diff --git a/README.md b/README.md
index 1358555..2afc450 100644
--- a/README.md
+++ b/README.md
@@ -9,7 +9,7 @@ The code in this repository is split into:
   *  a Python package `mlp`, a [NumPy](http://www.numpy.org/) based neural network package designed specifically for the course that students will implement parts of and extend during the course labs and assignments,
   *  a series of [Jupyter](http://jupyter.org/) notebooks in the `notebooks` directory containing explanatory material and coding exercises to be completed during the course labs.
 
-## Coursework 1
+## Coursework 2
 This branch contains the python code and latex files of the first coursework. The code follows the same structure as the labs, in particular the mlp package, and a specific notebook is provided to help you run experiments.
- * Detailed instructions are given in MLP2024_25_CW1_Spec.pdf (see Learn, Assessment, CW1).
- * The [report directory](https://github.com/VICO-UoE/mlpractical/tree/mlp2024-25/coursework1/report) contains the latex files that you will use to create your report.
+ * Detailed instructions are given in MLP2024_25_CW2_Spec.pdf (see Learn, Assessment, CW2).
+ * The [report directory](https://github.com/VICO-UoE/mlpractical/tree/mlp2024-25/coursework2/report) contains the latex files that you will use to create your report.
diff --git a/mlp/__init__.py b/mlp/__init__.py
index b41e667..73c9478 100644
--- a/mlp/__init__.py
+++ b/mlp/__init__.py
@@ -1,6 +1,6 @@
 # -*- coding: utf-8 -*-
 """Machine Learning Practical package."""
 
-__authors__ = ['Pawel Swietojanski', 'Steve Renals', 'Matt Graham']
+__authors__ = ['Pawel Swietojanski', 'Steve Renals', 'Matt Graham', 'Antreas Antoniou']
 
 DEFAULT_SEED = 123456  # Default random number generator seed if none provided.
diff --git a/mlp/data_providers.py b/mlp/data_providers.py
index 0bf3840..25e3b57 100644
--- a/mlp/data_providers.py
+++ b/mlp/data_providers.py
@@ -7,8 +7,17 @@ data points.
 
 import pickle
 import gzip
+import sys
+
 import numpy as np
 import os
+
+from PIL import Image
+from torch.utils import data
+from torch.utils.data import Dataset
+from torchvision import transforms
+from torchvision.datasets.utils import download_url, check_integrity
+
 from mlp import DEFAULT_SEED
 
 
@@ -16,7 +25,7 @@ class DataProvider(object):
     """Generic data provider."""
 
     def __init__(self, inputs, targets, batch_size, max_num_batches=-1,
-                 shuffle_order=True, rng=None, smooth_labels=False):
+                 shuffle_order=True, rng=None):
         """Create a new data provider object.
 
         Args:
@@ -32,7 +41,6 @@ class DataProvider(object):
             shuffle_order (bool): Whether to randomly permute the order of
                 the data before each epoch.
             rng (RandomState): A seeded random number generator.
-            smooth_labels (bool): turn on label smoothing
         """
         self.inputs = inputs
         self.targets = targets
@@ -44,12 +52,10 @@ class DataProvider(object):
         self._max_num_batches = max_num_batches
         self._update_num_batches()
         self.shuffle_order = shuffle_order
-        
         self._current_order = np.arange(inputs.shape[0])
         if rng is None:
             rng = np.random.RandomState(DEFAULT_SEED)
         self.rng = rng
-        self.smooth_labels = smooth_labels
         self.new_epoch()
 
     @property
@@ -140,7 +146,7 @@ class MNISTDataProvider(DataProvider):
     """Data provider for MNIST handwritten digit images."""
 
     def __init__(self, which_set='train', batch_size=100, max_num_batches=-1,
-                 shuffle_order=True, rng=None, smooth_labels=False):
+                 shuffle_order=True, rng=None):
         """Create a new MNIST data provider object.
 
         Args:
@@ -154,7 +160,6 @@ class MNISTDataProvider(DataProvider):
             shuffle_order (bool): Whether to randomly permute the order of
                 the data before each epoch.
             rng (RandomState): A seeded random number generator.
-            smooth_labels (bool): enable/disable label smoothing
         """
         # check a valid which_set was provided
         assert which_set in ['train', 'valid', 'test'], (
@@ -177,7 +182,7 @@ class MNISTDataProvider(DataProvider):
         inputs = inputs.astype(np.float32)
         # pass the loaded data to the parent class __init__
         super(MNISTDataProvider, self).__init__(
-            inputs, targets, batch_size, max_num_batches, shuffle_order, rng, smooth_labels)
+            inputs, targets, batch_size, max_num_batches, shuffle_order, rng)
 
     def next(self):
         """Returns next data batch or raises `StopIteration` if at end."""
@@ -207,7 +212,7 @@ 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, smooth_labels=False):
+                 shuffle_order=True, rng=None, flatten=False):
         """Create a new EMNIST data provider object.
 
         Args:
@@ -221,7 +226,6 @@ class EMNISTDataProvider(DataProvider):
             shuffle_order (bool): Whether to randomly permute the order of
                 the data before each epoch.
             rng (RandomState): A seeded random number generator.
-            smooth_labels (bool): enable/disable label smoothing
         """
         # check a valid which_set was provided
         assert which_set in ['train', 'valid', 'test'], (
@@ -243,23 +247,20 @@ class EMNISTDataProvider(DataProvider):
         print(loaded.keys())
         inputs, targets = loaded['inputs'], loaded['targets']
         inputs = inputs.astype(np.float32)
-        inputs = np.reshape(inputs, newshape=(-1, 28*28))
+        targets = targets.astype(np.int)
+        if flatten:
+            inputs = np.reshape(inputs, newshape=(-1, 28*28))
+        else:
+            inputs = np.reshape(inputs, newshape=(-1, 28, 28, 1))
         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, smooth_labels)
+            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()
-
-        if self.smooth_labels:
-            targets_batch_mat = self.label_smoothing(targets_batch)
-        else:
-            targets_batch_mat = self.to_one_of_k(targets_batch)
-        return inputs_batch, targets_batch_mat
-        
-
+        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.
@@ -279,26 +280,6 @@ class EMNISTDataProvider(DataProvider):
         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
-    
-    def label_smoothing(self, int_targets, alpha=0.1):
-        """Converts integer coded class target to 1 of K coded targets with label smoothing.
-
-        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,).
-        alpha (float): Smoothing factor.
-
-        Returns:
-        Array of 1 of K coded targets with label smoothing i.e. an array of shape
-        (num_data, num_classes)
-
-        """
-        
-        raise NotImplementedError
-  
-    
 
 class MetOfficeDataProvider(DataProvider):
     """South Scotland Met Office weather data provider."""
@@ -393,6 +374,21 @@ class CCPPDataProvider(DataProvider):
         super(CCPPDataProvider, self).__init__(
             inputs, targets, batch_size, max_num_batches, shuffle_order, rng)
 
+class EMNISTPytorchDataProvider(Dataset):
+    def __init__(self, which_set='train', batch_size=100, max_num_batches=-1,
+                 shuffle_order=True, rng=None, flatten=False, transforms=None):
+        self.numpy_data_provider = EMNISTDataProvider(which_set=which_set, batch_size=batch_size, max_num_batches=max_num_batches,
+                 shuffle_order=shuffle_order, rng=rng, flatten=flatten)
+        self.transforms = transforms
+
+    def __getitem__(self, item):
+        x = self.numpy_data_provider.inputs[item]
+        for augmentation in self.transforms:
+            x = augmentation(x)
+        return x, int(self.numpy_data_provider.targets[item])
+
+    def __len__(self):
+        return len(self.numpy_data_provider.targets)
 
 class AugmentedMNISTDataProvider(MNISTDataProvider):
     """Data provider for MNIST dataset which randomly transforms images."""
@@ -430,3 +426,321 @@ class AugmentedMNISTDataProvider(MNISTDataProvider):
             AugmentedMNISTDataProvider, self).next()
         transformed_inputs_batch = self.transformer(inputs_batch, self.rng)
         return transformed_inputs_batch, targets_batch
+
+class Omniglot(data.Dataset):
+    """`CIFAR10 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.
+    Args:
+        root (string): Root directory of dataset where directory
+            ``cifar-10-batches-py`` exists or will be saved to if download is set to True.
+        train (bool, optional): If True, creates dataset from training set, otherwise
+            creates from test set.
+        transform (callable, optional): A function/transform that  takes in an PIL image
+            and returns a transformed version. E.g, ``transforms.RandomCrop``
+        target_transform (callable, optional): A function/transform that takes in the
+            target and transforms it.
+        download (bool, optional): If true, downloads the dataset from the internet and
+            puts it in root directory. If dataset is already downloaded, it is not
+            downloaded again.
+    """
+    def collect_data_paths(self, root):
+        data_dict = dict()
+        print(root)
+        for subdir, dir, files in os.walk(root):
+            for file in files:
+                if file.endswith('.png'):
+                    filepath = os.path.join(subdir, file)
+                    class_label = '_'.join(subdir.split("/")[-2:])
+                    if class_label in data_dict:
+                        data_dict[class_label].append(filepath)
+                    else:
+                        data_dict[class_label] = [filepath]
+
+        return data_dict
+
+    def __init__(self, root, set_name,
+                 transform=None, target_transform=None,
+                 download=False):
+        self.root = os.path.expanduser(root)
+        self.root = os.path.abspath(os.path.join(self.root, 'omniglot_dataset'))
+        self.transform = transform
+        self.target_transform = target_transform
+        self.set_name = set_name  # training set or test set
+        self.data_dict = self.collect_data_paths(root=self.root)
+
+        x = []
+        label_to_idx = {label: idx for idx, label in enumerate(self.data_dict.keys())}
+        y = []
+
+        for key, value in self.data_dict.items():
+            x.extend(value)
+            y.extend(len(value) * [label_to_idx[key]])
+
+        y = np.array(y)
+
+
+        rng = np.random.RandomState(seed=0)
+
+        idx = np.arange(len(x))
+        rng.shuffle(idx)
+
+        x = [x[current_idx] for current_idx in idx]
+        y = y[idx]
+
+        train_sample_idx = rng.choice(a=[i for i in range(len(x))], size=int(len(x) * 0.80), replace=False)
+        evaluation_sample_idx = [i for i in range(len(x)) if i not in train_sample_idx]
+        validation_sample_idx = rng.choice(a=[i for i in range(len(evaluation_sample_idx))], size=int(len(evaluation_sample_idx) * 0.40), replace=False)
+        test_sample_idx = [i for i in range(len(evaluation_sample_idx)) if i not in evaluation_sample_idx]
+
+        if self.set_name=='train':
+            self.data = [item for idx, item in enumerate(x) if idx in train_sample_idx]
+            self.labels = y[train_sample_idx]
+
+        elif self.set_name=='val':
+            self.data = [item for idx, item in enumerate(x) if idx in validation_sample_idx]
+            self.labels = y[validation_sample_idx]
+
+        else:
+            self.data = [item for idx, item in enumerate(x) if idx in test_sample_idx]
+            self.labels = y[test_sample_idx]
+
+    def __getitem__(self, index):
+        """
+        Args:
+            index (int): Index
+        Returns:
+            tuple: (image, target) where target is index of the target class.
+        """
+        img, target = self.data[index], self.labels[index]
+
+        img = Image.open(img)
+        img.show()
+
+        if self.transform is not None:
+            img = self.transform(img)
+
+        if self.target_transform is not None:
+            target = self.target_transform(target)
+
+        return img, target
+
+    def __len__(self):
+        return len(self.data)
+
+
+    def __repr__(self):
+        fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
+        fmt_str += '    Number of datapoints: {}\n'.format(self.__len__())
+        tmp = self.set_name
+        fmt_str += '    Split: {}\n'.format(tmp)
+        fmt_str += '    Root Location: {}\n'.format(self.root)
+        tmp = '    Transforms (if any): '
+        fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
+        tmp = '    Target Transforms (if any): '
+        fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
+        return fmt_str
+
+class CIFAR10(data.Dataset):
+    """`CIFAR10 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.
+    Args:
+        root (string): Root directory of dataset where directory
+            ``cifar-10-batches-py`` exists or will be saved to if download is set to True.
+        train (bool, optional): If True, creates dataset from training set, otherwise
+            creates from test set.
+        transform (callable, optional): A function/transform that  takes in an PIL image
+            and returns a transformed version. E.g, ``transforms.RandomCrop``
+        target_transform (callable, optional): A function/transform that takes in the
+            target and transforms it.
+        download (bool, optional): If true, downloads the dataset from the internet and
+            puts it in root directory. If dataset is already downloaded, it is not
+            downloaded again.
+    """
+    base_folder = 'cifar-10-batches-py'
+    url = "https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz"
+    filename = "cifar-10-python.tar.gz"
+    tgz_md5 = 'c58f30108f718f92721af3b95e74349a'
+    train_list = [
+        ['data_batch_1', 'c99cafc152244af753f735de768cd75f'],
+        ['data_batch_2', 'd4bba439e000b95fd0a9bffe97cbabec'],
+        ['data_batch_3', '54ebc095f3ab1f0389bbae665268c751'],
+        ['data_batch_4', '634d18415352ddfa80567beed471001a'],
+        ['data_batch_5', '482c414d41f54cd18b22e5b47cb7c3cb'],
+    ]
+
+    test_list = [
+        ['test_batch', '40351d587109b95175f43aff81a1287e'],
+    ]
+
+    def __init__(self, root, set_name,
+                 transform=None, target_transform=None,
+                 download=False):
+        self.root = os.path.expanduser(root)
+        self.transform = transform
+        self.target_transform = target_transform
+        self.set_name = set_name  # training set or test set
+
+        if download:
+            self.download()
+
+        if not self._check_integrity():
+            raise RuntimeError('Dataset not found or corrupted.' +
+                               ' You can use download=True to download it')
+
+        # now load the picked numpy arrays
+        rng = np.random.RandomState(seed=0)
+
+        train_sample_idx = rng.choice(a=[i for i in range(50000)], size=47500, replace=False)
+        val_sample_idx = [i for i in range(50000) if i not in train_sample_idx]
+
+        if self.set_name=='train':
+            self.data = []
+            self.labels = []
+            for fentry in self.train_list:
+                f = fentry[0]
+                file = os.path.join(self.root, self.base_folder, f)
+                fo = open(file, 'rb')
+                if sys.version_info[0] == 2:
+                    entry = pickle.load(fo)
+                else:
+                    entry = pickle.load(fo, encoding='latin1')
+                self.data.append(entry['data'])
+                if 'labels' in entry:
+                    self.labels += entry['labels']
+                else:
+                    self.labels += entry['fine_labels']
+                fo.close()
+
+            self.data = np.concatenate(self.data)
+
+            self.data = self.data.reshape((50000, 3, 32, 32))
+            self.data = self.data.transpose((0, 2, 3, 1))  # convert to HWC
+            self.data = self.data[train_sample_idx]
+            self.labels = np.array(self.labels)[train_sample_idx]
+            print(set_name, self.data.shape)
+            print(set_name, self.labels.shape)
+
+        elif self.set_name=='val':
+            self.data = []
+            self.labels = []
+            for fentry in self.train_list:
+                f = fentry[0]
+                file = os.path.join(self.root, self.base_folder, f)
+                fo = open(file, 'rb')
+                if sys.version_info[0] == 2:
+                    entry = pickle.load(fo)
+                else:
+                    entry = pickle.load(fo, encoding='latin1')
+                self.data.append(entry['data'])
+                if 'labels' in entry:
+                    self.labels += entry['labels']
+                else:
+                    self.labels += entry['fine_labels']
+                fo.close()
+
+            self.data = np.concatenate(self.data)
+            self.data = self.data.reshape((50000, 3, 32, 32))
+            self.data = self.data.transpose((0, 2, 3, 1))  # convert to HWC
+            self.data = self.data[val_sample_idx]
+            self.labels = np.array(self.labels)[val_sample_idx]
+            print(set_name, self.data.shape)
+            print(set_name, self.labels.shape)
+
+        else:
+            f = self.test_list[0][0]
+            file = os.path.join(self.root, self.base_folder, f)
+            fo = open(file, 'rb')
+            if sys.version_info[0] == 2:
+                entry = pickle.load(fo)
+            else:
+                entry = pickle.load(fo, encoding='latin1')
+            self.data = entry['data']
+            if 'labels' in entry:
+                self.labels = entry['labels']
+            else:
+                self.labels = entry['fine_labels']
+            fo.close()
+            self.data = self.data.reshape((10000, 3, 32, 32))
+            self.data = self.data.transpose((0, 2, 3, 1))  # convert to HWC
+            self.labels = np.array(self.labels)
+            print(set_name, self.data.shape)
+            print(set_name, self.labels.shape)
+
+    def __getitem__(self, index):
+        """
+        Args:
+            index (int): Index
+        Returns:
+            tuple: (image, target) where target is index of the target class.
+        """
+        img, target = self.data[index], self.labels[index]
+
+        # doing this so that it is consistent with all other datasets
+        # to return a PIL Image
+
+        img = Image.fromarray(img)
+
+        if self.transform is not None:
+            img = self.transform(img)
+
+        if self.target_transform is not None:
+            target = self.target_transform(target)
+
+        return img, target
+
+    def __len__(self):
+        return len(self.data)
+
+    def _check_integrity(self):
+        root = self.root
+        for fentry in (self.train_list + self.test_list):
+            filename, md5 = fentry[0], fentry[1]
+            fpath = os.path.join(root, self.base_folder, filename)
+            if not check_integrity(fpath, md5):
+                return False
+        return True
+
+    def download(self):
+        import tarfile
+
+        if self._check_integrity():
+            print('Files already downloaded and verified')
+            return
+
+        root = self.root
+        download_url(self.url, root, self.filename, self.tgz_md5)
+
+        # extract file
+        cwd = os.getcwd()
+        tar = tarfile.open(os.path.join(root, self.filename), "r:gz")
+        os.chdir(root)
+        tar.extractall()
+        tar.close()
+        os.chdir(cwd)
+
+    def __repr__(self):
+        fmt_str = 'Dataset ' + self.__class__.__name__ + '\n'
+        fmt_str += '    Number of datapoints: {}\n'.format(self.__len__())
+        tmp = self.set_name
+        fmt_str += '    Split: {}\n'.format(tmp)
+        fmt_str += '    Root Location: {}\n'.format(self.root)
+        tmp = '    Transforms (if any): '
+        fmt_str += '{0}{1}\n'.format(tmp, self.transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
+        tmp = '    Target Transforms (if any): '
+        fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
+        return fmt_str
+
+
+class CIFAR100(CIFAR10):
+    """`CIFAR100 <https://www.cs.toronto.edu/~kriz/cifar.html>`_ Dataset.
+    This is a subclass of the `CIFAR10` Dataset.
+    """
+    base_folder = 'cifar-100-python'
+    url = "https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz"
+    filename = "cifar-100-python.tar.gz"
+    tgz_md5 = 'eb9058c3a382ffc7106e4002c42a8d85'
+    train_list = [
+        ['train', '16019d7e3df5f24257cddd939b257f8d'],
+    ]
+
+    test_list = [
+        ['test', 'f0ef6b0ae62326f3e7ffdfab6717acfc'],
+    ]
\ No newline at end of file
diff --git a/mlp/layers.py b/mlp/layers.py
index d3cb38e..c6641c9 100644
--- a/mlp/layers.py
+++ b/mlp/layers.py
@@ -16,6 +16,7 @@ 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."""
 
@@ -95,6 +96,7 @@ class LayerWithParameters(Layer):
         """
         raise NotImplementedError()
 
+
 class StochasticLayerWithParameters(Layer):
     """Specialised layer which uses a stochastic forward propagation."""
 
@@ -124,6 +126,7 @@ class StochasticLayerWithParameters(Layer):
             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.
 
@@ -166,6 +169,7 @@ class StochasticLayerWithParameters(Layer):
         """
         raise NotImplementedError()
 
+
 class StochasticLayer(Layer):
     """Specialised layer which uses a stochastic forward propagation."""
 
@@ -329,6 +333,7 @@ class AffineLayer(LayerWithParameters):
         return 'AffineLayer(input_dim={0}, output_dim={1})'.format(
             self.input_dim, self.output_dim)
 
+
 class SigmoidLayer(Layer):
     """Layer implementing an element-wise logistic sigmoid transformation."""
 
@@ -368,6 +373,152 @@ 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_height, 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_height = input_height - kernel_height + 1
+        output_width = input_width - kernel_width + 1
+    """
+
+    def __init__(self, num_input_channels, num_output_channels,
+                 input_height, input_width,
+                 kernel_height, kernel_width,
+                 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_height (int): Size of first input dimension of each 2D
+                channel of inputs.
+            input_width (int): Size of second input dimension of each 2D
+                channel of inputs.
+            kernel_height (int): Size of first dimension of each 2D channel of
+                kernels.
+            kernel_width (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_height = input_height
+        self.input_width = input_width
+        self.kernel_height = kernel_height
+        self.kernel_width = kernel_width
+        self.kernels_init = kernels_init
+        self.biases_init = biases_init
+        self.kernels_shape = (
+            num_output_channels, num_input_channels, kernel_height, kernel_width
+        )
+        self.inputs_shape = (
+            None, num_input_channels, input_height, input_width
+        )
+        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, num_input_channels, image_height, image_width).
+        Returns:
+            outputs: Array of layer outputs of shape (batch_size, num_output_channels, output_height, output_width).
+        """
+        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_height, input_width).
+            outputs: Array of layer outputs calculated in forward pass of
+                shape
+                (batch_size, num_output_channels, output_height, output_width).
+            grads_wrt_outputs: Array of gradients with respect to the layer
+                outputs of shape
+                (batch_size, num_output_channels, output_height, output_width).
+        Returns:
+            Array of gradients with respect to the layer inputs of shape
+            (batch_size, num_input_channels, input_height, input_width).
+        """
+        # 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_height, output_width).
+        Returns:
+            list of arrays of gradients with respect to the layer parameters
+            `[grads_wrt_kernels, grads_wrt_biases]`.
+        """
+        # Get inputs_col from previous fprop
+        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_height={2}, input_width={3},\n'
+            '    kernel_height={4}, kernel_width={5}\n'
+            ')'
+                .format(self.num_input_channels, self.num_output_channels,
+                        self.input_height, self.input_width, self.kernel_height,
+                        self.kernel_width)
+        )
+
+
 class ReluLayer(Layer):
     """Layer implementing an element-wise rectified linear transformation."""
 
@@ -406,84 +557,6 @@ class ReluLayer(Layer):
     def __repr__(self):
         return 'ReluLayer'
 
-class LeakyReluLayer(Layer):
-    """Layer implementing an element-wise leaky 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 = ..., else`.
-        """
-
-        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.
-        """
-        raise NotImplementedError
-
-    def __repr__(self):
-        return 'LeakyReluLayer'
-
-
-
-class ParametricReluLayer(LayerWithParameters):
-    """Layer implementing an element-wise parametric rectified linear transformation."""
-
-    def __init__(self, alpha=0.25):
-        self.alpha = np.array([alpha])
-
-    @property
-    def params(self):
-        """A list of layer parameter values: `[weights, biases]`."""
-        return [self.alpha]
-
-    def fprop(self, inputs):
-        """Forward propagates activations through the layer transformation.
-
-        For inputs `x` and outputs `y` this corresponds to `y = ..., else`.
-        """
-        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.
-        """
-        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_params]`. Where params is the alpha parameter.
-        """
-        raise NotImplementedError
-
-    @property
-    def params(self):
-        """A list of layer parameter values: `[weights, biases]`."""
-        return [self.alpha]
-
-    @params.setter
-    def params(self, values):
-        self.alpha = values[0]
-
-    def __repr__(self):
-        return 'ParametricReluLayer'
-
 
 class TanhLayer(Layer):
     """Layer implementing an element-wise hyperbolic tangent transformation."""
@@ -518,11 +591,12 @@ class TanhLayer(Layer):
             Array of gradients with respect to the layer inputs of shape
             (batch_size, input_dim).
         """
-        return (1. - outputs**2) * grads_wrt_outputs
+        return (1. - outputs ** 2) * grads_wrt_outputs
 
     def __repr__(self):
         return 'TanhLayer'
 
+
 class SoftmaxLayer(Layer):
     """Layer implementing a softmax transformation."""
 
@@ -567,6 +641,7 @@ class SoftmaxLayer(Layer):
     def __repr__(self):
         return 'SoftmaxLayer'
 
+
 class RadialBasisFunctionLayer(Layer):
     """Layer implementing projection to a grid of radial basis functions."""
 
