   member of the `plain' family, but with author-year citations.
+ %
+ % With version 6.0 of `natbib.sty', it may also be used for numerical
+ %   citations, while retaining the commands \citeauthor, \citefullauthor,
+ %   and \citeyear to print the corresponding information.
+ %
+ % For version 7.0 of `natbib.sty', the KEY field replaces missing
+ %   authors/editors, and the date is left blank in \bibitem.
+ %
+ % Includes field EID for the sequence/citation number of electronic journals
+ %  which is used instead of page numbers.
+ %
+ % Includes fields ISBN and ISSN.
+ %
+ % Includes field URL for Internet addresses.
+ %
+ % Includes field DOI for Digital Object Idenfifiers.
+ %
+ % Works best with the url.sty package of Donald Arseneau.
+ %
+ % Works with identical authors and year are further sorted by
+ %   citation key, to preserve any natural sequence.
+ %
+ENTRY
+  { address
+    author
+    booktitle
+    chapter
+    doi
+    eid
+    edition
+    editor
+    howpublished
+    institution
+    isbn
+    issn
+    journal
+    key
+    month
+    note
+    number
+    organization
+    pages
+    publisher
+    school
+    series
+    title
+    type
+    url
+    volume
+    year
+  }
+  {}
+  { label extra.label sort.label short.list }
+
+INTEGERS { output.state before.all mid.sentence after.sentence after.block }
+
+FUNCTION {init.state.consts}
+{ #0 'before.all :=
+  #1 'mid.sentence :=
+  #2 'after.sentence :=
+  #3 'after.block :=
+}
+
+STRINGS { s t }
+
+FUNCTION {output.nonnull}
+{ 's :=
+  output.state mid.sentence =
+    { ", " * write$ }
+    { output.state after.block =
+        { add.period$ write$
+          newline$
+          "\newblock " write$
+        }
+        { output.state before.all =
+            'write$
+            { add.period$ " " * write$ }
+          if$
+        }
+      if$
+      mid.sentence 'output.state :=
+    }
+  if$
+  s
+}
+
+FUNCTION {output}
+{ duplicate$ empty$
+    'pop$
+    'output.nonnull
+  if$
+}
+
+FUNCTION {output.check}
+{ 't :=
+  duplicate$ empty$
+    { pop$ "empty " t * " in " * cite$ * warning$ }
+    'output.nonnull
+  if$
+}
+
+FUNCTION {fin.entry}
+{ add.period$
+  write$
+  newline$
+}
+
+FUNCTION {new.block}
+{ output.state before.all =
+    'skip$
+    { after.block 'output.state := }
+  if$
+}
+
+FUNCTION {new.sentence}
+{ output.state after.block =
+    'skip$
+    { output.state before.all =
+        'skip$
+        { after.sentence 'output.state := }
+      if$
+    }
+  if$
+}
+
+FUNCTION {not}
+{   { #0 }
+    { #1 }
+  if$
+}
+
+FUNCTION {and}
+{   'skip$
+    { pop$ #0 }
+  if$
+}
+
+FUNCTION {or}
+{   { pop$ #1 }
+    'skip$
+  if$
+}
+
+FUNCTION {new.block.checka}
+{ empty$
+    'skip$
+    'new.block
+  if$
+}
+
+FUNCTION {new.block.checkb}
+{ empty$
+  swap$ empty$
+  and
+    'skip$
+    'new.block
+  if$
+}
+
+FUNCTION {new.sentence.checka}
+{ empty$
+    'skip$
+    'new.sentence
+  if$
+}
+
+FUNCTION {new.sentence.checkb}
+{ empty$
+  swap$ empty$
+  and
+    'skip$
+    'new.sentence
+  if$
+}
+
+FUNCTION {field.or.null}
+{ duplicate$ empty$
+    { pop$ "" }
+    'skip$
+  if$
+}
+
+FUNCTION {emphasize}
+{ duplicate$ empty$
+    { pop$ "" }
+    { "\emph{" swap$ * "}" * }
+  if$
+}
+
+INTEGERS { nameptr namesleft numnames }
+
+FUNCTION {format.names}
+{ 's :=
+  #1 'nameptr :=
+  s num.names$ 'numnames :=
+  numnames 'namesleft :=
+    { namesleft #0 > }
+    { s nameptr "{vv~}{ll}{, jj}{, ff}" format.name$ 't :=
+      nameptr #1 >
+        { namesleft #1 >
+            { ", " * t * }
+            { numnames #2 >
+                { "," * }
+                'skip$
+              if$
+              t "others" =
+                { " et~al." * }
+                { " and " * t * }
+              if$
+            }
+          if$
+        }
+        't
+      if$
+      nameptr #1 + 'nameptr :=
+      namesleft #1 - 'namesleft :=
+    }
+  while$
+}
+
+FUNCTION {format.key}
+{ empty$
+    { key field.or.null }
+    { "" }
+  if$
+}
+
+FUNCTION {format.authors}
+{ author empty$
+    { "" }
+    { author format.names }
+  if$
+}
+
+FUNCTION {format.editors}
+{ editor empty$
+    { "" }
+    { editor format.names
+      editor num.names$ #1 >
+        { " (eds.)" * }
+        { " (ed.)" * }
+      if$
+    }
+  if$
+}
+
+FUNCTION {format.isbn}
+{ isbn empty$
+    { "" }
+    { new.block "ISBN " isbn * }
+  if$
+}
+
+FUNCTION {format.issn}
+{ issn empty$
+    { "" }
+    { new.block "ISSN " issn * }
+  if$
+}
+
+FUNCTION {format.url}
+{ url empty$
+    { "" }
+    { new.block "URL \url{" url * "}" * }
+  if$
+}
+
+FUNCTION {format.doi}
+{ doi empty$
+    { "" }
+    { new.block "\doi{" doi * "}" * }
+  if$
+}
+
+FUNCTION {format.title}
+{ title empty$
+    { "" }
+    { title "t" change.case$ }
+  if$
+}
+
+FUNCTION {format.full.names}
+{'s :=
+  #1 'nameptr :=
+  s num.names$ 'numnames :=
+  numnames 'namesleft :=
+    { namesleft #0 > }
+    { s nameptr
+      "{vv~}{ll}" format.name$ 't :=
+      nameptr #1 >
+        {
+          namesleft #1 >
+            { ", " * t * }
+            {
+              numnames #2 >
+                { "," * }
+                'skip$
+              if$
+              t "others" =
+                { " et~al." * }
+                { " and " * t * }
+              if$
+            }
+          if$
+        }
+        't
+      if$
+      nameptr #1 + 'nameptr :=
+      namesleft #1 - 'namesleft :=
+    }
+  while$
+}
+
+FUNCTION {author.editor.full}
+{ author empty$
+    { editor empty$
+        { "" }
+        { editor format.full.names }
+      if$
+    }
+    { author format.full.names }
+  if$
+}
+
+FUNCTION {author.full}
+{ author empty$
+    { "" }
+    { author format.full.names }
+  if$
+}
+
+FUNCTION {editor.full}
+{ editor empty$
+    { "" }
+    { editor format.full.names }
+  if$
+}
+
+FUNCTION {make.full.names}
+{ type$ "book" =
+  type$ "inbook" =
+  or
+    'author.editor.full
+    { type$ "proceedings" =
+        'editor.full
+        'author.full
+      if$
+    }
+  if$
+}
+
+FUNCTION {output.bibitem}
+{ newline$
+  "\bibitem[" write$
+  label write$
+  ")" make.full.names duplicate$ short.list =
+     { pop$ }
+     { * }
+   if$
+  "]{" * write$
+  cite$ write$
+  "}" write$
+  newline$
+  ""
+  before.all 'output.state :=
+}
+
+FUNCTION {n.dashify}
+{ 't :=
+  ""
+    { t empty$ not }
+    { t #1 #1 substring$ "-" =
+        { t #1 #2 substring$ "--" = not
+            { "--" *
+              t #2 global.max$ substring$ 't :=
+            }
+            {   { t #1 #1 substring$ "-" = }
+                { "-" *
+                  t #2 global.max$ substring$ 't :=
+                }
+              while$
+            }
+          if$
+        }
+        { t #1 #1 substring$ *
+          t #2 global.max$ substring$ 't :=
+        }
+      if$
+    }
+  while$
+}
+
+FUNCTION {format.date}
+{ year duplicate$ empty$
+    { "empty year in " cite$ * warning$
+       pop$ "" }
+    'skip$
+  if$
+  month empty$
+    'skip$
+    { month
+      " " * swap$ *
+    }
+  if$
+  extra.label *
+}
+
+FUNCTION {format.btitle}
+{ title emphasize
+}
+
+FUNCTION {tie.or.space.connect}
+{ duplicate$ text.length$ #3 <
+    { "~" }
+    { " " }
+  if$
+  swap$ * *
+}
+
+FUNCTION {either.or.check}
+{ empty$
+    'pop$
+    { "can't use both " swap$ * " fields in " * cite$ * warning$ }
+  if$
+}
+
+FUNCTION {format.bvolume}
+{ volume empty$
+    { "" }
+    { "volume" volume tie.or.space.connect
+      series empty$
+        'skip$
+        { " of " * series emphasize * }
+      if$
+      "volume and number" number either.or.check
+    }
+  if$
+}
+
+FUNCTION {format.number.series}
+{ volume empty$
+    { number empty$
+        { series field.or.null }
+        { output.state mid.sentence =
+            { "number" }
+            { "Number" }
+          if$
+          number tie.or.space.connect
+          series empty$
+            { "there's a number but no series in " cite$ * warning$ }
+            { " in " * series * }
+          if$
+        }
+      if$
+    }
+    { "" }
+  if$
+}
+
+FUNCTION {format.edition}
+{ edition empty$
+    { "" }
+    { output.state mid.sentence =
+        { edition "l" change.case$ " edition" * }
+        { edition "t" change.case$ " edition" * }
+      if$
+    }
+  if$
+}
+
+INTEGERS { multiresult }
+
+FUNCTION {multi.page.check}
+{ 't :=
+  #0 'multiresult :=
+    { multiresult not
+      t empty$ not
+      and
+    }
+    { t #1 #1 substring$
+      duplicate$ "-" =
+      swap$ duplicate$ "," =
+      swap$ "+" =
+      or or
+        { #1 'multiresult := }
+        { t #2 global.max$ substring$ 't := }
+      if$
+    }
+  while$
+  multiresult
+}
+
+FUNCTION {format.pages}
+{ pages empty$
+    { "" }
+    { pages multi.page.check
+        { "pp.\ " pages n.dashify tie.or.space.connect }
+        { "pp.\ " pages tie.or.space.connect }
+      if$
+    }
+  if$
+}
+
+FUNCTION {format.eid}
+{ eid empty$
+    { "" }
+    { "art." eid tie.or.space.connect }
+  if$
+}
+
+FUNCTION {format.vol.num.pages}
+{ volume field.or.null
+  number empty$
+    'skip$
+    { "\penalty0 (" number * ")" * *
+      volume empty$
+        { "there's a number but no volume in " cite$ * warning$ }
+        'skip$
+      if$
+    }
+  if$
+  pages empty$
+    'skip$
+    { duplicate$ empty$
+        { pop$ format.pages }
+        { ":\penalty0 " * pages n.dashify * }
+      if$
+    }
+  if$
+}
+
+FUNCTION {format.vol.num.eid}
+{ volume field.or.null
+  number empty$
+    'skip$
+    { "\penalty0 (" number * ")" * *
+      volume empty$
+        { "there's a number but no volume in " cite$ * warning$ }
+        'skip$
+      if$
+    }
+  if$
+  eid empty$
+    'skip$
+    { duplicate$ empty$
+        { pop$ format.eid }
+        { ":\penalty0 " * eid * }
+      if$
+    }
+  if$
+}
+
+FUNCTION {format.chapter.pages}
+{ chapter empty$
+    'format.pages
+    { type empty$
+        { "chapter" }
+        { type "l" change.case$ }
+      if$
+      chapter tie.or.space.connect
+      pages empty$
+        'skip$
+        { ", " * format.pages * }
+      if$
+    }
+  if$
+}
+
+FUNCTION {format.in.ed.booktitle}
+{ booktitle empty$
+    { "" }
+    { editor empty$
+        { "In " booktitle emphasize * }
+        { "In " format.editors * ", " * booktitle emphasize * }
+      if$
+    }
+  if$
+}
+
+FUNCTION {empty.misc.check}
+{ author empty$ title empty$ howpublished empty$
+  month empty$ year empty$ note empty$
+  and and and and and
+  key empty$ not and
+    { "all relevant fields are empty in " cite$ * warning$ }
+    'skip$
+  if$
+}
+
+FUNCTION {format.thesis.type}
+{ type empty$
+    'skip$
+    { pop$
+      type "t" change.case$
+    }
+  if$
+}
+
+FUNCTION {format.tr.number}
+{ type empty$
+    { "Technical Report" }
+    'type
+  if$
+  number empty$
+    { "t" change.case$ }
+    { number tie.or.space.connect }
+  if$
+}
+
+FUNCTION {format.article.crossref}
+{ key empty$
+    { journal empty$
+        { "need key or journal for " cite$ * " to crossref " * crossref *
+          warning$
+          ""
+        }
+        { "In \emph{" journal * "}" * }
+      if$
+    }
+    { "In " }
+  if$
+  " \citet{" * crossref * "}" *
+}
+
+FUNCTION {format.book.crossref}
+{ volume empty$
+    { "empty volume in " cite$ * "'s crossref of " * crossref * warning$
+      "In "
+    }
+    { "Volume" volume tie.or.space.connect
+      " of " *
+    }
+  if$
+  editor empty$
+  editor field.or.null author field.or.null =
+  or
+    { key empty$
+        { series empty$
+            { "need editor, key, or series for " cite$ * " to crossref " *
+              crossref * warning$
+              "" *
+            }
+            { "\emph{" * series * "}" * }
+          if$
+        }
+        'skip$
+      if$
+    }
+    'skip$
+  if$
+  " \citet{" * crossref * "}" *
+}
+
+FUNCTION {format.incoll.inproc.crossref}
+{ editor empty$
+  editor field.or.null author field.or.null =
+  or
+    { key empty$
+        { booktitle empty$
+            { "need editor, key, or booktitle for " cite$ * " to crossref " *
+              crossref * warning$
+              ""
+            }
+            { "In \emph{" booktitle * "}" * }
+          if$
+        }
+        { "In " }
+      if$
+    }
+    { "In " }
+  if$
+  " \citet{" * crossref * "}" *
+}
+
+FUNCTION {article}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  crossref missing$
+    { journal emphasize "journal" output.check
+      eid empty$
+        { format.vol.num.pages output }
+        { format.vol.num.eid output }
+      if$
+      format.date "year" output.check
+    }
+    { format.article.crossref output.nonnull
+      eid empty$
+        { format.pages output }
+        { format.eid output }
+      if$
+    }
+  if$
+  format.issn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {book}
+{ output.bibitem
+  author empty$
+    { format.editors "author and editor" output.check
+      editor format.key output
+    }
+    { format.authors output.nonnull
+      crossref missing$
+        { "author and editor" editor either.or.check }
+        'skip$
+      if$
+    }
+  if$
+  new.block
+  format.btitle "title" output.check
+  crossref missing$
+    { format.bvolume output
+      new.block
+      format.number.series output
+      new.sentence
+      publisher "publisher" output.check
+      address output
+    }
+    { new.block
+      format.book.crossref output.nonnull
+    }
+  if$
+  format.edition output
+  format.date "year" output.check
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {booklet}
+{ output.bibitem
+  format.authors output
+  author format.key output
+  new.block
+  format.title "title" output.check
+  howpublished address new.block.checkb
+  howpublished output
+  address output
+  format.date output
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {inbook}
+{ output.bibitem
+  author empty$
+    { format.editors "author and editor" output.check
+      editor format.key output
+    }
+    { format.authors output.nonnull
+      crossref missing$
+        { "author and editor" editor either.or.check }
+        'skip$
+      if$
+    }
+  if$
+  new.block
+  format.btitle "title" output.check
+  crossref missing$
+    { format.bvolume output
+      format.chapter.pages "chapter and pages" output.check
+      new.block
+      format.number.series output
+      new.sentence
+      publisher "publisher" output.check
+      address output
+    }
+    { format.chapter.pages "chapter and pages" output.check
+      new.block
+      format.book.crossref output.nonnull
+    }
+  if$
+  format.edition output
+  format.date "year" output.check
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {incollection}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  crossref missing$
+    { format.in.ed.booktitle "booktitle" output.check
+      format.bvolume output
+      format.number.series output
+      format.chapter.pages output
+      new.sentence
+      publisher "publisher" output.check
+      address output
+      format.edition output
+      format.date "year" output.check
+    }
+    { format.incoll.inproc.crossref output.nonnull
+      format.chapter.pages output
+    }
+  if$
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {inproceedings}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  crossref missing$
+    { format.in.ed.booktitle "booktitle" output.check
+      format.bvolume output
+      format.number.series output
+      format.pages output
+      address empty$
+        { organization publisher new.sentence.checkb
+          organization output
+          publisher output
+          format.date "year" output.check
+        }
+        { address output.nonnull
+          format.date "year" output.check
+          new.sentence
+          organization output
+          publisher output
+        }
+      if$
+    }
+    { format.incoll.inproc.crossref output.nonnull
+      format.pages output
+    }
+  if$
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {conference} { inproceedings }
+
+FUNCTION {manual}
+{ output.bibitem
+  format.authors output
+  author format.key output
+  new.block
+  format.btitle "title" output.check
+  organization address new.block.checkb
+  organization output
+  address output
+  format.edition output
+  format.date output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {mastersthesis}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  "Master's thesis" format.thesis.type output.nonnull
+  school "school" output.check
+  address output
+  format.date "year" output.check
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {misc}
+{ output.bibitem
+  format.authors output
+  author format.key output
+  title howpublished new.block.checkb
+  format.title output
+  howpublished new.block.checka
+  howpublished output
+  format.date output
+  format.issn output
+  format.url output
+  new.block
+  note output
+  fin.entry
+  empty.misc.check
+}
+
+FUNCTION {phdthesis}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.btitle "title" output.check
+  new.block
+  "PhD thesis" format.thesis.type output.nonnull
+  school "school" output.check
+  address output
+  format.date "year" output.check
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {proceedings}
+{ output.bibitem
+  format.editors output
+  editor format.key output
+  new.block
+  format.btitle "title" output.check
+  format.bvolume output
+  format.number.series output
+  address output
+  format.date "year" output.check
+  new.sentence
+  organization output
+  publisher output
+  format.isbn output
+  format.doi output
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {techreport}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  format.tr.number output.nonnull
+  institution "institution" output.check
+  address output
+  format.date "year" output.check
+  format.url output
+  new.block
+  note output
+  fin.entry
+}
+
+FUNCTION {unpublished}
+{ output.bibitem
+  format.authors "author" output.check
+  author format.key output
+  new.block
+  format.title "title" output.check
+  new.block
+  note "note" output.check
+  format.date output
+  format.url output
+  fin.entry
+}
+
+FUNCTION {default.type} { misc }
+
+
+MACRO {jan} {"January"}
+
+MACRO {feb} {"February"}
+
+MACRO {mar} {"March"}
+
+MACRO {apr} {"April"}
+
+MACRO {may} {"May"}
+
+MACRO {jun} {"June"}
+
+MACRO {jul} {"July"}
+
+MACRO {aug} {"August"}
+
+MACRO {sep} {"September"}
+
+MACRO {oct} {"October"}
+
+MACRO {nov} {"November"}
+
+MACRO {dec} {"December"}
+
+
+
+MACRO {acmcs} {"ACM Computing Surveys"}
+
+MACRO {acta} {"Acta Informatica"}
+
+MACRO {cacm} {"Communications of the ACM"}
+
+MACRO {ibmjrd} {"IBM Journal of Research and Development"}
+
+MACRO {ibmsj} {"IBM Systems Journal"}
+
+MACRO {ieeese} {"IEEE Transactions on Software Engineering"}
+
+MACRO {ieeetc} {"IEEE Transactions on Computers"}
+
+MACRO {ieeetcad}
+ {"IEEE Transactions on Computer-Aided Design of Integrated Circuits"}
+
+MACRO {ipl} {"Information Processing Letters"}
+
+MACRO {jacm} {"Journal of the ACM"}
+
+MACRO {jcss} {"Journal of Computer and System Sciences"}
+
+MACRO {scp} {"Science of Computer Programming"}
+
+MACRO {sicomp} {"SIAM Journal on Computing"}
+
+MACRO {tocs} {"ACM Transactions on Computer Systems"}
+
+MACRO {tods} {"ACM Transactions on Database Systems"}
+
+MACRO {tog} {"ACM Transactions on Graphics"}
+
+MACRO {toms} {"ACM Transactions on Mathematical Software"}
+
+MACRO {toois} {"ACM Transactions on Office Information Systems"}
+
+MACRO {toplas} {"ACM Transactions on Programming Languages and Systems"}
+
+MACRO {tcs} {"Theoretical Computer Science"}
+
+
+READ
+
+FUNCTION {sortify}
+{ purify$
+  "l" change.case$
+}
+
+INTEGERS { len }
+
+FUNCTION {chop.word}
+{ 's :=
+  'len :=
+  s #1 len substring$ =
+    { s len #1 + global.max$ substring$ }
+    's
+  if$
+}
+
+FUNCTION {format.lab.names}
+{ 's :=
+  s #1 "{vv~}{ll}" format.name$
+  s num.names$ duplicate$
+  #2 >
+    { pop$ " et~al." * }
+    { #2 <
+        'skip$
+        { s #2 "{ff }{vv }{ll}{ jj}" format.name$ "others" =
+            { " et~al." * }
+            { " \& " * s #2 "{vv~}{ll}" format.name$ * }
+          if$
+        }
+      if$
+    }
+  if$
+}
+
+FUNCTION {author.key.label}
+{ author empty$
+    { key empty$
+        { cite$ #1 #3 substring$ }
+        'key
+      if$
+    }
+    { author format.lab.names }
+  if$
+}
+
+FUNCTION {author.editor.key.label}
+{ author empty$
+    { editor empty$
+        { key empty$
+            { cite$ #1 #3 substring$ }
+            'key
+          if$
+        }
+        { editor format.lab.names }
+      if$
+    }
+    { author format.lab.names }
+  if$
+}
+
+FUNCTION {author.key.organization.label}
+{ author empty$
+    { key empty$
+        { organization empty$
+            { cite$ #1 #3 substring$ }
+            { "The " #4 organization chop.word #3 text.prefix$ }
+          if$
+        }
+        'key
+      if$
+    }
+    { author format.lab.names }
+  if$
+}
+
+FUNCTION {editor.key.organization.label}
+{ editor empty$
+    { key empty$
+        { organization empty$
+            { cite$ #1 #3 substring$ }
+            { "The " #4 organization chop.word #3 text.prefix$ }
+          if$
+        }
+        'key
+      if$
+    }
+    { editor format.lab.names }
+  if$
+}
+
+FUNCTION {calc.short.authors}
+{ type$ "book" =
+  type$ "inbook" =
+  or
+    'author.editor.key.label
+    { type$ "proceedings" =
+        'editor.key.organization.label
+        { type$ "manual" =
+            'author.key.organization.label
+            'author.key.label
+          if$
+        }
+      if$
+    }
+  if$
+  'short.list :=
+}
+
+FUNCTION {calc.label}
+{ calc.short.authors
+  short.list
+  "("
+  *
+  year duplicate$ empty$
+  short.list key field.or.null = or
+     { pop$ "" }
+     'skip$
+  if$
+  *
+  'label :=
+}
+
+FUNCTION {sort.format.names}
+{ 's :=
+  #1 'nameptr :=
+  ""
+  s num.names$ 'numnames :=
+  numnames 'namesleft :=
+    { namesleft #0 > }
+    {
+      s nameptr "{vv{ } }{ll{ }}{  ff{ }}{  jj{ }}" format.name$ 't :=
+      nameptr #1 >
+        {
+          "   "  *
+          namesleft #1 = t "others" = and
+            { "zzzzz" * }
+            { numnames #2 > nameptr #2 = and
+                { "zz" * year field.or.null * "   " * }
+                'skip$
+              if$
+              t sortify *
+            }
+          if$
+        }
+        { t sortify * }
+      if$
+      nameptr #1 + 'nameptr :=
+      namesleft #1 - 'namesleft :=
+    }
+  while$
+}
+
+FUNCTION {sort.format.title}
+{ 't :=
+  "A " #2
+    "An " #3
+      "The " #4 t chop.word
+    chop.word
+  chop.word
+  sortify
+  #1 global.max$ substring$
+}
+
+FUNCTION {author.sort}
+{ author empty$
+    { key empty$
+        { "to sort, need author or key in " cite$ * warning$
+          ""
+        }
+        { key sortify }
+      if$
+    }
+    { author sort.format.names }
+  if$
+}
+
+FUNCTION {author.editor.sort}
+{ author empty$
+    { editor empty$
+        { key empty$
+            { "to sort, need author, editor, or key in " cite$ * warning$
+              ""
+            }
+            { key sortify }
+          if$
+        }
+        { editor sort.format.names }
+      if$
+    }
+    { author sort.format.names }
+  if$
+}
+
+FUNCTION {author.organization.sort}
+{ author empty$
+    { organization empty$
+        { key empty$
+            { "to sort, need author, organization, or key in " cite$ * warning$
+              ""
+            }
+            { key sortify }
+          if$
+        }
+        { "The " #4 organization chop.word sortify }
+      if$
+    }
+    { author sort.format.names }
+  if$
+}
+
+FUNCTION {editor.organization.sort}
+{ editor empty$
+    { organization empty$
+        { key empty$
+            { "to sort, need editor, organization, or key in " cite$ * warning$
+              ""
+            }
+            { key sortify }
+          if$
+        }
+        { "The " #4 organization chop.word sortify }
+      if$
+    }
+    { editor sort.format.names }
+  if$
+}
+
+
+FUNCTION {presort}
+{ calc.label
+  label sortify
+  "    "
+  *
+  type$ "book" =
+  type$ "inbook" =
+  or
+    'author.editor.sort
+    { type$ "proceedings" =
+        'editor.organization.sort
+        { type$ "manual" =
+            'author.organization.sort
+            'author.sort
+          if$
+        }
+      if$
+    }
+  if$
+  "    "
+  *
+  year field.or.null sortify
+  *
+  "    "
+  *
+  cite$
+  *
+  #1 entry.max$ substring$
+  'sort.label :=
+  sort.label *
+  #1 entry.max$ substring$
+  'sort.key$ :=
+}
+
+ITERATE {presort}
+
+SORT
+
+STRINGS { longest.label last.label next.extra }
+
+INTEGERS { longest.label.width last.extra.num number.label }
+
+FUNCTION {initialize.longest.label}
+{ "" 'longest.label :=
+  #0 int.to.chr$ 'last.label :=
+  "" 'next.extra :=
+  #0 'longest.label.width :=
+  #0 'last.extra.num :=
+  #0 'number.label :=
+}
+
+FUNCTION {forward.pass}
+{ last.label label =
+    { last.extra.num #1 + 'last.extra.num :=
+      last.extra.num int.to.chr$ 'extra.label :=
+    }
+    { "a" chr.to.int$ 'last.extra.num :=
+      "" 'extra.label :=
+      label 'last.label :=
+    }
+  if$
+  number.label #1 + 'number.label :=
+}
+
+FUNCTION {reverse.pass}
+{ next.extra "b" =
+    { "a" 'extra.label := }
+    'skip$
+  if$
+  extra.label 'next.extra :=
+  extra.label
+  duplicate$ empty$
+    'skip$
+    { "{\natexlab{" swap$ * "}}" * }
+  if$
+  'extra.label :=
+  label extra.label * 'label :=
+}
+
+EXECUTE {initialize.longest.label}
+
+ITERATE {forward.pass}
+
+REVERSE {reverse.pass}
+
+FUNCTION {bib.sort.order}
+{ sort.label  'sort.key$ :=
+}
+
+ITERATE {bib.sort.order}
+
+SORT
+
+FUNCTION {begin.bib}
+{   preamble$ empty$
+    'skip$
+    { preamble$ write$ newline$ }
+  if$
+  "\begin{thebibliography}{" number.label int.to.str$ * "}" *
+  write$ newline$
+  "\providecommand{\natexlab}[1]{#1}"
+  write$ newline$
+  "\providecommand{\url}[1]{\texttt{#1}}"
+  write$ newline$
+  "\expandafter\ifx\csname urlstyle\endcsname\relax"
+  write$ newline$
+  "  \providecommand{\doi}[1]{doi: #1}\else"
+  write$ newline$
+  "  \providecommand{\doi}{doi: \begingroup \urlstyle{rm}\Url}\fi"
+  write$ newline$
+}
+
+EXECUTE {begin.bib}
+
+EXECUTE {init.state.consts}
+
+ITERATE {call.type$}
+
+FUNCTION {end.bib}
+{ newline$
+  "\end{thebibliography}" write$ newline$
+}
+
+EXECUTE {end.bib}
diff --git a/report/icml_numpapers.pdf b/report/icml_numpapers.pdf
new file mode 100644
index 0000000..7d247e6
Binary files /dev/null and b/report/icml_numpapers.pdf differ
diff --git a/report/mlp-cw1-template.pdf b/report/mlp-cw1-template.pdf
new file mode 100644
index 0000000..720d2df
Binary files /dev/null and b/report/mlp-cw1-template.pdf differ
diff --git a/report/mlp-cw1-template.tex b/report/mlp-cw1-template.tex
new file mode 100644
index 0000000..22124a7
--- /dev/null
+++ b/report/mlp-cw1-template.tex
@@ -0,0 +1,207 @@
+%% Template for MLP Coursework 1 / 16 October 2017 
+
+%% Based on  LaTeX template for ICML 2017 - example_paper.tex at 
+%%  https://2017.icml.cc/Conferences/2017/StyleAuthorInstructions
+
+\documentclass{article}
+
+\usepackage[T1]{fontenc}
+\usepackage{amssymb,amsmath}
+\usepackage{txfonts}
+\usepackage{microtype}
+
+% For figures
+\usepackage{graphicx}
+\usepackage{subfigure} 
+
+% For citations
+\usepackage{natbib}
+
+% For algorithms
+\usepackage{algorithm}
+\usepackage{algorithmic}
+
+% the hyperref package is used to produce hyperlinks in the
+% resulting PDF.  If this breaks your system, please commend out the
+% following usepackage line and replace \usepackage{mlp2017} with
+% \usepackage[nohyperref]{mlp2017} below.