@@ -582,10 +657,10 @@ class RadialBasisFunctionLayer(Layer):
                 tile basis functions in grid across. For example for a 2D input
                 space spanning [0, 1] x [0, 1] use intervals=[[0, 1], [0, 1]].
         """
-        num_basis = grid_dim**len(intervals)
+        num_basis = grid_dim ** len(intervals)
         self.centres = np.array(np.meshgrid(*[
             np.linspace(low, high, grid_dim) for (low, high) in intervals])
-        ).reshape((len(intervals), -1))
+                                ).reshape((len(intervals), -1))
         self.scales = np.array([
             [(high - low) * 1. / grid_dim] for (low, high) in intervals])
 
@@ -598,8 +673,8 @@ class RadialBasisFunctionLayer(Layer):
         Returns:
             outputs: Array of layer outputs of shape (batch_size, output_dim).
         """
-        return np.exp(-(inputs[..., None] - self.centres[None, ...])**2 /
-                      self.scales**2).reshape((inputs.shape[0], -1))
+        return np.exp(-(inputs[..., None] - self.centres[None, ...]) ** 2 /
+                      self.scales ** 2).reshape((inputs.shape[0], -1))
 
     def bprop(self, inputs, outputs, grads_wrt_outputs):
         """Back propagates gradients through a layer.
@@ -620,13 +695,14 @@ class RadialBasisFunctionLayer(Layer):
         """
         num_basis = self.centres.shape[1]
         return -2 * (
-            ((inputs[..., None] - self.centres[None, ...]) / self.scales**2) *
-            grads_wrt_outputs.reshape((inputs.shape[0], -1, num_basis))
+                ((inputs[..., None] - self.centres[None, ...]) / self.scales ** 2) *
+                grads_wrt_outputs.reshape((inputs.shape[0], -1, num_basis))
         ).sum(-1)
 
     def __repr__(self):
         return 'RadialBasisFunctionLayer(grid_dim={0})'.format(self.grid_dim)
 
+
 class DropoutLayer(StochasticLayer):
     """Layer which stochastically drops input dimensions in its output."""
 
@@ -661,7 +737,12 @@ class DropoutLayer(StochasticLayer):
         Returns:
             outputs: Array of layer outputs of shape (batch_size, output_dim).
         """
-        raise NotImplementedError
+        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.
@@ -681,11 +762,12 @@ class DropoutLayer(StochasticLayer):
             Array of gradients with respect to the layer inputs of shape
             (batch_size, input_dim).
         """
-        raise NotImplementedError
+        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."""
 
@@ -739,4 +821,4 @@ class ReshapeLayer(Layer):
         return grads_wrt_outputs.reshape(inputs.shape)
 
     def __repr__(self):
-        return 'ReshapeLayer(output_shape={0})'.format(self.output_shape)
+        return 'ReshapeLayer(output_shape={0})'.format(self.output_shape)
\ No newline at end of file
diff --git a/mlp/learning_rules.py b/mlp/learning_rules.py
index 56fcb63..52f34cc 100644
--- a/mlp/learning_rules.py
+++ b/mlp/learning_rules.py
@@ -315,3 +315,74 @@ class AdaGradLearningRule(GradientDescentLearningRule):
             param -= (self.learning_rate * grad /
                       (sum_sq_grad + self.epsilon) ** 0.5)
 
+
+class RMSPropLearningRule(GradientDescentLearningRule):
+    """Root mean squared gradient normalised learning rule (RMSProp).
+    First-order gradient-descent based learning rule which normalises gradient
+    updates by a exponentially smoothed estimate of the gradient second
+    moments.
+    References:
+      [1]: Neural Networks for Machine Learning: Lecture 6a slides
+           University of Toronto,Computer Science Course CSC321
+      http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf
+    """
+
+    def __init__(self, learning_rate=1e-3, beta=0.9, epsilon=1e-8):
+        """Creates a new learning rule object.
+        Args:
+            learning_rate: A postive scalar to scale gradient updates to the
+                parameters by. This needs to be carefully set - if too large
+                the learning dynamic will be unstable and may diverge, while
+                if set too small learning will proceed very slowly.
+            beta: Exponential decay rate for gradient second moment
+                estimates. This should be a scalar value in [0, 1]. The running
+                gradient second moment estimate is calculated using
+                `m_2 = beta * m_2_prev + (1 - beta) * g**2`
+                 where `m_2_prev` is the previous estimate and `g` the current
+                 parameter gradients.
+            epsilon: 'Softening' parameter to stop updates diverging when
+                gradient second moment estimates are close to zero. Should be
+                set to a small positive value.
+        """
+        super(RMSPropLearningRule, self).__init__(learning_rate)
+        assert beta >= 0. and beta <= 1., 'beta should be in [0, 1].'
+        assert epsilon > 0., 'epsilon should be > 0.'
+        self.beta = beta
+        self.epsilon = epsilon
+
+    def initialise(self, params):
+        """Initialises the state of the learning rule for a set or parameters.
+        This must be called before `update_params` is first called.
+        Args:
+            params: A list of the parameters to be optimised. Note these will
+                be updated *in-place* to avoid reallocating arrays on each
+                update.
+        """
+        super(RMSPropLearningRule, self).initialise(params)
+        self.moms_2 = []
+        for param in self.params:
+            self.moms_2.append(np.zeros_like(param))
+
+    def reset(self):
+        """Resets any additional state variables to their initial values.
+        For this learning rule this corresponds to zeroing all gradient
+        second moment estimates.
+        """
+        for mom_2 in self.moms_2:
+            mom_2 *= 0.
+
+    def update_params(self, grads_wrt_params):
+        """Applies a single update to all parameters.
+        All parameter updates are performed using in-place operations and so
+        nothing is returned.
+        Args:
+            grads_wrt_params: A list of gradients of the scalar loss function
+                with respect to each of the parameters passed to `initialise`
+                previously, with this list expected to be in the same order.
+        """
+        for param, mom_2, grad in zip(
+                self.params, self.moms_2, grads_wrt_params):
+            mom_2 *= self.beta
+            mom_2 += (1. - self.beta) * grad ** 2
+            param -= (self.learning_rate * grad /
+                      (mom_2 + self.epsilon) ** 0.5)
diff --git a/mlp/models.py b/mlp/models.py
index 7f1273e..b292cf4 100644
--- a/mlp/models.py
+++ b/mlp/models.py
@@ -27,7 +27,7 @@ class SingleLayerModel(object):
         """A list of all of the parameters of the model."""
         return self.layer.params
 
-    def fprop(self, inputs):
+    def fprop(self, inputs, evaluation=False):
         """Calculate the model outputs corresponding to a batch of inputs.
 
         Args:
diff --git a/mlp/optimisers.py b/mlp/optimisers.py
index 61509ae..8ab313a 100644
--- a/mlp/optimisers.py
+++ b/mlp/optimisers.py
@@ -58,7 +58,7 @@ class Optimiser(object):
         parameters according to the learning rule.
         """
         with self.tqdm_progress(total=self.train_dataset.num_batches) as train_progress_bar:
-            train_progress_bar.set_description("Ep Prog")
+            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)
@@ -112,7 +112,7 @@ class Optimiser(object):
         """
         logger.info('Epoch {0}: {1:.1f}s to complete\n    {2}'.format(
             epoch, epoch_time,
-            ', '.join(['{}={:.2e}'.format(k, v) for (k, v) in stats.items()])
+            ', '.join(['{0}={1:.2e}'.format(k, v) for (k, v) in stats.items()])
         ))
 
     def train(self, num_epochs, stats_interval=5):
@@ -132,7 +132,7 @@ class Optimiser(object):
         start_train_time = time.time()
         run_stats = [list(self.get_epoch_stats().values())]
         with self.tqdm_progress(total=num_epochs) as progress_bar:
-            progress_bar.set_description("Exp Prog")
+            progress_bar.set_description("Experiment Progress")
             for epoch in range(1, num_epochs + 1):
                 start_time = time.time()
                 self.do_training_epoch()
diff --git a/mlp/penalties.py b/mlp/penalties.py
index ac2ae90..2876434 100644
--- a/mlp/penalties.py
+++ b/mlp/penalties.py
@@ -29,7 +29,7 @@ class L1Penalty(object):
         Returns:
             Value of penalty term.
         """
-        raise NotImplementedError
+        return self.coefficient * abs(parameter).sum()
 
     def grad(self, parameter):
         """Calculate the penalty gradient with respect to the parameter.
@@ -41,7 +41,7 @@ class L1Penalty(object):
             Value of penalty gradient with respect to parameter. This
             should be an array of the same shape as the parameter.
         """
-        raise NotImplementedError
+        return self.coefficient * np.sign(parameter)
 
     def __repr__(self):
         return 'L1Penalty({0})'.format(self.coefficient)
@@ -72,7 +72,7 @@ class L2Penalty(object):
         Returns:
             Value of penalty term.
         """
-        raise NotImplementedError
+        return 0.5 * self.coefficient * (parameter ** 2).sum()
 
     def grad(self, parameter):
         """Calculate the penalty gradient with respect to the parameter.
@@ -84,7 +84,7 @@ class L2Penalty(object):
             Value of penalty gradient with respect to parameter. This
             should be an array of the same shape as the parameter.
         """
-        raise NotImplementedError
+        return self.coefficient * parameter
 