+\usepackage{hyperref}
+\usepackage{url}
+\urlstyle{same}
+
+% Packages hyperref and algorithmic misbehave sometimes.  We can fix
+% this with the following command.
+\newcommand{\theHalgorithm}{\arabic{algorithm}}
+
+
+% Set up MLP coursework style (based on ICML style)
+\usepackage{mlp2017}
+\mlptitlerunning{MLP Coursework 1 (\studentNumber)}
+\bibliographystyle{icml2017}
+
+
+\DeclareMathOperator{\softmax}{softmax}
+\DeclareMathOperator{\sigmoid}{sigmoid}
+\DeclareMathOperator{\sgn}{sgn}
+\DeclareMathOperator{\relu}{relu}
+\DeclareMathOperator{\lrelu}{lrelu}
+\DeclareMathOperator{\elu}{elu}
+\DeclareMathOperator{\selu}{selu}
+\DeclareMathOperator{\maxout}{maxout}
+
+%% You probably do not need to change anything above this comment
+
+%% REPLACE this with your student number
+\def\studentNumber{s1754321}
+
+\begin{document} 
+
+\twocolumn[
+\mlptitle{MLP Coursework 1: Activation Functions}
+
+\centerline{\studentNumber}
+
+\vskip 7mm
+]
+
+\begin{abstract} 
+The abstract should be 100--200 words long,  providing a concise summary of the contents of your report.
+\end{abstract} 
+
+\section{Introduction}
+\label{sec:intro}
+This document provides a template for the MLP coursework 1 report.  In particular, it structures the document into five sections  (plus an abstract and the references) -- you should keep to this structure for your report.  If you want to use subsections within a section that is fine, but please do not use any deeper structuring.  In this template the text in each section will include an outline of what you should include in each section, along with some practical LaTeX examples (for example figures, tables, algorithms).  Your document should be no longer than \textbf{six pages},  with an additional page allowed for references.
+
+The introduction should place your work in context, giving the overall motivation for the work, and clearly outlining the research questions you have explored -- in this case comparison of the behaviour of the different activation functions,  experimental investigation of the impact of the depth of the network with respect to accuracy, and experimental investigation of different approaches to weight initialisation.  This section should also include a concise description of the MNIST task and  data -- be precise: for example state the size of the training and validation sets.
+
+
+\section{Activation functions}
+\label{sec:actfn}
+This section should cover the theoretical methodology -- in this case you should present the four activation functions: ReLU, Leaky ReLU, ELU, and SELU.  I didn't do it in this document, but the first time you use an acronym you should say what it stands for, for example Restricted Linear Unit (ReLU).  You should use equations to concisely describe each activation function.  For example, ReLU: 
+\begin{equation}
+  \relu(x) = \max(0, x) ,
+\end{equation} 
+which has the gradient:
+\begin{equation}
+  \frac{d}{dx} \relu(x) =
+     \begin{cases} 
+      0      & \quad \text{if } x \leq  0 \\
+      1       & \quad \text{if } x > 0 .
+    \end{cases} 
+\end{equation}
+The \LaTeX for the derivatives is slightly more complicated.  We provided definitions near the top of the file (the part before \verb+\begin{document}+) for \verb+\relu+, \verb+\lrelu+, \verb+\elu+, and \verb+\selu+.  There is no need to discuss the unit tests for these activation functions in this report.
+
+It is probably not needed in this report, but if you would like to include an algorithm in your report, please use the \verb+algorithm+ and \verb+algorithmic+ environments to format pseudocode (for instance, Algorithm~\ref{alg:example}). These require the corresponding style files, \verb+algorithm.sty+ and \verb+algorithmic.sty+ which are supplied with this package. 
+
+\begin{algorithm}[ht]
+\begin{algorithmic}
+   \STATE {\bfseries Input:} data $x_i$, size $m$
+   \REPEAT
+   \STATE Initialize $noChange = true$.
+   \FOR{$i=1$ {\bfseries to} $m-1$}
+   \IF{$x_i > x_{i+1}$} 
+   \STATE Swap $x_i$ and $x_{i+1}$
+   \STATE $noChange = false$
+   \ENDIF
+   \ENDFOR
+   \UNTIL{$noChange$ is $true$}
+\end{algorithmic}
+  \caption{Bubble Sort}
+  \label{alg:example}
+\end{algorithm}
+
+\section{Experimental comparison of activation functions}
+\label{sec:actexpts}
+In this section you should present the results and discussion of your experiments comparing networks using the different activation functions on the MNIST task.  As explained in the coursework document, you should use 2 hidden layers with 100 hidden units per layer for these experiments.  You can compare the learning curves (error vs epoch) for training and/or validation, and the validation set accuracies. 
+
+Your experimental sections should include graphs (for instance, figure~\ref{fig:sample-graph}) and/or tables (for instance, table~\ref{tab:sample-table})\footnote{These examples were taken from the ICML template paper.}, using the \verb+figure+ and \verb+table+ environments, in which you use \verb+\includegraphics+ to include an image (pdf, png, or jpg formats).  Please export graphs as 
+\href{https://en.wikipedia.org/wiki/Vector_graphics}{vector graphics}
+rather than \href{https://en.wikipedia.org/wiki/Raster_graphics}{raster
+files} as this will make sure all detail in the plot is visible.
+Matplotlib supports saving high quality figures in a wide range of
+common image formats using the
+\href{http://matplotlib.org/api/pyplot_api.html\#matplotlib.pyplot.savefig}{\texttt{savefig}}
+function. \textbf{You should use \texttt{savefig} rather than copying
+the screen-resolution raster images outputted in the notebook.} An
+example of using \texttt{savefig} to save a figure as a PDF file (which
+can be included as graphics in a \LaTeX document is given in the coursework document.
+
+If you need a figure or table to stretch across two columns use the \verb+figure*+ or \verb+table*+ environment instead of the \verb+figure+ or \verb+table+ environment.  Use the \verb+subfigure+ environment if you want to include multiple graphics in a single figure.
+
+\begin{figure}[tb]
+\vskip 5mm
+\begin{center}
+\centerline{\includegraphics[width=\columnwidth]{icml_numpapers}}
+\caption{Historical locations and number of accepted papers for International
+  Machine Learning Conferences (ICML 1993 -- ICML 2008) and
+  International Workshops on Machine Learning (ML 1988 -- ML
+  1992). At the time this figure was produced, the number of
+  accepted papers for ICML 2008 was unknown and instead estimated.}
+\label{fig:sample-graph}
+\end{center}
+\vskip -5mm
+\end{figure} 
+
+\begin{table}[tb]
+\vskip 3mm
+\begin{center}
+\begin{small}
+\begin{sc}
+\begin{tabular}{lcccr}
+\hline
+\abovespace\belowspace
+Data set & Naive & Flexible & Better? \\
+\hline
+\abovespace
+Breast    & 95.9$\pm$ 0.2& 96.7$\pm$ 0.2& $\surd$ \\
+Cleveland & 83.3$\pm$ 0.6& 80.0$\pm$ 0.6& $\times$\\
+Glass2    & 61.9$\pm$ 1.4& 83.8$\pm$ 0.7& $\surd$ \\
+Credit    & 74.8$\pm$ 0.5& 78.3$\pm$ 0.6&         \\
+Horse     & 73.3$\pm$ 0.9& 69.7$\pm$ 1.0& $\times$\\
+Meta      & 67.1$\pm$ 0.6& 76.5$\pm$ 0.5& $\surd$ \\
+Pima      & 75.1$\pm$ 0.6& 73.9$\pm$ 0.5&         \\
+\belowspace
+Vehicle   & 44.9$\pm$ 0.6& 61.5$\pm$ 0.4& $\surd$ \\
+\hline
+\end{tabular}
+\end{sc}
+\end{small}
+\caption{Classification accuracies for naive Bayes and flexible 
+Bayes on various data sets.}
+\label{tab:sample-table}
+\end{center}
+\vskip -3mm
+\end{table}
+
+\section{Deep neural network experiments}
+\label{sec:dnnexpts}
+This section should report on your experiments on deeper networks for MNIST.  The two sets of experiments are to explore the impact of the depth of the network (number of hidden layers), and a comparison of different approaches to weight initialisation.
+
+In this section, and in the previous section, you should present your experimental results clearly and concisely, followed by an interpretation and discussion of results. You need to present your results in a way that makes it easy for a reader to understand what they mean. You should facilitate comparisons either using graphs with multiple curves or (if appropriate, e.g. for accuracies) a results table. You need to avoid having too many figures, poorly labelled graphs, and graphs which should be comparable but which use different axis scales. A good presentation will enable the reader to compare trends in the same graph -- each graph should summarise the results relating to a particular research (sub)question.
+
+Your discussion should interpret the results, both in terms of summarising the outcomes of a particular experiment, and attempting to relate to the underlying models. A good report would have some analysis, resulting in an understanding of why particular results are observed, perhaps with reference to the literature. Use bibtex to organise your references -- in this case the references are in the file \verb+example-refs.bib+.  Here is a an example reference \citep{langley00}.  
+
+
+
+
+\section{Conclusions}
+\label{sec:concl}
+You should draw conclusions from the experiments, related to the research questions outlined in the introduction (section~\ref{sec:intro}). You should state the conclusions clearly and concisely. It is good if the conclusion from one experiment influenced what you did in later experiments -- your aim is to learn from your experiments. Extra credit if you relate your findings to what has been reported in the literature.
+
+A good conclusions section would also include a further work discussion, building on work done so far, and referencing the literature where appropriate.
+
+\bibliography{example-refs}
+
+\end{document} 
+
+
+% This document was modified from the file originally made available by
+% Pat Langley and Andrea Danyluk for ICML-2K. This version was
+% created by Lise Getoor and Tobias Scheffer, it was slightly modified  
+% from the 2010 version by Thorsten Joachims & Johannes Fuernkranz, 
+% slightly modified from the 2009 version by Kiri Wagstaff and 
+% Sam Roweis's 2008 version, which is slightly modified from 
+% Prasad Tadepalli's 2007 version which is a lightly 
+% changed version of the previous year's version by Andrew Moore, 
+% which was in turn edited from those of Kristian Kersting and 
+% Codrina Lauth. Alex Smola contributed to the algorithmic style files.  
diff --git a/report/mlp-cw2-template.pdf b/report/mlp-cw2-template.pdf
new file mode 100644
index 0000000..c1c4ea8
Binary files /dev/null and b/report/mlp-cw2-template.pdf differ
diff --git a/report/mlp-cw2-template.tex b/report/mlp-cw2-template.tex
new file mode 100644
index 0000000..bdb2de9
--- /dev/null
+++ b/report/mlp-cw2-template.tex
@@ -0,0 +1,195 @@
+%% Template for MLP Coursework 2 / 6 November 2017 
+
+%% Based on  LaTeX template for ICML 2017 - example_paper.tex at 
+%%  https://2017.icml.cc/Conferences/2017/StyleAuthorInstructions
+
+\documentclass{article}
+
+\usepackage[T1]{fontenc}
+\usepackage{amssymb,amsmath}
+\usepackage{txfonts}
+\usepackage{microtype}
+
+% For figures
+\usepackage{graphicx}
+\usepackage{subfigure} 
+
+% For citations
+\usepackage{natbib}
+
+% For algorithms
+\usepackage{algorithm}
+\usepackage{algorithmic}
+
+% the hyperref package is used to produce hyperlinks in the
+% resulting PDF.  If this breaks your system, please commend out the
+% following usepackage line and replace \usepackage{mlp2017} with
+% \usepackage[nohyperref]{mlp2017} below.
+\usepackage{hyperref}
+\usepackage{url}
+\urlstyle{same}
+
+% Packages hyperref and algorithmic misbehave sometimes.  We can fix
+% this with the following command.
+\newcommand{\theHalgorithm}{\arabic{algorithm}}
+
+
+% Set up MLP coursework style (based on ICML style)
+\usepackage{mlp2017}
+\mlptitlerunning{MLP Coursework 2 (\studentNumber)}
+\bibliographystyle{icml2017}
+
+
+\DeclareMathOperator{\softmax}{softmax}
+\DeclareMathOperator{\sigmoid}{sigmoid}
+\DeclareMathOperator{\sgn}{sgn}
+\DeclareMathOperator{\relu}{relu}
+\DeclareMathOperator{\lrelu}{lrelu}
+\DeclareMathOperator{\elu}{elu}
+\DeclareMathOperator{\selu}{selu}
+\DeclareMathOperator{\maxout}{maxout}
+
+%% You probably do not need to change anything above this comment
+
+%% REPLACE this with your student number
+\def\studentNumber{sXXXXXXX}
+
+\begin{document} 
+
+\twocolumn[
+\mlptitle{MLP Coursework 2: Learning rules, BatchNorm, and ConvNets}
+
+\centerline{\studentNumber}
+
+\vskip 7mm
+]
+
+\begin{abstract} 
+The abstract should be 100--200 words long,  providing a concise summary of the contents of your report.
+\end{abstract} 
+
+\section{Introduction}
+\label{sec:intro}
+This document provides a template for the MLP coursework 2 report.  This template structures the report into sections, which you are recommended to use, but can change if you wish.  If you want to use subsections within a section that is fine, but please do not use any deeper structuring.  In this template the text in each section will include an outline of what you should include in each section, along with some practical LaTeX examples (for example figures, tables, algorithms).  Your document should be no longer than \textbf{seven pages},  with an additional page allowed for references.
+
+The introduction should place your work in context, giving the overall motivation for the work, and clearly outlining the research questions you have explored.  This section should also include a concise description of the Balanced EMNIST task and  data -- be precise: for example state the size of the training, validation, and test sets.
+
+\section{Baseline systems} 
+In this section you should report your baseline experiments for EMNIST.  No need for theoretical explanations of things covered in the course, but should you go beyond what was covered please explain what you did with references to relevant paper(s) if appropriate.   In this section you should aim to cover the both the ``what'' and the ``why'': \emph{what} you did, giving sufficient information (hyperparameter settings, etc.) so that someone else (e.g. another student on the course) could reproduce your results; and \emph{why} you performed the experiments you are reporting - what you are aiming to discover what is the motivation for the particular experiments you undertook. You should also provide some discussion and interpretation of your results.  
+
+As before, your experimental sections should include graphs (for instance, figure~\ref{fig:sample-graph}) and/or tables (for instance, table~\ref{tab:sample-table})\footnote{These examples were taken from the ICML template paper.}, using the \verb+figure+ and \verb+table+ environments, in which you use \verb+\includegraphics+ to include an image (pdf, png, or jpg formats).  Please export graphs as 
+\href{https://en.wikipedia.org/wiki/Vector_graphics}{vector graphics}
+rather than \href{https://en.wikipedia.org/wiki/Raster_graphics}{raster
+files} as this will make sure all detail in the plot is visible.
+Matplotlib supports saving high quality figures in a wide range of
+common image formats using the
+\href{http://matplotlib.org/api/pyplot_api.html\#matplotlib.pyplot.savefig}{\texttt{savefig}}
+function. \textbf{You should use \texttt{savefig} rather than copying
+the screen-resolution raster images outputted in the notebook.} An
+example of using \texttt{savefig} to save a figure as a PDF file (which
+can be included as graphics in a \LaTeX document is given in the coursework 1 document.
+
+If you need a figure or table to stretch across two columns use the \verb+figure*+ or \verb+table*+ environment instead of the \verb+figure+ or \verb+table+ environment.  Use the \verb+subfigure+ environment if you want to include multiple graphics in a single figure.
+
+\begin{figure}[tb]
+\vskip 5mm
+\begin{center}
+\centerline{\includegraphics[width=\columnwidth]{icml_numpapers}}
+\caption{Historical locations and number of accepted papers for International
+  Machine Learning Conferences (ICML 1993 -- ICML 2008) and
+  International Workshops on Machine Learning (ML 1988 -- ML
+  1992). At the time this figure was produced, the number of
+  accepted papers for ICML 2008 was unknown and instead estimated.}
+\label{fig:sample-graph}
+\end{center}
+\vskip -5mm
+\end{figure} 
+
+\begin{table}[tb]
+\vskip 3mm
+\begin{center}
+\begin{small}
+\begin{sc}
+\begin{tabular}{lcccr}
+\hline
+\abovespace\belowspace
+Data set & Naive & Flexible & Better? \\
+\hline
+\abovespace
+Breast    & 95.9$\pm$ 0.2& 96.7$\pm$ 0.2& $\surd$ \\
+Cleveland & 83.3$\pm$ 0.6& 80.0$\pm$ 0.6& $\times$\\
+Glass2    & 61.9$\pm$ 1.4& 83.8$\pm$ 0.7& $\surd$ \\
+Credit    & 74.8$\pm$ 0.5& 78.3$\pm$ 0.6&         \\
+Horse     & 73.3$\pm$ 0.9& 69.7$\pm$ 1.0& $\times$\\
+Meta      & 67.1$\pm$ 0.6& 76.5$\pm$ 0.5& $\surd$ \\
+Pima      & 75.1$\pm$ 0.6& 73.9$\pm$ 0.5&         \\
+\belowspace
+Vehicle   & 44.9$\pm$ 0.6& 61.5$\pm$ 0.4& $\surd$ \\
+\hline
+\end{tabular}
+\end{sc}
+\end{small}
+\caption{Classification accuracies for naive Bayes and flexible 
+Bayes on various data sets.}
+\label{tab:sample-table}
+\end{center}
+\vskip -3mm
+\end{table}
+
+\section{Learning rules}
+In this section you should compare RMSProp and Adam with gradient descent, introducing these learning rules either as equations or as algorithmic pseudocode.  If you present the different approaches as algorithms, you can use the \verb+algorithm+ and \verb+algorithmic+ environments to format pseudocode (for instance, Algorithm~\ref{alg:example}). These require the corresponding style files, \verb+algorithm.sty+ and \verb+algorithmic.sty+ which are supplied with this package. 