     def __repr__(self):
-        return 'L2Penalty({0})'.format(self.coefficient)
+        return 'L2Penalty({0})'.format(self.coefficient)
\ No newline at end of file
diff --git a/mlp/schedulers.py b/mlp/schedulers.py
index 7abab2f..4f53e7e 100644
--- a/mlp/schedulers.py
+++ b/mlp/schedulers.py
@@ -32,42 +32,3 @@ class ConstantLearningRateScheduler(object):
             epoch_number: Integer index of training epoch about to be run.
         """
         learning_rule.learning_rate = self.learning_rate
-
-class CosineAnnealingWithWarmRestarts(object):
-    """Cosine annealing scheduler, implemented as in https://arxiv.org/pdf/1608.03983.pdf"""
-
-    def __init__(self, min_learning_rate, max_learning_rate, total_iters_per_period, max_learning_rate_discount_factor,
-                 period_iteration_expansion_factor):
-        """
-        Instantiates a new cosine annealing with warm restarts learning rate scheduler
-        :param min_learning_rate: The minimum learning rate the scheduler can assign
-        :param max_learning_rate: The maximum learning rate the scheduler can assign
-        :param total_epochs_per_period: The number of epochs in a period
-        :param max_learning_rate_discount_factor: The rate of discount for the maximum learning rate after each restart i.e. how many times smaller the max learning rate will be after a restart compared to the previous one
-        :param period_iteration_expansion_factor: The rate of expansion of the period epochs. e.g. if it's set to 1 then all periods have the same number of epochs, if it's larger than 1 then each subsequent period will have more epochs and vice versa.
-        """
-        self.min_learning_rate = min_learning_rate
-        self.max_learning_rate = max_learning_rate
-        self.total_epochs_per_period = total_iters_per_period
-
-        self.max_learning_rate_discount_factor = max_learning_rate_discount_factor
-        self.period_iteration_expansion_factor = period_iteration_expansion_factor
-
-
-    def update_learning_rule(self, learning_rule, epoch_number):
-        """Update the hyperparameters of the learning rule.
-
-        Run at the beginning of each epoch.
-
-        Args:
-            learning_rule: Learning rule object being used in training run,
-                any scheduled hyperparameters to be altered should be
-                attributes of this object.
-            epoch_number: Integer index of training epoch about to be run.
-        Returns:
-            effective_learning_rate at step 'epoch_number'
-        """
-        raise NotImplementedError
-
-
-
diff --git a/mlp/test_methods.py b/mlp/test_methods.py
deleted file mode 100644
index e9c33ca..0000000
--- a/mlp/test_methods.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# from mlp.learning_rules import AdamLearningRuleWithWeightDecay
-# from mlp.schedulers import CosineAnnealingWithWarmRestarts
-from mlp.layers import DropoutLayer
-from mlp.penalties import L1Penalty, L2Penalty
-import numpy as np
-import os
-
-
-
-def test_dropout_layer():
-    # loaded = np.load("../data/correct_results.npz")
-    rng = np.random.RandomState(92019)
-    
-    x = rng.normal(loc=0, scale=5.0, size=(50, 3, 64, 64))
-
-    correct_outputs = np.load(os.path.join(os.environ['MLP_DATA_DIR'], 'regularization_debug_pack.npy'), allow_pickle=True).item()
-    
-    rng = np.random.RandomState(92019)
-    layer = DropoutLayer(rng=rng)
-
-    out = layer.fprop(x)
-
-    grads = layer.bprop(inputs=x, outputs=out, grads_wrt_outputs=np.ones(x.shape))
-
-#     correct_outputs = correct_outputs['dropout']
-
-    fprop_test = np.allclose(correct_outputs['DropoutLayer_fprop'], out)
-
-    bprop_test = np.allclose(correct_outputs['DropoutLayer_bprop'], grads)
-
-    return fprop_test, out, correct_outputs['DropoutLayer_fprop'], bprop_test, grads, correct_outputs['DropoutLayer_bprop']
-
-
-def test_L1_Penalty():
-    
-
-    rng = np.random.RandomState(92019)
-    
-    x = rng.normal(loc=0, scale=5.0, size=(50, 3, 64, 64))
-
-    correct_outputs = np.load(os.path.join(os.environ['MLP_DATA_DIR'], 'regularization_debug_pack.npy'), allow_pickle=True).item()
-    
-    layer = L1Penalty(1e-4)
-
-    out = layer(x)
-
-    grads = layer.grad(x)
-
-#     correct_outputs = correct_outputs['l1penalty']
-
-    __call__test = np.allclose(correct_outputs['L1Penalty___call__correct'], out)
-
-    grad_test = np.allclose(correct_outputs['L1Penalty_grad_correct'], grads)
-
-    return __call__test, out, correct_outputs['L1Penalty___call__correct'], grad_test, grads, correct_outputs['L1Penalty_grad_correct']
-
-
-def test_L2_Penalty():
-    
-
-    rng = np.random.RandomState(92019)
-    
-    x = rng.normal(loc=0, scale=5.0, size=(50, 3, 64, 64))
-
-    correct_outputs = np.load(os.path.join(os.environ['MLP_DATA_DIR'], 'regularization_debug_pack.npy'), allow_pickle=True).item()
-    
-    layer = L2Penalty(1e-4)
-
-    out = layer(x)
-
-    grads = layer.grad(x)
-
-#     correct_outputs = correct_outputs['l2penalty']
-
-    __call__test = np.allclose(correct_outputs['L2Penalty___call__correct'], out)
-
-    grad_test = np.allclose(correct_outputs['L2Penalty_grad_correct'], grads)
-
-    return __call__test, out, correct_outputs['L2Penalty___call__correct'], grad_test, grads, correct_outputs['L2Penalty_grad_correct']
-
diff --git a/notebooks/00_notebook.ipynb b/notebooks/00_notebook.ipynb
deleted file mode 100644
index 59fae18..0000000
--- a/notebooks/00_notebook.ipynb
+++ /dev/null
@@ -1,242 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
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-    "# Introduction\n",
-    "\n",
-    "## Getting started with Jupyter notebooks\n",
-    "\n",
-    "The majority of your work in this course will be done using Jupyter notebooks so we will here introduce some of the basics of the notebook system. If you are already comfortable using notebooks or just would rather get on with some coding feel free to [skip straight to the exercises below](#Exercises).\n",
-    "\n",
-    "*Note: Jupyter notebooks are also known as IPython notebooks. The Jupyter system now supports languages other than Python [hence the name was changed to make it more language agnostic](https://ipython.org/#jupyter-and-the-future-of-ipython) however IPython notebook is still commonly used.*\n",
-    "\n",
-    "### Jupyter basics: the server, dashboard and kernels\n",
-    "\n",
-    "In launching this notebook you will have already come across two of the other key components of the Jupyter system - the notebook *server* and *dashboard* interface.\n",
-    "\n",
-    "We began by starting a notebook server instance in the terminal by running\n",
-    "\n",
-    "```\n",
-    "jupyter notebook\n",
-    "```\n",
-    "\n",
-    "This will have begun printing a series of log messages to terminal output similar to\n",
-    "\n",
-    "```\n",
-    "$ jupyter notebook\n",
-    "[I 08:58:24.417 NotebookApp] Serving notebooks from local directory: ~/mlpractical\n",
-    "[I 08:58:24.417 NotebookApp] 0 active kernels\n",
-    "[I 08:58:24.417 NotebookApp] The Jupyter Notebook is running at: http://localhost:8888/\n",
-    "```\n",
-    "\n",
-    "The last message included here indicates the URL the application is being served at. The default behaviour of the `jupyter notebook` command is to open a tab in a web browser pointing to this address after the server has started up. The server can be launched without opening a browser window by running `jupyter notebook --no-browser`. This can be useful for example when running a notebook server on a remote machine over SSH. Descriptions of various other command options can be found by displaying the command help page using\n",
-    "\n",
-    "```\n",
-    "jupyter notebook --help\n",
-    "```\n",
-    "\n",
-    "While the notebook server is running it will continue printing log messages to terminal it was started from. Unless you detach the process from the terminal session you will need to keep the session open to keep the notebook server alive. If you want to close down a running server instance from the terminal you can use `Ctrl+C` - this will bring up a confirmation message asking you to confirm you wish to shut the server down. You can either enter `y` or skip the confirmation by hitting `Ctrl+C` again.\n",
-    "\n",
-    "When the notebook application first opens in your browser you are taken to the notebook *dashboard*. This will appear something like this\n",
-    "\n",
-    "<img src='res/jupyter-dashboard.png' />\n",
-    "\n",
-    "The dashboard above is showing the `Files` tab, a list of files in the directory the notebook server was launched from. We can navigate in to a sub-directory by clicking on a directory name and back up to the parent directory by clicking the `..` link. An important point to note is that the top-most level that you will be able to navigate to is the directory you run the server from. This is a security feature and generally you should try to limit the access the server has by launching it in the highest level directory which gives you access to all the files you need to work with.\n",
-    "\n",
-    "As well as allowing you to launch existing notebooks, the `Files` tab of the dashboard also allows new notebooks to be created using the `New` drop-down on the right. It can also perform basic file-management tasks such as renaming and deleting files (select a file by checking the box alongside it to bring up a context menu toolbar).\n",
-    "\n",
-    "In addition to opening notebook files, we can also edit text files such as `.py` source files, directly in the browser by opening them from the dashboard. The in-built text-editor is less-featured than a full IDE but is useful for quick edits of source files and previewing data files.\n",
-    "\n",
-    "The `Running` tab of the dashboard gives a list of the currently running notebook instances. This can be useful to keep track of which notebooks are still running and to shutdown (or reopen) old notebook processes when the corresponding tab has been closed.\n",
-    "\n",
-    "### The notebook interface\n",
-    "\n",
-    "The top of your notebook window should appear something like this:\n",
-    "\n",
-    "<img src='res/jupyter-notebook-interface.png' />\n",
-    "\n",
-    "The name of the current notebook is displayed at the top of the page and can be edited by clicking on the text of the name. Displayed alongside this is an indication of the last manual *checkpoint* of the notebook file. On-going changes are auto-saved at regular intervals; the check-point mechanism is mainly meant as a way to recover an earlier version of a notebook after making unwanted changes. Note the default system only currently supports storing a single previous checkpoint despite the `Revert to checkpoint` dropdown under the `File` menu perhaps suggesting otherwise.\n",
-    "\n",
-    "As well as having options to save and revert to checkpoints, the `File` menu also allows new notebooks to be created in same directory as the current notebook, a copy of the current notebook to be made and the ability to export the current notebook to various formats.\n",
-    "\n",
-    "The `Edit` menu contains standard clipboard functions as well as options for reorganising notebook *cells*. Cells are the basic units of notebooks, and can contain formatted text like the one you are reading at the moment or runnable code as we will see below. The `Edit` and `Insert` drop down menus offer various options for moving cells around the notebook, merging and splitting cells and inserting new ones, while the `Cell` menu allow running of code cells and changing cell types.\n",
-    "\n",
-    "The `Kernel` menu offers some useful commands for managing the Python process (kernel) running in the notebook. In particular it provides options for interrupting a busy kernel (useful for example if you realise you have set a slow code cell running with incorrect parameters) and to restart the current kernel. This will cause all variables currently defined in the workspace to be lost but may be necessary to get the kernel back to a consistent state after polluting the namespace with lots of global variables or when trying to run code from an updated module and `reload` is failing to work. \n",
-    "\n",
-    "To the far right of the menu toolbar is a kernel status indicator. When a dark filled circle is shown this means the kernel is currently busy and any further code cell run commands will be queued to happen after the currently running cell has completed. An open status circle indicates the kernel is currently idle.\n",
-    "\n",
-    "The final row of the top notebook interface is the notebook toolbar which contains shortcut buttons to some common commands such as clipboard actions and cell / kernel management. If you are interested in learning more about the notebook user interface you may wish to run through the `User Interface Tour` under the `Help` menu drop down.\n",
-    "\n",
-    "### Markdown cells: easy text formatting\n",
-    "\n",
-    "This entire introduction has been written in what is termed a *Markdown* cell of a notebook. [Markdown](https://en.wikipedia.org/wiki/Markdown) is a lightweight markup language intended to be readable in plain-text. As you may wish to use Markdown cells to keep your own formatted notes in notebooks, a small sampling of the formatting syntax available is below (escaped mark-up on top and corresponding rendered output below that); there are many much more extensive syntax guides - for example [this cheatsheet](https://github.com/adam-p/markdown-here/wiki/Markdown-Cheatsheet).\n",
-    "\n",
-    "---\n",
-    "\n",
-    "```\n",
-    "## Level 2 heading\n",
-    "### Level 3 heading\n",
-    "\n",
-    "*Italicised* and **bold** text.\n",
-    "\n",
-    "  * bulleted\n",
-    "  * lists\n",
-    "  \n",
-    "and\n",
-    "\n",
-    "  1. enumerated\n",
-    "  2. lists\n",
-    "\n",
-    "Inline maths $y = mx + c$ using [MathJax](https://www.mathjax.org/) as well as display style\n",
-    "\n",
-    "$$ ax^2 + bx + c = 0 \\qquad \\Rightarrow \\qquad x = \\frac{-b \\pm \\sqrt{b^2 - 4ac}}{2a} $$\n",
-    "```\n",
-    "---\n",
-    "\n",
-    "## Level 2 heading\n",
-    "### Level 3 heading\n",
-    "\n",
-    "*Italicised* and **bold** text.\n",
-    "\n",
-    "  * bulleted\n",
-    "  * lists\n",
-    "  \n",
-    "and\n",
-    "\n",
-    "  1. enumerated\n",
-    "  2. lists\n",
-    "\n",
-    "Inline maths $y = mx + c$ using [MathJax]() as well as display maths\n",
-    "\n",
-    "$$ ax^2 + bx + c = 0 \\qquad \\Rightarrow \\qquad x = \\frac{-b \\pm \\sqrt{b^2 - 4ac}}{2a} $$\n",
-    "\n",
-    "---\n",
-    "\n",
-    "We can also directly use HTML tags in Markdown cells to embed rich content such as images and videos.\n",
-    "\n",
-    "---\n",
-    "```\n",
-    "<img src=\"http://placehold.it/350x150\" />\n",
-    "```\n",
-    "---\n",
-    "\n",
-    "<img src=\"http://placehold.it/350x150\" />\n",
-    "\n",
-    "---\n",
-    "\n",
-    "  \n",
-    "### Code cells: in browser code execution\n",
-    "\n",
-    "Up to now we have not seen any runnable code. An example of a executable code cell is below. To run it first click on the cell so that it is highlighted, then either click the <i class=\"fa-step-forward fa\"></i> button on the notebook toolbar, go to `Cell > Run Cells` or use the keyboard shortcut `Ctrl+Enter`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from __future__ import print_function\n",
-    "import sys\n",
-    "\n",
-    "print('Hello world!')\n",
-    "print('Alarming hello!', file=sys.stderr)\n",
-    "print('Hello again!')\n",
-    "'And again!'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This example shows the three main components of a code cell.\n",
-    "\n",
-    "The most obvious is the input area. This (unsuprisingly) is used to enter the code to be run which will be automatically syntax highlighted.\n",
-    "\n",
-    "To the immediate left of the input area is the execution indicator / counter. Before a code cell is first run this will display `In [ ]:`. After the cell is run this is updated to `In [n]:` where `n` is a number corresponding to the current execution counter which is incremented whenever any code cell in the notebook is run. This can therefore be used to keep track of the relative order in which cells were last run. There is no fundamental requirement to run cells in the order they are organised in the notebook, though things will usually be more readable if you keep things in roughly in order!\n",
-    "\n",
-    "Immediately below the input area is the output area. This shows any output produced by the code in the cell. This is dealt with a little bit confusingly in the current Jupyter version. At the top any output to [`stdout`](https://en.wikipedia.org/wiki/Standard_streams#Standard_output_.28stdout.29) is displayed. Immediately below that output to [`stderr`](https://en.wikipedia.org/wiki/Standard_streams#Standard_error_.28stderr.29) is displayed. All of the output to `stdout` is displayed together even if there has been output to `stderr` between as shown by the suprising ordering in the output here. \n",
-    "\n",
-    "The final part of the output area is the *display* area. By default this will just display the returned output of the last Python statement as would usually be the case in a (I)Python interpreter run in a terminal. What is displayed for a particular object is by default determined by its special `__repr__` method e.g. for a string it is just the quote enclosed value of the string itself.\n",
-    "\n",
-    "### Useful keyboard shortcuts\n",
-    "\n",
-    "There are a wealth of keyboard shortcuts available in the notebook interface. For an exhaustive list see the `Keyboard Shortcuts` option under the `Help` menu. We will cover a few of those we find most useful below.\n",
-    "\n",
-    "Shortcuts come in two flavours: those applicable in *command mode*, active when no cell is currently being edited and indicated by a blue highlight around the current cell; those applicable in *edit mode* when the content of a cell is being edited, indicated by a green current cell highlight.\n",
-    "\n",
-    "In edit mode of a code cell, two of the more generically useful keyboard shortcuts are offered by the `Tab` key.\n",
-    "\n",
-    "  * Pressing `Tab` a single time while editing code will bring up suggested completions of what you have typed so far. This is done in a scope aware manner so for example typing `a` + `[Tab]` in a code cell will come up with a list of objects beginning with `a` in the current global namespace, while typing `np.a` + `[Tab]` (assuming `import numpy as np` has been run already) will bring up a list of objects in the root NumPy namespace beginning with `a`.\n",
-    "  * Pressing `Shift+Tab` once immediately after opening parenthesis of a function or method will cause a tool-tip to appear with the function signature (including argument names and defaults) and its docstring. Pressing `Shift+Tab` twice in succession will cause an expanded version of the same tooltip to appear, useful for longer docstrings. Pressing `Shift+Tab` four times in succession will cause the information to be instead displayed in a pager docked to bottom of the notebook interface which stays attached even when making further edits to the code cell and so can be useful for keeping documentation visible when editing e.g. to help remember the name of arguments to a function and their purposes.\n",
-    "\n",
-    "A series of useful shortcuts available in both command and edit mode are `[modifier]+Enter` where `[modifier]` is one of `Ctrl` (run selected cell), `Shift` (run selected cell and select next) or `Alt` (run selected cell and insert a new cell after).\n",
-    "\n",
-    "A useful command mode shortcut to know about is the ability to toggle line numbers on and off for a cell by pressing `L` which can be useful when trying to diagnose stack traces printed when an exception is raised or when referring someone else to a section of code.\n",
-    "  \n",
-    "### Magics\n",
-    "\n",
-    "There are a range of *magic* commands in IPython notebooks, than provide helpful tools outside of the usual Python syntax. A full list of the inbuilt magic commands is given [here](http://ipython.readthedocs.io/en/stable/interactive/magics.html), however three that are particularly useful for this course:\n",
-    "\n",
-    "  * [`%%timeit`](http://ipython.readthedocs.io/en/stable/interactive/magics.html?highlight=matplotlib#magic-timeit) Put at the beginning of a cell to time its execution and print the resulting timing statistics.\n",
-    "  * [`%precision`](http://ipython.readthedocs.io/en/stable/interactive/magics.html?highlight=matplotlib#magic-precision) Set the precision for pretty printing of floating point values and NumPy arrays.\n",
-    "  * [`%debug`](http://ipython.readthedocs.io/en/stable/interactive/magics.html?highlight=matplotlib#magic-debug) Activates the interactive debugger in a cell. Run after an exception has been occured to help diagnose the issue.\n",
-    "  \n",
-    "### Plotting with `matplotlib`\n",
-    "\n",
-    "When setting up your environment one of the dependencies we asked you to install was `matplotlib`. This is an extensive plotting and data visualisation library which is tightly integrated with NumPy and Jupyter notebooks.\n",
-    "\n",
-    "When using `matplotlib` in a notebook you should first run the [magic command](http://ipython.readthedocs.io/en/stable/interactive/magics.html?highlight=matplotlib)\n",
-    "\n",
-    "```\n",
-    "%matplotlib inline\n",
-    "```\n",
-    "\n",
-    "This will cause all plots to be automatically displayed as images in the output area of the cell they are created in. Below we give a toy example of plotting two sinusoids using `matplotlib` to show case some of the basic plot options. To see the output produced select the cell and then run it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "\n",
-    "# generate a pair of sinusoids\n",
-    "x = np.linspace(0., 2. * np.pi, 100)\n",
-    "y1 = np.sin(x)\n",
-    "y2 = np.cos(x)\n",
-    "\n",
-    "# produce a new figure object with a defined (width, height) in inches\n",
-    "fig = plt.figure(figsize=(8, 4))\n",
-    "# add a single axis to the figure\n",
-    "ax = fig.add_subplot(111)\n",
-    "# plot the two sinusoidal traces on the axis, adjusting the line width\n",
-    "# and adding LaTeX legend labels\n",
-    "ax.plot(x, y1, linewidth=2, label=r'$\\sin(x)$')\n",
-    "ax.plot(x, y2, linewidth=2, label=r'$\\cos(x)$')\n",
-    "# set the axis labels\n",
-    "ax.set_xlabel('$x$', fontsize=16)\n",
-    "ax.set_ylabel('$y$', fontsize=16)\n",
-    "# force the legend to be displayed\n",
-    "ax.legend()\n",
-    "# adjust the limits of the horizontal axis\n",
-    "ax.set_xlim(0., 2. * np.pi)\n",
-    "# make a grid be displayed in the axis background\n",
-    "ax.grid(True)"
-   ]
-  }
- ],
- "metadata": {
-  "language_info": {
-   "name": "python"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
\ No newline at end of file
diff --git a/notebooks/01_Introduction.ipynb b/notebooks/01_Introduction.ipynb
deleted file mode 100644
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--- a/notebooks/01_Introduction.ipynb
+++ /dev/null
@@ -1,412 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "nbpresent": {
-     "id": "533c10f0-95ba-4684-a72d-fd52cef0d007"
-    }
-   },
-   "source": [
-    "# Exercises\n",
-    "\n",
-    "Today's exercises are meant to allow you to get some initial familiarisation with the `mlp` package and how data is provided to the learning functions. You are going to implement variants of a `DataProvider` class, which preprocesses data and serves data in batches when the `next()` function is called. \n",
-    "\n",
-    "If you are new to Python and/or NumPy and are struggling to complete the exercises, you may find going through [this Stanford University tutorial](http://cs231n.github.io/python-numpy-tutorial/) by Justin Johnson first helps. There is also a derived Jupyter notebook by Volodymyr Kuleshov and Isaac Caswell which you can download [from here](https://github.com/kuleshov/teaching-material/blob/master/tutorials/python/cs228-python-tutorial.ipynb) - if you save this in to your `mlpractical/notebooks` directory you should be able to open the notebook from the dashboard to run the examples.\n",
-    "\n",
-    "## Data providers\n",
-    "\n",
-    "Open (in the browser) the [`mlp.data_providers`](../mlp/data_providers.py) module. Have a look through the code and comments, then follow to the exercises.\n",
-    "\n",
-    "### Exercise 1 \n",
-    "\n",
-    "The `MNISTDataProvider` iterates over input images and target classes (digit IDs) from the [MNIST database of handwritten digit images](http://yann.lecun.com/exdb/mnist/), a common supervised learning benchmark task. Using the data provider and `matplotlib` we can for example iterate over the first couple of images in the dataset and display them using the following code:\n",
-    "\n",
-    "* NOTE: If you encounter `KeyError: 'MLP_DATA_DIR'`, check that you have correctly set the environment variable following the setup instructions, and that you are in the `mlp` environment."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "nbpresent": {
-     "id": "978c1095-a9ce-4626-a113-e0be5fe51ecb"
-    }
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-   "outputs": [
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-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 200x200 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Image target: [8]\n"
-     ]
-    }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "import numpy as np\n",
-    "import sys\n",
-    "# sys.path.append('/path/to/mlpractical')\n",
-    "import matplotlib.pyplot as plt\n",
-    "import mlp.data_providers as data_providers\n",
-    "# If error while importing mlp.data_providers: add path to your folder mlpractical using sys.path.append('/path/to/mlpractical/')\n",
-    "def show_single_image(img, fig_size=(2, 2)):\n",
-    "    fig = plt.figure(figsize=fig_size)\n",
-    "    ax = fig.add_subplot(111)\n",
-    "    ax.imshow(img, cmap='Greys')\n",
-    "    ax.axis('off')\n",
-    "    plt.show()\n",
-    "    return fig, ax\n",
-    "\n",
-    "# An example for a single MNIST image\n",
-    "mnist_dp = data_providers.MNISTDataProvider(\n",
-    "    which_set='valid', batch_size=1, max_num_batches=2, shuffle_order=True)\n",
-    "\n",
-    "for inputs, target in mnist_dp:\n",
-    "    # The reshape operation reorganizes data from 1D array of size 784 to 2D array of size 28x28\n",
-    "    # See notes in the next cell\n",
-    "    square_inputs = inputs.reshape((28, 28))\n",
-    "    show_single_image(square_inputs)\n",
-    "    print('Image target: {0}'.format(target))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Generally we will want to deal with batches of multiple images i.e. `batch_size > 1`. \n",
-    "\n",
-    "**Your tasks**:\n",
-    "\n",
-    "* Using `MNISTDataProvider`, write code that iterates over the first 5 minibatches of size 100 data-points. \n",
-    "* Display each batch of MNIST digits in a $10\\times10$ grid of images. \n",
-    "  \n",
-    "**Notes**:\n",
-    "\n",
-    "  * Images are returned from the provider as tuples of numpy arrays `(inputs, targets)`. The `inputs` matrix has shape `(batch_size, input_dim)` while the `targets` array is of shape `(batch_size,)`, where `batch_size` is the number of data points in a single batch and `input_dim` is dimensionality of the input features. \n",
-    "  * Each input data-point (image) is stored as a 784 dimensional vector of pixel intensities normalised to $[0, 1]$ from inital integer values in $[0, 255]$. However, the original spatial domain is two dimensional, so before plotting you will need to reshape the one dimensional input arrays in to two dimensional arrays 2D (MNIST images have the same height and width dimensions).\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 300x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 300x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 300x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 300x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 300x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# write your code here for iterating over five batches of \n",
-    "# 100 data points each and displaying as 10x10 grids\n",
-    "\n",
-    "def show_batch_of_images(img_batch, fig_size=(3, 3)):\n",
-    "    #Shape of img_batch: (batch_size, im_height, im_width)\n",
-    "    fig = plt.figure(figsize=fig_size)\n",
-    "    batch_size, im_height, im_width = img_batch.shape\n",
-    "    # calculate no. of 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')\n",
-    "    ax.axis('off')\n",
-    "    fig.tight_layout()\n",
-    "    plt.show()\n",
-    "    return fig, ax\n",
-    "\n",
-    "batch_size = 100\n",
-    "num_batches = 5\n",
-    "\n",
-    "mnist_dp = data_providers.MNISTDataProvider(\n",
-    "    which_set='valid', batch_size=batch_size, \n",
-    "    max_num_batches=num_batches, shuffle_order=True)\n",
-    "\n",
-    "for inputs, target in mnist_dp:\n",
-    "    # reshape inputs from batch of vectors to batch of 2D arrays (images)\n",
-    "    show_batch_of_images(inputs.reshape((batch_size, 28, 28)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "nbpresent": {
-     "id": "d2d525de-5d5b-41d5-b2fb-a83874dba986"
-    }
-   },
-   "source": [
-    "### Exercise 2\n",
-    "\n",
-    "The `targets` variable in `MNISTDataProvider` currently returns a vector of integers, where each element in this vector represents an the class of the corresponding data-point (0 to 9). \n",
-    "\n",
-    "It is easier to train neural networks using a 1-of-K representation for multi-class targets. Instead of representing class identity by an integer, each target is replaced by a vector of length equal to teh number of classes whose values are zero everywhere except on the index corresponding to the class.\n",
-    "\n",
-    "For instance, given a batch of 5 integer targets `[2, 2, 0, 1, 0]` and assuming there are 3 different classes \n",
-    "the corresponding 1-of-K encoded targets would be\n",
-    "```\n",
-    "[[0, 0, 1],\n",
-    " [0, 0, 1],\n",
-    " [1, 0, 0],\n",
-    " [0, 1, 0],\n",
-    " [1, 0, 0]]\n",
-    "```\n",
-    "**Your Tasks**:\n",
-    "  * Implement the `to_one_of_k` method of `MNISTDataProvider` class. \n",
-    "  * Uncomment the overloaded `next` method, so the raw targets are converted to 1-of-K coding. \n",
-    "  * Test your code by running the the cell below. As you have changed the `mlp` package, reload the notebook kernel before running the cell to make sure the changes are picked up."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
-      " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
-      " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
-      " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
-      " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]]\n",
-      "[[0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
-      " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
-      " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
-      " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
-      " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]]\n",
-      "[[0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
-      " [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
-      " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
-      " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
-      " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]]\n",
-      "[[0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
-      " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
-      " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
-      " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
-      " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]]\n",
-      "[[0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
-      " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
-      " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
-      " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
-      " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "import mlp.data_providers as data_providers\n",
-    "import numpy as np\n",
-    "\n",
-    "mnist_dp = data_providers.MNISTDataProvider(\n",
-    "    which_set='valid', batch_size=5, max_num_batches=5, shuffle_order=False)\n",
-    "\n",
-    "for inputs, targets in mnist_dp:\n",
-    "    # Check that values are either 0 or 1\n",
-    "    assert np.all(np.logical_or(targets == 0., targets == 1.))\n",
-    "    # Check that there is exactly a single 1\n",
-    "    assert np.all(targets.sum(-1) == 1.)\n",
-    "    print(targets)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "nbpresent": {
-     "id": "471093b7-4b94-4295-823a-5285c79d3119"
-    }
-   },
-   "source": [
-    "### Exercise 3\n",
-    "\n",
-    "Here you will write your own data provider `MetOfficeDataProvider` that wraps weather data for south Scotland. This data is stored in `data/HadSSP_daily_qc.txt` for your convenience and skeleton code for the class provided in `mlp/data_providers.py`.\n",
-    "\n",
-    "The data is organised in the text file as a table, with the first two columns indexing the year and month of the readings and the following 31 columns giving daily precipitation values for the corresponding month. As not all months have 31 days some of the entries correspond to non-existing days. These values are indicated by a non-physical value of `-99.9`.\n",
-    "\n",
-    "**Your tasks**:\n",
-    "\n",
-    "  * You should read all of the data from the file ([`np.loadtxt`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.loadtxt.html) may be useful for this) and then filter out the `-99.9` values and collapse the table to a one-dimensional array corresponding to a sequence of daily measurements for the whole period data is available for. [NumPy's boolean indexing feature](http://docs.scipy.org/doc/numpy/user/basics.indexing.html#boolean-or-mask-index-arrays) could be helpful here.\n",
-    "  * A common initial preprocessing step in machine learning tasks is to normalise data so that it has zero mean and a standard deviation of one. Normalise the data sequence so that its overall mean is zero and standard deviation one.\n",
-    "  * Each data point in the data provider should correspond to a window of length specified in the `__init__` method as `window_size` of this contiguous data sequence, with the model inputs being the first `window_size - 1` elements of the window and the target output being the last element of the window. For example if the original data sequence was `[1, 2, 3, 4, 5, 6]` and `window_size=3` then `input, target` pairs iterated over by the data provider should be\n",
-    "  ```\n",
-    "  [1, 2], 3\n",
-    "  [4, 5], 6\n",
-    "  ```\n",
-    "  * **Extension**: The current data provider only produces `len(data)/window_size` sample points. A better approach is to have it return overlapping windows of the sequence so that more training data instances are produced. For example for the same sequence `[1, 2, 3, 4, 5, 6]` the corresponding `input, target` pairs with `window_size=3` would be\n",
-    "\n",
-    "```\n",
-    "[1, 2], 3\n",
-    "[2, 3], 4\n",
-    "[3, 4], 5\n",
-    "[4, 5], 6\n",
-    "```\n",
-    "  * Test your code by running the cell below. (Remember to reload kernel after making changes in the `mlp` package)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "nbpresent": {