+
+\begin{algorithm}[ht]
+\begin{algorithmic}
+   \STATE {\bfseries Input:} data $x_i$, size $m$
+   \REPEAT
+   \STATE Initialize $noChange = true$.
+   \FOR{$i=1$ {\bfseries to} $m-1$}
+   \IF{$x_i > x_{i+1}$} 
+   \STATE Swap $x_i$ and $x_{i+1}$
+   \STATE $noChange = false$
+   \ENDIF
+   \ENDFOR
+   \UNTIL{$noChange$ is $true$}
+\end{algorithmic}
+  \caption{Bubble Sort}
+  \label{alg:example}
+\end{algorithm}
+
+You should, in your own words, explain what the different learning rules do, and how they differ.  You should then present your experiments and results, comparing and contrasting stochastic gradient descent, RMSProp, and Adam.  As before concentrate on the ``what'' (remember give enough information so someone can reproduce your experiments), the ``why'' (why did you choose the experiments that you performed -- you may have been motivated by your earlier results, by the literature, or by a specific research question), and the interpretation of your results.
+
+In every section, you should present your results in a way that makes it easy for a reader to understand what they mean. You should facilitate comparisons either using graphs with multiple curves or (if appropriate, e.g. for accuracies) a results table. You need to avoid having too many figures, poorly labelled graphs, and graphs which should be comparable but which use different axis scales. A good presentation will enable the reader to compare trends in the same graph -- each graph should summarise the results relating to a particular research (sub)question.
+
+Your discussion should interpret the results, both in terms of summarising the outcomes of a particular experiment, and attempting to relate to the underlying models. A good report would have some analysis, resulting in an understanding of why particular results are observed, perhaps with reference to the literature. Use bibtex to organise your references -- in this case the references are in the file \verb+example-refs.bib+.  Here is a an example reference \citep{langley00}.  
+
+\section{Batch normalisation}
+In this section you should present batch normalisation,  supported using equations or algorithmic pseudocode.  Following this present your experiments, again remembering to include the ``what'', the ``why'', and the interpretation of results.
+
+\section{Convolutional networks}
+In this section you should present your experiments with convolutional networks.  Explain the idea of convolutional layers and pooling layers, and briefly explain how you did the implementation.  There is no need to include chunks of code.  You should report the experiments you have undertaken, again remembering to include \emph{what} experiments you performed (include details of hyperparameters, etc.),  \emph{why} you performed them (what was the motivation for the experiments, what research questions are you exploring), and the interpretation and discussion of your results.
+
+\section{Test results}
+The results reported in the previous sections should be on the validation set.  You should finally report results on the EMNIST test set using what you judge to the be the best deep neural network (without convolutional layers) and the best convolutional network.  Again focus on what the experiments were (be precise), why you chose to do them (in particular, how did you choose the architectures/settings to use with the test set), and a discussion/interpretation of the results.
+
+
+\section{Conclusions}
+\label{sec:concl}
+You should draw conclusions from the experiments, related to the research questions outlined in the introduction (section~\ref{sec:intro}). You should state the conclusions clearly and concisely. It is good if the conclusion from one experiment influenced what you did in later experiments -- your aim is to learn from your experiments. Extra credit if you relate your findings to what has been reported in the literature.
+
+A good conclusions section would also include a further work discussion, building on work done so far, and referencing the literature where appropriate.
+
+\bibliography{example-refs}
+
+\end{document} 
+
+
+% This document was modified from the file originally made available by
+% Pat Langley and Andrea Danyluk for ICML-2K. This version was
+% created by Lise Getoor and Tobias Scheffer, it was slightly modified  
+% from the 2010 version by Thorsten Joachims & Johannes Fuernkranz, 
+% slightly modified from the 2009 version by Kiri Wagstaff and 
+% Sam Roweis's 2008 version, which is slightly modified from 
+% Prasad Tadepalli's 2007 version which is a lightly 
+% changed version of the previous year's version by Andrew Moore, 
+% which was in turn edited from those of Kristian Kersting and 
+% Codrina Lauth. Alex Smola contributed to the algorithmic style files.  
diff --git a/report/mlp2017.sty b/report/mlp2017.sty
new file mode 100644
index 0000000..969517b
--- /dev/null
+++ b/report/mlp2017.sty
@@ -0,0 +1,720 @@
+% File: mlp2017.sty (LaTeX style file for ICML-2017, version of 2017-05-31)
+
+% Modified by Daniel Roy 2017: changed byline to use footnotes for affiliations, and removed emails
+
+% This file contains the LaTeX formatting parameters for a two-column 
+% conference proceedings that is 8.5 inches wide by 11 inches high.  
+% 
+% Modified by Percy Liang 12/2/2013: changed the year, location from the previous template for ICML 2014
+
+% Modified by Fei Sha 9/2/2013: changed the year, location form the previous template for ICML 2013
+%
+% Modified by Fei Sha 4/24/2013: (1) remove the extra whitespace after the first author's email address (in %the camera-ready version) (2) change the Proceeding ... of ICML 2010 to 2014 so PDF's metadata will show up % correctly
+%
+% Modified by Sanjoy Dasgupta, 2013: changed years, location
+%
+% Modified by Francesco Figari, 2012: changed years, location
+%
+% Modified by Christoph Sawade and Tobias Scheffer, 2011: added line 
+% numbers, changed years
+%
+% Modified by Hal Daume III, 2010: changed years, added hyperlinks
+%
+% Modified by Kiri Wagstaff, 2009: changed years
+%
+% Modified by Sam Roweis, 2008: changed years
+%
+% Modified by Ricardo Silva, 2007: update of the ifpdf verification
+%
+% Modified by Prasad Tadepalli and Andrew Moore, merely changing years. 
+%
+% Modified by Kristian Kersting, 2005, based on Jennifer Dy's 2004 version
+% - running title. If the original title is to long or is breaking a line,
+%   use \mlptitlerunning{...} in the preamble to supply a shorter form.
+%   Added fancyhdr package to get a running head. 
+% - Updated to store the page size because pdflatex does compile the 
+%   page size into the pdf. 
+%
+% Hacked by Terran Lane, 2003:
+% - Updated to use LaTeX2e style file conventions (ProvidesPackage,
+%   etc.)
+% - Added an ``appearing in'' block at the base of the first column
+%   (thus keeping the ``appearing in'' note out of the bottom margin
+%   where the printer should strip in the page numbers).
+% - Added a package option [accepted] that selects between the ``Under
+%   review'' notice (default, when no option is specified) and the
+%   ``Appearing in'' notice (for use when the paper has been accepted
+%   and will appear).
+%
+%   Originally created as:  ml2k.sty (LaTeX style file for ICML-2000)
+%   by P. Langley (12/23/99)
+
+%%%%%%%%%%%%%%%%%%%%
+%% This version of the style file supports both a ``review'' version
+%% and a ``final/accepted'' version.  The difference is only in the
+%% text that appears in the note at the bottom of the first column of
+%% the first page.  The default behavior is to print a note to the
+%% effect that the paper is under review and don't distribute it.  The
+%% final/accepted version prints an ``Appearing in'' note.  To get the
+%% latter behavior, in the calling file change the ``usepackage'' line
+%% from:
+%%	\usepackage{icml2017}
+%% to
+%%	\usepackage[accepted]{icml2017}
+%%%%%%%%%%%%%%%%%%%%
+
+\NeedsTeXFormat{LaTeX2e}
+\ProvidesPackage{mlp2017}[2017/01/01 MLP Coursework Style File]
+
+% Use fancyhdr package
+\RequirePackage{fancyhdr}
+\RequirePackage{color}
+\RequirePackage{algorithm}
+\RequirePackage{algorithmic}
+\RequirePackage{natbib}
+\RequirePackage{eso-pic} % used by \AddToShipoutPicture 
+\RequirePackage{forloop}
+
+%%%%%%%% Options
+%\DeclareOption{accepted}{%
+% \renewcommand{\Notice@String}{\ICML@appearing}
+  \gdef\isaccepted{1}
+%}
+\DeclareOption{nohyperref}{%
+  \gdef\nohyperref{1}
+}
+
+\ifdefined\nohyperref\else\ifdefined\hypersetup
+  \definecolor{mydarkblue}{rgb}{0,0.08,0.45}
+  \hypersetup{ %
+    pdftitle={},
+    pdfauthor={},
+    pdfsubject={MLP Coursework 2017-18},
+    pdfkeywords={},
+    pdfborder=0 0 0,
+    pdfpagemode=UseNone,
+    colorlinks=true,
+    linkcolor=mydarkblue,
+    citecolor=mydarkblue,
+    filecolor=mydarkblue,
+    urlcolor=mydarkblue,
+    pdfview=FitH}
+
+  \ifdefined\isaccepted \else
+    \hypersetup{pdfauthor={Anonymous Submission}}
+  \fi
+\fi\fi
+
+%%%%%%%%%%%%%%%%%%%%
+% This string is printed at the bottom of the page for the
+% final/accepted version of the ``appearing in'' note.  Modify it to
+% change that text.
+%%%%%%%%%%%%%%%%%%%%
+\newcommand{\ICML@appearing}{\textit{MLP Coursework 1 2017-18}}
+
+%%%%%%%%%%%%%%%%%%%%
+% This string is printed at the bottom of the page for the draft/under
+% review version of the ``appearing in'' note.  Modify it to change
+% that text.
+%%%%%%%%%%%%%%%%%%%%
+\newcommand{\Notice@String}{MLP Coursework 1 2017-18}
+
+% Cause the declared options to actually be parsed and activated
+\ProcessOptions\relax
+
+% Uncomment the following for debugging.  It will cause LaTeX to dump
+% the version of the ``appearing in'' string that will actually appear
+% in the document.
+%\typeout{>> Notice string='\Notice@String'}
+
+% Change citation commands to be more like old ICML styles
+\newcommand{\yrcite}[1]{\citeyearpar{#1}}
+\renewcommand{\cite}[1]{\citep{#1}}
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+% to ensure the letter format is used. pdflatex does compile the
+% page size into the pdf. This is done using \pdfpagewidth and 
+% \pdfpageheight. As Latex does not know this directives, we first
+% check whether pdflatex or latex is used.
+%
+% Kristian Kersting 2005
+%
+% in order to account for the more recent use of pdfetex as the default
+% compiler, I have changed the pdf verification.
+%
+% Ricardo Silva 2007
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+\paperwidth=210mm
+\paperheight=297mm
+
+% old PDFLaTex verification, circa 2005
+%
+%\newif\ifpdf\ifx\pdfoutput\undefined
+%  \pdffalse % we are not running PDFLaTeX
+%\else
+%  \pdfoutput=1 % we are running PDFLaTeX
+%  \pdftrue
+%\fi
+
+\newif\ifpdf %adapted from ifpdf.sty
+\ifx\pdfoutput\undefined
+\else
+   \ifx\pdfoutput\relax
+   \else
+     \ifcase\pdfoutput
+     \else
+       \pdftrue
+     \fi
+   \fi
+\fi
+
+\ifpdf
+%    \pdfpagewidth=\paperwidth
+%    \pdfpageheight=\paperheight
+  \setlength{\pdfpagewidth}{210mm}
+  \setlength{\pdfpageheight}{297mm}
+\fi
+
+% Physical page layout 
+
+\evensidemargin -5.5mm  
+\oddsidemargin -5.5mm 
+\setlength\textheight{248mm}
+\setlength\textwidth{170mm} 
+\setlength\columnsep{6.5mm}
+\setlength\headheight{10pt}
+\setlength\headsep{10pt} 
+\addtolength{\topmargin}{-20pt}
+
+%\setlength\headheight{1em}
+%\setlength\headsep{1em}
+\addtolength{\topmargin}{-6mm}
+
+%\addtolength{\topmargin}{-2em}
+
+%% The following is adapted from code in the acmconf.sty conference
+%% style file.  The constants in it are somewhat magical, and appear
+%% to work well with the two-column format on US letter paper that
+%% ICML uses, but will break if you change that layout, or if you use
+%% a longer block of text for the copyright notice string.  Fiddle with
+%% them if necessary to get the block to fit/look right.
+%%
+%% -- Terran Lane, 2003
+%%
+%% The following comments are included verbatim from acmconf.sty:
+%%
+%%% This section (written by KBT) handles the 1" box in the lower left
+%%% corner of the left column of the first page by creating a picture,
+%%% and inserting the predefined string at the bottom (with a negative
+%%% displacement to offset the space allocated for a non-existent
+%%% caption).
+%%%
+\def\ftype@copyrightbox{8}
+\def\@copyrightspace{
+% Create a float object positioned at the bottom of the column.  Note
+% that because of the mystical nature of floats, this has to be called
+% before the first column is populated with text (e.g., from the title
+% or abstract blocks).  Otherwise, the text will force the float to
+% the next column.  -- TDRL.
+\@float{copyrightbox}[b]
+\begin{center}
+\setlength{\unitlength}{1pc}
+\begin{picture}(20,1.5)
+% Create a line separating the main text from the note block.
+% 4.818pc==0.8in.
+\put(0,2.5){\line(1,0){4.818}}
+% Insert the text string itself.  Note that the string has to be
+% enclosed in a parbox -- the \put call needs a box object to
+% position.  Without the parbox, the text gets splattered across the
+% bottom of the page semi-randomly.  The 19.75pc distance seems to be
+% the width of the column, though I can't find an appropriate distance
+% variable to substitute here.  -- TDRL.
+\put(0,0){\parbox[b]{19.75pc}{\small \Notice@String}}
+\end{picture}
+\end{center}
+\end@float}
+
+% Note: A few Latex versions need the next line instead of the former.
+% \addtolength{\topmargin}{0.3in}
+% \setlength\footheight{0pt}
+\setlength\footskip{0pt} 
+%\pagestyle{empty} 
+\flushbottom \twocolumn
+\sloppy
+
+% Clear out the addcontentsline command
+\def\addcontentsline#1#2#3{}
+ 
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%%% commands for formatting paper title, author names, and addresses. 
+
+%%start%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%%%%%% title as running head -- Kristian Kersting 2005 %%%%%%%%%%%%%
+
+
+%\makeatletter
+%\newtoks\mytoksa
+%\newtoks\mytoksb
+%\newcommand\addtomylist[2]{%
+%  \mytoksa\expandafter{#1}%
+%  \mytoksb{#2}%
+%  \edef#1{\the\mytoksa\the\mytoksb}%
+%}
+%\makeatother 
+
+% box to check the size of the running head
+\newbox\titrun
+
+% general page style
+\pagestyle{fancy}
+\fancyhf{}
+\fancyhead{}
+\fancyfoot{}
+% set the width of the head rule to 1 point
+\renewcommand{\headrulewidth}{1pt}
+
+% definition to set the head as running head in the preamble
+\def\mlptitlerunning#1{\gdef\@mlptitlerunning{#1}}
+
+% main definition adapting \mlptitle from 2004
+\long\def\mlptitle#1{%
+
+   %check whether @mlptitlerunning exists
+   % if not \mlptitle is used as running head
+   \ifx\undefined\@mlptitlerunning%
+	\gdef\@mlptitlerunning{#1}
+   \fi
+
+   %add it to pdf information
+  \ifdefined\nohyperref\else\ifdefined\hypersetup
+     \hypersetup{pdftitle={#1}}
+   \fi\fi
+
+   %get the dimension of the running title
+   \global\setbox\titrun=\vbox{\small\bf\@mlptitlerunning}
+
+   % error flag
+   \gdef\@runningtitleerror{0}
+
+   % running title too long
+   \ifdim\wd\titrun>\textwidth%
+	  {\gdef\@runningtitleerror{1}}%
+   % running title breaks a line
+   \else\ifdim\ht\titrun>6.25pt
+	   {\gdef\@runningtitleerror{2}}%
+	\fi
+   \fi 
+
+   % if there is somthing wrong with the running title
+   \ifnum\@runningtitleerror>0
+	   \typeout{}%
+           \typeout{}%
+           \typeout{*******************************************************}%
+           \typeout{Title exceeds size limitations for running head.}%
+           \typeout{Please supply a shorter form for the running head}
+           \typeout{with \string\mlptitlerunning{...}\space prior to \string\begin{document}}%
+           \typeout{*******************************************************}%
+ 	    \typeout{}%
+           \typeout{}%
+           % set default running title
+	   \chead{\small\bf Title Suppressed Due to Excessive Size}%
+    \else
+	   % 'everything' fine, set provided running title
+  	   \chead{\small\bf\@mlptitlerunning}%
+    \fi
+
+  % no running title on the first page of the paper
+  \thispagestyle{empty}
+
+%%%%%%%%%%%%%%%%%%%% Kristian Kersting %%%%%%%%%%%%%%%%%%%%%%%%%  
+%end%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+  {\center\baselineskip 18pt
+                       \toptitlebar{\Large\bf #1}\bottomtitlebar}
+}
+
+
+\gdef\icmlfullauthorlist{}
+\newcommand\addstringtofullauthorlist{\g@addto@macro\icmlfullauthorlist}
+\newcommand\addtofullauthorlist[1]{%
+  \ifdefined\icmlanyauthors%
+    \addstringtofullauthorlist{, #1}%
+  \else%
+    \addstringtofullauthorlist{#1}%
+    \gdef\icmlanyauthors{1}%
+  \fi%
+  \ifdefined\nohyperref\else\ifdefined\hypersetup%
+    \hypersetup{pdfauthor=\icmlfullauthorlist}%
+  \fi\fi}
+
+
+\def\toptitlebar{\hrule height1pt \vskip .25in} 
+\def\bottomtitlebar{\vskip .22in \hrule height1pt \vskip .3in} 
+
+\newenvironment{icmlauthorlist}{%
+  \setlength\topsep{0pt}
+  \setlength\parskip{0pt}
+  \begin{center}
+}{%
+  \end{center}
+}
+
+\newcounter{@affiliationcounter}
+\newcommand{\@pa}[1]{%
+% ``#1''
+\ifcsname the@affil#1\endcsname
+   % do nothing
+\else
+  \ifcsname @icmlsymbol#1\endcsname
+    % nothing
+  \else
+  \stepcounter{@affiliationcounter}%
+  \newcounter{@affil#1}%
+  \setcounter{@affil#1}{\value{@affiliationcounter}}%
+  \fi
+\fi%
+\ifcsname @icmlsymbol#1\endcsname
+  \textsuperscript{\csname @icmlsymbol#1\endcsname\,}%
+\else
+  %\expandafter\footnotemark[\arabic{@affil#1}\,]%
+  \textsuperscript{\arabic{@affil#1}\,}%
+\fi
+}
+
+%\newcommand{\icmlauthor}[2]{%
+%\addtofullauthorlist{#1}%
+%#1\@for\theaffil:=#2\do{\pa{\theaffil}}%
+%}
+\newcommand{\icmlauthor}[2]{%
+  \ifdefined\isaccepted
+    \mbox{\bf #1}\,\@for\theaffil:=#2\do{\@pa{\theaffil}} \addtofullauthorlist{#1}%
+   \else
+    \ifdefined\@icmlfirsttime
+    \else
+      \gdef\@icmlfirsttime{1}
+      \mbox{\bf Anonymous Authors}\@pa{@anon} \addtofullauthorlist{Anonymous Authors}
+     \fi
+    \fi
+}
+
+\newcommand{\icmlsetsymbol}[2]{%
+  \expandafter\gdef\csname @icmlsymbol#1\endcsname{#2}
+ }
+   
+
+\newcommand{\icmlaffiliation}[2]{%
+\ifdefined\isaccepted
+\ifcsname the@affil#1\endcsname
+ \expandafter\gdef\csname @affilname\csname the@affil#1\endcsname\endcsname{#2}%
+\else
+  {\bf AUTHORERR: Error in use of \textbackslash{}icmlaffiliation command. Label ``#1'' not mentioned in some \textbackslash{}icmlauthor\{author name\}\{labels here\} command beforehand. }
+  \typeout{}%
+  \typeout{}%
+  \typeout{*******************************************************}%
+  \typeout{Affiliation label undefined. }%
+  \typeout{Make sure \string\icmlaffiliation\space follows }
+  \typeout{all of \string\icmlauthor\space commands}%
+  \typeout{*******************************************************}%
+  \typeout{}%
+  \typeout{}%
+\fi
+\else % \isaccepted
+ % can be called multiple times... it's idempotent
+ \expandafter\gdef\csname @affilname1\endcsname{Anonymous Institution, Anonymous City, Anonymous Region, Anonymous Country}
+\fi
+}
+
+\newcommand{\icmlcorrespondingauthor}[2]{
+\ifdefined\isaccepted
+ \ifdefined\icmlcorrespondingauthor@text
+   \g@addto@macro\icmlcorrespondingauthor@text{, #1 \textless{}#2\textgreater{}}
+ \else
+   \gdef\icmlcorrespondingauthor@text{#1 \textless{}#2\textgreater{}}
+ \fi
+\else
+\gdef\icmlcorrespondingauthor@text{Anonymous Author \textless{}anon.email@domain.com\textgreater{}}
+\fi
+}
+
+\newcommand{\icmlEqualContribution}{\textsuperscript{*}Equal contribution }
+
+\newcounter{@affilnum}
+\newcommand{\printAffiliationsAndNotice}[1]{%
+\stepcounter{@affiliationcounter}%
+{\let\thefootnote\relax\footnotetext{\hspace*{-\footnotesep}#1%
+\forloop{@affilnum}{1}{\value{@affilnum} < \value{@affiliationcounter}}{
+\textsuperscript{\arabic{@affilnum}}\ifcsname @affilname\the@affilnum\endcsname%
+\csname @affilname\the@affilnum\endcsname%
+\else
+{\bf AUTHORERR: Missing \textbackslash{}icmlaffiliation.}
+\fi
+}.
+\ifdefined\icmlcorrespondingauthor@text
+Correspondence to: \icmlcorrespondingauthor@text.
+\else
+{\bf AUTHORERR: Missing \textbackslash{}icmlcorrespondingauthor.}
+\fi
+
+\ \\
+\Notice@String
+}
+}
+}
+
+  
+%\makeatother
+
+\long\def\icmladdress#1{%
+ {\bf The \textbackslash{}icmladdress command is no longer used.  See the example\_paper PDF .tex for usage of \textbackslash{}icmlauther and \textbackslash{}icmlaffiliation.}
+}
+
+%% keywords as first class citizens
+\def\icmlkeywords#1{%
+%  \ifdefined\isaccepted \else
+%    \par {\bf Keywords:} #1%
+%  \fi
+%  \ifdefined\nohyperref\else\ifdefined\hypersetup
+%    \hypersetup{pdfkeywords={#1}}
+%  \fi\fi
+%  \ifdefined\isaccepted \else
+%    \par {\bf Keywords:} #1%
+%  \fi
+  \ifdefined\nohyperref\else\ifdefined\hypersetup
+    \hypersetup{pdfkeywords={#1}}
+  \fi\fi
+}
+
+% modification to natbib citations
+\setcitestyle{authoryear,round,citesep={;},aysep={,},yysep={;}}
+
+% Redefinition of the abstract environment. 