-     "id": "c8553a56-9f25-4198-8a1a-d7e9572b4382"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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oN1CCiRCiZivIKxYwShu9eOhYTmrVrmPvVs7kTo+HAocHWFob9dcUJUkgMaKaEEiKpOWm8f3Z74mIieBe/j1AG0xmBc9ioN9ALC2qdyMkIUQ9VZBbSpgoZXJn0TyM3LQqXEQFDu7lj17owocn2LuDpXSyqGkkkBhRTQokRdJy04iIieCHsz+QkZ8BQHN1c2YFz2KQ/yAJJkIIw+TnlLM09eFjyZCbbvg1VBbaUYniIxRljV44NtCOdsjfZbWeBBIjqomBpEh6XjoRZyP4PuZ7MvK0waSZuhmzgmcx2H+wBBMh6qu8rEqOXtzvgZF3z/BrqCzvB4kKJncW/dfeDSzk9nJ9I4HEiGpyICmSkZdBREwE35/9nvQ87b9c/F38CQ0OZUizIVhZyDCmELVaXmbJiZylTe4sChz355oZxMLqodGL0pamNnjwuJ2rBAxRIQkkRlQbAkmRe3n3WHVuFd+d/Y60+/ds/Vz8CA0OZWizoRJMhKgJSmx0Vs7kzqLRjft9iQxiYV350QtHT7BT63XxFMIYJJAYUW0KJEXu5d3jx3M/svLsSl0waerclNDgUJ5u/rQEEyGMSVG0cyoqWppafD5GKRudVcjStvKjF46eYOsiAUOYnQQSI6qNgaRIZn6mNpicWUlqbioAvs6+zAyaybAWw7C2kCVvQpSgt9FZeaMXj7rRmb1+gChv9MLRU9tWXAKGqGUkkBhRbQ4kRbLys/jp/E+sOL2ClNwUAHycfJgZPJNnWjwjwUTUbQ9vdFbe5M6i0FHVjc4qWppa/JiNo9F/VSFqGgkkRlQXAkmRrPwsVp9fzYozK7ibcxeAJk5NmBk0k+EthmMtTYJEbaDRaDtzlrs09aGdVKu0D4nTQ0tTK2i2ZYSNzoSoaySQGFFdCiRFsvKzWHNhDctOL9MFk8aOjZkRPIORLUZKMBHVq/hGZ2XupFpsbkZVNzqzdXno9kg5oxcOHmbb6EyIukQCiRHVxUBSJLsgmzXntcEkOScZAG9Hb2YEzWBUy1ESTETVlNjorIylqUXhI+suVdqHxFZdxk6qZTTbsrI1+q8qhCifBBIjqsuBpEh2QTZrL6xl2ellJGUnAdDIsREz2s9gVKtR2FjKhlL1WmF+yW3Zy91JNRXTbHT20E6qstGZEDWeBBIjqg+BpEhOQQ7rLq5j6aml3Mm+A0BDh4ZMD5rO6FajsbWUf2HWCXobnZWzNLXo+ypvdOZe8dJU3U6q7rLRmRB1kAQSI6pPgaRIbmEu6y6sY+nppdzOug2Al4MX09tPZ0zrMRJMappSNzorY/QiM9kIG51VotmWbHQmhEACiVHVx0BSJLcwl/UX17P01FJuZd0CwMvei2lB0xjTagx2VnZmrrCOys8uJUyUs5Pq/X2MDPLwRmcPb8uut7LEUzY6E0JUSZ0MJCkpKcydO5dNmzYBMHz4cL766itcXV3LfM78+fP56aefiI+Px8bGhk6dOvHBBx/QrVu3Sl+3PgeSInmFefxy8ReWnF5CYmYiAJ72nkxrP43nWj8nwaQixTc6K3NparHRjapudFbZyZ2ODWQfEiFEtaiTgWTIkCFcv36dRYsWARAaGoq/vz+bN28u8zmrVq3Cy8uL5s2bk52dzWeffcaaNWu4dOkSDRo0qNR1JZA8kFeYx4ZLG1hyagk3M28C2mAytd1Ungt4DnurerBMUlG0G51VdvQiKwnyswy/TvGNzipstiUbnQkhaqY6F0hiYmIIDAzk0KFDutGNQ4cO0aNHD86dO0dAQEClXqfojfnjjz/o37+/Qc+RQPJAfmE+Gy5vYEn0EhIyEwDwsPNgavupPNf6ORysa1GDqOIbnZUYvXj42CNsdGZpU4nJncVGNGSjMyFEHWDIZ2itmHV28OBB1Gq13q2W7t27o1arOXDgQKUCSV5eHosWLUKtVhMSElLmebm5ueTmPtj4Kj09/dGKr4OsLa15rvVzjGwxkk2XN7H41GJu3LvBf47+h2WnlzGl3RTGBow1TzApbaOzilqFV3mjs8rspHp/NMPWWQKGEEKUo1YEksTERLy8vEoc9/LyIjExsdznbtmyhXHjxpGVlYW3tzc7duzA09OzzPMXLFjAe++998g11wfWltaMaT2G4S2Hs+XyFhZGL+TGvRt8euxTVpxZweR2kxkXMO7RgomiaJedlliaWsZy1axkI2x0VsbS1OLHZKMzIYQwKrPespk/f36FH/5Hjhxh+/btrFy5kvPnz+s91qpVK6ZPn86bb75Z5vMzMzO5efMmSUlJLF68mF27dnH48OFSAw6UPkLi6+srt2wqIV+Tz5bLW1h8ajHxGfEAuNm6MandJF5o8wKO1o4PbXRW1tLU4tu4V3WjM8dKjF4U30lVNjoTQghjqzVzSJKSkkhKSir3HH9/f1atWsVrr71Gamqq3mOurq589tlnTJ06tdLXbNWqFdOmTeOtt96q1Pkyh6QCpWx0VnDvFr/ePsqi1CjiCrUTOtUKTM7M54XkOzhpCgy/jo1T5SZ3ykZnQghRY9SaOSSenp7l3j4p0qNHD9LS0vjzzz/p2rUrAIcPHyYtLY3HH3/coGsqiqI3AiIeoinU7i1S2q2QEqMX979XNHovYQWMAJ4Gtjo5sNBVzTVra750smalfUMmpWUwPhec7D0oe2nqQ+HDWpYWCyGEqRQWFrJ3715u3ryJt7c3vXr1wtKyensP1YpVNqBd9puQkMDChQsB7bJfPz8/vWW/bdq0YcGCBYwaNYrMzEw++OADhg8fjre3N8nJyXz77bf88MMPHDt2jHbt2lXqurV+hKT4RmcVTe58lI3O7NRljl4UOLizNfMai27s5GqWdrmwi40LEwMnMqHtBJxtnI37OwshhKi09evXM2/ePK5fv6475uPjwxdffMHo0aMf6bVrzS0bQ9y9e7dEY7Svv/5arzGaSqVi+fLlTJkyhZycHMaPH8/hw4dJSkrCw8ODLl268Pbbb9OlS5dKX9ckgSTtBty9DO4tQN3EsOeWttFZec22HmmjswqWphq40VmhppBtV7exMHohsWmxADjbODOx7UQmBE7AxaYWBj4hhKjF1q9fz7PPPsvDUUB1f9L+2rVrHymU1MlAYi5GDyTHv4PN87S3OVQWMPS/EDCkcqMXVd7oTKVt/V3R0lRdwDDtRmeFmkK2X9tOeFQ4V9KuAOBs7cyLgS/yYuCLEkyEEKIaFBYW4u/vrzcyUpxKpcLHx4fY2Ngq376RQGJERg0kaTfg8/Yl5lwYTvXQqEUFO6nau9XIjc4KNYXsuLaDhdELuZR6CQAnaycmtJ3AxMCJqG3VZq5QCCHqpnu5BazZtI1pzz9T4bm7d++mT58+VbpOrZnUWu/cvVxGGFFVfvSiDm10ZmlhyeBmg3nK/yl2XNtBeFQ4l1IvsTB6IT/E/MD4NuOZ3G6yBBMhhKiCrLwCriZlcTU5k9ikTK4mZd7/Pouke7lknv1fpV7n5s2bJq5USwJJdXJvob1NUzyUqCxhXhS4+pqvLjOzUFkwyH8QA/0GsjNuJ2FRYVxMucjiU4tZdW4V49uMZ1LgJFztXM1dqhBC1Cg5+YXE3c16KHBov26ll7+i1M3Ti/Ibb2h5e3sbp9gKyC2bCphmDskroBRqw8gzn0PHSY/+unWIRtGwK24X4VHhnE/RNsNzsHLghTYvMLndZNzs3MxcoRBCVJ+8Ag3xKVnE3nkQOK4mZ3I1KYuEtGzK+xR3dbDG38ORZp6O+Hs44u/poP3e0xFHawv8/f25ceNGiUmtIHNIahzTrbK5Au7NDV9lU49oFA2743cTHhXOubvnALC3smdcm3FMaTcFdzt3M1cohBDGUVCo4XpKNrHJ90c6kjKJTc7ialIm11Oy0JTzSe1sa4X//ZDRzNORZp4OuhDi6lD+CsiiVTaAXiiRVTY1UK3vQ1IHKIrCnvg9hEWFEXM3BrgfTALGMbndZDzsPcxboBBCVEKhRiEhNfv+6EYmV3S3WbKIv5tFQTmpw8HG8sFIR7HA4e/piIejjS5AVEVpfUh8fX35/PPPa3Yfkscee6zUX1ylUmFnZ0fLli2ZMmUKffv2NazqGkoCSc2hKAqR1yMJiwrjbPJZQBtMnm/9PFPaT8HTvuKuv0IIYUoajUJies79EY77Ix33J5bGJWeRV1j2KktbKwvdbRV/T0eaeWgDR3NPRxo42z5S6KiIqTq1mjSQvPXWW4SFhREUFETXrl1RFIWjR48SHR3NlClTOHv2LDt37mT9+vWMGDHikX6RmkACSc2jKAp7b+wl7GQYp5NPA2BnacfzAc8ztf1UCSZCCJNSFIU7Gbm6uRyxSVm6CaVXkzPJyS87dNhYWuDrbn//1oqjXvBo5GKHhUXd2kXcpIFk5syZNG3alHfeeUfv+L/+9S+uXbvG4sWLeffdd/n11185evSo4dXXMBJIai5FUdh3Yx9hUWGcSjoFgK2lLc+1fo5p7afRwKGBmSsUQtRWiqJwNzOPq8mZXLnzYBJpbFIm15IzycwrLPO5VhYqfN0d8Pdw0M3rKLrF0tjVHss6FjrKY9JAolarOXbsGC1bttQ7funSJTp16kRaWhrnzp2jS5cuZGRkGF59DSOBpOZTFIX9CfsJiwoj+k40oA0mz7Z+lmntp+Hl4GXmCoUQNVVqVl6pIx2xSZlk5JS9M7mFCpq42eutYCka9WjiZo+1pUU1/hY1l0kbo9nZ2XHgwIESgeTAgQPY2Wl3ZNVoNNja2hr60kJUiUql4okmT9CzcU8OJhwkLCqMk3dOEhETwZrza3TBpKFjQ3OXKoQwg4ycfO3oht4KFm3oSM3KL/e5jdV2NGvg+NDSWUd83e2xtar9DSprEoMDyf/93/8xe/Zsjh07RpcuXVCpVPz5558sWbKEv//97wD8/vvvPPbYY0YvVojyqFQqHm/yOD0a9+DQzUOERYVx4vYJVp1bxZoLaxjTagzTg6bTyLGRuUsVQhhZUVfS2GIjHEWjHUn38sp9bkMXW71VK0Xf+3k4YGctoaO6VGnZb0REBF9//TXnz2ubVgUEBPB///d/jB8/HoDs7GzdqpvaTm7Z1F6KonA48TBhJ8M4fvs4ANYW1oxuNZoZQTMkmAhRy+TkF3ItOatYY7AHTcIq6krq6WSjG91o5qnfKMzBRpqWm4r0ITEiCSS1n6IoHEk8QlhUGEdvaSdaW1lYMbqlNph4O1VPW2QhRMXyCjTE3c0q0Qb9alImN9Nzyu1K6uZgrbdq5cH3DjjbmW4Hc1G2agkkeXl53L59G41Gf3lT06ZNq/JyNZYEkrqlKJgcSTwCaIPJyJYjmRk0k8ZOjc1cnRD1Q/79rqTFRziK/nsjJbv8rqR2VnpzOQzpSiqqn0kDycWLF5k2bRoHDhzQO64oCiqVisLCspdC1UYSSOqmo4lHCY8K53DiYQCsVFaMaDmCmcEzaeIk7fyFeFRFXUlLzukwrCuprlfH/eDh/ohdSUX1Mmkg6dmzJ1ZWVrz55pt4e3uX+IMREhJieMU1mASSuu3YrWOERYVx+OaDYDK85XBmBM3A17n+7sAsRGUU70p6pdgk0tikTOLvZpfbldTO+n5X0lJGOkzdlVRUH5MGEkdHR44dO0abNm0eqcjaQgJJ/XDi9gnCToZx8OZBACxVljzT4hlCg0LxdZFgIuovRVG4XdSVtFg79Kv326HnFpTflbSpR1HQeNAOvVkDRxo6172upKIkk/YhCQwMJCkpqcrFCVETPeb1GIueWsTJ2ycJjwpnf8J+NlzawObLmxnWfBihwaE0dalb86OEKKIoCsmZeXpzOoovoc2qRFfSB83BHHRLZ+tbV1LxaAweIdm1axdvv/02H374IUFBQVhb689crmujCDJCUj9F3YkiLCqM/Tf2A9oRk6ebP01ocCh+Ln5mrk6IqinqSvpgtCNL1ygsI7f8rqQ+bkUjHA56K1h83Oyxkq6kogwmvWVjYaH9g/fw/T2Z1CrqolN3ThEWFcbeG3sBsFBZMLTZUEKDQ2mmbmbm6oQoKT0n/8FIx/3bKkUjHeV1JVWpoLHaXm9r+6IJpb5uDthYSegQhjNpIImMjCz38d69exvycjWeBBIBcDrpNOFR4URe1/75t1BZMKTZEEKDQ2mubm7m6kR9k5lboLut8nCvjuTM8ruSNnKxw9/T4aGls440dZeupML4pDGaEUkgEcWdSTpDeFQ4e67vAUCFisHNBjM7eDbNXSWYCOPJyS8s1o00S29C6e2MirqS2upWrfhLV1JhRkYPJNHR0bRv3x4LCwuio6PLPTc4ONiwams4CSSiNGeTzxIeFc7u+N2ANpgM8h/ErOBZtHRrWcGzhdDKLSgk/m5WicBxNSmThLSccp+r60parDNp0f4r0pVU1BRGDyQWFhYkJibi5eWFhYUFKpWK0p4mc0hEfXPu7jnCo8LZGbcT0AaTp/yfYlbwLFq5tTJzdaImeLgrafFGYQmp5XcldSnqSlpsw7eiyaRqBwkdouYzeiC5du0aTZs2RaVSce3atXLP9fOrWysQJJCIyjh/9zzhUeH8EfeH7thAv4HMDplNa7fWZqxMVIdCjcKNlGzdCEfxzd/iU7IpLCd1ONpYPrTvinQlFXWHzCExIgkkwhDn755nYfRCdlzboTs2oOkAZofMJsA9wIyViUel0SjcvN+VtPjW9oZ2JW3WoPjmbw40cJKupKLuMnog2bRpU6UvPnz48EqfWxtIIBFVcTHlIgujF7L96nYUtP+L9W/an9khs2njXj+6HNdGJbqSFhvtuJacVX5XUisL/NwdSkwibeYpXUlF/WWSOSR6T3poDknxdC9zSIR44FLKJRZGL+T3q7/rgklf377MCZlDW4+2Zq6uflIUhaR7eQ9t+KZdyXKtEl1Jm7o7FJvT4UAzTyf8PR3wVktXUiEeZtJbNn/88Qd/+9vf+PDDD+nRowcqlYoDBw7ourcOHDjwkYqvaSSQCGO4nHqZhdEL2Ra7TRdM+vj0YXaH2bTzaGfm6uqmlMw8vVUrsclZxCbd41pSVoVdSX3dHzQH8/d4MOrRxFW6kgphCJMGkvbt2xMeHs4TTzyhd3zv3r2EhoYSExNjeMU1mAQSYUxX0q6wKHoRW2O3olG0w/+9fXozJ2QO7TwlmBgqLTtfby5H8XboadkVdyVtdn8eR/EVLNKVVAjjMWkgsbe3588//yQoKEjveHR0NN26dSM7O9vwimswCSTCFGLTYlkUvYjfYn/TBZNeTXoxJ2QOQQ2CKnh2/ZKZW6C3aiX2fndSQ7uSNiu2dNZXupIKUS1MGkiefPJJrK2t+eGHH/D29gYgMTGRiRMnkpeXV2Fr+dpGAokwpatpV1l8ajFbrmzRBZOeTXoyJ2QOIQ1CzFxd9cnOK+Ta3QeBIzbpnna32eRM7lTQlbSBs+39VSsOektn/T0csbeR0CGEOZk0kFy6dIlRo0Zx/vx5mjbVbsceFxdH69at2bBhAy1b1q0ulRJIRHWIS49jUfQitlzZQqGinVTZs3FPZofMpoNXB/MWZyS5BYXEJWcVawyWpbvdcrOCrqTujjYP5nJIV1Ihag2T9yFRFIUdO3Zw7tw5FEUhMDCQAQMG1Mm19BJIRHWKT49n0alFbL68WRdMenj3YE6HOTzm9ZiZq6tYfqGG+LtZJQJHbFImN1KzKe9vm+JdSZvpLZ11RG0voUOI2kgaoxmRBBJhDvEZ8Sw5tYRNlzZRoGhXhHTz7sackDl0atjJrLWV1pW0aNTjegVdSZ1srfQnkRYb7XBzsK6T/6gRoj4zeSDJzMwkMjKSuLg48vL0J5XNnTvX0Jer0SSQCHO6nnGdJaeWsPHSxgfBpFE3ZofMpnOjzia7rkajkJCWrZvH8WDpbCbxd7PILyz7rw17a0v8PBz09l0pCh2eTtIKXYj6xKSB5MSJEwwdOpSsrCwyMzNxd3cnKSkJBwcHvLy8uHLlyiMVX5aUlBTmzp2r6xo7fPhwvvrqK1xdXSv1/FmzZrFo0SI+++wzXnnllUpfVwKJqAlu3LvBklNL2HBpAwUabTDp0qgLc0Lm0KVRlyq9pqIo3ErPfWgFy/3vk7PIq6Arqb+H/nLZou8bukgrdCGEliGfoVaGvvirr77KM888Q1hYGK6urhw6dAhra2tefPFF5s2bV+WiKzJ+/HiuX7/Otm3bAAgNDWXixIls3ry5wudu2LCBw4cP07hxY5PVJ4QpNXFqwrs93mVm0EyWnlrK+kvrOZJ4hCOJR+jcsLMumDwcBIq6ksYWG+EoCh7XkrPIzi+7K6m1pQpfd4di+6446lazNFbbSyt0IYRRGTxC4urqyuHDhwkICMDV1ZWDBw/Stm1bDh8+zOTJkzl37pzRi4yJiSEwMJBDhw7RrVs3AA4dOkSPHj04d+4cAQFlb1p248YNunXrxu+//87TTz/NK6+8IiMkotZLzExkyaklrL+4nnyNtgFYa3Uw3d3GocppxdXkol4dWdwrpyuppYUKHzd73ehG8dssjV3tpCupEOKRmHSExNr6wcSzhg0bEhcXR9u2bVGr1cTFxVWt4gocPHgQtVqtCyMA3bt3R61Wc+DAgTIDiUajYeLEibz++uu0a1e5Lpi5ubnk5j7oe5Cenv5oxQthJA93Jb2T9DTe6SHEabagOB3mQlo0F9KiKcjyI+/OAAqzWgIqVCpo4mr/0CRS7e0WX3cHrCV0CCFqAIMDyWOPPcbRo0dp3bo1ffv25Z///CdJSUl8//33Jbq3GktiYiJeXl4ljnt5eZGYmFjm8z7++GOsrKwMmmi7YMEC3nvvvSrVKcSjupdboLe9vW5CaXIWd0vtSmoBDEdl1Rs3730UOB7EyuEaVn5L8XcMZFLbmQxr1Qd7G4P/VxdCiGpl8N9SH374IRkZGQC8//77TJ48mTlz5tCyZUuWL19u0GvNnz+/wg//I0eOAJQ6SU5RlDInzx07dowvvviC48ePGzTB7q233uK1117T/Zyeno6vr2+lny9ERbLzCh9MItWtYDG8K2kzTyftSIenI37ujtjbjOd21m2WnV7G2gtruZp5lv939FU2XAtmTsgcejbuKZNNhRA1lln7kCQlJZGUlFTuOf7+/qxatYrXXnuN1NRUvcdcXV357LPPmDp1aonnff7557z22mtYWDwYji4sLMTCwgJfX1+uXr1aqRplDomoipz8QuLvFu9KWjTqkUVievldST0cbfS2t/cvdqvFybZy/4a4k3WHZaeXsebCGnILtSEn2DOY2SGzeaLJExJMhBDVwuR9SAoKCtizZw+XL19m/PjxODs7k5CQgIuLC05OTlUuvCxFk1oPHz5M165dATh8+DDdu3cvc1JrcnIyN2/e1Ds2aNAgJk6cyNSpU8udCFucBBJRlrwCDfEpWfrLZZO0ISQhrfyupGp76/uTRx9sbW+KrqRJ2UksP72cn8//TE6hNgi192jPnA5z6NWklwQTIYRJmTSQXLt2jcGDBxMXF0dubi4XLlygefPmvPLKK+Tk5BAeHv5IxZdlyJAhJCQksHDhQkC77NfPz09v2W+bNm1YsGABo0aNKvU1/P39ZZWNMEhBoYYbqdm6OR1Xi+3FUtmupM08nXTBo2gFi5ujTTX+FtpgsuL0ClafX60LJu082jE7ZDa9fXpLMBFCmIRJV9nMmzePzp07ExUVhYeHh+74qFGjmDFjhuHVVlJERARz587lqaeeArSN0b7++mu9c86fP09aWprJahB1k15X0qR7etvbx6dU3JW0+KqV4qMdNakrqae9J3/t8lemtp/KyjMr+en8T5xJPsP/7fo/2rq3ZU7IHPr49qkx9Qoh6h+DR0g8PT3Zv38/AQEBODs7ExUVRfPmzbl69SqBgYFkZWWZqlazkBGSukGjUbiVkaObx1E0r+NqUibX7hrelbSoZ4eXc+3sSno35y4rz6zkx3M/kl2QDUBb97bMCplFP99+tfJ3EkLUPCYdIdFoNBQWluzueP36dZydnQ19OSGMRlEU7tzL1QaOh7qSXk3OJCe/7NBhbamiqbtDiQ3f/D0d8Xaxq3NdSd3t3Hm106tMaTdFF0xi7sbwyu5XCHALYHbIbPo17YeFSnqUCCGqh8EjJGPHjkWtVrNo0SKcnZ2Jjo6mQYMGjBgxgqZNmxq89LemkxGSmkVRFO5m5ultb18UPK4lV9yV1NfNXm/fFelKqpWak8p3Z78jIiaCrALtKGdrt9bMDplN/6b9JZgIIarEpJNab9y4Qb9+/bC0tOTixYt07tyZixcv4unpyf/+979SG5jVZhJIzCMtK7/ECEfR9+k5ZYeO0rqSNr8fPHzc7KUraQXSctN0wSQzPxOAlq4tmR0ym4F+AyWYCCEMYvJlv9nZ2fz0008cO3YMjUZDx44dmTBhAvb29lUuuqaSQGI6JbqSFhvtSMnKL/e5jdV2D234pp1Y6uvugK2VZTX9BnVXWm4a35/9noiYCO7l3wO0wWRW8CwG+g3E0kLeYyFExUwWSPLz8wkICGDLli0EBgY+cqG1gQSSR5OVV1BiEmnR7Zake+V3JfVyti0ROB50JZUPxOqQlptGREwEP5z9gYx8bYfm5urmzA6ZzVN+T0kwEUKUy6QjJE2aNOGPP/6gbdu2j1RkbSGBpGI5+YXEFXUlLdaZ1JCupM30moNpV7Q4VrIrqTC99Lx0Is5G8H3M92TkaYNJM3UzZgXPYrD/YAkmQohSmTSQfPTRR5w7d44lS5ZgZVX3PzAkkGg93JW0eGfSirqSujpYP5hEqtuHRTvq4WJnvK6kwvQy8jKIiIng+7Pfk56n3Qnb38Wf0OBQhjQbgpVF3f87QQhReSYNJKNGjWLnzp04OTkRFBSEo6Oj3uPr1683vOIarD4FkoJCDddTsott+JZJbLI2hFxPyaKcpqQ421oVm9PhoDfq4epQvV1Jhendy7vHqnOr+O7sd6TlapsR+rn4MSt4lgQTIYSOSQNJaRvZFSfLfmu2Qo1CQmp2sVUrD+Z3xN/NoqCc1OFgY1lsuax+ozAPx5rTlVRUn3t59/jx3I+sPLtSF0yaOjclNDiUp5s/LcFEiHrO5Kts6pPaGEh0XUnvFG8Opg0ecclZ5BWW3SDM1sriwTwOvQmltbcrqTC9zPxMbTA5s5LU3FQAfJ19mRk0k2EthmFtIbfmhKiPJJAYUU0NJIqicCcjt9j29lm6CaUVdSW1sbTA1730Xh2N6mBXUlF9svKz+On8T6w4vYKU3BQAfJx8CA0OlWAiRD0kgcSITBFIbqZpd49t5umIt7rs3i2ldiW9/3UtOZPMvJIt/ItYWmhbofs/tL19M09HGrvaYymhQ5hQVn4Wq8+vZsWZFdzNuQtAE6cmzAyayfCWwyWYCFFPSCAxImMHktVH4nhr/Sk0ClioYMHoIAa1a1TqSEdsUiYZ5XQltVBBEzd7vRUsRXM6pCupqAmy8rNYc2ENy04v0wWTxo6NmRE8g5EtRmJtKcFEiLpMAokRGTOQ3EzLpudHu8pdrVKaoq6k+r06HPF1t5eupKJWyC7IZs15bTBJzkkGwNvRmxlBMxjVcpQEEyHqKAkkRmTMQHLgchLjFx8u9bGGLrZ6IxxF3/t5OGBnLaFD1A3ZBdmsvbCWZaeXkZSdBEAjx0bMaD+DUa1GYWMpS8SFqEuMHki+/PLLSl987ty5lT63NjD1CImFCv54rTfNGzg9YqVC1B45BTmsu7iOpaeWcif7DgANHRoyPWg6o1uNxtbS1swVCiGMweiBpFmzZno/37lzh6ysLFxdXQFITU3FwcEBLy8vrly5UvXKayBTzCH5+/rTFCoKlioVH45uz9guTY1QqRC1T25hLusurGPp6aXczroNgJeDF9PbT2dM6zESTISo5Ux6y2bVqlV8++23LF26lICAAADOnz/PzJkzmTVrFhMmTKh65TWQqVbZXE3Kwt/TodxVNkLUF7mFuay/uJ6lp5ZyK+sWAF72XkwLmsaYVmOws7Izc4VCiKowaSBp0aIFa9eu5bHHHtM7fuzYMZ599lliY2MNr7gGq6l9SISoi/IK8/jl4i8sOb2ExMxEABrYN2Bq+6k81/o5CSZC1DKGfIYavC705s2b5OfnlzheWFjIrVu3DH05IYTQsbG0YWybsfw66lfe6f4O3o7e3Mm+wydHPmHI+iF8d+Y7sguyzV2mEMIEDB4heeaZZ4iLi2Pp0qV06tQJlUrF0aNHmTlzJr6+vmzatMlUtZqFjJAIYT75hflsuLyBJdFLSMhMAMDDzkM3YuJg7WDmCoUQ5THpLZs7d+4wefJktm3bhrW1tndAQUEBgwYNYsWKFXh5eVW98hpIAokQ5pdfmM+my5tYfGoxN+7dAMDdzp0p7aYwNmCsBBMhaqhq6UNy4cIFzp07h6IotG3bltatW1ep2JpOAokQNUe+Jp8tl7ewMHqhXjCZ3G4y4wLGSTARooaplkCSl5dHbGwsLVq0wMqq7m4xLoFEiJqnKJgsPrWY+Ix4ANxs3ZjUbhIvtHkBR2tHM1cohAATT2rNyspi+vTpODg40K5dO+Li4gBtQ7SPPvqoahULIYQBrC2sGdVqFJtGbuJfPf9FU+empOSm8MXxLxi8bjBLTi0hMz/T3GUKIQxgcCB56623iIqKYs+ePdjZPViCN2DAAFavXm3U4oQQojxWFlaMaDmCjSM38uETH+Ln4kdqbipfHP+CQesGsSh6Effy7pm7TCFEJRh8y8bPz4/Vq1fTvXt3nJ2diYqKonnz5ly6dImOHTuSnp5uqlrNQm7ZCFF7FGgK2Bq7lUXRi7iafhUAFxsXJgZOZELbCTjbOJu3QCHqGZPesrlz506pK2kyMzNRqVSGvpwQQhiNlYUVz7R4hg0jNvBRr49opm5Gel4635z8hkHrBhEWFUZ6Xt36R5MQdYXBgaRLly78+uuvup+LQsjixYvp0aOH8SoTQogqsrSw5OnmT/PL8F/45MlPaK5uTkZeBt+e/JbBawfz7clvJZgIUcMYfMvmwIEDDB48mAkTJrBixQpmzZrFmTNnOHjwIJGRkXTq1MlUtZqF3LIRovYr1BSy49oOwqPCuZx2GQAnaycmtJ3AxMCJqG3VZq5QiLrJ5Mt+T506xX/+8x+OHTuGRqOhY8eO/O1vfyMoKKjKRddUEkiEqDs0ikYXTC6lXgK0wWR82/FMCpwkwUQII6uWPiT1hQQSIeoejaJhZ9xOwqLCuJhyEQBHa0fGt9EGE1c7V/MWKEQdYdJAcvz4caytrXWjIRs3bmT58uUEBgYyf/58bGxsql55DSSBRIi6S6No2BW3i/CocM6nnAfAwcqBF9q8wOR2k3GzczNzhULUbiZdZTNr1iwuXLgAwJUrVxg7diwODg6sWbOGN954o2oVCyGEGVioLBjgN4Cfn/mZz/t+Thv3NmQVZLH09FIGrRvEZ8c+427OXXOXKUS9YPAIiVqt5vjx47Ro0YKPP/6YXbt28fvvv7N//37GjRtHfHy8qWo1CxkhEaL+UBSFPfF7CIsKI+ZuDAD2VvaMCxjH5HaT8bD3MG+BQtQyJh0hURQFjUYDwB9//MHQoUMB8PX1JSkpqQrlCiFEzaBSqejbtC+rh63mq35fEegRSHZBNsvPLGfI+iH858h/SMqWv+eEMAWDR0j69euHr68vAwYMYPr06Zw9e5aWLVsSGRnJ5MmTuXr1qolKNQ8ZIRGi/lIUhb039hJ2MozTyacBsLO04/mA55nafiqe9p5mrlCIms2kIySff/45x48f5+WXX+Yf//gHLVu2BGDt2rU8/vjjVau4ElJSUpg4cSJqtRq1Ws3EiRNJTU0t9zlTpkxBpVLpfXXv3t1kNQoh6haVSsWTPk+y6ulVfNv/W4I8g8gpzOG7s98xeN1gPv7zY+5k3TF3mULUCUZb9puTk4OlpSXW1tbGeLkShgwZwvXr11m0aBEAoaGh+Pv7s3nz5jKfM2XKFG7dusXy5ct1x2xsbHB3d6/0dWWERAhRRFEU9ifsJywqjOg70QDYWtrybOtnmdZ+Gl4OJbfVEKI+q3N9SGJiYggMDOTQoUN069YNgEOHDtGjRw/OnTtHQEBAqc+bMmUKqampbNiwodLXys3NJTc3V/dzeno6vr6+EkiEEDqKonAw4SBhUWGcvHMSABsLG10waejY0LwFClFDGP2Wjbu7u27CqpubG+7u7mV+mcLBgwdRq9W6MALQvXt31Go1Bw4cKPe5e/bswcvLi9atWzNz5kxu375d7vkLFizQ3RZSq9X4+voa5XcQQtQdKpWKx5s8zndDvmPRwEU85vUYeZo8Vp1bxdD1Q/ng0AckZiaau0whahWrypz02Wef4eys3bb7888/N2U9pUpMTCx1h2EvLy8SE8v+n37IkCE899xz+Pn5ERsbyzvvvEO/fv04duwYtra2pT7nrbfe4rXXXtP9XDRCIoQQD1OpVPRo3IPu3t05nHiYsJNhHL99nJ/O/8S6i+sY3Wo0M4Jm0MixkblLFaLGM+stm/nz5/Pee++Ve86RI0fYvn07K1eu5Pz583qPtWrViunTp/Pmm29W6no3b97Ez8+Pn376idGjR1fqOTKHRAhRWYqicCTxCGFRYRy9dRQAKwsrRrfUBhNvJ28zVyhE9TLkM7RSIyTp6ZXfptuQD+2XX36ZcePGlXuOv78/0dHR3Lp1q8Rjd+7coWHDyt+r9fb2xs/Pj4sXL1b6OUIIUVkqlYqu3l3p6t1VF0yOJB7h5ws/s/7Seka2HMnMoJk0dmps7lKFqHEqFUhcXV1RqVTlnqMoCiqVisLCwkpf3NPTE0/Pitfx9+jRg7S0NP7880+6du0KwOHDh0lLSzNoqXFycjLx8fF4e8u/UoQQptWlURe6NOrC0cSjhEeFczjxMGsvrGXDxQ2MaDmCmcEzaeLUxNxlClFjVOqWTWRkZKVfsHfv3o9UUFmGDBlCQkICCxcuBLTLfv38/PSW/bZp04YFCxYwatQo7t27x/z58xkzZgze3t5cvXqVv//978TFxRETE6ObE1MRuWUjhDCGY7eOERYVxuGbhwGwUlkxvOVwZgTNwNdZ5qmJuqnOLfsFuHv3LnPnzmXTpk0ADB8+nK+//hpXV1fdOSqViuXLlzNlyhSys7MZOXIkJ06cIDU1FW9vb/r27cv7779v0CRVCSRCCGM6cfsEYSfDOHjzIACWKkueafEMoUGh+LpIMBF1S7UEkqysLOLi4sjLy9M7HhwcXJWXq7EkkAghTOHk7ZOER4WzP2E/oA0mw5oPIzQ4lKYuTc1cnRDGYdJAcufOHaZOncrWrVtLfdyQOSS1gQQSIYQpRd2JIiwqjP03HgSTp5s/TWhwKH4ufmauTohHY9K9bF555RVSUlI4dOgQ9vb2bNu2jZUrV9KqVSvd7RQhhBCVE9IghPAB4awauopeTXpRqBSy6fImhm8Yzt/3/p2raVfNXaIQ1cLgERJvb282btxI165dcXFx4ejRo7Ru3ZpNmzbxySefsG/fPlPVahYyQiKEqE6nk04THhVO5HXtYgILlQVDmg0hNDiU5urmZq5OCMOYdIQkMzNT1zXV3d2dO3e0O10GBQVx/PjxKpQrhBCiSHvP9nzd/2t+evon+vj0QaNo+PXKr4zcMJI3/vcGV1KvmLtEIUzC4EASEBCg65jaoUMHFi5cyI0bNwgPD5f+HkIIYSTtPNvxVf+vWD1sNX19+6KgsDV2KyM3juSNyDe4nHrZ3CUKYVQG37KJiIggPz+fKVOmcOLECQYNGkRycjI2NjasWLGCsWPHmqpWs5BbNkKImuDc3XOER4WzM24nACpUPOX/FLOCZ9HKrZWZqxOidNXahyQrK4tz587RtGnTSnVdrW0kkAghapLzd88THhXOH3F/6I4N9BvI7JDZtHZrbcbKhCipTjZGMxcJJEKImuj83fMsjF7Ijms7dMcG+g1kVvAsAtwDzFiZEA+YNJAoisLatWvZvXs3t2/fRqPR6D2+fv16wyuuwSSQCCFqsospF1kYvZDtV7ejoP3rvH/T/swOmU0b9zZmrk7UdyZdZTNv3jwmTpxIbGwsTk5OqNVqvS8hhBDVp5VbK/7T+z+sH76ewf6DUaFiZ9xOntv8HHN3zSUmOcbcJQpRKQaPkLi7u/PDDz8wdOhQU9VUo8gIiRCiNrmcepmF0QvZFrtNN2LSx7cPs0Nm086jnZmrE/WNSUdI1Go1zZtLcx4hhKiJWri24JMnP2HDyA083fxpLFQW7Infw7gt43h558ucSTpj7hKFKJXBIyQrV65k27ZtLFu2DHt7e1PVVWPICIkQojaLTYtlUfQifov9DY2infPXq0kv5oTMIahBkJmrE3WdSSe1ZmVlMXr0aPbv34+/vz/W1tZ6j9e1bq0SSIQQdcHVtKssPrWYLVe26ILJE02eYE7IHIIb1K1d2kXNYdJA8vzzz7N7926effZZGjZsiEql0nv83XffNbziGkwCiRCiLolLj2NR9CK2XNlCoaLdnb1n457MDplNB68O5i1O1DkmDSSOjo78/vvvPPHEE49UZG0hgUQIURfFp8ez6NQiNl/erAsmPbx7MKfDHB7zeszM1Ym6wqSTWn19feWDWQghajlfF1/e7/k+m0dtZnSr0ViprDh48yCTtk5i5vaZHL9Vt26/i5rP4BGSX3/9la+++orw8HD8/f1NVFbNISMkQoj64HrGdZacWsLGSxspUAoA6NaoG7NDZtO5UWczVydqK5PesnFzcyMrK4uCggIcHBxKTGq9e/eu4RXXYBJIhBD1yY17N1hyagkbLm2gQKMNJl0adWFOyBy6NOpi5upEbWPSQLJy5cpyH588ebIhL1fjSSARQtRHCfcSWHpqKesvrdcFk84NO+uCycMLGoQojckCSX5+PqGhobzzzjv1pjmaBBIhRH2WmJnIklNLWH9xPfmafAA6enVkToc5dGvUTYKJKJdJR0hcXV05fvy4BBIhhKhHEjMTWXpqKesurtMFk8e8HmNOyBy6e3eXYCJKZdJVNqNGjWLDhg1VrU0IIUQt1MixEf/o/g+2jt7K+DbjsbGw4cTtE4TuCGXS1kkcuHEAA/99K4Qeg0dIPvjgA/7zn//Qv39/OnXqhKOjo97jc+fONWqB5iYjJEIIUdLtrNssO72MtRfWkluYC0Bwg2BeCnmJxxs/LiMmAjDxLZtmzZqV/WIqFVeuXDHk5Wo8CSRCCFG2O1l3WHZ6GWsurHkQTDyDmR0ymyeaPCHBpJ4zaSCpbySQCCFExZKyk1h+ejk/n/+ZnMIcANp7tGdOhzn0atJLgkk9VW2BpOipdfkPmgQSIYSovKTsJFacXsHq86t1waSdRzvmhMzhSZ8n6/TnhSjJpJNaAb777juCgoKwt7fH3t6e4OBgvv/++yoVK4QQou7wtPfkr13+yrYx25jabir2VvacST7Dy7teZtyv49gdt1smv4pSGTxC8umnn/LOO+/w8ssv07NnTxRFYf/+/XzzzTf861//4tVXXzVVrWYhIyRCCFF1d3PusvLMSn489yPZBdkAtHVvy6yQWfTz7ScjJnWcySe1vvfee0yaNEnv+MqVK5k/fz6xsbGGV1yDSSARQohHl5KTogsmWQVZAAS4BTAnZA59m/bFQlWlAXtRw5k0kNjZ2XH69Glatmypd/zixYsEBQWRk5NjeMU1mAQSIYQwntScVL47+x0RMRG6YNLarTWzQ2bTv2l/CSZ1jEnnkLRs2ZKff/65xPHVq1fTqlUrQ19OCCFEPeJq58rcjnPZ/ux2QoNDcbR25ELKBV7b8xpjNo3h96u/o1E05i5TmIHBIyTr1q1j7NixDBgwgJ49e6JSqdi3bx87d+7k559/ZtSoUaaq1SxkhEQIIUwnLTeN789+T0RMBPfy7wHQ0rUls0Jm8ZTfUzJiUsuZfNnvsWPH+Oyzz4iJiUFRFAIDA/nLX/7CY489VuWiayoJJEIIYXppuWlExETww9kfyMjPAKCFuoUumFhaWJq5QlEV0hjNiCSQCCFE9UnPSyfibATfx3xPRp42mDRTN2NW8CwG+w+WYFLLSCAxIgkkQghR/TLyMoiIieD7s9+TnpcOgL+LP7NCZjHEf4gEk1rCJJNaLSwssLS0LPfLysrqkYsvS0pKChMnTkStVqNWq5k4cSKpqakVPi8mJobhw4ejVqtxdname/fuxMXFmaxOIYQQj87ZxpnZIbP5fczv/N9j/4faVs3V9Ku8tfctRm4cyebLmynQFJi7TGFElR4h2bhxY5mPHThwgK+++gpFUcjOzjZaccUNGTKE69evs2jRIgBCQ0Px9/dn8+bNZT7n8uXLdO3alenTp/PCCy+gVquJiYmhS5cueHl5Veq6MkIihBDmdy/vHj+e+5GVZ1eSlpsGQFPnpoQGh/J086exsjDdP4hF1VXbLZtz587x1ltvsXnzZiZMmMD7779P06ZNq/pyZYqJiSEwMJBDhw7RrVs3AA4dOkSPHj04d+4cAQEBpT5v3LhxWFtbP1JbewkkQghRc2TmZ2qDyZmVpOamAuDr7EtocCjDmg+TYFLDmHwvm4SEBGbOnElwcDAFBQWcPHmSlStXmiSMABw8eBC1Wq0LIwDdu3dHrVZz4MCBUp+j0Wj49ddfad26NYMGDcLLy4tu3bqxYcOGcq+Vm5tLenq63pcQQoiawdHakRlBM/h9zO+82ulV3GzdiM+I55397/DML8/wy8VfyNfkm7tMUQUGBZK0tDT+9re/0bJlS86cOcPOnTvZvHkz7du3N1V9ACQmJpZ6i8XLy4vExMRSn3P79m3u3bvHRx99xODBg9m+fTujRo1i9OjRREZGlnmtBQsW6OapqNVqfH19jfZ7CCGEMA4HawemtZ/GtjHbeK3Ta7jbuXP93nX+eeCfPPPLM6y7sE6CSS1T6UDyySef0Lx5c7Zs2cKPP/7IgQMH6NWr1yNdfP78+ahUqnK/jh49ClDqBkyKopS5MZNGo+30N2LECF599VU6dOjAm2++ybBhwwgPDy+zprfeeou0tDTdV3x8/CP9jkIIIUzHwdqBqe2nsnX0Vv7a+a+427lz494N5h+cz7D1w1h7YS35hRJMaoNK32x78803sbe3p2XLlqxcuZKVK1eWet769esrffGXX36ZcePGlXuOv78/0dHR3Lp1q8Rjd+7coWHDhqU+z9PTEysrKwIDA/WOt23bln379pV5PVtbW2xtbStRvRBCiJrCwdqBye0m83zA86w5v4Zlp5eRkJnAewffY1H0ImYEzWBUy1FYW1qbu1RRhkoHkkmTJhl9m2hPT088PT0rPK9Hjx6kpaXx559/0rVrVwAOHz5MWloajz/+eKnPsbGxoUuXLpw/f17v+IULF/Dz83v04oUQQtQ49lb2TGo3iecCnmPthbUsO72Mm5k3ef/Q+yw+tZgZ7WcwqtUobCxtzF2qeEitaYw2ZMgQEhISWLhwIaBd9uvn56e37LdNmzYsWLBAt5/OL7/8wtixY/nmm2/o27cv27Zt45VXXmHPnj088cQTlbqurLIRQojaK6cgh3UX17H01FLuZN8BoKFDQ2YEzWB0q9ESTEysTnZqvXv3LnPnzmXTpk0ADB8+nK+//hpXV1fdOSqViuXLlzNlyhTdsWXLlrFgwQKuX79OQEAA7733HiNGjKj0dSWQCCFE7ZdbmMu6C+tYenopt7NuA+Dl4MX09tMZ03oMtpZyq94U6mQgMRcJJEIIUXfkFuay/uJ6lpxa8iCY2HsxLWgaY1qNwc7KzswV1i0SSIxIAokQQtQ9eYV5/HLxF5acXkJiprZ9RAP7BkxrP41nWz8rwcRIJJAYkQQSIYSou/IK89hwaQNLTi3hZuZNADztPZnabirPBTyHvZW9mSus3SSQGJEEEiGEqPvyC/PZcHkDS6KXkJCZAICHnQdT20/ludbP4WDtYOYKaycJJEYkgUQIIeqP/MJ8Nl3exOJTi7lx7wYA7nbuTG03lecDnpdgYiAJJEYkgUQIIeqffE0+Wy5vYWH0Qr1gMrndZMYFjJNgUkkSSIxIAokQQtRfRcFk8anFxGdotxJxs3VjUrtJvNDmBRytHc1cYc0mgcSIJJAIIYQo0BTw65VfWRS9iLiMOABcbV2Z3G6yBJNySCAxIgkkQgghihRoCtgau5WF0Qu5ln4NALWtmkmBkxjfZjxONk5mrrBmkUBiRBJIhBBCPKwomCyKXsTV9KsAuNi4MDFwIhPaTsDZxtm8BdYQEkiMSAKJEEKIshRqCtl2dRsLoxcSmxYLgLONsy6YuNjU788NCSRGJIFECCFERQo1hWy/tp3wqHCupF0BwNnamRcDX+TFwBfrbTCRQGJEEkiEEEJUVqGmkB3XdhAeFc7ltMuANphMCJzAi21fRG2rNnOF1UsCiRFJIBFCCGEojaLRBZNLqZcAcLJ2Ynzb8UwKnFRvgokEEiOSQCKEEKKqNIqGnXE7CYsK42LKRQAcrR0Z30YbTFztXM1boIlJIDEiCSRCCCEelUbRsCtuF+FR4ZxPOQ+Ag5WDbsTEzc7NzBWahgQSI5JAIoQQwlg0iobd8bsJjwrn3N1zANhb2fNCmxeY3G4y7nbuZq7QuCSQGJEEEiGEEMamKAp74vcQFhVGzN0YQBtMxgWMY3K7yXjYe5i3QCORQGJEEkiEEEKYiqIoRF6PJCwqjLPJZwFtMBkbMJbJ7Sbjae9p5gofjQQSI5JAIoQQwtQURWHvjb2EnQzjdPJpAOws7Xg+4Hmmtp9aa4OJBBIjkkAihBCiuiiKwr4b+wiLCuNU0ikAbC1tea71c0xrP40GDg3MXKFhJJAYkQQSIYQQ1U1RFPYn7CcsKozoO9FA7QwmEkiMSAKJEEIIc1EUhYMJBwmLCuPknZMA2FjY8GzrZ5nWfhoNHRuat8AKSCAxIgkkQgghzE1RFA7dPERYVBgnbp8AtMFkdKvRTA+aTiPHRmausHQSSIxIAokQQoiaQlEUDiceJuxkGMdvHwfA2sKa0a1GMyNoRo0LJhJIjEgCiRBCiJpGURSOJB4hLCqMo7eOAtpgMqrlKGYEzcDbydvMFWpJIDEiCSRCCCFqsqJgciTxCABWFla6YNLYqbFZa5NAYkQSSIQQQtQGRxOPEh4VzuHEw4A2mIxoMYKZwTNp4tTELDVJIDEiCSRCCCFqk2O3jhEWFcbhm/eDicqK4S2HMyNoBr7OvtVaiwQSI5JAIoQQojY6cfsEYSfDOHjzIACWKkuGtxjOzOCZJYJJYWEhe/fu5ebNm3h7e9OrVy8sLS0fuQYJJEYkgUQIIURtdvL2ScKjwtmfsB/QBpNhzYcRGhxKU5emrF+/nnnz5nH9+nXdc3x8fPjiiy8YPXr0I11bAokRSSARQghRF0TdiSIsKoz9Nx4EkxbxLfjlnV94OAqoVCoA1q5d+0ihRAKJEUkgEUIIUZdE34kmPCqc/8X/j/N/OU9BSkGp56lUKnx8fIiNja3y7RtDPkMtqnQFIYQQQtRKwQ2C+XbAt/zF/S9lhhHQ9jqJj49n79691VKXBBIhhBCiHrLLsqvUeTdv3jRxJVoSSIQQQoh6yNu7ct1cK3veo5JAIoQQQtRDvXr1wsfHRzeB9WEqlQpfX1969epVLfVIIBFCCCHqIUtLS7744guAEqGk6OfPP//cKP1IKqPWBJKUlBQmTpyIWq1GrVYzceJEUlNTy32OSqUq9evf//539RQthBBC1GCjR49m7dq1NGmi31rex8fnkZf8GqrWLPsdMmQI169fZ9GiRQCEhobi7+/P5s2by3xOYmKi3s9bt25l+vTpXLp0iebNm1fqurLsVwghRF0nnVorKSYmhsDAQA4dOkS3bt0AOHToED169ODcuXMEBARU6nVGjhxJRkYGO3furPS1JZAIIYQQVVPn+pAcPHgQtVqtCyMA3bt3R61Wc+DAgUq9xq1bt/j111+ZPn16uefl5uaSnp6u9yWEEEII06oVgSQxMREvL68Sx728vErclinLypUrcXZ2rvB+2IIFC3TzVNRqNb6+1bszohBCCFEfmTWQzJ8/v8yJp0VfR48eBUrOAAZtF7mylis9bNmyZUyYMAE7u/Ibwbz11lukpaXpvuLj4w3/xYQQQghhECtzXvzll19m3Lhx5Z7j7+9PdHQ0t27dKvHYnTt3aNiwYYXX2bt3L+fPn2f16tUVnmtra4utra3u56IpNnLrRgghhDBM0WdnZaarmjWQeHp64unpWeF5PXr0IC0tjT///JOuXbsCcPjwYdLS0nj88ccrfP7SpUvp1KkTISEhBteYkZEBILduhBBCiCrKyMhArVaXe06tWGUD2mW/CQkJLFy4ENAu+/Xz89Nb9tumTRsWLFjAqFGjdMfS09Px9vbmv//9L7Nnzzb4uhqNhoSEBJydnSt9e6gi6enp+Pr6Eh8fLyt3jETeU+OS99P45D01Lnk/jc8U76miKGRkZNC4cWMsLMqfJWLWERJDREREMHfuXJ566ikAhg8fztdff613zvnz50lLS9M79tNPP6EoCi+88EKVrmthYYGPj0/Viq6Ai4uL/I9kZPKeGpe8n8Yn76lxyftpfMZ+TysaGSlSawKJu7s7P/zwQ7nnlDbYExoaSmhoqKnKEkIIIYQR1Iplv0IIIYSo2ySQmIGtrS3vvvuu3moe8WjkPTUueT+NT95T45L30/jM/Z7WmkmtQgghhKi7ZIRECCGEEGYngUQIIYQQZieBRAghhBBmJ4FECCGEEGYngcREvv32W5o1a4adnR2dOnVi79695Z4fGRlJp06dsLOzo3nz5oSHh1dTpbWHIe/p+vXrGThwIA0aNMDFxYUePXrw+++/V2O1NZ+hf0aL7N+/HysrKzp06GDaAmshQ9/T3Nxc/vGPf+Dn54etrS0tWrRg2bJl1VRtzWfo+xkREUFISAgODg54e3szdepUkpOTq6namu1///sfzzzzDI0bN0alUrFhw4YKn1Ptn0uKMLqffvpJsba2VhYvXqycPXtWmTdvnuLo6Khcu3at1POvXLmiODg4KPPmzVPOnj2rLF68WLG2tlbWrl1bzZXXXIa+p/PmzVM+/vhj5c8//1QuXLigvPXWW4q1tbVy/Pjxaq68ZjL0/SySmpqqNG/eXHnqqaeUkJCQ6im2lqjKezp8+HClW7duyo4dO5TY2Fjl8OHDyv79+6ux6prL0Pdz7969ioWFhfLFF18oV65cUfbu3au0a9dOGTlyZDVXXjP99ttvyj/+8Q9l3bp1CqD88ssv5Z5vjs8lCSQm0LVrV2X27Nl6x9q0aaO8+eabpZ7/xhtvKG3atNE7NmvWLKV79+4mq7G2MfQ9LU1gYKDy3nvvGbu0Wqmq7+fYsWOVt99+W3n33XclkDzE0Pd069atilqtVpKTk6ujvFrH0Pfz3//+t9K8eXO9Y19++aXi4+Njshprq8oEEnN8LsktGyPLy8vj2LFjuj13ijz11FMcOHCg1OccPHiwxPmDBg3i6NGj5Ofnm6zW2qIq7+nDNBoNGRkZuLu7m6LEWqWq7+fy5cu5fPky7777rqlLrHWq8p5u2rSJzp0788knn9CkSRNat27NX//6V7Kzs6uj5BqtKu/n448/zvXr1/ntt99QFIVbt26xdu1ann766eoouc4xx+dSrdnLprZISkqisLCQhg0b6h1v2LAhiYmJpT4nMTGx1PMLCgpISkrC29vbZPXWBlV5Tx/23//+l8zMTJ5//nlTlFirVOX9vHjxIm+++SZ79+7Fykr+2nhYVd7TK1eusG/fPuzs7Pjll19ISkripZde4u7du/V+HklV3s/HH3+ciIgIxo4dS05ODgUFBQwfPpyvvvqqOkquc8zxuSQjJCaiUqn0flYUpcSxis4v7Xh9Zuh7WuTHH39k/vz5rF69Gi8vL1OVV+tU9v0sLCxk/PjxvPfee7Ru3bq6yquVDPkzqtFoUKlURERE0LVrV4YOHcqnn37KihUrZJTkPkPez7NnzzJ37lz++c9/cuzYMbZt20ZsbCyzZ8+ujlLrpOr+XJJ/6hiZp6cnlpaWJVL87du3S6TNIo0aNSr1fCsrKzw8PExWa21Rlfe0yOrVq5k+fTpr1qxhwIABpiyz1jD0/czIyODo0aOcOHGCl19+GdB+mCqKgpWVFdu3b6dfv37VUntNVZU/o97e3jRp0kRva/a2bduiKArXr1+nVatWJq25JqvK+7lgwQJ69uzJ66+/DkBwcDCOjo706tWLf/3rX/V+pNlQ5vhckhESI7OxsaFTp07s2LFD7/iOHTt4/PHHS31Ojx49Spy/fft2OnfujLW1tclqrS2q8p6CdmRkypQprFq1Su4jF2Po++ni4sKpU6c4efKk7mv27NkEBARw8uRJunXrVl2l11hV+TPas2dPEhISuHfvnu7YhQsXsLCwwMfHx6T11nRVeT+zsrKwsND/SLO0tAQe/MteVJ5ZPpdMNl22HitarrZ06VLl7NmzyiuvvKI4OjoqV69eVRRFUd58801l4sSJuvOLlle9+uqrytmzZ5WlS5fKst+HGPqerlq1SrGyslK++eYb5ebNm7qv1NRUc/0KNYqh7+fDZJVNSYa+pxkZGYqPj4/y7LPPKmfOnFEiIyOVVq1aKTNmzDDXr1CjGPp+Ll++XLGyslK+/fZb5fLly8q+ffuUzp07K127djXXr1CjZGRkKCdOnFBOnDihAMqnn36qnDhxQreMuiZ8LkkgMZFvvvlG8fPzU2xsbJSOHTsqkZGRuscmT56s9O7dW+/8PXv2KI899phiY2Oj+Pv7K2FhYdVccc1nyHvau3dvBSjxNXny5OovvIYy9M9ocRJISmfoexoTE6MMGDBAsbe3V3x8fJTXXntNycrKquaqay5D388vv/xSCQwMVOzt7RVvb29lwoQJyvXr16u56ppp9+7d5f6dWBM+l1SKImNZQgghhDAvmUMihBBCCLOTQCKEEEIIs5NAIoQQQgizk0AihBBCCLOTQCKEEEIIs5NAIoQQQgizk0AihBBCCLOTQCKEEEIIs5NAIoQwmz59+vDKK69U2/VWrFiBq6vrI79OddctRH0ggUSIemzKlCmoVCpUKhXW1tY0bNiQgQMHsmzZMjQajcmvv379et5//32TX6fI2LFjuXDhQrVdTwhReRJIhKjnBg8ezM2bN7l69Spbt26lb9++zJs3j2HDhlFQUGDSa7u7u+Ps7GzSaxRnb2+Pl5dXtV1PCFF5EkiEqOdsbW1p1KgRTZo0oWPHjvz9739n48aNbN26lRUrVujO+/TTTwkKCsLR0RFfX19eeukl7t27B0BmZiYuLi6sXbtW77U3b96Mo6MjGRkZpV774Vsf/v7+fPjhh0ybNg1nZ2eaNm3KokWLyqx98+bNuLq66kZzTp48iUql4vXXX9edM2vWLF544QWg5C2b+fPn06FDB77//nv8/f1Rq9WMGzdOr97MzEwmTZqEk5MT3t7e/Pe//y1RR0pKCpMmTcLNzQ0HBweGDBnCxYsXAVAUhQYNGrBu3Trd+R06dNALRgcPHsTa2lr3fgpRH0kgEUKU0K9fP0JCQli/fr3umIWFBV9++SWnT59m5cqV7Nq1izfeeAMAR0dHxo0bx/Lly/VeZ/ny5Tz77LMGjYL897//pXPnzpw4cYKXXnqJOXPmcO7cuVLPffLJJ8nIyODEiRMAREZG4unpSWRkpO6cPXv20Lt37zKvd/nyZTZs2MCWLVvYsmULkZGRfPTRR7rHX3/9dXbv3s0vv/zC9u3b2bNnD8eOHdN7jSlTpnD06FE2bdrEwYMHURSFoUOHkp+fj0ql4sknn2TPnj2ANrycPXuW/Px8zp49q6uxU6dOODk5Vfp9EqKukUAihChVmzZtuHr1qu7nV155hb59+9KsWTP69evH+++/z88//6x7fMaMGfz+++8kJCQAkJSUxJYtW5g2bZpB1x06dCgvvfQSLVu25G9/+xuenp66D/OHqdVqOnTooHt8z549vPrqq0RFRZGRkUFiYiIXLlygT58+ZV5Po9GwYsUK2rdvT69evZg4cSI7d+4E4N69eyxdupT//Oc/DBw4kKCgIFauXElhYaHu+RcvXmTTpk0sWbKEXr16ERISQkREBDdu3GDDhg2AdiSoqMb//e9/hISE0K9fP726y6tRiPpAAokQolSKoqBSqXQ/7969m4EDB9KkSROcnZ2ZNGkSycnJZGZmAtC1a1fatWvHd999B8D3339P06ZNefLJJw26bnBwsO57lUpFo0aNuH37dpnnF33YK4rC3r17GTFiBO3bt2ffvn3s3r2bhg0b0qZNmzKf7+/vrzeC4+3trbve5cuXycvLo0ePHrrH3d3dCQgI0P0cExODlZUV3bp10x3z8PAgICCAmJgYXY1nzpwhKSmJyMhI+vTpQ58+fYiMjKSgoIADBw6UO4ojRH0ggUQIUaqYmBiaNWsGwLVr1xg6dCjt27dn3bp1HDt2jG+++QaA/Px83XNmzJihu22zfPlypk6dqhdqKsPa2lrvZ5VKVe6Knz59+rB3716ioqKwsLAgMDCQ3r17ExkZWeHtmoqupyhKhfWWdU7xQNe+fXs8PDyIjIzUBZKiGo8cOUJ2djZPPPFEhdcSoi6TQCKEKGHXrl2cOnWKMWPGAHD06FEKCgr473//S/fu3WndurXu1kxxL774InFxcXz55ZecOXOGyZMnm7zWonkkn3/+Ob1790alUtG7d2/27NlTqUBSnpYtW2Jtbc2hQ4d0x1JSUvSWDgcGBlJQUMDhw4d1x5KTk7lw4QJt27YF0M0j2bhxI6dPn6ZXr14EBQWRn59PeHg4HTt2rNbVRkLURBJIhKjncnNzSUxM5MaNGxw/fpwPP/yQESNGMGzYMCZNmgRAixYtKCgo4KuvvuLKlSt8//33hIeHl3gtNzc3Ro8ezeuvv85TTz2Fj4+Pyesvmkfyww8/6OZhPPnkkxw/frzC+SMVcXJyYvr06bz++uvs3LmT06dPM2XKFCwsHvzV2apVK0aMGMHMmTPZt28fUVFRvPjiizRp0oQRI0bozuvTpw+rVq0iODgYFxcXXUiJiIiQ+SNCIIFEiHpv27ZteHt74+/vz+DBg9m9ezdffvklGzduxNLSEtAuU/3000/5+OOPad++PRERESxYsKDU15s+fTp5eXkGT2Z9FH379qWwsFD3we7m5kZgYCANGjTQjVJU1b///W+efPJJhg8fzoABA3jiiSfo1KmT3jnLly+nU6dODBs2jB49eqAoCr/99pve7aCHawTo3bs3hYWFMn9ECEClVOYmqRBCVFJERATz5s0jISEBGxsbc5cjhKglrMxdgBCibsjKyiI2NpYFCxYwa9YsCSNCCIPILRshhFF88skndOjQgYYNG/LWW2+ZuxwhRC0jt2yEEEIIYXYyQiKEEEIIs5NAIoQQQgizk0AihBBCCLOTQCKEEEIIs5NAIoQQQgizk0AihBBCCLOTQCKEEEIIs5NAIoQQQgiz+/8D/9LmeOZnDAAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 600x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 600x300 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import mlp.data_providers as data_providers\n",
-    "import numpy as np\n",
-    "\n",
-    "batch_size = 3\n",
-    "for window_size in [2, 5, 10]:\n",
-    "    met_dp = data_providers.MetOfficeDataProvider(\n",
-    "        window_size=window_size, batch_size=batch_size,\n",
-    "        max_num_batches=1, shuffle_order=False)\n",
-    "    fig = plt.figure(figsize=(6, 3))\n",
-    "    ax = fig.add_subplot(111)\n",
-    "    ax.set_title('Window size {0}'.format(window_size))\n",
-    "    ax.set_xlabel('Day in window')\n",
-    "    ax.set_ylabel('Normalised reading')\n",
-    "    # iterate over data provider batches checking size and plotting\n",
-    "    for inputs, targets in met_dp:\n",
-    "        assert inputs.shape == (batch_size, window_size - 1)\n",
-    "        assert targets.shape == (batch_size, )\n",
-    "        ax.plot(np.c_[inputs, targets].T, '.-')\n",
-    "        ax.plot([window_size - 1] * batch_size, targets, 'ko')"
-   ]
-  }
- ],
- "metadata": {
-  "anaconda-cloud": {},
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.12.5"
-  }
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- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/02_Single_layer_models.ipynb b/notebooks/02_Single_layer_models.ipynb
deleted file mode 100644
index f25556f..0000000
--- a/notebooks/02_Single_layer_models.ipynb
+++ /dev/null
@@ -1,1322 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Single layer models\n",
-    "\n",
-    "The objective of this lab is to implement a single-layer network model consisting of solely of an affine transformation of the inputs. The relevant material for this is covered in slides 12-23 of the first lecture.\n",
-    "\n",
-    "We will first implement the forward propagation of inputs to the network to produce predicted outputs. We will then move on to considering how to use gradients of an error function evaluated on the outputs to compute the gradients with respect to the model parameters to allow us to perform an iterative gradient-descent training procedure. In the final exercise you will use an interactive visualisation to explore the role of some of the different hyperparameters of gradient-descent based training methods.\n",
-    "\n",
-    "#### A note on random number generators\n",
-    "\n",
-    "It is generally a good practice (for machine learning applications **not** for cryptography!) to seed a pseudo-random number generator once at the beginning of each experiment. This makes it easier to reproduce results as the same random draws will produced each time the experiment is run (e.g. the same random initialisations used for parameters). Therefore generally when we need to generate random values during this course, we will create a seeded random number generator object as we do in the cell below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "seed = 27092016 \n",
-    "rng = np.random.RandomState(seed)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercise 1: linear and affine transforms\n",
-    "\n",
-    "Any *linear transform* (also called a linear map) on a finite-dimensional vector space can be parametrised by a matrix. For example if we consider $\\boldsymbol{x} \\in \\mathbb{R}^{D}$ as the input space of a model with $D$ dimensional real-valued inputs, then a matrix $\\mathbf{W} \\in \\mathbb{R}^{K\\times D}$ can be used to define a prediction model consisting solely of a linear transform of the inputs\n",
-    "\n",
-    "\\begin{equation}\n",
-    "    \\boldsymbol{y} = \\mathbf{W} \\boldsymbol{x}\n",
-    "    \\qquad\n",
-    "    \\Leftrightarrow\n",
-    "    \\qquad\n",
-    "    y_k = \\sum_{d=1}^D \\left( W_{kd} x_d \\right) \\quad \\forall k \\in \\left\\lbrace 1 \\dots K\\right\\rbrace\n",
-    "\\end{equation}\n",
-    "\n",
-    "with $\\boldsymbol{y} \\in \\mathbb{R}^K$ the $K$-dimensional real-valued output of the model. Geometrically we can think of a linear transform as some combination of rotation, scaling, reflection and shearing of the input.\n",
-    "\n",
-    "An *affine transform* consists of a linear transform plus an additional translation on the output space parameterised by a vector $\\boldsymbol{b} \\in \\mathbb{R}^K$. A model consisting of an affine transformation of the inputs can then be defined as\n",
-    "\n",
-    "\\begin{equation}\n",
-    "    \\boldsymbol{y} = \\mathbf{W}\\boldsymbol{x} + \\boldsymbol{b}\n",
-    "    \\qquad\n",
-    "    \\Leftrightarrow\n",
-    "    \\qquad\n",
-    "    y_k = \\sum_{d=1}^D \\left( W_{kd} x_d \\right) + b_k \\quad \\forall k \\in \\left\\lbrace 1 \\dots K\\right\\rbrace\n",
-    "\\end{equation}\n",
-    "\n",
-    "In machine learning we will usually refer to the matrix $\\mathbf{W}$ as a *weight matrix* and the vector $\\boldsymbol{b}$ as a *bias vector*.\n",
-    "\n",
-    "Generally, rather than working with a single data vector $\\boldsymbol{x}$ we will work with batches of datapoints $\\left\\lbrace \\boldsymbol{x}^{(b)}\\right\\rbrace_{b=1}^B$. We could calculate the outputs for each input in the batch sequentially\n",
-    "\n",
-    "\\begin{align}\n",
-    "    \\boldsymbol{y}^{(1)} &= \\mathbf{W}\\boldsymbol{x}^{(1)} + \\boldsymbol{b}\\\\\n",
-    "    \\boldsymbol{y}^{(2)} &= \\mathbf{W}\\boldsymbol{x}^{(2)} + \\boldsymbol{b}\\\\\n",
-    "    \\dots &\\\\\n",
-    "    \\boldsymbol{y}^{(B)} &= \\mathbf{W}\\boldsymbol{x}^{(B)} + \\boldsymbol{b}\\\\\n",
-    "\\end{align}\n",
-    "\n",
-    "by looping over each input in the batch and calculating the output. However, loops in Python are slow (particularly compared to compiled and typed languages such as C). This is due at least in part to the large overhead in dynamically inferring variable types. In consequence, we want to avoid having loops in which this overhead would be the dominant computational cost.\n",
-    "\n",
-    "For array-based numerical operations, one way of overcoming this bottleneck is to *vectorise* operations, that is, computing all of them at once. NumPy `ndarrays` are typed arrays for which operations, like basic elementwise arithmetic and linear algebra operations (*e.g.*  computing matrix-matrix or matrix-vector products) are implemented by calls to highly-optimised compiled libraries. Therefore, implementing code directly using NumPy operations on arrays rather than by looping over array elements usually leads to very substantial performance gains.\n",
-    "\n",
-    "As a simple example, we can consider adding up two arrays `a` and `b` and writing the result to a third array `c`. Let us start by initialising `a` and `b` with arbitrary values by running the cell below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "size = 1000\n",
-    "a = np.random.randn(size)\n",
-    "b = np.random.randn(size)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, we are going to measure how long it takes to add up each pair of values in the two array and write the results to a third array using a loop-based implementation. We will use the `%%timeit` magic briefly mentioned in the previous lab notebook, specifying the number of times to loop the code as 100 and repeating it 3 times for better consistency. Run the cell below to get a print out of the average time taken."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "145 μs ± 76.5 μs per loop (mean ± std. dev. of 3 runs, 100 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit -n 100 -r 3\n",
-    "c = np.empty(size)\n",
-    "for i in range(size):\n",
-    "    c[i] = a[i] + b[i]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "And now we will perform the corresponding summation with the overloaded addition operator of NumPy arrays. Again run the cell below to get a print out of the average time taken."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The slowest run took 11.72 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
-      "9.79 μs ± 10.8 μs per loop (mean ± std. dev. of 3 runs, 100 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit -n 100 -r 3\n",
-    "c = a + b"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The first loop-based implementation should have taken on the order of milliseconds ( $10^{-3}$ s) while the vectorised implementation should have taken on the order of microseconds ( $10^{-6}$ s), i.e. a $\\sim1000\\times$ speedup. Hopefully this simple example should make it clear why we want to vectorise operations whenever possible!\n",
-    "\n",
-    "Getting back to our affine model, ideally rather than individually computing the output corresponding to each input we should compute the outputs for all inputs in a batch using a vectorised implementation. As you saw last week, data providers return batches of inputs as arrays of shape `(batch_size, input_dim)`. In the mathematical notation used earlier we can consider the input as a matrix $\\mathbf{X}$ of dimensionality $B \\times D$:\n",
-    "\n",
-    "\\begin{equation}\n",
-    "    \\mathbf{X} = \\left[ \\boldsymbol{x}^{(1)} ~ \\boldsymbol{x}^{(2)} ~ \\dots ~ \\boldsymbol{x}^{(B)} \\right]^\\mathrm{T}\n",
-    "\\end{equation}\n",
-    "\n",
-    "i.e. the $b^{\\textrm{th}}$ input vector $\\boldsymbol{x}^{(b)}$ corresponds to the $b^{\\textrm{th}}$ row of $\\mathbf{X}$. Similarly, we can define the $B \\times K$ matrix of outputs $\\mathbf{Y}$ as\n",
-    "\n",
-    "\\begin{equation}\n",
-    "    \\mathbf{Y} = \\left[ \\boldsymbol{y}^{(1)} ~ \\boldsymbol{y}^{(2)} ~ \\dots ~ \\boldsymbol{y}^{(B)} \\right]^\\mathrm{T}\n",
-    "\\end{equation}\n",
-    "\n",
-    "We can then express the relationship between $\\mathbf{X}$ and $\\mathbf{Y}$ using [matrix multiplication](https://en.wikipedia.org/wiki/Matrix_multiplication) and addition as\n",
-    "\n",
-    "\\begin{equation}\n",
-    "    \\mathbf{Y} = \\mathbf{X} \\mathbf{W}^\\mathrm{T} + \\mathbf{B}\n",
-    "\\end{equation}\n",
-    "\n",
-    "where $\\mathbf{B} = \\left[ \\boldsymbol{b} ~ \\boldsymbol{b} ~ \\dots ~ \\boldsymbol{b} \\right]^\\mathrm{T}$ i.e. a $B \\times K$ matrix with each row corresponding to the same bias vector. The weight matrix needs to be transposed here as the inner dimensions of a matrix multiplication must match i.e. for $\\mathbf{C} = \\mathbf{A} \\mathbf{B}$ then if $\\mathbf{A}$ is of dimensionality $K \\times L$ and $\\mathbf{B}$ is of dimensionality $M \\times N$ then it must be the case that $L = M$ and $\\mathbf{C}$ will be of dimensionality $K \\times N$.\n",
-    "\n",
-    "**Your Tasks:**\n",
-    "\n",
-    "The first exercise for this lab is to implement *forward propagation* for a single-layer model consisting of an affine transformation of the inputs in the `fprop` function given as skeleton code in the cell below. This should work for a batch of inputs of shape `(batch_size, input_dim)` producing a batch of outputs of shape `(batch_size, output_dim)`.\n",
-    "  \n",
-    "You will probably want to use the NumPy `dot` function and [broadcasting features](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) to implement this efficiently. If you are not familiar with either of these, you may wish to read the [hints](#Hints:-Using-the-dot-function-and-broadcasting) section below which provides some tips before attempting the exercise."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def fprop(inputs, weights, biases):\n",
-    "    \"\"\"Forward propagates activations through the layer transformation.\n",
-    "\n",
-    "    For inputs `x`, outputs `y`, weights `W` and biases `b` the layer\n",
-    "    corresponds to `y = W x + b`.\n",
-    "\n",
-    "    Args:\n",
-    "        inputs: Array of layer inputs of shape (batch_size, input_dim).\n",
-    "        weights: Array of weight parameters of shape \n",
-    "            (output_dim, input_dim).\n",
-    "        biases: Array of bias parameters of shape (output_dim, ).\n",
-    "\n",
-    "    Returns:\n",
-    "        outputs: Array of layer outputs of shape (batch_size, output_dim).\n",
-    "    \"\"\"\n",
-    "    return inputs.dot(weights.T) + biases"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Once you have implemented `fprop` in the cell above you can test your implementation by running the cell below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "All outputs correct!\n"
-     ]
-    }
-   ],
-   "source": [
-    "inputs = np.array([[0., -1., 2.], [-6., 3., 1.]])\n",
-    "weights = np.array([[2., -3., -1.], [-5., 7., 2.]])\n",
-    "biases = np.array([5., -3.])\n",
-    "true_outputs = np.array([[6., -6.], [-17., 50.]])\n",
-    "\n",
-    "if not np.allclose(fprop(inputs, weights, biases), true_outputs):\n",
-    "    print('Wrong outputs computed.')\n",
-    "else:\n",
-    "    print('All outputs correct!')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Hints: Using the `dot` function and broadcasting\n",
-    "\n",
-    "For those new to NumPy below are some details on the `dot` function and broadcasting feature of NumPy that you may want to use for implementing the first exercise. If you are already familiar with these and have already completed the first exercise you can move on straight to [second exercise](#Exercise-2:-visualising-random-models).\n",