+\renewenvironment{abstract}
+   {%
+% Insert the ``appearing in'' copyright notice.
+%\@copyrightspace
+\centerline{\large\bf Abstract}
+    \vspace{-0.12in}\begin{quote}}
+   {\par\end{quote}\vskip 0.12in}
+
+% numbered section headings with different treatment of numbers
+
+\def\@startsection#1#2#3#4#5#6{\if@noskipsec \leavevmode \fi
+   \par \@tempskipa #4\relax
+   \@afterindenttrue
+% Altered the following line to indent a section's first paragraph. 
+%  \ifdim \@tempskipa <\z@ \@tempskipa -\@tempskipa \@afterindentfalse\fi
+   \ifdim \@tempskipa <\z@ \@tempskipa -\@tempskipa \fi
+   \if@nobreak \everypar{}\else
+     \addpenalty{\@secpenalty}\addvspace{\@tempskipa}\fi \@ifstar
+     {\@ssect{#3}{#4}{#5}{#6}}{\@dblarg{\@sict{#1}{#2}{#3}{#4}{#5}{#6}}}}
+
+\def\@sict#1#2#3#4#5#6[#7]#8{\ifnum #2>\c@secnumdepth
+     \def\@svsec{}\else 
+     \refstepcounter{#1}\edef\@svsec{\csname the#1\endcsname}\fi
+     \@tempskipa #5\relax
+      \ifdim \@tempskipa>\z@
+        \begingroup #6\relax
+          \@hangfrom{\hskip #3\relax\@svsec.~}{\interlinepenalty \@M #8\par}
+        \endgroup
+       \csname #1mark\endcsname{#7}\addcontentsline
+         {toc}{#1}{\ifnum #2>\c@secnumdepth \else
+                      \protect\numberline{\csname the#1\endcsname}\fi
+                    #7}\else
+        \def\@svsechd{#6\hskip #3\@svsec #8\csname #1mark\endcsname
+                      {#7}\addcontentsline
+                           {toc}{#1}{\ifnum #2>\c@secnumdepth \else
+                             \protect\numberline{\csname the#1\endcsname}\fi
+                       #7}}\fi
+     \@xsect{#5}}
+
+\def\@sect#1#2#3#4#5#6[#7]#8{\ifnum #2>\c@secnumdepth
+     \def\@svsec{}\else 
+     \refstepcounter{#1}\edef\@svsec{\csname the#1\endcsname\hskip 0.4em }\fi
+     \@tempskipa #5\relax
+      \ifdim \@tempskipa>\z@ 
+        \begingroup #6\relax
+          \@hangfrom{\hskip #3\relax\@svsec}{\interlinepenalty \@M #8\par}
+        \endgroup
+       \csname #1mark\endcsname{#7}\addcontentsline
+         {toc}{#1}{\ifnum #2>\c@secnumdepth \else
+                      \protect\numberline{\csname the#1\endcsname}\fi
+                    #7}\else
+        \def\@svsechd{#6\hskip #3\@svsec #8\csname #1mark\endcsname
+                      {#7}\addcontentsline
+                           {toc}{#1}{\ifnum #2>\c@secnumdepth \else
+                             \protect\numberline{\csname the#1\endcsname}\fi
+                       #7}}\fi
+     \@xsect{#5}}
+
+% section headings with less space above and below them
+\def\thesection {\arabic{section}}
+\def\thesubsection {\thesection.\arabic{subsection}}
+\def\section{\@startsection{section}{1}{\z@}{-0.12in}{0.02in}
+             {\large\bf\raggedright}}
+\def\subsection{\@startsection{subsection}{2}{\z@}{-0.10in}{0.01in}
+                {\normalsize\bf\raggedright}}
+\def\subsubsection{\@startsection{subsubsection}{3}{\z@}{-0.08in}{0.01in}
+                {\normalsize\sc\raggedright}}
+\def\paragraph{\@startsection{paragraph}{4}{\z@}{1.5ex plus
+  0.5ex minus .2ex}{-1em}{\normalsize\bf}}
+\def\subparagraph{\@startsection{subparagraph}{5}{\z@}{1.5ex plus
+  0.5ex minus .2ex}{-1em}{\normalsize\bf}}
+ 
+% Footnotes 
+\footnotesep 6.65pt % 
+\skip\footins 9pt 
+\def\footnoterule{\kern-3pt \hrule width 0.8in \kern 2.6pt } 
+\setcounter{footnote}{0} 
+ 
+% Lists and paragraphs 
+\parindent 0pt 
+\topsep 4pt plus 1pt minus 2pt 
+\partopsep 1pt plus 0.5pt minus 0.5pt 
+\itemsep 2pt plus 1pt minus 0.5pt 
+\parsep 2pt plus 1pt minus 0.5pt 
+\parskip 6pt
+ 
+\leftmargin 2em \leftmargini\leftmargin \leftmarginii 2em 
+\leftmarginiii 1.5em \leftmarginiv 1.0em \leftmarginv .5em  
+\leftmarginvi .5em 
+\labelwidth\leftmargini\advance\labelwidth-\labelsep \labelsep 5pt 
+ 
+\def\@listi{\leftmargin\leftmargini} 
+\def\@listii{\leftmargin\leftmarginii 
+   \labelwidth\leftmarginii\advance\labelwidth-\labelsep 
+   \topsep 2pt plus 1pt minus 0.5pt 
+   \parsep 1pt plus 0.5pt minus 0.5pt 
+   \itemsep \parsep} 
+\def\@listiii{\leftmargin\leftmarginiii 
+    \labelwidth\leftmarginiii\advance\labelwidth-\labelsep 
+    \topsep 1pt plus 0.5pt minus 0.5pt  
+    \parsep \z@ \partopsep 0.5pt plus 0pt minus 0.5pt 
+    \itemsep \topsep} 
+\def\@listiv{\leftmargin\leftmarginiv 
+     \labelwidth\leftmarginiv\advance\labelwidth-\labelsep} 
+\def\@listv{\leftmargin\leftmarginv 
+     \labelwidth\leftmarginv\advance\labelwidth-\labelsep} 
+\def\@listvi{\leftmargin\leftmarginvi 
+     \labelwidth\leftmarginvi\advance\labelwidth-\labelsep} 
+ 
+\abovedisplayskip 7pt plus2pt minus5pt% 
+\belowdisplayskip \abovedisplayskip 
+\abovedisplayshortskip  0pt plus3pt%    
+\belowdisplayshortskip  4pt plus3pt minus3pt% 
+ 
+% Less leading in most fonts (due to the narrow columns) 
+% The choices were between 1-pt and 1.5-pt leading 
+\def\@normalsize{\@setsize\normalsize{11pt}\xpt\@xpt} 
+\def\small{\@setsize\small{10pt}\ixpt\@ixpt} 
+\def\footnotesize{\@setsize\footnotesize{10pt}\ixpt\@ixpt} 
+\def\scriptsize{\@setsize\scriptsize{8pt}\viipt\@viipt} 
+\def\tiny{\@setsize\tiny{7pt}\vipt\@vipt} 
+\def\large{\@setsize\large{14pt}\xiipt\@xiipt} 
+\def\Large{\@setsize\Large{16pt}\xivpt\@xivpt} 
+\def\LARGE{\@setsize\LARGE{20pt}\xviipt\@xviipt} 
+\def\huge{\@setsize\huge{23pt}\xxpt\@xxpt} 
+\def\Huge{\@setsize\Huge{28pt}\xxvpt\@xxvpt} 
+
+% Revised formatting for figure captions and table titles. 
+\newsavebox\newcaptionbox\newdimen\newcaptionboxwid
+
+\long\def\@makecaption#1#2{
+ \vskip 10pt 
+        \baselineskip 11pt
+        \setbox\@tempboxa\hbox{#1. #2}
+        \ifdim \wd\@tempboxa >\hsize
+        \sbox{\newcaptionbox}{\small\sl #1.~}
+        \newcaptionboxwid=\wd\newcaptionbox
+        \usebox\newcaptionbox {\footnotesize #2}
+%        \usebox\newcaptionbox {\small #2}
+        \else 
+          \centerline{{\small\sl #1.} {\small #2}} 
+        \fi}
+
+\def\fnum@figure{Figure \thefigure}
+\def\fnum@table{Table \thetable}
+
+% Strut macros for skipping spaces above and below text in tables. 
+\def\abovestrut#1{\rule[0in]{0in}{#1}\ignorespaces}
+\def\belowstrut#1{\rule[-#1]{0in}{#1}\ignorespaces}
+
+\def\abovespace{\abovestrut{0.20in}}
+\def\aroundspace{\abovestrut{0.20in}\belowstrut{0.10in}}
+\def\belowspace{\belowstrut{0.10in}}
+
+% Various personal itemization commands. 
+\def\texitem#1{\par\noindent\hangindent 12pt
+               \hbox to 12pt {\hss #1 ~}\ignorespaces}
+\def\icmlitem{\texitem{$\bullet$}}
+
+% To comment out multiple lines of text.
+\long\def\comment#1{}
+
+
+
+
+%% Line counter (not in final version). Adapted from NIPS style file by Christoph Sawade
+
+% Vertical Ruler
+% This code is, largely, from the CVPR 2010 conference style file
+% ----- define vruler
+\makeatletter
+\newbox\icmlrulerbox
+\newcount\icmlrulercount
+\newdimen\icmlruleroffset
+\newdimen\cv@lineheight
+\newdimen\cv@boxheight
+\newbox\cv@tmpbox
+\newcount\cv@refno
+\newcount\cv@tot
+% NUMBER with left flushed zeros  \fillzeros[<WIDTH>]<NUMBER>
+\newcount\cv@tmpc@ \newcount\cv@tmpc
+\def\fillzeros[#1]#2{\cv@tmpc@=#2\relax\ifnum\cv@tmpc@<0\cv@tmpc@=-\cv@tmpc@\fi
+\cv@tmpc=1 %
+\loop\ifnum\cv@tmpc@<10 \else \divide\cv@tmpc@ by 10 \advance\cv@tmpc by 1 \fi
+   \ifnum\cv@tmpc@=10\relax\cv@tmpc@=11\relax\fi \ifnum\cv@tmpc@>10 \repeat
+\ifnum#2<0\advance\cv@tmpc1\relax-\fi
+\loop\ifnum\cv@tmpc<#1\relax0\advance\cv@tmpc1\relax\fi \ifnum\cv@tmpc<#1 \repeat
+\cv@tmpc@=#2\relax\ifnum\cv@tmpc@<0\cv@tmpc@=-\cv@tmpc@\fi \relax\the\cv@tmpc@}%
+% \makevruler[<SCALE>][<INITIAL_COUNT>][<STEP>][<DIGITS>][<HEIGHT>]
+\def\makevruler[#1][#2][#3][#4][#5]{
+	\begingroup\offinterlineskip
+		\textheight=#5\vbadness=10000\vfuzz=120ex\overfullrule=0pt%
+		\global\setbox\icmlrulerbox=\vbox to \textheight{%
+			{
+				\parskip=0pt\hfuzz=150em\cv@boxheight=\textheight
+				\cv@lineheight=#1\global\icmlrulercount=#2%
+				\cv@tot\cv@boxheight\divide\cv@tot\cv@lineheight\advance\cv@tot2%
+				\cv@refno1\vskip-\cv@lineheight\vskip1ex%
+				\loop\setbox\cv@tmpbox=\hbox to0cm{					 % side margin
+					\hfil {\hfil\fillzeros[#4]\icmlrulercount}
+				}%
+				\ht\cv@tmpbox\cv@lineheight\dp\cv@tmpbox0pt\box\cv@tmpbox\break
+				\advance\cv@refno1\global\advance\icmlrulercount#3\relax
+				\ifnum\cv@refno<\cv@tot\repeat
+			}
+		}
+	\endgroup
+}%
+\makeatother
+% ----- end of vruler
+
+
+% \makevruler[<SCALE>][<INITIAL_COUNT>][<STEP>][<DIGITS>][<HEIGHT>]
+\def\icmlruler#1{\makevruler[12pt][#1][1][3][\textheight]\usebox{\icmlrulerbox}}
+\AddToShipoutPicture{%
+\icmlruleroffset=\textheight
+\advance\icmlruleroffset by 5.2pt % top margin
+  \color[rgb]{.7,.7,.7}
+  \ifdefined\isaccepted \else
+	  \AtTextUpperLeft{%
+	    \put(\LenToUnit{-35pt},\LenToUnit{-\icmlruleroffset}){%left ruler
+	      \icmlruler{\icmlrulercount}}
+%	    \put(\LenToUnit{1.04\textwidth},\LenToUnit{-\icmlruleroffset}){%right ruler
+%	      \icmlruler{\icmlrulercount}}
+	  }
+	 \fi
+}
+\endinput
diff --git a/report/natbib.sty b/report/natbib.sty
new file mode 100644
index 0000000..ff0d0b9
--- /dev/null
+++ b/report/natbib.sty
@@ -0,0 +1,1246 @@
+%%
+%% This is file `natbib.sty',
+%% generated with the docstrip utility.
+%%
+%% The original source files were:
+%%
+%% natbib.dtx  (with options: `package,all')
+%% =============================================
+%% IMPORTANT NOTICE:
+%% 
+%% This program can be redistributed and/or modified under the terms
+%% of the LaTeX Project Public License Distributed from CTAN
+%% archives in directory macros/latex/base/lppl.txt; either
+%% version 1 of the License, or any later version.
+%% 
+%% This is a generated file.
+%% It may not be distributed without the original source file natbib.dtx.
+%% 
+%% Full documentation can be obtained by LaTeXing that original file.
+%% Only a few abbreviated comments remain here to describe the usage.
+%% =============================================
+%% Copyright 1993-2009 Patrick W Daly
+%% Max-Planck-Institut f\"ur Sonnensystemforschung
+%% Max-Planck-Str. 2
+%% D-37191 Katlenburg-Lindau
+%% Germany
+%% E-mail: daly@mps.mpg.de
+\NeedsTeXFormat{LaTeX2e}[1995/06/01]
+\ProvidesPackage{natbib}
+        [2009/07/16 8.31 (PWD, AO)]
+
+ % This package reimplements the LaTeX \cite command to be used for various
+ % citation styles, both author-year and numerical. It accepts BibTeX
+ % output intended for many other packages, and therefore acts as a
+ % general, all-purpose citation-style interface.
+ %
+ % With standard numerical .bst files, only numerical citations are
+ % possible. With an author-year .bst file, both numerical and
+ % author-year citations are possible.
+ %
+ % If author-year citations are selected, \bibitem must have one of the
+ %   following forms:
+ %   \bibitem[Jones et al.(1990)]{key}...
+ %   \bibitem[Jones et al.(1990)Jones, Baker, and Williams]{key}...
+ %   \bibitem[Jones et al., 1990]{key}...
+ %   \bibitem[\protect\citeauthoryear{Jones, Baker, and Williams}{Jones
+ %       et al.}{1990}]{key}...
+ %   \bibitem[\protect\citeauthoryear{Jones et al.}{1990}]{key}...
+ %   \bibitem[\protect\astroncite{Jones et al.}{1990}]{key}...
+ %   \bibitem[\protect\citename{Jones et al., }1990]{key}...
+ %   \harvarditem[Jones et al.]{Jones, Baker, and Williams}{1990}{key}...
+ %
+ % This is either to be made up manually, or to be generated by an
+ % appropriate .bst file with BibTeX.
+ %                            Author-year mode     ||   Numerical mode
+ % Then, \citet{key}  ==>>  Jones et al. (1990)    ||   Jones et al. [21]
+ %       \citep{key}  ==>> (Jones et al., 1990)    ||   [21]
+ % Multiple citations as normal:
+ % \citep{key1,key2}  ==>> (Jones et al., 1990; Smith, 1989) || [21,24]
+ %                           or  (Jones et al., 1990, 1991)  || [21,24]
+ %                           or  (Jones et al., 1990a,b)     || [21,24]
+ % \cite{key} is the equivalent of \citet{key} in author-year mode
+ %                         and  of \citep{key} in numerical mode
+ % Full author lists may be forced with \citet* or \citep*, e.g.
+ %       \citep*{key}      ==>> (Jones, Baker, and Williams, 1990)
+ % Optional notes as:
+ %   \citep[chap. 2]{key}    ==>> (Jones et al., 1990, chap. 2)
+ %   \citep[e.g.,][]{key}    ==>> (e.g., Jones et al., 1990)
+ %   \citep[see][pg. 34]{key}==>> (see Jones et al., 1990, pg. 34)
+ %  (Note: in standard LaTeX, only one note is allowed, after the ref.
+ %   Here, one note is like the standard, two make pre- and post-notes.)
+ %   \citealt{key}          ==>> Jones et al. 1990
+ %   \citealt*{key}         ==>> Jones, Baker, and Williams 1990
+ %   \citealp{key}          ==>> Jones et al., 1990
+ %   \citealp*{key}         ==>> Jones, Baker, and Williams, 1990
+ % Additional citation possibilities (both author-year and numerical modes)
+ %   \citeauthor{key}       ==>> Jones et al.
+ %   \citeauthor*{key}      ==>> Jones, Baker, and Williams
+ %   \citeyear{key}         ==>> 1990
+ %   \citeyearpar{key}      ==>> (1990)
+ %   \citetext{priv. comm.} ==>> (priv. comm.)
+ %   \citenum{key}          ==>> 11 [non-superscripted]
+ % Note: full author lists depends on whether the bib style supports them;
+ %       if not, the abbreviated list is printed even when full requested.
+ %
+ % For names like della Robbia at the start of a sentence, use
+ %   \Citet{dRob98}         ==>> Della Robbia (1998)
+ %   \Citep{dRob98}         ==>> (Della Robbia, 1998)
+ %   \Citeauthor{dRob98}    ==>> Della Robbia
+ %
+ %
+ % Citation aliasing is achieved with
+ %   \defcitealias{key}{text}
+ %   \citetalias{key}  ==>> text
+ %   \citepalias{key}  ==>> (text)
+ %
+ % Defining the citation mode and punctual (citation style)
+ %   \setcitestyle{<comma-separated list of keywords, same
+ %     as the package options>}
+ % Example: \setcitestyle{square,semicolon}
+ % Alternatively:
+ % Use \bibpunct with 6 mandatory arguments:
+ %    1. opening bracket for citation
+ %    2. closing bracket
+ %    3. citation separator (for multiple citations in one \cite)
+ %    4. the letter n for numerical styles, s for superscripts
+ %        else anything for author-year
+ %    5. punctuation between authors and date
+ %    6. punctuation between years (or numbers) when common authors missing
+ % One optional argument is the character coming before post-notes. It
+ %   appears in square braces before all other arguments. May be left off.
+ % Example (and default) \bibpunct[, ]{(}{)}{;}{a}{,}{,}
+ %
+ % To make this automatic for a given bib style, named newbib, say, make
+ % a local configuration file, natbib.cfg, with the definition
+ %   \newcommand{\bibstyle@newbib}{\bibpunct...}
+ % Then the \bibliographystyle{newbib} will cause \bibstyle@newbib to
+ % be called on THE NEXT LATEX RUN (via the aux file).
+ %
+ % Such preprogrammed definitions may be invoked anywhere in the text
+ %  by calling \citestyle{newbib}. This is only useful if the style specified
+ %  differs from that in \bibliographystyle.
+ %
+ % With \citeindextrue and \citeindexfalse, one can control whether the
+ % \cite commands make an automatic entry of the citation in the .idx
+ % indexing file. For this, \makeindex must also be given in the preamble.
+ %
+ % Package Options: (for selecting punctuation)
+ %   round  -  round parentheses are used (default)
+ %   square -  square brackets are used   [option]
+ %   curly  -  curly braces are used      {option}
+ %   angle  -  angle brackets are used    <option>
+ %   semicolon  -  multiple citations separated by semi-colon (default)
+ %   colon  - same as semicolon, an earlier confusion
+ %   comma  -  separated by comma
+ %   authoryear - selects author-year citations (default)
+ %   numbers-  selects numerical citations
+ %   super  -  numerical citations as superscripts
+ %   sort   -  sorts multiple citations according to order in ref. list
+ %   sort&compress   -  like sort, but also compresses numerical citations
+ %   compress - compresses without sorting
+ %   longnamesfirst  -  makes first citation full author list
+ %   sectionbib - puts bibliography in a \section* instead of \chapter*
+ %   merge - allows the citation key to have a * prefix,
+ %           signifying to merge its reference with that of the previous citation.
+ %   elide - if references are merged, repeated portions of later ones may be removed.
+ %   mcite - recognizes and ignores the * prefix for merging.
+ % Punctuation so selected dominates over any predefined ones.
+ % Package options are called as, e.g.
+ %        \usepackage[square,comma]{natbib}
+ % LaTeX the source file natbib.dtx to obtain more details
+ % or the file natnotes.tex for a brief reference sheet.
+ %-----------------------------------------------------------
+\providecommand\@ifxundefined[1]{%
+ \ifx#1\@undefined\expandafter\@firstoftwo\else\expandafter\@secondoftwo\fi
+}%
+\providecommand\@ifnum[1]{%
+ \ifnum#1\expandafter\@firstoftwo\else\expandafter\@secondoftwo\fi
+}%
+\providecommand\@ifx[1]{%
+ \ifx#1\expandafter\@firstoftwo\else\expandafter\@secondoftwo\fi
+}%
+\providecommand\appdef[2]{%
+ \toks@\expandafter{#1}\@temptokena{#2}%
+ \edef#1{\the\toks@\the\@temptokena}%
+}%
+\@ifclassloaded{agu2001}{\PackageError{natbib}
+  {The agu2001 class already includes natbib coding,\MessageBreak
+   so you should not add it explicitly}
+  {Type <Return> for now, but then later remove\MessageBreak
+   the command \protect\usepackage{natbib} from the document}
+  \endinput}{}
+\@ifclassloaded{agutex}{\PackageError{natbib}
+  {The AGUTeX class already includes natbib coding,\MessageBreak
+   so you should not add it explicitly}
+  {Type <Return> for now, but then later remove\MessageBreak
+   the command \protect\usepackage{natbib} from the document}
+  \endinput}{}
+\@ifclassloaded{aguplus}{\PackageError{natbib}
+  {The aguplus class already includes natbib coding,\MessageBreak
+   so you should not add it explicitly}
+  {Type <Return> for now, but then later remove\MessageBreak
+   the command \protect\usepackage{natbib} from the document}
+  \endinput}{}
+\@ifclassloaded{nlinproc}{\PackageError{natbib}
+  {The nlinproc class already includes natbib coding,\MessageBreak
+   so you should not add it explicitly}
+  {Type <Return> for now, but then later remove\MessageBreak
+   the command \protect\usepackage{natbib} from the document}
+  \endinput}{}
+\@ifclassloaded{egs}{\PackageError{natbib}
+  {The egs class already includes natbib coding,\MessageBreak
+   so you should not add it explicitly}
+  {Type <Return> for now, but then later remove\MessageBreak
+   the command \protect\usepackage{natbib} from the document}
+  \endinput}{}
+\@ifclassloaded{egu}{\PackageError{natbib}
+  {The egu class already includes natbib coding,\MessageBreak
+   so you should not add it explicitly}
+  {Type <Return> for now, but then later remove\MessageBreak
+   the command \protect\usepackage{natbib} from the document}
+  \endinput}{}
+ % Define citation punctuation for some author-year styles
+ % One may add and delete at this point
+ % Or put additions into local configuration file natbib.cfg
+\newcommand\bibstyle@chicago{\bibpunct{(}{)}{;}{a}{,}{,}}
+\newcommand\bibstyle@named{\bibpunct{[}{]}{;}{a}{,}{,}}
+\newcommand\bibstyle@agu{\bibpunct{[}{]}{;}{a}{,}{,~}}%Amer. Geophys. Union
+\newcommand\bibstyle@copernicus{\bibpunct{(}{)}{;}{a}{,}{,}}%Copernicus Publications
+\let\bibstyle@egu=\bibstyle@copernicus
+\let\bibstyle@egs=\bibstyle@copernicus
+\newcommand\bibstyle@agsm{\bibpunct{(}{)}{,}{a}{}{,}\gdef\harvardand{\&}}
+\newcommand\bibstyle@kluwer{\bibpunct{(}{)}{,}{a}{}{,}\gdef\harvardand{\&}}
+\newcommand\bibstyle@dcu{\bibpunct{(}{)}{;}{a}{;}{,}\gdef\harvardand{and}}
+\newcommand\bibstyle@aa{\bibpunct{(}{)}{;}{a}{}{,}} %Astronomy & Astrophysics
+\newcommand\bibstyle@pass{\bibpunct{(}{)}{;}{a}{,}{,}}%Planet. & Space Sci
+\newcommand\bibstyle@anngeo{\bibpunct{(}{)}{;}{a}{,}{,}}%Annales Geophysicae
+\newcommand\bibstyle@nlinproc{\bibpunct{(}{)}{;}{a}{,}{,}}%Nonlin.Proc.Geophys.