-    "\n",
-    "#### `numpy.dot` function\n",
-    "\n",
-    "Matrix-matrix, matrix-vector and vector-vector (dot) products can all be computed in NumPy using the [`dot`](http://docs.scipy.org/doc/numpy/reference/generated/numpy.dot.html) operator which generalizes all of these operation. For example if `A` and `B` are both two dimensional arrays, then `C = np.dot(A, B)` or equivalently `C = A.dot(B)` will compute the matrix product of `A` and `B` assuming `A` and `B` have compatible dimensions. Similarly if `a` and `b` are one dimensional arrays then `c = np.dot(a, b)` (which is equivalent to `c = a.dot(b)`) will compute the [scalar / dot product](https://en.wikipedia.org/wiki/Dot_product) of the two arrays. If `A` is a two-dimensional array and `b` a one-dimensional array `np.dot(A, b)` (which is equivalent to `A.dot(b)`) will compute the matrix-vector product of `A` and `b`. Examples of all three of these product types are shown in the cell below:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[ 6.  6.  6.]\n",
-      " [24. 24. 24.]\n",
-      " [42. 42. 42.]]\n",
-      "[[18. 24. 30.]\n",
-      " [18. 24. 30.]\n",
-      " [18. 24. 30.]]\n",
-      "[0.8 2.6 4.4]\n",
-      "[2.4 3.  3.6]\n",
-      "0.19999999999999998\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Initiliase arrays with arbitrary values\n",
-    "A = np.arange(9).reshape((3, 3))\n",
-    "B = np.ones((3, 3)) * 2\n",
-    "a = np.array([-1., 0., 1.])\n",
-    "b = np.array([0.1, 0.2, 0.3])\n",
-    "print(A.dot(B))  # Matrix-matrix product\n",
-    "print(B.dot(A))  # Reversed product of above. A.dot(B) != B.dot(A) in general\n",
-    "print(A.dot(b))  # Matrix-vector product\n",
-    "print(b.dot(A))  # Again A.dot(b) != b.dot(A) unless A is symmetric i.e. A == A.T\n",
-    "print(a.dot(b))  # Vector-vector scalar product"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Broadcasting\n",
-    "\n",
-    "Another NumPy feature it will be helpful to get familiar with is [broadcasting](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html). Broadcasting allows you to apply operations to arrays of different shapes by letting numpy infer the missing parts, for example to add a one-dimensional array to a two-dimensional array or multiply a multidimensional array by a scalar. The complete set of rules for broadcasting as explained in the official documentation page just linked to can sound a bit complex: you might find the [visual explanation on this page](http://www.scipy-lectures.org/intro/numpy/operations.html#broadcasting) more intuitive.\n",
-    "Keep in mind that the shapes must be compatible with one another, and that it may lead to erroneous results if the shapes are not as intended, so you are advised to make sure your arrays have the proper shapes.\n",
-    "The cell below gives a few examples:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[[0.1 1.2]\n",
-      " [2.1 3.2]\n",
-      " [4.1 5.2]]\n",
-      "[[-1.  0.]\n",
-      " [ 2.  3.]\n",
-      " [ 5.  6.]]\n",
-      "[[0.  0.2]\n",
-      " [0.2 0.6]\n",
-      " [0.4 1. ]]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Initiliase arrays with arbitrary values\n",
-    "A = np.arange(6).reshape((3, 2))\n",
-    "b = np.array([0.1, 0.2])\n",
-    "c = np.array([-1., 0., 1.])\n",
-    "print(A + b)  # Add b elementwise to all rows of A\n",
-    "print((A.T + c).T)  # Add b elementwise to all columns of A\n",
-    "print(A * b)  # Multiply each row of A elementise by b "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercise 2: visualising random models"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In this exercise you will use your `fprop` implementation to visualise the outputs of a single-layer affine transform model with two-dimensional inputs and a one-dimensional output. In this simple case, we can visualise the joint input-output space on a 3D axis.\n",
-    "\n",
-    "For this task and the learning experiments later in the notebook we will use a regression dataset from the UCI machine learning repository. In particular we will use a version of the [Combined Cycle Power Plant dataset](http://archive.ics.uci.edu/ml/datasets/Combined+Cycle+Power+Plant), where the task is to predict the energy output of a power plant given observations of the local ambient conditions (e.g. temperature, pressure and humidity).\n",
-    "\n",
-    "The original dataset has four input dimensions and a single target output dimension. We have preprocessed the dataset by [whitening](https://en.wikipedia.org/wiki/Whitening_transformation) it. Geometrically, this process rotates the data so that it's [principle components](https://en.wikipedia.org/wiki/Principal_component_analysis) are aligned with the basis vectors, and then scales the data so that variance along each dimension is one (see [here](https://www.quora.com/What-is-the-use-of-Whitening-images-as-a-preprocessing-step-for-a-Convolutional-Neural-Network)).\n",
-    "\n",
-    "If the original dataset has a covariance $\\mathbf{C}$, a whitening transformation $\\mathbf{D}$ is one which satisfies:\n",
-    "\\begin{equation}\n",
-    "    \\mathbf{D}^{\\mathrm{T}} \\mathbf{C} \\mathbf{D} = \\mathbf{I},\n",
-    "\\end{equation}\n",
-    "where $\\mathbf{I}$ is the identity matrix.\n",
-    "\n",
-    "This can be considered a change of basis, where newly formed input features are decorrelated and have equivalent scale, which can lead to reduced learning times (see [here](https://proceedings.neurips.cc/paper/1990/file/758874998f5bd0c393da094e1967a72b-Paper.pdf)). We will only use the first two dimensions of the whitened inputs (corresponding to the first two principal components of the original inputs) so we can easily visualise the joint input-output space.\n",
-    "\n",
-    "The dataset has been wrapped in the `CCPPDataProvider` class in the `mlp.data_providers` module and the data included as a compressed file in the data directory as `ccpp_data.npz`. Running the cell below will initialise an instance of this class, get a single batch of inputs and targets, and import the necessary `matplotlib` objects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "# import sys\n",
-    "# sys.path.append('/path/to/mlpractical')\n",
-    "from mpl_toolkits.mplot3d import Axes3D\n",
-    "from mlp.data_providers import CCPPDataProvider\n",
-    "\n",
-    "data_provider = CCPPDataProvider(\n",
-    "    which_set='train',\n",
-    "    input_dims=[0, 1],\n",
-    "    batch_size=5000, \n",
-    "    max_num_batches=1, \n",
-    "    shuffle_order=False\n",
-    ")\n",
-    "\n",
-    "input_dim, output_dim = 2, 1\n",
-    "\n",
-    "inputs, targets = data_provider.next()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now run the cell below to plot the predicted outputs of a randomly initialised model across the two dimensional input space as well as the true target outputs. This sort of visualisation can be a useful method (in low dimensions) to assess how well the model is likely to be able to fit the data and to judge appropriate initialisation scales for the parameters. Each time you re-run the cell a new set of random parameters will be sampled\n",
-    "\n",
-    "**Your Tasks:**\n",
-    "\n",
-    "Here you don't need to implement anything. Just run the cell for several times and try to answer the following questions:\n",
-    "\n",
-    "  * How do the weights and bias initialisation scale affect the sort of predicted input-output relationships?\n",
-    "  * Do you think a linear model is a good choice for this data?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "weights_init_range = 0.5\n",
-    "biases_init_range = 0.1\n",
-    "\n",
-    "# Randomly initialise weights matrix\n",
-    "weights = rng.uniform(\n",
-    "    low=-weights_init_range, \n",
-    "    high=weights_init_range, \n",
-    "    size=(output_dim, input_dim)\n",
-    ")\n",
-    "\n",
-    "#  Randomly initialise biases vector\n",
-    "biases = rng.uniform(\n",
-    "    low=-biases_init_range, \n",
-    "    high=biases_init_range, \n",
-    "    size=output_dim\n",
-    ")\n",
-    "# Calculate predicted model outputs\n",
-    "outputs = fprop(inputs, weights, biases)\n",
-    "\n",
-    "# Plot target and predicted outputs against inputs on same axis\n",
-    "fig = plt.figure(figsize=(8, 8))\n",
-    "ax = fig.add_subplot(111, projection='3d')\n",
-    "ax.plot(inputs[:, 0], inputs[:, 1], targets[:, 0], 'r.', ms=2)\n",
-    "ax.plot(inputs[:, 0], inputs[:, 1], outputs[:, 0], 'b.', ms=2)\n",
-    "ax.set_xlabel('Input dim 1')\n",
-    "ax.set_ylabel('Input dim 2')\n",
-    "ax.set_zlabel('Output')\n",
-    "ax.legend(['Targets', 'Predictions'], frameon=False)\n",
-    "fig.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercise 3: computing the error function and its gradient\n",
-    "\n",
-    "We will now consider the task of regression as covered in the first lecture. Given a set of inputs $\\left\\lbrace \\boldsymbol{x}^{(n)}\\right\\rbrace_{n=1}^N$, the aim in a regression problem is to produce outputs $\\left\\lbrace \\boldsymbol{y}^{(n)}\\right\\rbrace_{n=1}^N$ that are as 'close' as possible to a set of targets $\\left\\lbrace \\boldsymbol{t}^{(n)}\\right\\rbrace_{n=1}^N$. The measure of 'closeness' or distance between target and predicted outputs can vary and is usually a design choice. \n",
-    "\n",
-    "A very common choice is the squared Euclidean distance between the predicted and target outputs. This can be computed as the sum of the squared differences between each element in the target and predicted outputs. A widespread convention is to multiply this value by $\\frac{1}{2}$ as this gives a slightly nicer expression for the error gradient. The error for the $n^{\\textrm{th}}$ training example is then expresed by\n",
-    "\n",
-    "\\begin{equation}\n",
-    "    E^{(n)} = \\frac{1}{2} \\sum_{k=1}^K \\left\\lbrace \\left( y^{(n)}_k - t^{(n)}_k \\right)^2 \\right\\rbrace.\n",
-    "\\end{equation}\n",
-    "\n",
-    "The overall error is defined as the *average* of this value across all training examples\n",
-    "\n",
-    "\\begin{equation}\n",
-    "    \\bar{E} = \\frac{1}{N} \\sum_{n=1}^N \\left\\lbrace E^{(n)} \\right\\rbrace. \n",
-    "\\end{equation}\n",
-    "\n",
-    "*Note here we are using a slightly different convention from the lectures. There the overall error was considered to be the sum of the individual error terms rather than the mean. To differentiate between the two we will use $\\bar{E}$ to represent the average error here as opposed to sum of errors $E$ as used in the slides with $\\bar{E} = \\frac{E}{N}$. Normalising by the number of training examples is helpful to do in practice as this means we can more easily compare errors across data sets / batches of different sizes, and more importantly it means the size of our gradient updates will be independent of the number of training examples summed over.*\n",
-    "\n",
-    "Solving the regression problem means finding parameters of the model which minimise $\\bar{E}$. For our simple single-layer affine model here, that corresponds to finding weights $\\mathbf{W}$ and biases $\\boldsymbol{b}$ which minimise $\\bar{E}$. \n",
-    "\n",
-    "As mentioned in the lecture, in this case there is actually a closed form solution for the optimal weights and bias parameters. This is the linear least-squares solution those doing MLPR will have come across.\n",
-    "\n",
-    "However in general we will be interested in models where closed form solutions do not exist. Therefore, we will generally use iterative gradient descent based optimization methods to find parameters which (locally) minimise the error function. A basic requirement of being able to do gradient-descent based training is (unsuprisingly) the ability to evaluate gradients of the error function.\n",
-    "\n",
-    "Our end goal is to calculate gradients of the error function with respect to the model parameters $\\mathbf{W}$ and $\\boldsymbol{b}$. As a first step here we will consider the gradient of the error function with respect to the model outputs $\\left\\lbrace \\boldsymbol{y}^{(n)}\\right\\rbrace_{n=1}^N$. This can be written\n",
-    "\n",
-    "\\begin{equation}\n",
-    "    \\frac{\\partial \\bar{E}}{\\partial \\boldsymbol{y}^{(n)}} = \\frac{1}{N} \\left( \\boldsymbol{y}^{(n)} - \\boldsymbol{t}^{(n)} \\right)\n",
-    "    \\qquad \\Leftrightarrow \\qquad\n",
-    "    \\frac{\\partial \\bar{E}}{\\partial y^{(n)}_k} = \\frac{1}{N} \\left( y^{(n)}_k - t^{(n)}_k \\right) \\quad \\forall k \\in \\left\\lbrace 1 \\dots K\\right\\rbrace\n",
-    "\\end{equation}\n",
-    "\n",
-    "*i.e.* the gradient of the error function with respect to the $n^{\\textrm{th}}$ model output is the difference between the $n^{\\textrm{th}}$ model and target outputs, corresponding to the $\\boldsymbol{\\delta}^{(n)}$ terms mentioned in the lecture slides.\n",
-    "\n",
-    "**Your Tasks:** \n",
-    "\n",
-    "Using the equations given above, implement functions computing the mean sum of squared differences error and its gradient with respect to the model outputs. You should implement the functions using the provided skeleton definitions in the cell below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def error(outputs, targets):\n",
-    "    \"\"\"Calculates error function given a batch of outputs and targets.\n",
-    "\n",
-    "    Args:\n",
-    "        outputs: Array of model outputs of shape (batch_size, output_dim).\n",
-    "        targets: Array of target outputs of shape (batch_size, output_dim).\n",
-    "\n",
-    "    Returns:\n",
-    "        Scalar error function value.\n",
-    "    \"\"\"\n",
-    "    return 0.5 * ((outputs - targets)**2).sum() / outputs.shape[0]\n",
-    "    \n",
-    "def error_grad(outputs, targets):\n",
-    "    \"\"\"Calculates gradient of error function with respect to model outputs.\n",
-    "\n",
-    "    Args:\n",
-    "        outputs: Array of model outputs of shape (batch_size, output_dim).\n",
-    "        targets: Array of target outputs of shape (batch_size, output_dim).\n",
-    "\n",
-    "    Returns:\n",
-    "        Gradient of error function with respect to outputs.\n",
-    "        This will be an array of shape (batch_size, output_dim).\n",
-    "    \"\"\"\n",
-    "    return (outputs - targets) / outputs.shape[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Check your implementation by running the test cell below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Error function and gradient computed correctly!\n"
-     ]
-    }
-   ],
-   "source": [
-    "outputs = np.array([[1., 2.], [-1., 0.], [6., -5.], [-1., 1.]])\n",
-    "targets = np.array([[0., 1.], [3., -2.], [7., -3.], [1., -2.]])\n",
-    "true_error = 5.\n",
-    "true_error_grad = np.array([[0.25, 0.25], [-1., 0.5], [-0.25, -0.5], [-0.5, 0.75]])\n",
-    "\n",
-    "if not error(outputs, targets) == true_error:\n",
-    "    print('Error calculated incorrectly.')\n",
-    "elif not np.allclose(error_grad(outputs, targets), true_error_grad):\n",
-    "    print('Error gradient calculated incorrectly.')\n",
-    "else:\n",
-    "    print('Error function and gradient computed correctly!')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercise 4: computing gradients with respect to the parameters\n",
-    "\n",
-    "In the previous exercise you implemented a function computing the gradient of the error function with respect to the model outputs. For gradient-descent based training, we need to be able to evaluate the gradient of the error function with respect to the model parameters.\n",
-    "\n",
-    "Using the [chain rule for derivatives](https://en.wikipedia.org/wiki/Chain_rule#Higher_dimensions), we can write the partial deriviative of the error function with respect to single elements of the weight matrix and bias vector as\n",
-    "\n",
-    "\\begin{equation}\n",
-    "    \\frac{\\partial E}{\\partial W_{kj}} = \\sum_{n=1}^N \\left\\lbrace \\frac{\\partial E}{\\partial y^{(n)}_k} \\frac{\\partial y^{(n)}_k}{\\partial W_{kj}} \\right\\rbrace\n",
-    "    \\quad \\textrm{and} \\quad\n",
-    "    \\frac{\\partial E}{\\partial b_k} = \\sum_{n=1}^N \\left\\lbrace \\frac{\\partial E}{\\partial y^{(n)}_k} \\frac{\\partial y^{(n)}_k}{\\partial b_k} \\right\\rbrace.\n",
-    "\\end{equation}\n",
-    "\n",
-    "From the definition of our model at the beginning we have \n",
-    "\n",
-    "\\begin{equation}\n",
-    "    y^{(n)}_k = \\sum_{d=1}^D \\left\\lbrace W_{kd} x^{(n)}_d \\right\\rbrace + b_k\n",
-    "    \\quad \\Rightarrow \\quad\n",
-    "    \\frac{\\partial y^{(n)}_k}{\\partial W_{kj}} = x^{(n)}_j\n",
-    "    \\quad \\textrm{and} \\quad\n",
-    "    \\frac{\\partial y^{(n)}_k}{\\partial b_k} = 1.\n",
-    "\\end{equation}\n",
-    "\n",
-    "Putting this together we get that\n",
-    "\n",
-    "\\begin{equation}\n",
-    "    \\frac{\\partial E}{\\partial W_{kj}} = \n",
-    "    \\sum_{n=1}^N \\left\\lbrace \\frac{\\partial E}{\\partial y^{(n)}_k} x^{(n)}_j \\right\\rbrace\n",
-    "    \\quad \\textrm{and} \\quad\n",
-    "    \\frac{\\partial E}{\\partial b_{k}} = \n",
-    "    \\sum_{n=1}^N \\left\\lbrace \\frac{\\partial E}{\\partial y^{(n)}_k} \\right\\rbrace.\n",
-    "\\end{equation}\n",
-    "\n",
-    "Although this may seem a bit of a roundabout way to get to these results, this method of decomposing the error gradient with respect to the parameters in terms of the gradient of the error function with respect to the model outputs and the derivatives of the model outputs with respect to the model parameters is the key element that allows calculating the parameter gradients of more complex models we will study later in the course.\n",
-    "\n",
-    "**Your Tasks:** \n",
-    "\n",
-    "Implement a function calculating the gradient of the error function with respect to the weight and bias parameters of the model given the already computed gradient of the error function with respect to the model outputs. You should implement this in the `grads_wrt_params` function in the cell below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def grads_wrt_params(inputs, grads_wrt_outputs):\n",
-    "    \"\"\"Calculates gradients with respect to model parameters.\n",
-    "\n",
-    "    Args:\n",
-    "        inputs: array of inputs to model of shape (batch_size, input_dim)\n",
-    "        grads_wrt_to_outputs: array of gradients of with respect to the model\n",
-    "            outputs of shape (batch_size, output_dim).\n",
-    "\n",
-    "    Returns:\n",
-    "        list of arrays of gradients with respect to the model parameters\n",
-    "        `[grads_wrt_weights, grads_wrt_biases]`.\n",
-    "    \"\"\"\n",
-    "    grads_wrt_weights = grads_wrt_outputs.T.dot(inputs)\n",
-    "    grads_wrt_biases = grads_wrt_outputs.sum(0)\n",
-    "    return [grads_wrt_weights, grads_wrt_biases]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Check your implementation by running the test cell below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "All parameter gradients calculated correctly!\n"