+ % Define citation punctuation for some numerical styles
+\newcommand\bibstyle@cospar{\bibpunct{/}{/}{,}{n}{}{}%
+     \gdef\bibnumfmt##1{##1.}}
+\newcommand\bibstyle@esa{\bibpunct{(Ref.~}{)}{,}{n}{}{}%
+     \gdef\bibnumfmt##1{##1.\hspace{1em}}}
+\newcommand\bibstyle@nature{\bibpunct{}{}{,}{s}{}{\textsuperscript{,}}%
+     \gdef\bibnumfmt##1{##1.}}
+ % The standard LaTeX styles
+\newcommand\bibstyle@plain{\bibpunct{[}{]}{,}{n}{}{,}}
+\let\bibstyle@alpha=\bibstyle@plain
+\let\bibstyle@abbrv=\bibstyle@plain
+\let\bibstyle@unsrt=\bibstyle@plain
+ % The author-year modifications of the standard styles
+\newcommand\bibstyle@plainnat{\bibpunct{[}{]}{,}{a}{,}{,}}
+\let\bibstyle@abbrvnat=\bibstyle@plainnat
+\let\bibstyle@unsrtnat=\bibstyle@plainnat
+\newif\ifNAT@numbers \NAT@numbersfalse
+\newif\ifNAT@super \NAT@superfalse
+\let\NAT@merge\z@
+\DeclareOption{numbers}{\NAT@numberstrue
+   \ExecuteOptions{square,comma,nobibstyle}}
+\DeclareOption{super}{\NAT@supertrue\NAT@numberstrue
+   \renewcommand\NAT@open{}\renewcommand\NAT@close{}
+   \ExecuteOptions{nobibstyle}}
+\DeclareOption{authoryear}{\NAT@numbersfalse
+   \ExecuteOptions{round,semicolon,bibstyle}}
+\DeclareOption{round}{%
+      \renewcommand\NAT@open{(} \renewcommand\NAT@close{)}
+   \ExecuteOptions{nobibstyle}}
+\DeclareOption{square}{%
+      \renewcommand\NAT@open{[} \renewcommand\NAT@close{]}
+   \ExecuteOptions{nobibstyle}}
+\DeclareOption{angle}{%
+      \renewcommand\NAT@open{$<$} \renewcommand\NAT@close{$>$}
+   \ExecuteOptions{nobibstyle}}
+\DeclareOption{curly}{%
+      \renewcommand\NAT@open{\{} \renewcommand\NAT@close{\}}
+   \ExecuteOptions{nobibstyle}}
+\DeclareOption{comma}{\renewcommand\NAT@sep{,}
+   \ExecuteOptions{nobibstyle}}
+\DeclareOption{semicolon}{\renewcommand\NAT@sep{;}
+   \ExecuteOptions{nobibstyle}}
+\DeclareOption{colon}{\ExecuteOptions{semicolon}}
+\DeclareOption{nobibstyle}{\let\bibstyle=\@gobble}
+\DeclareOption{bibstyle}{\let\bibstyle=\@citestyle}
+\newif\ifNAT@openbib \NAT@openbibfalse
+\DeclareOption{openbib}{\NAT@openbibtrue}
+\DeclareOption{sectionbib}{\def\NAT@sectionbib{on}}
+\def\NAT@sort{\z@}
+\def\NAT@cmprs{\z@}
+\DeclareOption{sort}{\def\NAT@sort{\@ne}}
+\DeclareOption{compress}{\def\NAT@cmprs{\@ne}}
+\DeclareOption{sort&compress}{\def\NAT@sort{\@ne}\def\NAT@cmprs{\@ne}}
+\DeclareOption{mcite}{\let\NAT@merge\@ne}
+\DeclareOption{merge}{\@ifnum{\NAT@merge<\tw@}{\let\NAT@merge\tw@}{}}
+\DeclareOption{elide}{\@ifnum{\NAT@merge<\thr@@}{\let\NAT@merge\thr@@}{}}
+\@ifpackageloaded{cite}{\PackageWarningNoLine{natbib}
+  {The `cite' package should not be used\MessageBreak
+   with natbib. Use option `sort' instead}\ExecuteOptions{sort}}{}
+\@ifpackageloaded{mcite}{\PackageWarningNoLine{natbib}
+  {The `mcite' package should not be used\MessageBreak
+   with natbib. Use option `merge' instead}\ExecuteOptions{merge}}{}
+\@ifpackageloaded{citeref}{\PackageError{natbib}
+  {The `citeref' package must be loaded after natbib}%
+  {Move \protect\usepackage{citeref} to after \string\usepackage{natbib}}}{}
+\newif\ifNAT@longnames\NAT@longnamesfalse
+\DeclareOption{longnamesfirst}{\NAT@longnamestrue}
+\DeclareOption{nonamebreak}{\def\NAT@nmfmt#1{\mbox{\NAT@up#1}}}
+\def\NAT@nmfmt#1{{\NAT@up#1}}
+\renewcommand\bibstyle[1]{\csname bibstyle@#1\endcsname}
+\AtBeginDocument{\global\let\bibstyle=\@gobble}
+\let\@citestyle\bibstyle
+\newcommand\citestyle[1]{\@citestyle{#1}\let\bibstyle\@gobble}
+\newcommand\bibpunct[7][, ]%
+  {\gdef\NAT@open{#2}\gdef\NAT@close{#3}\gdef
+   \NAT@sep{#4}\global\NAT@numbersfalse
+     \ifx #5n\global\NAT@numberstrue\global\NAT@superfalse
+   \else
+     \ifx #5s\global\NAT@numberstrue\global\NAT@supertrue
+   \fi\fi
+   \gdef\NAT@aysep{#6}\gdef\NAT@yrsep{#7}%
+   \gdef\NAT@cmt{#1}%
+   \NAT@@setcites
+  }
+\newcommand\setcitestyle[1]{
+ \@for\@tempa:=#1\do
+ {\def\@tempb{round}\ifx\@tempa\@tempb
+    \renewcommand\NAT@open{(}\renewcommand\NAT@close{)}\fi
+  \def\@tempb{square}\ifx\@tempa\@tempb
+    \renewcommand\NAT@open{[}\renewcommand\NAT@close{]}\fi
+  \def\@tempb{angle}\ifx\@tempa\@tempb
+    \renewcommand\NAT@open{$<$}\renewcommand\NAT@close{$>$}\fi
+  \def\@tempb{curly}\ifx\@tempa\@tempb
+    \renewcommand\NAT@open{\{}\renewcommand\NAT@close{\}}\fi
+  \def\@tempb{semicolon}\ifx\@tempa\@tempb
+    \renewcommand\NAT@sep{;}\fi
+  \def\@tempb{colon}\ifx\@tempa\@tempb
+    \renewcommand\NAT@sep{;}\fi
+  \def\@tempb{comma}\ifx\@tempa\@tempb
+    \renewcommand\NAT@sep{,}\fi
+  \def\@tempb{authoryear}\ifx\@tempa\@tempb
+    \NAT@numbersfalse\fi
+  \def\@tempb{numbers}\ifx\@tempa\@tempb
+    \NAT@numberstrue\NAT@superfalse\fi
+  \def\@tempb{super}\ifx\@tempa\@tempb
+    \NAT@numberstrue\NAT@supertrue\fi
+  \expandafter\NAT@find@eq\@tempa=\relax\@nil
+  \if\@tempc\relax\else
+    \expandafter\NAT@rem@eq\@tempc
+    \def\@tempb{open}\ifx\@tempa\@tempb
+     \xdef\NAT@open{\@tempc}\fi
+    \def\@tempb{close}\ifx\@tempa\@tempb
+     \xdef\NAT@close{\@tempc}\fi
+    \def\@tempb{aysep}\ifx\@tempa\@tempb
+     \xdef\NAT@aysep{\@tempc}\fi
+    \def\@tempb{yysep}\ifx\@tempa\@tempb
+     \xdef\NAT@yrsep{\@tempc}\fi
+    \def\@tempb{notesep}\ifx\@tempa\@tempb
+     \xdef\NAT@cmt{\@tempc}\fi
+    \def\@tempb{citesep}\ifx\@tempa\@tempb
+     \xdef\NAT@sep{\@tempc}\fi
+  \fi
+ }%
+ \NAT@@setcites
+}
+ \def\NAT@find@eq#1=#2\@nil{\def\@tempa{#1}\def\@tempc{#2}}
+ \def\NAT@rem@eq#1={\def\@tempc{#1}}
+ \def\NAT@@setcites{\global\let\bibstyle\@gobble}
+\AtBeginDocument{\let\NAT@@setcites\NAT@set@cites}
+\newcommand\NAT@open{(} \newcommand\NAT@close{)}
+\newcommand\NAT@sep{;}
+\ProcessOptions
+\newcommand\NAT@aysep{,} \newcommand\NAT@yrsep{,}
+\newcommand\NAT@cmt{, }
+\newcommand\NAT@cite%
+    [3]{\ifNAT@swa\NAT@@open\if*#2*\else#2\NAT@spacechar\fi
+        #1\if*#3*\else\NAT@cmt#3\fi\NAT@@close\else#1\fi\endgroup}
+\newcommand\NAT@citenum%
+    [3]{\ifNAT@swa\NAT@@open\if*#2*\else#2\NAT@spacechar\fi
+        #1\if*#3*\else\NAT@cmt#3\fi\NAT@@close\else#1\fi\endgroup}
+\newcommand\NAT@citesuper[3]{\ifNAT@swa
+\if*#2*\else#2\NAT@spacechar\fi
+\unskip\kern\p@\textsuperscript{\NAT@@open#1\NAT@@close}%
+   \if*#3*\else\NAT@spacechar#3\fi\else #1\fi\endgroup}
+\providecommand\textsuperscript[1]{\mbox{$^{\mbox{\scriptsize#1}}$}}
+\begingroup \catcode`\_=8
+\gdef\NAT@ifcat@num#1{%
+ \ifcat_\ifnum\z@<0#1_\else A\fi
+  \expandafter\@firstoftwo
+ \else
+  \expandafter\@secondoftwo
+ \fi
+}%
+\endgroup
+\providecommand\@firstofone[1]{#1}
+\newcommand\NAT@citexnum{}
+\def\NAT@citexnum[#1][#2]#3{%
+  \NAT@reset@parser
+  \NAT@sort@cites{#3}%
+  \NAT@reset@citea
+  \@cite{\def\NAT@num{-1}\let\NAT@last@yr\relax\let\NAT@nm\@empty
+    \@for\@citeb:=\NAT@cite@list\do
+    {\@safe@activestrue
+     \edef\@citeb{\expandafter\@firstofone\@citeb\@empty}%
+     \@safe@activesfalse
+     \@ifundefined{b@\@citeb\@extra@b@citeb}{%
+       {\reset@font\bfseries?}
+        \NAT@citeundefined\PackageWarning{natbib}%
+       {Citation `\@citeb' on page \thepage \space undefined}}%
+     {\let\NAT@last@num\NAT@num\let\NAT@last@nm\NAT@nm
+      \NAT@parse{\@citeb}%
+      \ifNAT@longnames\@ifundefined{bv@\@citeb\@extra@b@citeb}{%
+        \let\NAT@name=\NAT@all@names
+        \global\@namedef{bv@\@citeb\@extra@b@citeb}{}}{}%
+      \fi
+      \ifNAT@full\let\NAT@nm\NAT@all@names\else
+        \let\NAT@nm\NAT@name\fi
+      \ifNAT@swa
+       \@ifnum{\NAT@ctype>\@ne}{%
+        \@citea
+        \NAT@hyper@{\@ifnum{\NAT@ctype=\tw@}{\NAT@test{\NAT@ctype}}{\NAT@alias}}%
+       }{%
+        \@ifnum{\NAT@cmprs>\z@}{%
+         \NAT@ifcat@num\NAT@num
+          {\let\NAT@nm=\NAT@num}%
+          {\def\NAT@nm{-2}}%
+         \NAT@ifcat@num\NAT@last@num
+          {\@tempcnta=\NAT@last@num\relax}%
+          {\@tempcnta\m@ne}%
+         \@ifnum{\NAT@nm=\@tempcnta}{%
+          \@ifnum{\NAT@merge>\@ne}{}{\NAT@last@yr@mbox}%
+         }{%
+           \advance\@tempcnta by\@ne
+           \@ifnum{\NAT@nm=\@tempcnta}{%
+             \ifx\NAT@last@yr\relax
+               \def@NAT@last@yr{\@citea}%
+             \else
+               \def@NAT@last@yr{--\NAT@penalty}%
+             \fi
+           }{%
+             \NAT@last@yr@mbox
+           }%
+         }%
+        }{%
+         \@tempswatrue
+         \@ifnum{\NAT@merge>\@ne}{\@ifnum{\NAT@last@num=\NAT@num\relax}{\@tempswafalse}{}}{}%
+         \if@tempswa\NAT@citea@mbox\fi
+        }%
+       }%
+       \NAT@def@citea
+      \else
+        \ifcase\NAT@ctype
+          \ifx\NAT@last@nm\NAT@nm \NAT@yrsep\NAT@penalty\NAT@space\else
+            \@citea \NAT@test{\@ne}\NAT@spacechar\NAT@mbox{\NAT@super@kern\NAT@@open}%
+          \fi
+          \if*#1*\else#1\NAT@spacechar\fi
+          \NAT@mbox{\NAT@hyper@{{\citenumfont{\NAT@num}}}}%
+          \NAT@def@citea@box
+        \or
+          \NAT@hyper@citea@space{\NAT@test{\NAT@ctype}}%
+        \or
+          \NAT@hyper@citea@space{\NAT@test{\NAT@ctype}}%
+        \or
+          \NAT@hyper@citea@space\NAT@alias
+        \fi
+      \fi
+     }%
+    }%
+      \@ifnum{\NAT@cmprs>\z@}{\NAT@last@yr}{}%
+      \ifNAT@swa\else
+        \@ifnum{\NAT@ctype=\z@}{%
+          \if*#2*\else\NAT@cmt#2\fi
+        }{}%
+        \NAT@mbox{\NAT@@close}%
+      \fi
+  }{#1}{#2}%
+}%
+\def\NAT@citea@mbox{%
+ \@citea\mbox{\NAT@hyper@{{\citenumfont{\NAT@num}}}}%
+}%
+\def\NAT@hyper@#1{%
+ \hyper@natlinkstart{\@citeb\@extra@b@citeb}#1\hyper@natlinkend
+}%
+\def\NAT@hyper@citea#1{%
+ \@citea
+ \NAT@hyper@{#1}%
+ \NAT@def@citea
+}%
+\def\NAT@hyper@citea@space#1{%
+ \@citea
+ \NAT@hyper@{#1}%
+ \NAT@def@citea@space
+}%
+\def\def@NAT@last@yr#1{%
+ \protected@edef\NAT@last@yr{%
+  #1%
+  \noexpand\mbox{%
+   \noexpand\hyper@natlinkstart{\@citeb\@extra@b@citeb}%
+   {\noexpand\citenumfont{\NAT@num}}%
+   \noexpand\hyper@natlinkend
+  }%
+ }%
+}%
+\def\NAT@last@yr@mbox{%
+ \NAT@last@yr\let\NAT@last@yr\relax
+ \NAT@citea@mbox
+}%
+\newcommand\NAT@test[1]{%
+ \@ifnum{#1=\@ne}{%
+  \ifx\NAT@nm\NAT@noname
+   \begingroup\reset@font\bfseries(author?)\endgroup
+   \PackageWarning{natbib}{%
+    Author undefined for citation`\@citeb' \MessageBreak on page \thepage%
+   }%
+  \else \NAT@nm
+  \fi
+ }{%
+  \if\relax\NAT@date\relax
+   \begingroup\reset@font\bfseries(year?)\endgroup
+   \PackageWarning{natbib}{%
+    Year undefined for citation`\@citeb' \MessageBreak on page \thepage%
+   }%
+  \else \NAT@date
+  \fi
+ }%
+}%
+\let\citenumfont=\@empty
+\newcommand\NAT@citex{}
+\def\NAT@citex%
+  [#1][#2]#3{%
+  \NAT@reset@parser
+  \NAT@sort@cites{#3}%
+  \NAT@reset@citea
+  \@cite{\let\NAT@nm\@empty\let\NAT@year\@empty
+    \@for\@citeb:=\NAT@cite@list\do
+    {\@safe@activestrue
+     \edef\@citeb{\expandafter\@firstofone\@citeb\@empty}%
+     \@safe@activesfalse
+     \@ifundefined{b@\@citeb\@extra@b@citeb}{\@citea%
+       {\reset@font\bfseries ?}\NAT@citeundefined
+                 \PackageWarning{natbib}%
+       {Citation `\@citeb' on page \thepage \space undefined}\def\NAT@date{}}%
+     {\let\NAT@last@nm=\NAT@nm\let\NAT@last@yr=\NAT@year
+      \NAT@parse{\@citeb}%
+      \ifNAT@longnames\@ifundefined{bv@\@citeb\@extra@b@citeb}{%
+        \let\NAT@name=\NAT@all@names
+        \global\@namedef{bv@\@citeb\@extra@b@citeb}{}}{}%
+      \fi
+     \ifNAT@full\let\NAT@nm\NAT@all@names\else
+       \let\NAT@nm\NAT@name\fi
+     \ifNAT@swa\ifcase\NAT@ctype
+       \if\relax\NAT@date\relax
+         \@citea\NAT@hyper@{\NAT@nmfmt{\NAT@nm}\NAT@date}%
+       \else
+         \ifx\NAT@last@nm\NAT@nm\NAT@yrsep
+            \ifx\NAT@last@yr\NAT@year
+              \def\NAT@temp{{?}}%
+              \ifx\NAT@temp\NAT@exlab\PackageWarningNoLine{natbib}%
+               {Multiple citation on page \thepage: same authors and
+               year\MessageBreak without distinguishing extra
+               letter,\MessageBreak appears as question mark}\fi
+              \NAT@hyper@{\NAT@exlab}%
+            \else\unskip\NAT@spacechar
+              \NAT@hyper@{\NAT@date}%
+            \fi
+         \else
+           \@citea\NAT@hyper@{%
+             \NAT@nmfmt{\NAT@nm}%
+             \hyper@natlinkbreak{%
+               \NAT@aysep\NAT@spacechar}{\@citeb\@extra@b@citeb
+             }%
+             \NAT@date
+           }%
+         \fi
+       \fi
+     \or\@citea\NAT@hyper@{\NAT@nmfmt{\NAT@nm}}%
+     \or\@citea\NAT@hyper@{\NAT@date}%
+     \or\@citea\NAT@hyper@{\NAT@alias}%
+     \fi \NAT@def@citea
+     \else
+       \ifcase\NAT@ctype
+        \if\relax\NAT@date\relax
+          \@citea\NAT@hyper@{\NAT@nmfmt{\NAT@nm}}%
+        \else
+         \ifx\NAT@last@nm\NAT@nm\NAT@yrsep
+            \ifx\NAT@last@yr\NAT@year
+              \def\NAT@temp{{?}}%
+              \ifx\NAT@temp\NAT@exlab\PackageWarningNoLine{natbib}%
+               {Multiple citation on page \thepage: same authors and
+               year\MessageBreak without distinguishing extra
+               letter,\MessageBreak appears as question mark}\fi
+              \NAT@hyper@{\NAT@exlab}%
+            \else
+              \unskip\NAT@spacechar
+              \NAT@hyper@{\NAT@date}%
+            \fi
+         \else
+           \@citea\NAT@hyper@{%
+             \NAT@nmfmt{\NAT@nm}%
+             \hyper@natlinkbreak{\NAT@spacechar\NAT@@open\if*#1*\else#1\NAT@spacechar\fi}%
+               {\@citeb\@extra@b@citeb}%
+             \NAT@date
+           }%
+         \fi
+        \fi
+       \or\@citea\NAT@hyper@{\NAT@nmfmt{\NAT@nm}}%
+       \or\@citea\NAT@hyper@{\NAT@date}%
+       \or\@citea\NAT@hyper@{\NAT@alias}%
+       \fi
+       \if\relax\NAT@date\relax
+         \NAT@def@citea
+       \else
+         \NAT@def@citea@close
+       \fi
+     \fi
+     }}\ifNAT@swa\else\if*#2*\else\NAT@cmt#2\fi
+     \if\relax\NAT@date\relax\else\NAT@@close\fi\fi}{#1}{#2}}
+\def\NAT@spacechar{\ }%
+\def\NAT@separator{\NAT@sep\NAT@penalty}%
+\def\NAT@reset@citea{\c@NAT@ctr\@ne\let\@citea\@empty}%
+\def\NAT@def@citea{\def\@citea{\NAT@separator\NAT@space}}%
+\def\NAT@def@citea@space{\def\@citea{\NAT@separator\NAT@spacechar}}%
+\def\NAT@def@citea@close{\def\@citea{\NAT@@close\NAT@separator\NAT@space}}%
+\def\NAT@def@citea@box{\def\@citea{\NAT@mbox{\NAT@@close}\NAT@separator\NAT@spacechar}}%
+\newif\ifNAT@par \NAT@partrue
+\newcommand\NAT@@open{\ifNAT@par\NAT@open\fi}
+\newcommand\NAT@@close{\ifNAT@par\NAT@close\fi}
+\newcommand\NAT@alias{\@ifundefined{al@\@citeb\@extra@b@citeb}{%
+  {\reset@font\bfseries(alias?)}\PackageWarning{natbib}
+  {Alias undefined for citation `\@citeb'
+  \MessageBreak on page \thepage}}{\@nameuse{al@\@citeb\@extra@b@citeb}}}
+\let\NAT@up\relax
+\newcommand\NAT@Up[1]{{\let\protect\@unexpandable@protect\let~\relax
+  \expandafter\NAT@deftemp#1}\expandafter\NAT@UP\NAT@temp}
+\newcommand\NAT@deftemp[1]{\xdef\NAT@temp{#1}}
+\newcommand\NAT@UP[1]{\let\@tempa\NAT@UP\ifcat a#1\MakeUppercase{#1}%
+  \let\@tempa\relax\else#1\fi\@tempa}
+\newcommand\shortcites[1]{%
+  \@bsphack\@for\@citeb:=#1\do
+  {\@safe@activestrue
+   \edef\@citeb{\expandafter\@firstofone\@citeb\@empty}%
+   \@safe@activesfalse
+   \global\@namedef{bv@\@citeb\@extra@b@citeb}{}}\@esphack}
+\newcommand\NAT@biblabel[1]{\hfill}
+\newcommand\NAT@biblabelnum[1]{\bibnumfmt{#1}}
+\let\bibnumfmt\@empty
+\providecommand\@biblabel[1]{[#1]}
+\AtBeginDocument{\ifx\bibnumfmt\@empty\let\bibnumfmt\@biblabel\fi}
+\newcommand\NAT@bibsetnum[1]{\settowidth\labelwidth{\@biblabel{#1}}%
+   \setlength{\leftmargin}{\labelwidth}\addtolength{\leftmargin}{\labelsep}%
+   \setlength{\itemsep}{\bibsep}\setlength{\parsep}{\z@}%
+   \ifNAT@openbib
+     \addtolength{\leftmargin}{\bibindent}%
+     \setlength{\itemindent}{-\bibindent}%
+     \setlength{\listparindent}{\itemindent}%
+     \setlength{\parsep}{0pt}%
+   \fi
+}
+\newlength{\bibhang}
+\setlength{\bibhang}{1em}
+\newlength{\bibsep}
+ {\@listi \global\bibsep\itemsep \global\advance\bibsep by\parsep}
+
+\newcommand\NAT@bibsetup%
+   [1]{\setlength{\leftmargin}{\bibhang}\setlength{\itemindent}{-\leftmargin}%
+       \setlength{\itemsep}{\bibsep}\setlength{\parsep}{\z@}}
+\newcommand\NAT@set@cites{%
+  \ifNAT@numbers
+    \ifNAT@super \let\@cite\NAT@citesuper
+       \def\NAT@mbox##1{\unskip\nobreak\textsuperscript{##1}}%
+       \let\citeyearpar=\citeyear
+       \let\NAT@space\relax
+       \def\NAT@super@kern{\kern\p@}%
+    \else
+       \let\NAT@mbox=\mbox
+       \let\@cite\NAT@citenum
+       \let\NAT@space\NAT@spacechar
+       \let\NAT@super@kern\relax
+    \fi
+    \let\@citex\NAT@citexnum
+    \let\@biblabel\NAT@biblabelnum
+    \let\@bibsetup\NAT@bibsetnum
+    \renewcommand\NAT@idxtxt{\NAT@name\NAT@spacechar\NAT@open\NAT@num\NAT@close}%
+    \def\natexlab##1{}%
+    \def\NAT@penalty{\penalty\@m}%
+  \else
+    \let\@cite\NAT@cite
+    \let\@citex\NAT@citex
+    \let\@biblabel\NAT@biblabel
+    \let\@bibsetup\NAT@bibsetup
+    \let\NAT@space\NAT@spacechar
+    \let\NAT@penalty\@empty
+    \renewcommand\NAT@idxtxt{\NAT@name\NAT@spacechar\NAT@open\NAT@date\NAT@close}%
+    \def\natexlab##1{##1}%
+  \fi}
+\AtBeginDocument{\NAT@set@cites}
+\AtBeginDocument{\ifx\SK@def\@undefined\else
+\ifx\SK@cite\@empty\else
+  \SK@def\@citex[#1][#2]#3{\SK@\SK@@ref{#3}\SK@@citex[#1][#2]{#3}}\fi
+\ifx\SK@citeauthor\@undefined\def\HAR@checkdef{}\else
+  \let\citeauthor\SK@citeauthor
+  \let\citefullauthor\SK@citefullauthor
+  \let\citeyear\SK@citeyear\fi
+\fi}
+\newif\ifNAT@full\NAT@fullfalse
+\newif\ifNAT@swa
+\DeclareRobustCommand\citet
+   {\begingroup\NAT@swafalse\let\NAT@ctype\z@\NAT@partrue
+     \@ifstar{\NAT@fulltrue\NAT@citetp}{\NAT@fullfalse\NAT@citetp}}
+\newcommand\NAT@citetp{\@ifnextchar[{\NAT@@citetp}{\NAT@@citetp[]}}
+\newcommand\NAT@@citetp{}
+\def\NAT@@citetp[#1]{\@ifnextchar[{\@citex[#1]}{\@citex[][#1]}}
+\DeclareRobustCommand\citep
+   {\begingroup\NAT@swatrue\let\NAT@ctype\z@\NAT@partrue
+         \@ifstar{\NAT@fulltrue\NAT@citetp}{\NAT@fullfalse\NAT@citetp}}
+\DeclareRobustCommand\cite
+    {\begingroup\let\NAT@ctype\z@\NAT@partrue\NAT@swatrue
+      \@ifstar{\NAT@fulltrue\NAT@cites}{\NAT@fullfalse\NAT@cites}}
+\newcommand\NAT@cites{\@ifnextchar [{\NAT@@citetp}{%
+     \ifNAT@numbers\else
+     \NAT@swafalse
+     \fi
+    \NAT@@citetp[]}}
+\DeclareRobustCommand\citealt
+   {\begingroup\NAT@swafalse\let\NAT@ctype\z@\NAT@parfalse
+         \@ifstar{\NAT@fulltrue\NAT@citetp}{\NAT@fullfalse\NAT@citetp}}
+\DeclareRobustCommand\citealp
+   {\begingroup\NAT@swatrue\let\NAT@ctype\z@\NAT@parfalse
+         \@ifstar{\NAT@fulltrue\NAT@citetp}{\NAT@fullfalse\NAT@citetp}}
+\DeclareRobustCommand\citenum
+   {\begingroup
+     \NAT@swatrue\let\NAT@ctype\z@\NAT@parfalse\let\textsuperscript\NAT@spacechar
+     \NAT@citexnum[][]}
+\DeclareRobustCommand\citeauthor
+   {\begingroup\NAT@swafalse\let\NAT@ctype\@ne\NAT@parfalse
+    \@ifstar{\NAT@fulltrue\NAT@citetp}{\NAT@fullfalse\NAT@citetp}}
+\DeclareRobustCommand\Citet
+   {\begingroup\NAT@swafalse\let\NAT@ctype\z@\NAT@partrue
+     \let\NAT@up\NAT@Up
+     \@ifstar{\NAT@fulltrue\NAT@citetp}{\NAT@fullfalse\NAT@citetp}}
+\DeclareRobustCommand\Citep
+   {\begingroup\NAT@swatrue\let\NAT@ctype\z@\NAT@partrue
+     \let\NAT@up\NAT@Up
+         \@ifstar{\NAT@fulltrue\NAT@citetp}{\NAT@fullfalse\NAT@citetp}}
+\DeclareRobustCommand\Citealt
+   {\begingroup\NAT@swafalse\let\NAT@ctype\z@\NAT@parfalse
+     \let\NAT@up\NAT@Up
+         \@ifstar{\NAT@fulltrue\NAT@citetp}{\NAT@fullfalse\NAT@citetp}}
+\DeclareRobustCommand\Citealp
+   {\begingroup\NAT@swatrue\let\NAT@ctype\z@\NAT@parfalse
+     \let\NAT@up\NAT@Up
+         \@ifstar{\NAT@fulltrue\NAT@citetp}{\NAT@fullfalse\NAT@citetp}}
+\DeclareRobustCommand\Citeauthor
+   {\begingroup\NAT@swafalse\let\NAT@ctype\@ne\NAT@parfalse
+     \let\NAT@up\NAT@Up
+    \@ifstar{\NAT@fulltrue\NAT@citetp}{\NAT@fullfalse\NAT@citetp}}
+\DeclareRobustCommand\citeyear
+   {\begingroup\NAT@swafalse\let\NAT@ctype\tw@\NAT@parfalse\NAT@citetp}
+\DeclareRobustCommand\citeyearpar
+   {\begingroup\NAT@swatrue\let\NAT@ctype\tw@\NAT@partrue\NAT@citetp}
+\newcommand\citetext[1]{\NAT@open#1\NAT@close}
+\DeclareRobustCommand\citefullauthor
+   {\citeauthor*}
+\newcommand\defcitealias[2]{%
+   \@ifundefined{al@#1\@extra@b@citeb}{}
+   {\PackageWarning{natbib}{Overwriting existing alias for citation #1}}
+   \@namedef{al@#1\@extra@b@citeb}{#2}}
+\DeclareRobustCommand\citetalias{\begingroup
+   \NAT@swafalse\let\NAT@ctype\thr@@\NAT@parfalse\NAT@citetp}
+\DeclareRobustCommand\citepalias{\begingroup
+   \NAT@swatrue\let\NAT@ctype\thr@@\NAT@partrue\NAT@citetp}
+\renewcommand\nocite[1]{\@bsphack
+  \@for\@citeb:=#1\do{%
+    \@safe@activestrue
+    \edef\@citeb{\expandafter\@firstofone\@citeb\@empty}%
+    \@safe@activesfalse
+    \if@filesw\immediate\write\@auxout{\string\citation{\@citeb}}\fi
+    \if*\@citeb\else
+    \@ifundefined{b@\@citeb\@extra@b@citeb}{%
+       \NAT@citeundefined \PackageWarning{natbib}%
+       {Citation `\@citeb' undefined}}{}\fi}%
+  \@esphack}
+\newcommand\NAT@parse[1]{%
+  \begingroup
+   \let\protect=\@unexpandable@protect
+   \let~\relax
+   \let\active@prefix=\@gobble
+   \edef\NAT@temp{\csname b@#1\@extra@b@citeb\endcsname}%
+   \aftergroup\NAT@split
+   \expandafter
+  \endgroup
+  \NAT@temp{}{}{}{}{}@@%
+  \expandafter\NAT@parse@date\NAT@date??????@@%
+  \ifciteindex\NAT@index\fi
+}%
+\def\NAT@split#1#2#3#4#5@@{%
+  \gdef\NAT@num{#1}\gdef\NAT@name{#3}\gdef\NAT@date{#2}%
+  \gdef\NAT@all@names{#4}%
+  \ifx\NAT@num\@empty\gdef\NAT@num{0}\fi
+  \ifx\NAT@noname\NAT@all@names \gdef\NAT@all@names{#3}\fi
+}%
+\def\NAT@reset@parser{%
+  \global\let\NAT@num\@empty
+  \global\let\NAT@name\@empty
+  \global\let\NAT@date\@empty
+  \global\let\NAT@all@names\@empty
+}%
+\newcommand\NAT@parse@date{}
+\def\NAT@parse@date#1#2#3#4#5#6@@{%
+  \ifnum\the\catcode`#1=11\def\NAT@year{}\def\NAT@exlab{#1}\else
+  \ifnum\the\catcode`#2=11\def\NAT@year{#1}\def\NAT@exlab{#2}\else
+  \ifnum\the\catcode`#3=11\def\NAT@year{#1#2}\def\NAT@exlab{#3}\else
+  \ifnum\the\catcode`#4=11\def\NAT@year{#1#2#3}\def\NAT@exlab{#4}\else
+    \def\NAT@year{#1#2#3#4}\def\NAT@exlab{{#5}}\fi\fi\fi\fi}
+\newcommand\NAT@index{}
+\let\NAT@makeindex=\makeindex
+\renewcommand\makeindex{\NAT@makeindex
+  \renewcommand\NAT@index{\@bsphack\begingroup
+     \def~{\string~}\@wrindex{\NAT@idxtxt}}}
+\newcommand\NAT@idxtxt{\NAT@name\NAT@spacechar\NAT@open\NAT@date\NAT@close}
+\@ifxundefined\@indexfile{}{\let\NAT@makeindex\relax\makeindex}
+\newif\ifciteindex \citeindexfalse
+\newcommand\citeindextype{default}
+\newcommand\NAT@index@alt{{\let\protect=\noexpand\let~\relax
+  \xdef\NAT@temp{\NAT@idxtxt}}\expandafter\NAT@exp\NAT@temp\@nil}
+\newcommand\NAT@exp{}
+\def\NAT@exp#1\@nil{\index[\citeindextype]{#1}}
+
+\AtBeginDocument{%
+\@ifpackageloaded{index}{\let\NAT@index=\NAT@index@alt}{}}
+\newcommand\NAT@ifcmd{\futurelet\NAT@temp\NAT@ifxcmd}
+\newcommand\NAT@ifxcmd{\ifx\NAT@temp\relax\else\expandafter\NAT@bare\fi}
+\def\NAT@bare#1(#2)#3(@)#4\@nil#5{%
+  \if @#2
+    \expandafter\NAT@apalk#1, , \@nil{#5}%
+  \else
+  \NAT@wrout{\the\c@NAT@ctr}{#2}{#1}{#3}{#5}%
+\fi
+}
+\newcommand\NAT@wrout[5]{%
+\if@filesw
+      {\let\protect\noexpand\let~\relax
+       \immediate
+       \write\@auxout{\string\bibcite{#5}{{#1}{#2}{{#3}}{{#4}}}}}\fi
+\ignorespaces}
+\def\NAT@noname{{}}
+\renewcommand\bibitem{\@ifnextchar[{\@lbibitem}{\@lbibitem[]}}%
+\let\NAT@bibitem@first@sw\@secondoftwo
+\def\@lbibitem[#1]#2{%
+  \if\relax\@extra@b@citeb\relax\else
+    \@ifundefined{br@#2\@extra@b@citeb}{}{%
+     \@namedef{br@#2}{\@nameuse{br@#2\@extra@b@citeb}}%
+    }%
+  \fi
+  \@ifundefined{b@#2\@extra@b@citeb}{%
+   \def\NAT@num{}%
+  }{%
+   \NAT@parse{#2}%
+  }%
+  \def\NAT@tmp{#1}%
+  \expandafter\let\expandafter\bibitemOpen\csname NAT@b@open@#2\endcsname
+  \expandafter\let\expandafter\bibitemShut\csname NAT@b@shut@#2\endcsname
+  \@ifnum{\NAT@merge>\@ne}{%
+   \NAT@bibitem@first@sw{%
+    \@firstoftwo
+   }{%
+    \@ifundefined{NAT@b*@#2}{%
+     \@firstoftwo
+    }{%
+     \expandafter\def\expandafter\NAT@num\expandafter{\the\c@NAT@ctr}%
+     \@secondoftwo
+    }%
+   }%
+  }{%
+   \@firstoftwo
+  }%
+  {%
+   \global\advance\c@NAT@ctr\@ne
+   \@ifx{\NAT@tmp\@empty}{\@firstoftwo}{%
+    \@secondoftwo
+   }%
+   {%
+    \expandafter\def\expandafter\NAT@num\expandafter{\the\c@NAT@ctr}%
+    \global\NAT@stdbsttrue
+   }{}%
+   \bibitem@fin
+   \item[\hfil\NAT@anchor{#2}{\NAT@num}]%
+   \global\let\NAT@bibitem@first@sw\@secondoftwo
+   \NAT@bibitem@init
+  }%
+  {%
+   \NAT@anchor{#2}{}%
+   \NAT@bibitem@cont
+   \bibitem@fin
+  }%
+  \@ifx{\NAT@tmp\@empty}{%
+    \NAT@wrout{\the\c@NAT@ctr}{}{}{}{#2}%
+  }{%
+    \expandafter\NAT@ifcmd\NAT@tmp(@)(@)\@nil{#2}%
+  }%
+}%
+\def\bibitem@fin{%
+ \@ifxundefined\@bibstop{}{\csname bibitem@\@bibstop\endcsname}%
+}%
+\def\NAT@bibitem@init{%
+ \let\@bibstop\@undefined
+}%
+\def\NAT@bibitem@cont{%
+ \let\bibitem@Stop\bibitemStop
+ \let\bibitem@NoStop\bibitemContinue
+}%
+\def\BibitemOpen{%
+ \bibitemOpen
+}%
+\def\BibitemShut#1{%
+ \bibitemShut
+ \def\@bibstop{#1}%
+ \let\bibitem@Stop\bibitemStop
+ \let\bibitem@NoStop\bibitemNoStop
+}%
+\def\bibitemStop{}%
+\def\bibitemNoStop{.\spacefactor\@mmm\space}%
+\def\bibitemContinue{\spacefactor\@mmm\space}%
+\mathchardef\@mmm=3000 %
+\providecommand{\bibAnnote}[3]{%
+  \BibitemShut{#1}%
+  \def\@tempa{#3}\@ifx{\@tempa\@empty}{}{%
+   \begin{quotation}\noindent
+    \textsc{Key:}\ #2\\\textsc{Annotation:}\ \@tempa
+   \end{quotation}%
+  }%
+}%
+\providecommand{\bibAnnoteFile}[2]{%
+  \IfFileExists{#2}{%
+    \bibAnnote{#1}{#2}{\input{#2}}%
+  }{%
+    \bibAnnote{#1}{#2}{}%
+  }%
+}%
+\let\bibitemOpen\relax
+\let\bibitemShut\relax
+\def\bibfield{\@ifnum{\NAT@merge>\tw@}{\@bibfield}{\@secondoftwo}}%
+\def\@bibfield#1#2{%
+ \begingroup
+  \let\Doi\@gobble
+  \let\bibinfo\relax
+  \let\restore@protect\@empty
+  \protected@edef\@tempa{#2}%
+  \aftergroup\def\aftergroup\@tempa
+ \expandafter\endgroup\expandafter{\@tempa}%
+ \expandafter\@ifx\expandafter{\csname @bib#1\endcsname\@tempa}{%
+  \expandafter\let\expandafter\@tempa\csname @bib@X#1\endcsname
+ }{%
+  \expandafter\let\csname @bib#1\endcsname\@tempa
+  \expandafter\let\expandafter\@tempa\csname @bib@Y#1\endcsname
+ }%
+ \@ifx{\@tempa\relax}{\let\@tempa\@firstofone}{}%
+ \@tempa{#2}%
+}%
+\def\bibinfo#1{%
+ \expandafter\let\expandafter\@tempa\csname bibinfo@X@#1\endcsname
+ \@ifx{\@tempa\relax}{\@firstofone}{\@tempa}%
+}%
+\def\@bib@Xauthor#1{\let\@bib@Xjournal\@gobble}%
+\def\@bib@Xjournal#1{\begingroup\let\bibinfo@X@journal\@bib@Z@journal#1\endgroup}%
+\def\@bibibid@#1{\textit{ibid}.}%
+\appdef\NAT@bibitem@init{%
+ \let\@bibauthor  \@empty
+ \let\@bibjournal \@empty
+ \let\@bib@Z@journal\@bibibid@
+}%
+\ifx\SK@lbibitem\@undefined\else
+   \let\SK@lbibitem\@lbibitem
+   \def\@lbibitem[#1]#2{%
+     \SK@lbibitem[#1]{#2}\SK@\SK@@label{#2}\ignorespaces}\fi
+\newif\ifNAT@stdbst \NAT@stdbstfalse
+
+\AtEndDocument{%
+  \ifNAT@stdbst\if@filesw
+   \immediate\write\@auxout{%
+    \string\providecommand\string\NAT@force@numbers{}%
+    \string\NAT@force@numbers
+   }%
+  \fi\fi
+ }
+\newcommand\NAT@force@numbers{%
+  \ifNAT@numbers\else
+  \PackageError{natbib}{Bibliography not compatible with author-year
+  citations.\MessageBreak
+  Press <return> to continue in numerical citation style}
+  {Check the bibliography entries for non-compliant syntax,\MessageBreak
+   or select author-year BibTeX style, e.g. plainnat}%
+  \global\NAT@numberstrue\fi}
+
+\providecommand\bibcite{}
+\renewcommand\bibcite[2]{%
+ \@ifundefined{b@#1\@extra@binfo}{\relax}{%
+   \NAT@citemultiple
+   \PackageWarningNoLine{natbib}{Citation `#1' multiply defined}%
+ }%
+ \global\@namedef{b@#1\@extra@binfo}{#2}%
+}%
+\AtEndDocument{\NAT@swatrue\let\bibcite\NAT@testdef}
+\newcommand\NAT@testdef[2]{%
+  \def\NAT@temp{#2}%
+  \expandafter \ifx \csname b@#1\@extra@binfo\endcsname\NAT@temp
+  \else
+    \ifNAT@swa \NAT@swafalse
+      \PackageWarningNoLine{natbib}{%
+        Citation(s) may have changed.\MessageBreak
+        Rerun to get citations correct%
+      }%
+    \fi
+  \fi
+}%
+\newcommand\NAT@apalk{}
+\def\NAT@apalk#1, #2, #3\@nil#4{%
+  \if\relax#2\relax
+    \global\NAT@stdbsttrue
+    \NAT@wrout{#1}{}{}{}{#4}%
+  \else
+    \NAT@wrout{\the\c@NAT@ctr}{#2}{#1}{}{#4}%
+  \fi
+}%
+\newcommand\citeauthoryear{}
+\def\citeauthoryear#1#2#3(@)(@)\@nil#4{%
+  \if\relax#3\relax
+    \NAT@wrout{\the\c@NAT@ctr}{#2}{#1}{}{#4}%
+  \else
+    \NAT@wrout{\the\c@NAT@ctr}{#3}{#2}{#1}{#4}%
+  \fi
+}%
+\newcommand\citestarts{\NAT@open}%
+\newcommand\citeends{\NAT@close}%
+\newcommand\betweenauthors{and}%
+\newcommand\astroncite{}
+\def\astroncite#1#2(@)(@)\@nil#3{%
+ \NAT@wrout{\the\c@NAT@ctr}{#2}{#1}{}{#3}%
+}%
+\newcommand\citename{}
+\def\citename#1#2(@)(@)\@nil#3{\expandafter\NAT@apalk#1#2, \@nil{#3}}
+\newcommand\harvarditem[4][]{%
+ \if\relax#1\relax
+   \bibitem[#2(#3)]{#4}%
+ \else
+   \bibitem[#1(#3)#2]{#4}%
+ \fi
+}%
+\newcommand\harvardleft{\NAT@open}
+\newcommand\harvardright{\NAT@close}
+\newcommand\harvardyearleft{\NAT@open}
+\newcommand\harvardyearright{\NAT@close}
+\AtBeginDocument{\providecommand{\harvardand}{and}}
+\newcommand\harvardurl[1]{\textbf{URL:} \textit{#1}}
+\providecommand\bibsection{}
+\@ifundefined{chapter}{%
+  \renewcommand\bibsection{%
+   \section*{\refname\@mkboth{\MakeUppercase{\refname}}{\MakeUppercase{\refname}}}%
+  }%
+}{%
+  \@ifxundefined\NAT@sectionbib{%
+    \renewcommand\bibsection{%
+      \chapter*{\bibname\@mkboth{\MakeUppercase{\bibname}}{\MakeUppercase{\bibname}}}%
+    }%
+  }{%
+    \renewcommand\bibsection{%
+      \section*{\bibname\ifx\@mkboth\@gobbletwo\else\markright{\MakeUppercase{\bibname}}\fi}%
+    }%
+  }%
+}%
+\@ifclassloaded{amsart}{\renewcommand\bibsection{\section*{\refname}}}{}%
+\@ifclassloaded{amsbook}{\renewcommand\bibsection{\chapter*{\bibname}}}{}%
+\@ifxundefined\bib@heading{}{\let\bibsection\bib@heading}%
+\newcounter{NAT@ctr}
+\renewenvironment{thebibliography}[1]{%
+ \bibsection
+ \parindent\z@
+ \bibpreamble
+ \bibfont
+ \list{\@biblabel{\the\c@NAT@ctr}}{\@bibsetup{#1}\global\c@NAT@ctr\z@}%
+ \ifNAT@openbib
+   \renewcommand\newblock{\par}%
+ \else
+   \renewcommand\newblock{\hskip .11em \@plus.33em \@minus.07em}%
+ \fi
+ \sloppy\clubpenalty4000\widowpenalty4000
+ \sfcode`\.\@m
+ \let\NAT@bibitem@first@sw\@firstoftwo
+    \let\citeN\cite \let\shortcite\cite
+    \let\citeasnoun\cite
+}{%
+ \bibitem@fin
+ \bibpostamble
+ \def\@noitemerr{%
+  \PackageWarning{natbib}{Empty `thebibliography' environment}%
+ }%
+ \endlist
+ \bibcleanup
+}%
+\let\bibfont\@empty
+\let\bibpreamble\@empty
+\let\bibpostamble\@empty
+\def\bibcleanup{\vskip-\lastskip}%
+\providecommand\reset@font{\relax}
+\providecommand\bibname{Bibliography}
+\providecommand\refname{References}
+\newcommand\NAT@citeundefined{\gdef \NAT@undefined {%
+    \PackageWarningNoLine{natbib}{There were undefined citations}}}
+\let \NAT@undefined \relax
+\newcommand\NAT@citemultiple{\gdef \NAT@multiple {%
+    \PackageWarningNoLine{natbib}{There were multiply defined citations}}}
+\let \NAT@multiple \relax
+\AtEndDocument{\NAT@undefined\NAT@multiple}
+\providecommand\@mkboth[2]{}
+\providecommand\MakeUppercase{\uppercase}
+\providecommand{\@extra@b@citeb}{}
+\gdef\@extra@binfo{}
+\def\NAT@anchor#1#2{%
+ \hyper@natanchorstart{#1\@extra@b@citeb}%
+  \def\@tempa{#2}\@ifx{\@tempa\@empty}{}{\@biblabel{#2}}%
+ \hyper@natanchorend
+}%
+\providecommand\hyper@natanchorstart[1]{}%
+\providecommand\hyper@natanchorend{}%
+\providecommand\hyper@natlinkstart[1]{}%
+\providecommand\hyper@natlinkend{}%
+\providecommand\hyper@natlinkbreak[2]{#1}%
+\AtBeginDocument{%
+  \@ifpackageloaded{babel}{%
+     \let\org@@citex\@citex}{}}
+\providecommand\@safe@activestrue{}%
+\providecommand\@safe@activesfalse{}%
+
+\newcommand\NAT@sort@cites[1]{%
+  \let\NAT@cite@list\@empty
+  \@for\@citeb:=#1\do{\expandafter\NAT@star@cite\@citeb\@@}%
+  \if@filesw
+    \expandafter\immediate\expandafter\write\expandafter\@auxout
+      \expandafter{\expandafter\string\expandafter\citation\expandafter{\NAT@cite@list}}%
+  \fi
+  \@ifnum{\NAT@sort>\z@}{%
+    \expandafter\NAT@sort@cites@\expandafter{\NAT@cite@list}%
+  }{}%
+}%
+\def\NAT@star@cite{%
+  \let\NAT@star@sw\@secondoftwo
+  \@ifnum{\NAT@merge>\z@}{%
+   \@ifnextchar*{%
+    \let\NAT@star@sw\@firstoftwo
+    \NAT@star@cite@star
+   }{%
+    \NAT@star@cite@nostar
+   }%
+  }{%
+   \NAT@star@cite@noextension
+  }%
+}%
+\def\NAT@star@cite@star*{%
+ \NAT@star@cite@nostar
+}%
+\def\NAT@star@cite@nostar{%
+ \let\nat@keyopt@open\@empty
+ \let\nat@keyopt@shut\@empty
+ \@ifnextchar[{\NAT@star@cite@pre}{\NAT@star@cite@pre[]}%
+}%
+\def\NAT@star@cite@pre[#1]{%
+ \def\nat@keyopt@open{#1}%
+ \@ifnextchar[{\NAT@star@cite@post}{\NAT@star@cite@post[]}%
+}%
+\def\NAT@star@cite@post[#1]#2\@@{%
+ \def\nat@keyopt@shut{#1}%
+ \NAT@star@sw{\expandafter\global\expandafter\let\csname NAT@b*@#2\endcsname\@empty}{}%
+ \NAT@cite@list@append{#2}%
+}%
+\def\NAT@star@cite@noextension#1\@@{%
+  \let\nat@keyopt@open\@empty
+  \let\nat@keyopt@shut\@empty
+  \NAT@cite@list@append{#1}%
+}%
+\def\NAT@cite@list@append#1{%
+  \edef\@citeb{\@firstofone#1\@empty}%
+  \if@filesw\@ifxundefined\@cprwrite{}{\expandafter\@cprwrite\@citeb=}\fi
+  \if\relax\nat@keyopt@open\relax\else
+   \global\expandafter\let\csname NAT@b@open@\@citeb\endcsname\nat@keyopt@open
+  \fi
+  \if\relax\nat@keyopt@shut\relax\else
+   \global\expandafter\let\csname NAT@b@shut@\@citeb\endcsname\nat@keyopt@shut
+  \fi
+  \toks@\expandafter{\NAT@cite@list}%
+  \ifx\NAT@cite@list\@empty
+    \@temptokena\expandafter{\@citeb}%
+  \else
+    \@temptokena\expandafter{\expandafter,\@citeb}%
+  \fi
+  \edef\NAT@cite@list{\the\toks@\the\@temptokena}%
+}%
+\newcommand\NAT@sort@cites@[1]{%
+  \count@\z@
+  \@tempcntb\m@ne
+  \let\@celt\delimiter
+  \def\NAT@num@list{}%
+  \let\NAT@cite@list\@empty
+  \let\NAT@nonsort@list\@empty
+  \@for \@citeb:=#1\do{\NAT@make@cite@list}%
+  \ifx\NAT@nonsort@list\@empty\else
+   \protected@edef\NAT@cite@list{\NAT@cite@list\NAT@nonsort@list}%
+  \fi
+  \ifx\NAT@cite@list\@empty\else
+   \protected@edef\NAT@cite@list{\expandafter\NAT@xcom\NAT@cite@list @@}%
+  \fi
+}%
+\def\NAT@make@cite@list{%
+  \advance\count@\@ne
+  \@safe@activestrue
+  \edef\@citeb{\expandafter\@firstofone\@citeb\@empty}%
+  \@safe@activesfalse
+  \@ifundefined{b@\@citeb\@extra@b@citeb}%
+   {\def\NAT@num{A}}%
+   {\NAT@parse{\@citeb}}%
+  \NAT@ifcat@num\NAT@num
+   {\@tempcnta\NAT@num \relax
+    \@ifnum{\@tempcnta<\@tempcntb}{%
+      \let\NAT@@cite@list=\NAT@cite@list
+      \let\NAT@cite@list\@empty
+      \begingroup\let\@celt=\NAT@celt\NAT@num@list\endgroup
+      \protected@edef\NAT@num@list{%
+       \expandafter\NAT@num@celt \NAT@num@list \@gobble @%
+      }%
+    }{%
+      \protected@edef\NAT@num@list{\NAT@num@list \@celt{\NAT@num}}%
+      \protected@edef\NAT@cite@list{\NAT@cite@list\@citeb,}%
+      \@tempcntb\@tempcnta
+    }%
+   }%
+   {\protected@edef\NAT@nonsort@list{\NAT@nonsort@list\@citeb,}}%
+}%
+\def\NAT@celt#1{%
+  \@ifnum{#1>\@tempcnta}{%
+    \xdef\NAT@cite@list{\NAT@cite@list\@citeb,\NAT@@cite@list}%
+    \let\@celt\@gobble
+  }{%
+    \expandafter\def@NAT@cite@lists\NAT@@cite@list\@@
+  }%
+}%
+\def\NAT@num@celt#1#2{%
+ \ifx#1\@celt
+  \@ifnum{#2>\@tempcnta}{%
+    \@celt{\number\@tempcnta}%
+    \@celt{#2}%
+  }{%
+    \@celt{#2}%
+    \expandafter\NAT@num@celt
+  }%
+ \fi
+}%
+\def\def@NAT@cite@lists#1,#2\@@{%
+  \xdef\NAT@cite@list{\NAT@cite@list#1,}%
+  \xdef\NAT@@cite@list{#2}%
+}%
+\def\NAT@nextc#1,#2@@{#1,}
+\def\NAT@restc#1,#2{#2}
+\def\NAT@xcom#1,@@{#1}
+\InputIfFileExists{natbib.cfg}
+       {\typeout{Local config file natbib.cfg used}}{}
+%% 
+%% <<<<< End of generated file <<<<<<
+%%
+%% End of file `natbib.sty'.
diff --git a/scripts/generate_inputs.py b/scripts/generate_inputs.py
new file mode 100644
index 0000000..ee7c46b
--- /dev/null
+++ b/scripts/generate_inputs.py
@@ -0,0 +1,63 @@
+import numpy as np
+from mlp.layers import ConvolutionalLayer, BatchNormalizationLayer
+import argparse
+
+parser = argparse.ArgumentParser(description='Welcome to GAN-Shot-Learning script')
+
+parser.add_argument('--student_id', nargs="?", type=str, help='Your student id in the format "sxxxxxxx"')
+
+args = parser.parse_args()
+
+student_id = args.student_id
+
+def generate_inputs(student_id):
+    student_number = student_id
+    tests = np.arange(96).reshape((2, 3, 4, 4))
+    tests[:, 0, :, :] = float(student_number[1:3]) / 10 - 5
+    tests[:, :, 1, :] = float(student_number[3:5]) / 10 - 5
+    tests[:, 2, :, :] = float(student_number[5:7]) / 10 - 5
+    tests[0, 1, :, :] = float(student_number[7]) / 10 - 5
+    return tests
+
+
+
+test_inputs = generate_inputs(student_id)
+test_grads_wrt_outputs = np.arange(-20, 16).reshape((2, 2, 3, 3))
+inputs = np.arange(96).reshape((2, 3, 4, 4))
+kernels = np.arange(-12, 12).reshape((2, 3, 2, 2))
+biases = np.arange(2)
+
+#produce ConvolutionalLayer fprop, bprop and grads_wrt_params
+activation_layer = ConvolutionalLayer(num_input_channels=3, num_output_channels=2, input_dim_1=4, input_dim_2=4,
+                                      kernel_dim_1=2, kernel_dim_2=2)
+activation_layer.params = [kernels, biases]
+conv_fprop = activation_layer.fprop(test_inputs)
+conv_bprop = activation_layer.bprop(
+    test_inputs, conv_fprop, test_grads_wrt_outputs)
+conv_grads_wrt_params = activation_layer.grads_wrt_params(test_inputs, test_grads_wrt_outputs)
+
+test_inputs = np.reshape(test_inputs, newshape=(2, -1))
+test_grads_wrt_outputs = np.arange(-48, 48).reshape((2, -1))
+
+#produce ELU fprop and bprop
+activation_layer = BatchNormalizationLayer(input_dim=48)
+
+beta = np.array(48*[0.3])
+gamma = np.array(48*[0.8])
+
+activation_layer.params = [gamma, beta]
+BN_fprop = activation_layer.fprop(test_inputs)
+BN_bprop = activation_layer.bprop(
+    test_inputs, BN_fprop, test_grads_wrt_outputs)
+BN_grads_wrt_params = activation_layer.grads_wrt_params(
+    test_inputs, test_grads_wrt_outputs)
+
+test_output = "ConvolutionalLayer:\nFprop: {}\nBprop: {}\nGrads_wrt_params: {}\n" \
+              "BatchNormalization:\nFprop: {}\nBprop: {}\nGrads_wrt_params: {}\n"\
+    .format(conv_fprop,
+            conv_bprop,
+            conv_grads_wrt_params,
+            BN_fprop, BN_bprop, BN_grads_wrt_params)
+
+with open("{}_test_file.txt".format(student_id), "w+") as out_file:
+    out_file.write(test_output)
\ No newline at end of file
diff --git a/scripts/s1473470_test_file.txt b/scripts/s1473470_test_file.txt
new file mode 100644
index 0000000..aed1bf3
--- /dev/null
+++ b/scripts/s1473470_test_file.txt
@@ -0,0 +1,119 @@
+ConvolutionalLayer:
+Fprop: [[[[  141   141   141]
+   [  161   161   161]
+   [  256   256   256]]
+
+  [[ -122  -122  -122]
+   [ -102  -102  -102]
+   [ -127  -127  -127]]]
+
+
+ [[[ -729  -740  -751]
+   [-1079 -1094 -1109]
+   [-1820 -1846 -1872]]
+
+  [[  844   857   870]
+   [  686   695   704]
+   [ 1613  1635  1657]]]]
+Bprop: [[[[ 147  319  305  162]
+   [ 338  716  680  354]
+   [ 290  608  572  294]
+   [ 149  307  285  144]]
+
+  [[  23   79   81   54]
+   [ 114  284  280  162]
+   [ 114  272  268  150]
+   [  73  163  157   84]]
+
+  [[-101 -161 -143  -54]
+   [-110 -148 -120  -30]
+   [ -62  -64  -36    6]
+   [  -3   19   29   24]]]
+
+
+ [[[  39   67   53   18]
+   [  50   68   32   -6]
+   [   2  -40  -76  -66]
+   [ -31  -89 -111  -72]]
+
+  [[  59  115  117   54]
+   [ 114  212  208   90]
+   [ 114  200  196   78]
+   [  37   55   49   12]]
+
+  [[  79  163  181   90]
+   [ 178  356  384  186]
+   [ 226  440  468  222]
+   [ 105  199  209   96]]]]
+Grads_wrt_params: (array([[[[  78,   78],
+         [ 168,  168]],
+
+        [[2332, 2311],
+         [1648, 1636]],
+
+        [[   0,    0],
+         [   0,    0]]],
+
+
+       [[[-138, -138],
+         [ -48,  -48]],
+
+        [[6085, 6010],
+         [5077, 5011]],
+
+        [[   0,    0],
+         [   0,    0]]]]), array([-126,   36]))
+BatchNormalization:
+Fprop: [[ 0.2         0.2         0.2         0.2         0.2         0.2         0.2
+   0.2         0.2         0.2         0.2         0.2         0.2         0.2
+   0.2         0.2        -0.7        -0.7        -0.7        -0.7
+  -0.69999963 -0.69999963 -0.69999963 -0.69999963 -0.7        -0.7        -0.7
+  -0.7        -0.7        -0.7        -0.7        -0.7         0.2         0.2
+   0.2         0.2         0.2         0.2         0.2         0.2         0.2
+   0.2         0.2         0.2         0.2         0.2         0.2         0.2       ]
+ [ 0.2         0.2         0.2         0.2         0.2         0.2         0.2
+   0.2         0.2         0.2         0.2         0.2         0.2         0.2
+   0.2         0.2         1.1         1.1         1.1         1.1
+   1.09999963  1.09999963  1.09999963  1.09999963  1.1         1.1         1.1
+   1.1         1.1         1.1         1.1         1.1         0.2         0.2
+   0.2         0.2         0.2         0.2         0.2         0.2         0.2
+   0.2         0.2         0.2         0.2         0.2         0.2         0.2       ]]
+Bprop: [[ -6.83051975e+03  -6.83051975e+03  -6.83051975e+03  -6.83051975e+03
+   -6.83051975e+03  -6.83051975e+03  -6.83051975e+03  -6.83051975e+03
+   -6.83051975e+03  -6.83051975e+03  -6.83051975e+03  -6.83051975e+03
+   -6.83051975e+03  -6.83051975e+03  -6.83051975e+03  -6.83051975e+03
+   -5.26012975e-09  -5.03790086e-09  -4.82801592e-09  -4.62962964e-09
+   -5.03789471e-06  -5.03789471e-06  -5.03789471e-06  -5.03789471e-06
+   -3.78504939e-09  -3.64132903e-09  -3.50479365e-09  -3.37499995e-09
+   -3.25153687e-09  -3.13402298e-09  -3.02210435e-09  -2.91545187e-09
+   -6.83051975e+03  -6.83051975e+03  -6.83051975e+03  -6.83051975e+03
+   -6.83051975e+03  -6.83051975e+03  -6.83051975e+03  -6.83051975e+03
+   -6.83051975e+03  -6.83051975e+03  -6.83051975e+03  -6.83051975e+03
+   -6.83051975e+03  -6.83051975e+03  -6.83051975e+03  -6.83051975e+03]
+ [  6.83051975e+03   6.83051975e+03   6.83051975e+03   6.83051975e+03
+    6.83051975e+03   6.83051975e+03   6.83051975e+03   6.83051975e+03
+    6.83051975e+03   6.83051975e+03   6.83051975e+03   6.83051975e+03
+    6.83051975e+03   6.83051975e+03   6.83051975e+03   6.83051975e+03
+    5.26012975e-09   5.03790086e-09   4.82801592e-09   4.62962964e-09
+    5.03789471e-06   5.03789471e-06   5.03789471e-06   5.03789471e-06
+    3.78504939e-09   3.64132903e-09   3.50479365e-09   3.37499995e-09
+    3.25153687e-09   3.13402298e-09   3.02210435e-09   2.91545187e-09
+    6.83051975e+03   6.83051975e+03   6.83051975e+03   6.83051975e+03
+    6.83051975e+03   6.83051975e+03   6.83051975e+03   6.83051975e+03
+    6.83051975e+03   6.83051975e+03   6.83051975e+03   6.83051975e+03
+    6.83051975e+03   6.83051975e+03   6.83051975e+03   6.83051975e+03]]
+Grads_wrt_params: [array([  0.        ,   0.        ,   0.        ,   0.        ,
+         0.        ,   0.        ,   0.        ,   0.        ,
+         0.        ,   0.        ,   0.        ,   0.        ,
+         0.        ,   0.        ,   0.        ,   0.        ,
+        47.9999998 ,  47.9999998 ,  47.99999981,  47.99999981,
+        47.99998041,  47.99998041,  47.99998041,  47.99998041,
+        47.99999984,  47.99999984,  47.99999985,  47.99999985,
+        47.99999985,  47.99999986,  47.99999986,  47.99999986,
+         0.        ,   0.        ,   0.        ,   0.        ,
+         0.        ,   0.        ,   0.        ,   0.        ,
+         0.        ,   0.        ,   0.        ,   0.        ,
+         0.        ,   0.        ,   0.        ,   0.        ]), array([-48, -46, -44, -42, -40, -38, -36, -34, -32, -30, -28, -26, -24,
+       -22, -20, -18, -16, -14, -12, -10,  -8,  -6,  -4,  -2,   0,   2,
+         4,   6,   8,  10,  12,  14,  16,  18,  20,  22,  24,  26,  28,
+        30,  32,  34,  36,  38,  40,  42,  44,  46])]
diff --git a/spec/coursework1.pdf b/spec/coursework1.pdf
new file mode 100644
index 0000000..5f34c4c
Binary files /dev/null and b/spec/coursework1.pdf differ
diff --git a/spec/coursework1.tex b/spec/coursework1.tex
new file mode 100644
index 0000000..45bf31a
--- /dev/null
+++ b/spec/coursework1.tex
@@ -0,0 +1,493 @@
+\documentclass[11pt,]{article}
+\usepackage[T1]{fontenc}
+\usepackage{amssymb,amsmath}
+\usepackage{txfonts}
+\usepackage{microtype}
+\usepackage{amssymb,amsmath}
+\usepackage{graphicx}
+\usepackage{subfigure} 
+\usepackage{natbib}
+\usepackage{paralist}
+\usepackage{hyperref}
+\usepackage{url}
+\urlstyle{same}
+\usepackage{color}
+\usepackage{fancyvrb}
+\newcommand{\VerbBar}{|}
+\newcommand{\VERB}{\Verb[commandchars=\\\{\}]}
+\DefineVerbatimEnvironment{Highlighting}{Verbatim}{commandchars=\\\{\}}
+% Add ',fontsize=\small' for more characters per line
+\newenvironment{Shaded}{}{}
+\newcommand{\KeywordTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{\textbf{{#1}}}}
+\newcommand{\DataTypeTok}[1]{\textcolor[rgb]{0.56,0.13,0.00}{{#1}}}
+\newcommand{\DecValTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
+\newcommand{\BaseNTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
+\newcommand{\FloatTok}[1]{\textcolor[rgb]{0.25,0.63,0.44}{{#1}}}
+\newcommand{\CharTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
+\newcommand{\StringTok}[1]{\textcolor[rgb]{0.25,0.44,0.63}{{#1}}}
+\newcommand{\CommentTok}[1]{\textcolor[rgb]{0.38,0.63,0.69}{\textit{{#1}}}}
+\newcommand{\OtherTok}[1]{\textcolor[rgb]{0.00,0.44,0.13}{{#1}}}
+\newcommand{\AlertTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}}
+\newcommand{\FunctionTok}[1]{\textcolor[rgb]{0.02,0.16,0.49}{{#1}}}
+\newcommand{\RegionMarkerTok}[1]{{#1}}
+\newcommand{\ErrorTok}[1]{\textcolor[rgb]{1.00,0.00,0.00}{\textbf{{#1}}}}
+\newcommand{\NormalTok}[1]{{#1}}
+
+\hypersetup{breaklinks=true,
+            pdfauthor={},
+            pdftitle={},
+            colorlinks=true,
+            citecolor=blue,
+            urlcolor=blue,
+            linkcolor=magenta,
+            pdfborder={0 0 0}}
+
+\setlength{\parindent}{0pt}
+\setlength{\parskip}{6pt plus 2pt minus 1pt}
+\setlength{\emergencystretch}{3em}  % prevent overfull lines
+\setcounter{secnumdepth}{0}
+
+\usepackage[a4paper,body={170mm,250mm},top=25mm,left=25mm]{geometry}
+\usepackage[sf,bf,small]{titlesec}
+\usepackage{fancyhdr}
+
+\pagestyle{fancy}
+\lhead{\sffamily MLP Coursework 1}
+\rhead{\sffamily Due: 30 October 2017}
+\cfoot{\sffamily \thepage}
+
+\author{}
+\date{}
+
+\DeclareMathOperator{\softmax}{softmax}
+\DeclareMathOperator{\sigmoid}{sigmoid}
+\DeclareMathOperator{\sgn}{sgn}
+\DeclareMathOperator{\relu}{relu}
+\DeclareMathOperator{\lrelu}{lrelu}
+\DeclareMathOperator{\elu}{elu}
+\DeclareMathOperator{\selu}{selu}
+\DeclareMathOperator{\maxout}{maxout}
+
+\begin{document}
+
+\section{Machine Learning Practical: Coursework
+1}
+\label{sec:machine-learning-practical-coursework-1}
+
+\textbf{Release date: Monday 16th October 2017}\\
+\textbf{Due date: 16:00 Monday 30th October 2017}
+
+\subsection{Introduction}
+\label{sec:introduction}
+This coursework is concerned with training multi-layer networks to
+address the MNIST digit classification problem. It builds on the
+material covered in the first three lab notebooks and the first four
+lectures. \textbf{You should complete the first three lab
+notebooks before starting the coursework.} The aim of the coursework is
+to investigate variants of the ReLU activation function for hidden units 
+in multi-layer networks, with respect to the validation set accuracies 
+achieved by the trained models.
+
+
+ 
+
+
+\subsection{Code}
+\label{sec:code}
+
+You should run all of the experiments for the coursework inside the
+Conda environment you set up in first labs.  The code for the coursework is available on the course
+\href{https://github.com/CSTR-Edinburgh/mlpractical/}{Github repository}
+on a branch \texttt{mlp2017-8/coursework1}. To create a local working
+copy of this branch in your local repository you need to do the
+following.
+
+\begin{enumerate}
+\def\labelenumi{\arabic{enumi}.}
+\itemsep1pt\parskip0pt\parsep0pt
+\item
+  Make sure all modified files on the branch you are currently have been
+  committed
+  (\href{https://github.com/CSTR-Edinburgh/mlpractical/blob/mlp2017-8/master/notes/getting-started-in-a-lab.md}{see
+  details here} if you are unsure how to do this).
+\item
+  Fetch changes to the upstream \texttt{origin} repository by running\\
+  \texttt{git fetch origin}
+\item
+  Checkout a new local branch from the fetched branch using\\
+  \texttt{git checkout -b coursework1 origin/mlp2017-8/coursework1}
+\end{enumerate}
+
+You will now have a new branch in your local repository with all the
+code necessary for the coursework in it. In the \texttt{notebooks}
+directory there is a notebook \texttt{Coursework\_1.ipynb} which is
+intended as a starting point for structuring the code for your
+experiments. You will probably want to add additional code cells to this
+as you go along and run new experiments (e.g.~doing each new training
+run in a new cell). You may also wish to use Markdown cells to keep
+notes on the results of experiments.
+
+There will also be a \verb+report+ directory which contains the LaTeX template and style files for the report.  You should copy all these files into the directory which will contain your report.
+
+
+\subsection{Standard network
+architecture}
+\label{sec:standard-network-architecture}
+
+To make the results of your experiments more easily comparable, you
+should try to keep as many of the free choices in the specification of
+the model and learning problem the same across different experiments. If
+you vary only a small number of aspects of the problem at a time this
+will make it easier to interpret the effect of those changes.
+
+In these experiments you should use a multi-layer network with two hidden layers 
+(corresponding to three affine transformations) and a softmax output layer.  The initial baseline
+should use a sigmoid activation function for the hidden layer;   other experiments will explore
+different nonlinear activation functions.  The hidden layers should each contain 100 hidden units.  
+The baseline network can this be defined with the following code (which should be familiar to you from Lab 3):
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\CharTok{import} \NormalTok{numpy }\CharTok{as} \NormalTok{np}
+\CharTok{from} \NormalTok{mlp.layers }\CharTok{import} \NormalTok{AffineLayer, SoftmaxLayer, SigmoidLayer}
+\CharTok{from} \NormalTok{mlp.errors }\CharTok{import} \NormalTok{CrossEntropySoftmaxError}
+\CharTok{from} \NormalTok{mlp.models }\CharTok{import} \NormalTok{MultipleLayerModel}
+\CharTok{from} \NormalTok{mlp.initialisers }\CharTok{import} \NormalTok{ConstantInit, GlorotUniformInit}
+
+\NormalTok{seed = }\DecValTok{10102016}
+\NormalTok{rng = np.random.RandomState(seed)}
+
+\NormalTok{input_dim, output_dim, hidden_dim = }\DecValTok{784}\NormalTok{, }\DecValTok{10}\NormalTok{, }\DecValTok{100}
+
+\NormalTok{weights_init = GlorotUniformInit(rng=rng)}
+\NormalTok{biases_init = ConstantInit(}\DecValTok{0}\NormalTok{.)}
+
+\NormalTok{model = MultipleLayerModel([}
+    \NormalTok{AffineLayer(input_dim, hidden_dim, weights_init, biases_init),}
+    \NormalTok{SigmoidLayer(),}
+    \NormalTok{AffineLayer(hidden_dim, hidden_dim, weights_init, biases_init),}
+    \NormalTok{SigmoidLayer(),}
+    \NormalTok{AffineLayer(hidden_dim, output_dim, weights_init, biases_init)}
+\NormalTok{])}
+
+\NormalTok{error = CrossEntropySoftmaxError()}
+\end{Highlighting}
+\end{Shaded}
+
+Here we are using the Glorot initialisation scheme, discussed in lecture 4.  In part 2B of this coursework you will explore the effect of different initialisation schemes.
+
+The above code creates a network using sigmoid hidden layers; you should modify it to also create a network using ReLU activation functions (see Lab 3).  These two networks will form your baseline systems.
+
+As well as standardising the network architecture, you should also fix
+the hyperparameters of the training procedure not being investigated to
+be the same across different runs. In particular for all experiments you
+should use a \textbf{batch size of 50 and train for a total of 100
+epochs} for all reported runs. You may of course use a smaller number of
+epochs for initial pilot runs.
+
+\subsection{Part 1: Implementing Activation Functions}
+\label{sec:actfns}
+
+In the first part of the assignment you will implement three further 
+activation functions, each of which is related to ReLU \citep{nair2010rectified}:  Leaky ReLU, ELU (Exponential Linear Unit), and SELU (Scaled Exponential Linear Unit).   Each of these units defines an activation function for which $f(x) = x$ when $x>0$, as for ReLU, but avoid having a zero gradient when $x<0$.
+
+\textbf{Leaky ReLU} ($\lrelu(x)$) \citep{maas2013rectifier} has the following form:
+\begin{equation}
+  \lrelu(x) =
+     \begin{cases} 
+      \alpha x     & \quad \text{if } x \leq  0 \\
+      x       & \quad \text{if } x > 0 \\
+    \end{cases} 
+\end{equation}
+Where $\alpha$ is a constant;  typically $\alpha=0.01$, and you can use this value in this coursework.  Note that $\alpha$ can be taken to be a parameter which is learned by back-propagation along with the weights and biases -- this is called Parametric ReLU (PReLU).  
+
+\textbf{ELU} ($\elu(x)$) \citep{clevert2015fast} has the following form:
+\begin{equation}
+  \elu(x) =
+     \begin{cases} 
+      \alpha (\exp(x) - 1)     & \quad \text{if } x \leq  0 \\
+      x       & \quad \text{if } x > 0 \\
+    \end{cases} 
+\end{equation}
+Again $\alpha$ can be taken as a constant or a tunable parameter.  Typically $\alpha=1$, which results in a smooth function, and you can use this value in this coursework.
+
+\textbf{SELU} ($\selu(x)$) \citep{klambauer2017self} has the following form:
+\begin{equation}
+  \selu(x) =
+     \lambda \begin{cases} 
+      \alpha (\exp(x) - 1)     & \quad \text{if } x \leq  0 \\
+      x       & \quad \text{if } x > 0 \\
+    \end{cases} 
+\end{equation}
+In the case of SELU, there is a theoretical argument for optimal values of the two parameters: $\alpha \approx 1.6733$ and $\lambda \approx 1.0507$, and you can use these values in this coursework.
+
+\begin{enumerate}
+  \item Implement each of these activations function as classes \verb+LeakyReluLayer+, \verb+EluLayer+ and \verb+SeluLayer+.  You need to implement \verb+fprop+ and \verb+bprop+ methods for each class.
+  \item Verify the correctness of your implementation using the supplied unit tests in \verb+Activation\_Tests.ipynb+
+  \item Automatically create a test file \verb+sXXXXXXX_test_file.txt+, by running the provided program \verb+generate_inputs.py+ which uses your code for  \verb+LeakyReluLayer+, \verb+EluLayer+ and \verb+SeluLayer+ to run your fprop and bprop methods for each layer on a unique test vector generated using your student ID number.
+\end{enumerate}
+
+For Part 1 of the coursework you need to submit the test file \verb+sXXXXXXX_test_file.txt+ (where sXXXXXXX is replaced with your student number) created in step 3 above.
+
+\subsection{Part 2: MNIST Experiments}
+\label{sec:expts}
+In  Part 2 of the coursework you will experiment with \verb+LeakyReluLayer+, \verb+EluLayer+ and \verb+SeluLayer+ in multi-layer networks trained on MNIST.
+
+\subsubsection{2A:  Comparing activation functions}
+In this sub-part you should compare the behaviour of Leaky ReLU, ELU, and SELU activation functions on the MNIST task.  Carry out all experiments using 2 hidden layers, with 100 units per hidden layer.  You should compare the results with baseline systems of the same architecture using sigmoid units and using ReLU units.  
+
+\subsubsection{2B: Deep neural network experiments}
+In this subpart you will explore the behaviour of deeper networks.  Based on the results of Part 2A, choose one activation function, and compare networks with 2--8 hidden layers, using 100 hidden units per hidden layer.  
+
+Also compare the effect of different initialisation strategies, as discussed in lecture 4.  First look at the effect of weight initialisation based on
+\begin{compactitem}
+\item Fan-in: $w_i \sim U(-\sqrt{3/n_{in}}, \sqrt{3/n_{in}}$
+\item Fan-out: $w_i \sim U(-\sqrt{3/n_{out}}, \sqrt{3/n_{out}}$
+\item Fan-in and Fan-out: $w_i \sim U \left(-\sqrt{6/(n_{in}+n_{out})}, \sqrt{6/(n_{in}+n_{out})}\right)$
+\end{compactitem}
+where $U$ is the uniform distribution.   The first of these corresponds to constraining the estimated variance of a unit to be independent of the number of incoming connections ($n_{in}$);  the second to  constraining the estimated variance of a unit's gradient to be independent of the number of outgoing connections ($n_{out}$);  the third corresponds to Glorot and Bengio's combined initialisation.  
+
+Additionally you could also explore the effect of drawing from a Gaussian distribution compared with a uniform distribution.  In particular you might like to explore initialising a SELU layer drawing from a Gaussian with mean 0 and variance $1/n_{out}$ as recommended by \cite{klambauer2017self}.
+
+For Part 2 of the coursework you need to write and submit a report, using the template provided, in the directory \verb+report+.  Please read the template document \verb+mlp-cw1-template.pdf+ very carefully, as it provides advice and instructions on writing your report.  You can use the LaTeX source file \verb+mlp-cw1-template.tex+ as a template for your report (see below, in the section 'Report').
+
+It is highly recommended that you use LaTeX for your report.  If you have not used LaTeX previously, now is a good time to learn how to use it!
+
+\subsection{Backing up your work}
+\label{sec:backing-up-your-work}
+
+It is \textbf{strongly recommended} you use some method for backing up
+your work. Those working in their AFS homespace on DICE will have their
+work automatically backed up as part of the
+\href{http://computing.help.inf.ed.ac.uk/backups-and-mirrors}{routine
+backup} of all user homespaces. If you are working on a personal
+computer you should have your own backup method in place (e.g.~saving
+additional copies to an external drive, syncing to a cloud service or
+pushing commits to your local Git repository to a private repository on
+Github). \textbf{Loss of work through failure to back up
+\href{http://tinyurl.com/edinflate}{does not consitute a good reason for
+late submission}}.
+
+You may \emph{additionally} wish to keep your coursework under version
+control in your local Git repository on the \texttt{coursework1} branch.
+% This does not need to be limited to the coursework notebook and
+% \texttt{mlp} Python modules - you can also add your report document to
+% the repository.
+
+If you make regular commits of your work on the coursework this will
+allow you to better keep track of the changes you have made and if
+necessary revert to previous versions of files and/or restore
+accidentally deleted work. This is not however required and you should
+note that keeping your work under version control is a distinct issue
+from backing up to guard against hard drive failure. If you are working
+on a personal computer you should still keep an additional back up of
+your work as described above.
+
+\subsection{Report}
+\label{sec:report}
+
+Part two of your coursework submission, worth 70 marks will be a report. The directory
+\verb+coursework1/report+ contains a template for your report (\verb+mlp-cw1-template.txt+);  the generated pdf file (\verb+mlp-cw1-template.pdf+) is also provided, and you should read this file carefully as it contains information about the required structure and experimentation. The template is written in LaTeX, and we strongly recommend that you write your own report using LaTeX, using the supplied document style \verb+mlp2017+ (as in the template).
+
+You should copy the files in the \verb+report+ directory to the directory containing the LaTeX file of your report, as \verb+pdflatex+ will need to access these files when building the pdf document from the LaTeX source file.
+
+Your report should be in a 2-column format, based on the document format used for the ICML conference. The report should be a \textbf{maximum of 6 pages long}, with a further page for references.  We will not read or assess any parts of the report beyond the allowed 6+1 pages.  
+
+Ideally, all figures should be included in your report file as
+\href{https://en.wikipedia.org/wiki/Vector_graphics}{vector graphics}
+rather than \href{https://en.wikipedia.org/wiki/Raster_graphics}{raster
+files} as this will make sure all detail in the plot is visible.
+Matplotlib supports saving high quality figures in a wide range of
+common image formats using the
+\href{http://matplotlib.org/api/pyplot_api.html\#matplotlib.pyplot.savefig}{\texttt{savefig}}
+function. \textbf{You should use \texttt{savefig} rather than copying
+the screen-resolution raster images outputted in the notebook.} An
+example of using \texttt{savefig} to save a figure as a PDF file (which
+can be included as graphics in
+\href{https://en.wikibooks.org/wiki/LaTeX/Importing_Graphics}{LaTeX}
+compiled with \texttt{pdflatex} and in Apple Pages and
+\href{https://support.office.com/en-us/article/Add-a-PDF-to-your-Office-file-74819342-8f00-4ab4-bcbe-0f3df15ab0dc}{Microsoft
+Word} documents) is given below.
+
+\begin{Shaded}
+\begin{Highlighting}[]
+\CharTok{import} \NormalTok{matplotlib.pyplot }\CharTok{as} \NormalTok{plt}
+\CharTok{import} \NormalTok{numpy }\CharTok{as} \NormalTok{np}
+\CommentTok{# Generate some example data to plot}
+\NormalTok{x = np.linspace(}\DecValTok{0}\NormalTok{., }\DecValTok{1}\NormalTok{., }\DecValTok{100}\NormalTok{)}
+\NormalTok{y1 = np.sin(}\DecValTok{2}\NormalTok{. * np.pi * x)}
+\NormalTok{y2 = np.cos(}\DecValTok{2}\NormalTok{. * np.pi * x)}
+\NormalTok{fig_size = (}\DecValTok{6}\NormalTok{, }\DecValTok{3}\NormalTok{)  }\CommentTok{# Set figure size in inches (width, height)}
+\NormalTok{fig = plt.figure(figsize=fig_size)  }\CommentTok{# Create a new figure object}
+\NormalTok{ax = fig.add_subplot(}\DecValTok{1}\NormalTok{, }\DecValTok{1}\NormalTok{, }\DecValTok{1}\NormalTok{)  }\CommentTok{# Add a single axes to the figure}
+\CommentTok{# Plot lines giving each a label for the legend and setting line width to 2}
+\NormalTok{ax.plot(x, y1, linewidth=}\DecValTok{2}\NormalTok{, label=}\StringTok{'$y = \textbackslash{}sin(2\textbackslash{}pi x)$'}\NormalTok{)}
+\NormalTok{ax.plot(x, y2, linewidth=}\DecValTok{2}\NormalTok{, label=}\StringTok{'$y = \textbackslash{}cos(2\textbackslash{}pi x)$'}\NormalTok{)}
+\CommentTok{# Set the axes labels. Can use LaTeX in labels within $...$ delimiters.}
+\NormalTok{ax.set_xlabel(}\StringTok{'$x$'}\NormalTok{, fontsize=}\DecValTok{12}\NormalTok{)}
+\NormalTok{ax.set_ylabel(}\StringTok{'$y$'}\NormalTok{, fontsize=}\DecValTok{12}\NormalTok{)}
+\NormalTok{ax.grid(}\StringTok{'on'}\NormalTok{)  }\CommentTok{# Turn axes grid on}
+\NormalTok{ax.legend(loc=}\StringTok{'best'}\NormalTok{, fontsize=}\DecValTok{11}\NormalTok{)  }\CommentTok{# Add a legend}
+\NormalTok{fig.tight_layout()  }\CommentTok{# This minimises whitespace around the axes.}
+\NormalTok{fig.savefig(}\StringTok{'file-name.pdf'}\NormalTok{) }\CommentTok{# Save figure to current directory in PDF format}
+\end{Highlighting}
+\end{Shaded}
+
+(If you are using Libre/OpenOffice you should use Scalable Vector Format
+plots instead using \\
+\texttt{fig.savefig('file-name.svg')}. If the
+document editor you are using for the report does not support including
+either PDF or SVG graphics you can instead output high-resolution raster
+images using \texttt{fig.savefig('file-name.png', dpi=200)} however note
+these files will generally be larger than either SVG or PDF formatted
+graphics.)
+
+However to emphasise again: \textbf{It is highly recommended that you use LaTeX.}
+
+If you make use of any any books, articles, web pages or other resources
+you should appropriately cite these in your report. You do not need to
+cite material from the course lecture slides or lab notebooks.
+
+To create a pdf file \verb+mlp-cw1-template.pdf+ from a LaTeX source file (\verb+mlp-cw1-template.tex+), you can run the following in a terminal:
+\begin{verbatim}
+pdflatex mlp-cw1-template
+bibtex mlp-cw1-template
+pdflatex mlp-cw1-template
+pdflatex mlp-cw1-template
+\end{verbatim}
+(Yes, you have to run pdflatex multiple times, in order  for latex to construct the internal document references.)
+
+An alternative, simpler approach uses the \verb+latexmk+ program:
+\begin{verbatim}
+latexmk -pdf mlp-cw1-template
+\end{verbatim}
+
+It is worth learning how to use LaTeX effectively, as it is particularly powerful for mathematical and academic writing.  There are many tutorials on the web.
+
+
+\subsection{Mechanics}
+\label{sec:mechanics}
+
+\textbf{Marks:} 
+This assignment will be assessed out of 100 marks and
+forms 10\% of your final grade for the course.
+
+\textbf{Academic conduct:} 
+Assessed work is subject to University
+regulations on academic
+conduct:\\\url{http://web.inf.ed.ac.uk/infweb/admin/policies/academic-misconduct}
+
+\textbf{Submission:} 
+You can submit more than once up until the submission deadline. All
+submissions are timestamped automatically. Identically named files
+will overwrite earlier submitted versions, so we will mark the latest
+submission that comes in before the deadline.
+
+If you submit anything before the deadline, you may not resubmit
+afterward. (This policy allows us to begin marking submissions
+immediately after the deadline, without having to worry that some may
+need to be re-marked).
+
+If you do not submit anything before the deadline, you may submit {\em
+exactly once} after the deadline, and a late penalty will be applied
+to this submission unless you have received an approved extension.
+Please be aware that late submissions may receive lower priority for
+marking, and marks may not be returned within the same timeframe as
+for on-time submissions.
+
+{\em Warning:} Unfortunately the \verb+submit+ command will technically
+allow you to submit late even if you submitted before the deadline
+(i.e.\ it does not enforce the above policy). Don't do this! We will
+mark the version that we retrieve just after the deadline, and (even
+worse) you may still be penalized for submitting late because the
+timestamp will update.
+
+For additional information about late penalties and extension
+requests, see the School web page below. Do {\bf not} email any course
+staff directly about extension requests; you must follow the
+instructions on the web page.
+
+\url{http://web.inf.ed.ac.uk/infweb/student-services/ito/admin/coursework-projects/late-coursework-extension-requests}
+
+\textbf{Late submission penalty:}  
+Following the University guidelines, 
+late coursework submitted without an authorised extension will be
+recorded as late and the following penalties will apply: 5
+percentage points will be deducted for every calendar day or part
+thereof it is late, up to a maximum of 7 calendar days. After this
+time a mark of zero will be recorded.
+
+\subsection{Submission}
+\label{sec:submission}
+
+Your coursework submission should be done electronically using the
+\href{http://computing.help.inf.ed.ac.uk/submit}{\texttt{submit}}
+command available on DICE machines.
+
+Your submission should include
+
+\begin{itemize}
+\itemsep1pt\parskip0pt\parsep0pt
+\item
+  the unit test file generated in part 1, \verb+sXXXXXXX_test_file.txt+, where your student number replaces \verb+sXXXXXXX+
+\item
+  your completed report as a PDF file, using the provided template
+\item
+  the notebook (\verb+.ipynb+) file you used to run the experiments in
+\item
+  and your local version of the \texttt{mlp} code including any changes
+  you made to the modules (\texttt{.py} files).
+\end{itemize}
+
+You should copy all of the files to a single directory, \verb+coursework1+, e.g.
+
+\begin{verbatim}
+mkdir coursework1
+cp notebooks/Coursework_1.ipynb mlp/*.py coursework1
+cp reports/coursework1.pdf reports/sXXXXXXX_test_file.txt coursework1
+\end{verbatim}
+
+
+and then submit this directory using
+
+\begin{verbatim}
+submit mlp cw1 coursework1
+\end{verbatim}
+
+The \texttt{submit} command will prompt you with the details of the
+submission including the name of the files / directories you are
+submitting and the name of the course and exercise you are submitting
+for and ask you to check if these details are correct. You should check
+these carefully and reply \texttt{y} to submit if you are sure the files
+are correct and \texttt{n} otherwise.
+
+You can amend an existing submission by rerunning the \texttt{submit}
+command any time up to the deadline. It is therefore a good idea
+(particularly if this is your first time using the DICE submit
+mechanism) to do an initial run of the \texttt{submit} command early on
+and then rerun the command if you make any further updates to your
+submisison rather than leaving submission to the last minute.
+
+
+\subsection{Marking Scheme}
+\label{sec:marking-scheme}
+
+\begin{itemize}
+\item
+  Part 1, Activation function implementation (30 marks). Based on your submitted test file.
+\item
+  Part 2, Report (70 marks).  The following aspects will contribute to the mark for your report:
+  \begin{itemize}
+    \item Abstract - how clear is it? does it cover what is reported in the document
+    \item Introduction - do you clear outline and motivate the paper, and describe the research questions investigated?
+    \item Description of activation functions -- is it clear and correct?
+    \item Experiments -- did you carry out the experiments correctly?  are the results clearly presented and described?  
+    \item Interpretation and discussion of results
+    \item Conclusions
+    \item Presentation and clarity of report 
+  \end{itemize}
+\end{itemize}
+
+\bibliographystyle{plainnat}
+\bibliography{cw1-references}
+\end{document}
diff --git a/spec/cw1-references.bib b/spec/cw1-references.bib
new file mode 100644
index 0000000..a7d0eb9
--- /dev/null
+++ b/spec/cw1-references.bib
@@ -0,0 +1,29 @@
+@inproceedings{maas2013rectifier,
+  title={Rectifier nonlinearities improve neural network acoustic models},
+  author={Maas, Andrew L and Hannun, Awni Y and Ng, Andrew Y},
+  booktitle={Proc. ICML},
+  volume={30},
+  number={1},
+  year={2013}
+}
+
+@inproceedings{nair2010rectified,
+  title={Rectified linear units improve restricted {Boltzmann} machines},
+  author={Nair, Vinod and Hinton, Geoffrey E},
+  booktitle={Proc ICML},
+  pages={807--814},
+  year={2010}
+}
+
+@article{clevert2015fast,
+  title={Fast and accurate deep network learning by exponential linear units ({ELU}s)},
+  author={Clevert, Djork-Arn{\'e} and Unterthiner, Thomas and Hochreiter, Sepp},
+  journal={arXiv preprint arXiv:1511.07289},
+  year={2015}
+}
+@article{klambauer2017self,
+  title={Self-Normalizing Neural Networks},
+  author={Klambauer, G{\"u}nter and Unterthiner, Thomas and Mayr, Andreas and Hochreiter, Sepp},
+  journal={arXiv preprint arXiv:1706.02515},
+  year={2017}
+}
\ No newline at end of file