-     ]
-    }
-   ],
-   "source": [
-    "inputs = np.array([[1., 2., 3.], [-1., 4., -9.]])\n",
-    "grads_wrt_outputs = np.array([[-1., 1.], [2., -3.]])\n",
-    "true_grads_wrt_weights = np.array([[-3., 6., -21.], [4., -10., 30.]])\n",
-    "true_grads_wrt_biases = np.array([1., -2.])\n",
-    "\n",
-    "grads_wrt_weights, grads_wrt_biases = grads_wrt_params(\n",
-    "    inputs, grads_wrt_outputs)\n",
-    "\n",
-    "if not np.allclose(true_grads_wrt_weights, grads_wrt_weights):\n",
-    "    print('Gradients with respect to weights incorrect.')\n",
-    "elif not np.allclose(true_grads_wrt_biases, grads_wrt_biases):\n",
-    "    print('Gradients with respect to biases incorrect.')\n",
-    "else:\n",
-    "    print('All parameter gradients calculated correctly!')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercise 5: wrapping the functions into reusable components\n",
-    "\n",
-    "In exercises 1, 3 and 4 you implemented methods to compute the predicted outputs of our model, evaluate the error function and its gradient on the outputs and finally to calculate the gradients of the error with respect to the model parameters. Together they constitute all the basic ingredients we need to implement a gradient-descent based iterative learning procedure.\n",
-    "\n",
-    "Although you could implement training code which directly uses the functions you defined, this would only be usable for this particular model architecture. In subsequent labs we will want to use the affine transform functions as the basis for more interesting multi-layer models. We will therefore wrap the implementations you just wrote in to reusable components that we can combine to build more complex models later in the course.\n",
-    "\n",
-    "**Your Tasks:**\n",
-    "\n",
-    "  * In the [`mlp.layers`](../mlp/layers.py) module, use your implementations of `fprop` and `grad_wrt_params` above to implement the corresponding methods in the skeleton `AffineLayer` class provided.\n",
-    "  * In the [`mlp.errors`](../mlp/errors.py) module use your implementation of `error` and `error_grad` to implement the `__call__` and `grad` methods respectively of the skeleton `SumOfSquaredDiffsError` class provided. Note `__call__` is a special Python method that allows an object to be used with a function call syntax.\n",
-    "\n",
-    "Run the cell below to use your completed `AffineLayer` and `SumOfSquaredDiffsError` implementations to train a single-layer model using batch gradient descent on the CCPP dataset. Remember to reload the notebook if you made changes to the `mlp` module."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 1: 0.0s to complete\n",
-      "    error(train)=1.67e-01\n",
-      "Epoch 2: 0.0s to complete\n",
-      "    error(train)=9.30e-02\n",
-      "Epoch 3: 0.0s to complete\n",
-      "    error(train)=7.95e-02\n",
-      "Epoch 4: 0.0s to complete\n",
-      "    error(train)=7.71e-02\n",
-      "Epoch 5: 0.0s to complete\n",
-      "    error(train)=7.66e-02\n",
-      "Epoch 6: 0.0s to complete\n",
-      "    error(train)=7.65e-02\n",
-      "Epoch 7: 0.0s to complete\n",
-      "    error(train)=7.65e-02\n",
-      "Epoch 8: 0.0s to complete\n",
-      "    error(train)=7.65e-02\n",
-      "Epoch 9: 0.0s to complete\n",
-      "    error(train)=7.63e-02\n",
-      "Epoch 10: 0.0s to complete\n",
-      "    error(train)=7.64e-02\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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aSlPf7Bb6ZgEAQAAjzAaotKHNfbMH6JsFAACBizAboCYk9lVIkE0lVbU6UFJtdTkAAADnBGE2QIUG2zUhsa+kptlZAACAQESYDWCn1pvlJjAAABCYuhxmGxoaFBQUpJ07d56LeuBFU1LomwUAAIGty2E2KChISUlJamxsPBf1wIsmDumnYLuhooqTyi+rsbocAAAAr+tWm8HSpUu1ePFilZXRi+nLwkLsGj+4ryT6ZgEAQGAK6s5JzzzzjL788kslJCQoKSlJERERHq/v2LHDK8Xh7KUNjdH2Q8e0Ja9UN1+UaHU5AAAAXtWtMDtr1iwvl4FzJS2lv5774CtmZgEAQEDqVph99NFHvV0HzpHUpH6y2wwdPn5CXx+r0eB+4VaXBAAA4DXdCrMu2dnZ2r17twzD0OjRozVx4kRv1QUviXAE6cLzopVbcFxbD5RpcCphFgAABI5uhdni4mL98Ic/1IYNG9S3b1+Zpqny8nJdccUVev311zVw4EBv14mzkDY0RrkFx7XlQKluSh1sdTkAAABe063VDBYsWKCKigrt2rVLZWVlOnbsmHbu3KmKigo9+OCD3q4RZ2lKiuvhCfTNAgCAwNKtmdl3331X77//vkaNGuUeGz16tJ577jmlp6d7rTh4x+TkfrIZUn5ZjQrLTyg+OszqkgAAALyiWzOzTqdTwcHBrcaDg4PldDrPuih4V2RosMYkREtivVkAABBYuhVmr7zySj300EM6cuSIe+zw4cN6+OGHNWPGDK8VB+9Jcz3aNq/U4koAAAC8p1thdvXq1aqsrFRycrKGDRum4cOHKyUlRZWVlXr22We9XSO8IG1oc98sM7MAACCAdKtnNjExUTt27FBWVpb27Nkj0zQ1evRoXXXVVd6uD15ycXKMDEM6UFKt4oqTio0KtbokAACAs9blMNvQ0KDQ0FDl5ubq6quv1tVXX30u6oKXRYcHa9SgKP2rsEJb88p0w/gEq0sCAAA4a11uMwgKClJSUpIaGxvPRT04h9KG0jcLAAACS7d6ZpcuXarFixerrIz+S3+SlkLfLAAACCzd6pl95pln9OWXXyohIUFJSUmKiIjweH3Hjh1eKQ7edXHzigb7i6tUWlWr/n0cFlcEAABwdroVZmfNmuXlMtATYiJCNCIuUnuPVurTvDLNvDDe6pIAAADOSrduAJOku+66S4mJiV4vCOdW2tAY7T1aqa2EWQAAEAC6dQPYL3/5S24A81OuvtktB7gJDAAA+L9u3QA2Y8YMbdiwwculoCe4+mb3Hq3U8Zo6i6sBAAA4O93qmZ05c6YWL16snTt3KjU1tdUNYN/5zne8Uhy8b2CkQ8MGRuirb6q1Na9M14wZZHVJAAAA3datmdn7779fR48e1cqVK3Xrrbdq1qxZ7u3GG2/s0rXWrFmjlJQUhYaGKjU1VZs2bWr32I8++kiXXHKJ+vfvr7CwMI0cOVK/+tWvuvMRejUebQsAAAJFt8Ks0+lsd+tKL+3atWu1cOFCLVmyRDk5OZo+fbpmzpyp/Pz8No+PiIjQAw88oI0bN2r37t1aunSpli5dqszMzO58jF4rLYWHJwAAgMDQpTB73XXXqby83L3/xBNP6Pjx4+790tJSjR49utPXW7lype6++27dc889GjVqlFatWqXExERlZGS0efzEiRN1yy23aMyYMUpOTtZtt92ma665psPZXLQ2pXlm9l+FFSo/UW9xNQAAAN3XpTD73nvvqba21r3/1FNPeTwFrKGhQXv37u3Uterq6pSdna309HSP8fT0dG3evLlT18jJydHmzZv1rW99q91jamtrVVFR4bH1dnFRoUruHy7TlLYfpNUAAAD4ry6FWdM0O9zvipKSEjU2NiouLs5jPC4uTkVFRR2eO3jwYDkcDk2ePFnz58/XPffc0+6xy5cvV3R0tHtjbdwm7kfb5hFmAQCA/+pWz6w3GYbhsW+aZqux023atEnbt2/X888/r1WrVum1115r99jFixervLzcvRUUFHilbn83ZVhz3yzrzQIAAD/WpaW5DMNoFTTPFDzbM2DAANnt9lazsMXFxa1ma0+XkpIiSbrwwgt19OhRLVu2TLfcckubxzocDjkcjm7VGMhcM7M7j1SoqrZBfRzdWqUNAADAUl1KMKZp6s4773SHw5MnT2revHnudWZb9tOeSUhIiFJTU5WVleWxnFdWVpa++93vdqmmrrwvmiT0DVNiTJgKyk5o+8EyXT4i1uqSAAAAuqxLYXbu3Lke+7fddlurY+64445OX2/RokW6/fbbNXnyZE2dOlWZmZnKz8/XvHnzJDW1CBw+fFivvPKKJOm5557TkCFDNHLkSElN687+8pe/1IIFC7ryMdAsLaW/Csq+1tY8wiwAAPBPXQqzL730klfffPbs2SotLdXjjz+uwsJCjR07VuvXr1dSUpIkqbCw0GPNWafTqcWLFysvL09BQUEaNmyY/vu//1v33XefV+vqLdJSYvTn7K/pmwUAAH7LMM9mSQI/VFFRoejoaJWXlysqKsrqcixVUFaj6U9/oCCboc+XpSs8hL5ZAABgva7kNctXM4B1BvcLU0J0qBqcpnYcOm51OQAAAF1GmO3FDMNQ2lDXerO0GgAAAP9DmO3l0lJc683y8AQAAOB/CLO9nGtmNrfguE7WN1pcDQAAQNcQZnu55P7hio10qK7RqR35x6wuBwAAoEsIs72cR98srQYAAMDPEGZxqm+Wm8AAAICfIcxCU4Y2hdmc/OOqbaBvFgAA+A/CLDRsYB8N6BOi2ganPisot7ocAACATiPMoqlvNsXVN0urAQAA8B+EWUiS0oa6+ma5CQwAAPgPwiwkyT0zm33omOobnRZXAwAA0DmEWUiSzo/to37hwTpR36jPv6ZvFgAA+AfCLCRJNpuhi1miCwAA+BnCLNxO3QRG3ywAAPAPhFm4uW4C236wTA30zQIAAD9AmIXbyEFRigoNUnVdo3YdqbC6HAAAgDMizMLNTt8sAADwM4RZeHD1zW6hbxYAAPgBwiw8uPpmt+WVqdFpWlwNAABAxwiz8DA6Pkp9HEGqrG3Q7kL6ZgEAgG8jzMJDkN2mycn9JElbDtA3CwAAfBthFq2415vNo28WAAD4NsIsWnH3zR4sk5O+WQAA4MMIs2jlwvOiFR5i1/Gaeu09Wml1OQAAAO0izKKVYLtNqUlNfbNb6ZsFAAA+jDCLNk0ZSt8sAADwfYRZtCmt+Ulgn+aVyTTpmwUAAL6JMIs2jRvcV6HBNpVW1+nL4iqrywEAAGgTYRZtCgmyadKQ5vVmaTUAAAA+ijCLdrnXm+UmMAAA4KMIs2iXa73ZrfTNAgAAH0WYRbsmJPZVSJBN31TWKq+k2upyAAAAWiHMol2hwXZNSOwrSdpygL5ZAADgewiz6NCUFFerAX2zAADA9xBm0aE018MTDtA3CwAAfA9hFh2aNKSfgu2GiipOKr+sxupyAAAAPBBm0aGwELvGDe4rqWl2FgAAwJcQZnFGrkfbbqFvFgAA+BjCLM5oSou+WQAAAF9CmMUZpSb1k91m6PDxE/r6GH2zAADAdxBmcUYRjiBdeF60JGZnAQCAbyHMolNOPdqWvlkAAOA7CLPolCkpzX2zeczMAgAA30GYRadMTu4nmyEdKq1RUflJq8sBAACQ5ANhds2aNUpJSVFoaKhSU1O1adOmdo/9y1/+oquvvloDBw5UVFSUpk6dqvfee68Hq+29IkODNSahuW+WVgMAAOAjLA2za9eu1cKFC7VkyRLl5ORo+vTpmjlzpvLz89s8fuPGjbr66qu1fv16ZWdn64orrtANN9ygnJycHq68d3KvN8tNYAAAwEcYpmmaVr15WlqaJk2apIyMDPfYqFGjNGvWLC1fvrxT1xgzZoxmz56tn/3sZ506vqKiQtHR0SovL1dUVFS36u6tsv51VD96ZbuGDozQ//7b5VaXAwAAAlRX8pplM7N1dXXKzs5Wenq6x3h6ero2b97cqWs4nU5VVlYqJibmXJSI01ycHCPDkA58U63iCvpmAQCA9SwLsyUlJWpsbFRcXJzHeFxcnIqKijp1jRUrVqi6ulo333xzu8fU1taqoqLCY0P3RIcHa+Sgpn8dsaoBAADwBZbfAGYYhse+aZqtxtry2muvadmyZVq7dq1iY2PbPW758uWKjo52b4mJiWddc2/m6pvlJjAAAOALLAuzAwYMkN1ubzULW1xc3Gq29nRr167V3XffrT/96U+66qqrOjx28eLFKi8vd28FBQVnXXtvNsX18ARuAgMAAD7AsjAbEhKi1NRUZWVleYxnZWVp2rRp7Z732muv6c4779Srr76q66+//ozv43A4FBUV5bGh+y5ufnjC/uIqlVbVWlwNAADo7SxtM1i0aJF++9vf6sUXX9Tu3bv18MMPKz8/X/PmzZPUNKt6xx13uI9/7bXXdMcdd2jFihWaMmWKioqKVFRUpPLycqs+Qq8TExGiEXGRkqRP6ZsFAAAWszTMzp49W6tWrdLjjz+uCRMmaOPGjVq/fr2SkpIkSYWFhR5rzr7wwgtqaGjQ/PnzFR8f794eeughqz5Cr5TmajUgzAIAAItZus6sFVhn9uy9/Xmh5r+6QyMHRerdhZdZXQ4AAAgwfrHOLPzXxc0rGuw9WqnjNXUWVwMAAHozwiy6bGCkQ8MGRsg06ZsFAADWIsyiW9KGNq1qQN8sAACwEmEW3cLDEwAAgC8gzKJbpjTPzP7rSIUqTtZbXA0AAOitCLPolrioUCX3D5fTlLYfpNUAAABYgzCLbktrfhrYFh5tCwAALEKYRbe5H55wgL5ZAABgDcIsus21osHOIxWqqm2wuBoAANAbEWbRbef1DdPgfmFqdJr0zQIAAEsQZnFWXH2zrDcLAACsQJjFWXH1zX7yValM07S4GgAA0NsQZnFWpjb3zeYWHNeiP31G7ywAAOhRhFmclcSYcC29fpRshvRmzmF959mPtOtIudVlAQCAXoIwi7N2z/Shev3eqRoUFaoDJdW6cc1mvfLJQdoOAADAOUeYhVdcnBKj9Q9N14yRsaprcOpnf9ul+/+wQ+UneNQtAAA4dwiz8JqYiBD9du5kLb1+lILtht7dVaTrn9mknPxjVpcGAAACFGEWXmUYhu6ZPlR/njdNiTFh+vrYCf3g+U+UufErOZ20HQAAAO8izOKcGJ/YV28/OF3XXxivBqepJ9fv0d0vb1NZdZ3VpQEAgABCmMU5ExUarNVzJuqJG8cqJMimD/Z+o5m/3qgtB0qtLg0AAAQIwizOKcMwdGtakv42/xINGxihoxW1mvObLfr1+/vVSNsBAAA4S4RZ9IhR8VF664FLddOkwXKa0q/e36fbfrtVxRUnrS4NAAD4McIsekyEI0grbh6vFT8Yr/AQuz45UKqZv96kD/d9Y3VpAADATxFm0eNuSh2stx64VCMHRaq0uk5zX/xUT727R/WNTqtLAwAAfoYwC0sMj+2jv86/RLdNGSJJytjwlWa/8IkOHz9hcWUAAMCfEGZhmdBgu34+60I9N2eSIh1B2pF/XNf9epP+savI6tIAAICfIMzCctePi9fbD07X+MHRKj9Rr3t/n61lb+1SbUOj1aUBAAAfR5iFTxjSP1xvzJumey5NkST9bvNB3ZSxWQdLqi2uDAAA+DLCLHxGSJBNS789Wv8zd7L6hgdr5+EKffvZj/TWZ0esLg0AAPgowix8zoxRcXrnoem6ODlGVbUNevC1HD2y7nOdqKPtAAAAeCLMwifFR4fp1R+lacGVw2UY0uvbCvTd5z7S/qOVVpcGAAB8CGEWPivIbtO/pY/QH+5O04A+Du07WqUbVn+kP20rkGnyKFwAAECYhR+4ZPgAvfPQdE0/f4BO1jv1f9d9rofX5qqqtsHq0gAAgMUIs/ALAyMdevn/XKz/uGaE7DZDf809ohue/Ug7D5dbXRoAALAQYRZ+w2YzNP+K4Vp77xTFR4cqr6Ra31uzWa98cpC2AwAAeinCLPzO5OQYrX9wuq4aFau6Rqd+9rddmveHbJXX1FtdGgAA6GGEWfilfhEh+s0dk/XTb49WsN3Qe7uO6rpnNmlH/jGrSwMAAD2IMAu/ZRiG7r40Revun6YhMeE6fPyEbn7+E73w4VdyOmk7AACgNyDMwu+NG9xXf3/wUl0/Ll4NTlPL39mju17eptKqWqtLAwAA5xhhFgEhKjRYq2+ZqCdvvFCOIJs27P1G1z2zSZ98VWp1aQAA4BwizCJgGIahOWlD9LcHLtGwgRE6WlGrW3+7Rave36dG2g4AAAhIhFkEnJGDovT/FlyqH6QOltOUVr2/X7f9dquOVpy0ujQAAOBlhFkEpPCQIP3iB+O18ubxCg+x65MDpbru15u0YW+x1aUBAAAvIswioH1v0mD9vwWXalR8lEqr63TnS9v03+/sUX2j0+rSAACAFxBmEfCGDeyjN388TbdPSZIkPf/hV5r9wif6+liNxZUBAICzRZhFrxAabNd/zRqrjFsnKTI0SDvyj+u6X2/Se7uKrC4NAACcBcvD7Jo1a5SSkqLQ0FClpqZq06ZN7R5bWFioOXPmaMSIEbLZbFq4cGHPFYqAMPPCeK1/cLrGJ/ZVxckG3ff7bC17a5dqGxqtLg0AAHSDpWF27dq1WrhwoZYsWaKcnBxNnz5dM2fOVH5+fpvH19bWauDAgVqyZInGjx/fw9UiUCTGhOuN+6bq3suGSpJ+t/mgbsrYrLySaosrAwAAXWWYpmnZApxpaWmaNGmSMjIy3GOjRo3SrFmztHz58g7PvfzyyzVhwgStWrWqS+9ZUVGh6OholZeXKyoqqjtlI4B8sKdYi/6Uq2M19YoIsevJ712o7044z+qyAADo1bqS1yybma2rq1N2drbS09M9xtPT07V582avvU9tba0qKio8NsDlipGxWv/QdF2cEqPqukY99HqufvLnz3WijrYDAAD8gWVhtqSkRI2NjYqLi/MYj4uLU1GR927KWb58uaKjo91bYmKi166NwBAfHaZX70nTgzPOl2FIa7cX6LvPfaR9RyutLg0AAJyB5TeAGYbhsW+aZquxs7F48WKVl5e7t4KCAq9dG4EjyG7Toqsv0B/vTtPASIf2Ha3Sd1Z/pLXb8mVhJw4AADgDy8LsgAEDZLfbW83CFhcXt5qtPRsOh0NRUVEeG9CeacMHaP2D0zX9/AE6We/UT9Z9oYVrc1VV22B1aQAAoA1BVr1xSEiIUlNTlZWVpRtvvNE9npWVpe9+97tWlQVoYKRDL/+fi/X8xq+04h/79LfcI9p+8JguSu6nIf0jlBQTrqT+4RrSP1wD+zi8+psEAADQNZaFWUlatGiRbr/9dk2ePFlTp05VZmam8vPzNW/ePElNLQKHDx/WK6+84j4nNzdXklRVVaVvvvlGubm5CgkJ0ejRo634CAhQNpuhH18+XGkpMVrwao4OHz+hw7knWh0XFmzXkJimYOsKuYkx4UrqH6Hz+oYpJMjyTh4AAAKapUtzSU0PTXj66adVWFiosWPH6le/+pUuu+wySdKdd96pgwcPasOGDe7j25oFS0pK0sGDBzv1fizNha6qPFmvTftLdKi0Rvll1TpUWqNDpTUqLD8hZwf/9dgMKaFvWNMsbkyEhrhmdJv/jAwN7rkPAQCAH+lKXrM8zPY0wiy8pa7BqcPHT+hQabXyy2rcITe/rGn/ZL2zw/NjIkKaZnU9Qm6EkprbF2w22hcAAL1TV/KapW0GgD8LCbIpZUCEUgZEtHrNNE19U1mrQ80hN7+0WofKapRfVqP80hqVVteprHnLLTje6nxHkK1FyI1wh90h/cM1uF+YHEH2HviEAAD4PsIscA4YhqHYqFDFRoXqouSYVq9Xnqx3B9v8spqmoFtao0Nl1Tpy/KRqG5zaX1yl/cVVbVxbSogOc4fdxOY/k2IiNKR/uKLDaF8AAPQetBkAPqa+0anDx060CLnVze0LTVvNGZ5O1jc82KN9wRVyh8SEa1BUKO0LAACfR5sB4MeC7TYlD4hQcjvtCyVVdR43ojX161Yrv+yESqpqdbymXsdryvX51+Wtzg8JsimxX5iS+nvekDYkpumGtGC7oeAgm0LsNgXbbbITfAEAPo4wC/gRwzA0MNKhgZEOpSa1bl+orm1w34zmuhHNFXgPHzuhuganvvqmWl99U92p97MZTeE6xG5TcJCtKezaT4Xd4CDD/XVIUPNYG8d47J92nuucU+c377vfs+nckObruLaQFtcOshms9wsAvRRhFgggEY4gjYqP0qj41r+SaWh06sjxkzrUHHLzm2d2D5XV6OuyGp2ob1TDaWuNOU2ptsGp2ganVNtTn6J7QlxBukUAdgXrYLtNQXZDhiQZTX/ajKZ/HBhNQ55fy5DN1vSnKyPbDKP5taZjmyatW461PKZp0OZxzaZ9Gaeua2vxdZs1GKeWIzROv16L92pZQ3NV7nOa9ltwXc9zt8Nz3GOn/YOhvXM7c/1T1zA6OKft9z1b3uyu82afXltltXf9rnyG9g4127h6+8d2/tptXbellj8LLb/nbf4cGcZpx7QYb/lz0uLnuuXPXMtjPH4WPcbbONfwPF/tHXfae5z6O2jxtdly3GxjrOWxHX9PWv7dusY7cy2zjQPaulbLQ9p73xkj4zQoOrRVnVYizAK9RJDd1tQ72z+83WOcTlP1TqfqG03VNzhV3+hUXWPzfqNTdc1j7v1GZ/NxLfZbjLn3m885db7rWqcd09DxOa7j6xpaL3tW1+hUXaOkM/QUAwC6L/meCMIsAN9lsxly2OxyBElyWF1N+0zTVKPT7EQYdqquoSloNzidMs2m2Qaz+RpNE9Gme8xpmh6vN31tus9zms3zE83jTvf1XMeYzeee+tppnpohaXmNVue1UYOaazRPq1GnfQbXtVzvcfrflftr95hr3zxt3/P1lqOnzwK1d25bx6jV9c0OzmnnGFOnTTGfe1Y0rrQ1C91WHW1NVrd9XOeu19ag0c7fQOffu/VYy++zx8+Jx7jpcUzLn4mWPw8tfwZbXsvzHNeV2r9Wy//mW9dpevzsm6df67TjPH870fpXFW3+xqPFqOdMcMevnxpr/7cbnmOtj/M8tp06ThuLiQhpXYTFCLMA/I5hGAqyGwqyS2FizV0A6M14cDwAAAD8FmEWAAAAfoswCwAAAL9FmAUAAIDfIswCAADAbxFmAQAA4LcIswAAAPBbhFkAAAD4LcIsAAAA/BZhFgAAAH6LMAsAAAC/FWR1AT3NNE1JUkVFhcWVAAAAoC2unObKbR3pdWG2srJSkpSYmGhxJQAAAOhIZWWloqOjOzzGMDsTeQOI0+nUkSNHFBkZKcMwrC4nIFRUVCgxMVEFBQWKioqyuhx0A99D/8b3z//xPfR/fA+9yzRNVVZWKiEhQTZbx12xvW5m1mazafDgwVaXEZCioqL4D9jP8T30b3z//B/fQ//H99B7zjQj68INYAAAAPBbhFkAAAD4LcIszprD4dCjjz4qh8NhdSnoJr6H/o3vn//je+j/+B5ap9fdAAYAAIDAwcwsAAAA/BZhFgAAAH6LMAsAAAC/RZgFAACA3yLMotuWL1+uiy66SJGRkYqNjdWsWbO0d+9eq8tCNy1fvlyGYWjhwoVWl4IuOHz4sG677Tb1799f4eHhmjBhgrKzs60uC53U0NCgpUuXKiUlRWFhYRo6dKgef/xxOZ1Oq0tDOzZu3KgbbrhBCQkJMgxDf/3rXz1eN01Ty5YtU0JCgsLCwnT55Zdr165d1hTbSxBm0W0ffvih5s+fry1btigrK0sNDQ1KT09XdXW11aWhi7Zt26bMzEyNGzfO6lLQBceOHdMll1yi4OBgvfPOO/rXv/6lFStWqG/fvlaXhk566qmn9Pzzz2v16tXavXu3nn76af3iF7/Qs88+a3VpaEd1dbXGjx+v1atXt/n6008/rZUrV2r16tXatm2bBg0apKuvvlqVlZU9XGnvwdJc8JpvvvlGsbGx+vDDD3XZZZdZXQ46qaqqSpMmTdKaNWv085//XBMmTNCqVausLgud8Mgjj+jjjz/Wpk2brC4F3fTtb39bcXFx+p//+R/32E033aTw8HD9/ve/t7AydIZhGHrzzTc1a9YsSU2zsgkJCVq4cKF+8pOfSJJqa2sVFxenp556Svfdd5+F1QYuZmbhNeXl5ZKkmJgYiytBV8yfP1/XX3+9rrrqKqtLQRe99dZbmjx5sn7wgx8oNjZWEydO1G9+8xury0IXXHrppfrnP/+pffv2SZI+++wzffTRR7ruuussrgzdkZeXp6KiIqWnp7vHHA6HvvWtb2nz5s0WVhbYgqwuAIHBNE0tWrRIl156qcaOHWt1Oeik119/XTt27NC2bdusLgXdcODAAWVkZGjRokX6z//8T3366ad68MEH5XA4dMcdd1hdHjrhJz/5icrLyzVy5EjZ7XY1NjbqiSee0C233GJ1aeiGoqIiSVJcXJzHeFxcnA4dOmRFSb0CYRZe8cADD+jzzz/XRx99ZHUp6KSCggI99NBD+sc//qHQ0FCry0E3OJ1OTZ48WU8++aQkaeLEidq1a5cyMjIIs35i7dq1+sMf/qBXX31VY8aMUW5urhYuXKiEhATNnTvX6vLQTYZheOybptlqDN5DmMVZW7Bggd566y1t3LhRgwcPtrocdFJ2draKi4uVmprqHmtsbNTGjRu1evVq1dbWym63W1ghziQ+Pl6jR4/2GBs1apTWrVtnUUXoqv/4j//QI488oh/+8IeSpAsvvFCHDh3S8uXLCbN+aNCgQZKaZmjj4+Pd48XFxa1ma+E99Myi20zT1AMPPKC//OUv+t///V+lpKRYXRK6YMaMGfriiy+Um5vr3iZPnqxbb71Vubm5BFk/cMkll7RaDm/fvn1KSkqyqCJ0VU1NjWw2z/8V2+12lubyUykpKRo0aJCysrLcY3V1dfrwww81bdo0CysLbMzMotvmz5+vV199VX/7298UGRnp7hWKjo5WWFiYxdXhTCIjI1v1N0dERKh///70PfuJhx9+WNOmTdOTTz6pm2++WZ9++qkyMzOVmZlpdWnopBtuuEFPPPGEhgwZojFjxignJ0crV67UXXfdZXVpaEdVVZW+/PJL935eXp5yc3MVExOjIUOGaOHChXryySd1/vnn6/zzz9eTTz6p8PBwzZkzx8KqAxtLc6Hb2uv/eemll3TnnXf2bDHwissvv5ylufzM3//+dy1evFj79+9XSkqKFi1apB/96EdWl4VOqqys1E9/+lO9+eabKi4uVkJCgm655Rb97Gc/U0hIiNXloQ0bNmzQFVdc0Wp87ty5+t3vfifTNPXYY4/phRde0LFjx5SWlqbnnnuOSYJziDALAAAAv0XPLAAAAPwWYRYAAAB+izALAAAAv0WYBQAAgN8izAIAAMBvEWYBAADgtwizAAAA8FuEWQDwEYZh6K9//avVZXRacnIyD9gAYDnCLIBe784775RhGK22a6+91urSAABnEGR1AQDgC6699lq99NJLHmMOh8Oianqv+vp6BQcHW10GAD/CzCwAqCm4Dho0yGPr16+f+3XDMJSRkaGZM2cqLCxMKSkpeuONNzyu8cUXX+jKK69UWFiY+vfvr3vvvVdVVVUex7z44osaM2aMHA6H4uPj9cADD3i8XlJSohtvvFHh4eE6//zz9dZbb3VYd3Jysp588kndddddioyM1JAhQ5SZmel+fcOGDTIMQ8ePH3eP5ebmyjAMHTx4UJL0u9/9Tn379tXf//53jRgxQuHh4fr+97+v6upqvfzyy0pOTla/fv20YMECNTY2erx/ZWWl5syZoz59+ighIUHPPvusx+vl5eW69957FRsbq6ioKF155ZX67LPP3K8vW7ZMEyZM0IsvvqihQ4fK4XCIp6wD6ArCLAB00k9/+lPddNNN+uyzz3Tbbbfplltu0e7duyVJNTU1uvbaa9WvXz9t27ZNb7zxht5//32PsJqRkaH58+fr3nvv1RdffKG33npLw4cP93iPxx57TDfffLM+//xzXXfddbr11ltVVlbWYV0rVqzQ5MmTlZOTox//+Me6//77tWfPni59tpqaGj3zzDN6/fXX9e6772rDhg363ve+p/Xr12v9+vX6/e9/r8zMTP35z3/2OO8Xv/iFxo0bpx07dmjx4sV6+OGHlZWVJUkyTVPXX3+9ioqKtH79emVnZ2vSpEmaMWOGx2f68ssv9ac//Unr1q1Tbm5ul+oGAJkA0MvNnTvXtNvtZkREhMf2+OOPu4+RZM6bN8/jvLS0NPP+++83TdM0MzMzzX79+plVVVXu199++23TZrOZRUVFpmmaZkJCgrlkyZJ265BkLl261L1fVVVlGoZhvvPOO+2ek5SUZN52223ufafTacbGxpoZGRmmaZrmBx98YEoyjx075j4mJyfHlGTm5eWZpmmaL730kinJ/PLLL93H3HfffWZ4eLhZWVnpHrvmmmvM++67z+O9r732Wo96Zs+ebc6cOdM0TdP85z//aUZFRZknT570OGbYsGHmCy+8YJqmaT766KNmcHCwWVxc3O5nBICO0DMLAJKuuOIKZWRkeIzFxMR47E+dOrXVvmsmcffu3Ro/frwiIiLcr19yySVyOp3au3evDMPQkSNHNGPGjA7rGDdunPvriIgIRUZGqri4uNPnGIahQYMGnfGc04WHh2vYsGHu/bi4OCUnJ6tPnz4eY6dft62/E9cKB9nZ2aqqqlL//v09jjlx4oS++uor935SUpIGDhzYpXoBwIUwCwBqCo6n/8q/MwzDkNT0K3XX120dExYW1qnrnX7zk2EYcjqd3T7HZrO563Opr6/v1DW6U4vrOElyOp2Kj4/Xhg0bWh3Tt29f99ct/wEAAF1FzywAdNKWLVta7Y8cOVKSNHr0aOXm5qq6utr9+scffyybzaYLLrhAkZGRSk5O1j//+c8erdk141lYWOge82Zfakd/J5MmTVJRUZGCgoI0fPhwj23AgAFeqwFA70aYBQBJtbW1Kioq8thKSko8jnnjjTf04osvat++fXr00Uf16aefum/wuvXWWxUaGqq5c+dq586d+uCDD7RgwQLdfvvtiouLk9R05/6KFSv0zDPPaP/+/dqxY0eru/+9bfjw4UpMTNSyZcu0b98+vf3221qxYoXXrv/xxx/r6aef1r59+/Tcc8/pjTfe0EMPPSRJuuqqqzR16lTNmjVL7733ng4ePKjNmzdr6dKl2r59u9dqANC7EWYBQNK7776r+Ph4j+3SSy/1OOaxxx7T66+/rnHjxunll1/WH//4R40ePVpSU8/pe++9p7KyMl100UX6/ve/rxkzZmj16tXu8+fOnatVq1ZpzZo1GjNmjL797W9r//795/RzBQcH67XXXtOePXs0fvx4PfXUU/r5z3/utev/27/9m7KzszVx4kT913/9l1asWKFrrrlGUlO7wfr163XZZZfprrvu0gUXXKAf/vCHOnjwoDvgA8DZMkyTBf0A4EwMw9Cbb76pWbNmWV0KAKAFZmYBAADgtwizAAAA8FsszQUAnUBHFgD4JmZmAQAA4LcIswAAAPBbhFkAAAD4LcIsAAAA/BZhFgAAAH6LMAsAAAC/RZgFAACA3yLMAgAAwG8RZgEAAOC3/j+9m8Fo6LxSdgAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from mlp.layers import AffineLayer\n",
-    "from mlp.errors import SumOfSquaredDiffsError\n",
-    "from mlp.models import SingleLayerModel\n",
-    "from mlp.initialisers import UniformInit, ConstantInit\n",
-    "from mlp.learning_rules import GradientDescentLearningRule\n",
-    "from mlp.optimisers import Optimiser\n",
-    "import logging\n",
-    "\n",
-    "# Seed a random number generator\n",
-    "seed = 27092016 \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 CCPP training set\n",
-    "train_data = CCPPDataProvider('train', [0, 1], batch_size=100, rng=rng)\n",
-    "input_dim, output_dim = 2, 1\n",
-    "\n",
-    "# Create a parameter initialiser which will sample random uniform values\n",
-    "# from [-0.1, 0.1]\n",
-    "param_init = UniformInit(-0.1, 0.1, rng=rng)\n",
-    "\n",
-    "# Create our single layer model\n",
-    "layer = AffineLayer(input_dim, output_dim, param_init, param_init)\n",
-    "model = SingleLayerModel(layer)\n",
-    "\n",
-    "# Initialise the error object\n",
-    "error = SumOfSquaredDiffsError()\n",
-    "\n",
-    "# Use a basic gradient descent learning rule with a small learning rate\n",
-    "learning_rule = GradientDescentLearningRule(learning_rate=1e-2)\n",
-    "\n",
-    "# Use the created objects to initialise a new Optimiser instance.\n",
-    "optimiser = Optimiser(model, error, learning_rule, train_data)\n",
-    "\n",
-    "# Run the optimiser for 5 epochs (full passes through the training set)\n",
-    "# printing statistics every epoch.\n",
-    "stats, keys, _ = optimiser.train(num_epochs=10, stats_interval=1)\n",
-    "\n",
-    "# Plot the change in the error over training.\n",
-    "fig = plt.figure(figsize=(8, 4))\n",
-    "ax = fig.add_subplot(111)\n",
-    "ax.plot(np.arange(1, stats.shape[0] + 1), stats[:, keys['error(train)']])\n",
-    "ax.set_xlabel('Epoch number')\n",
-    "ax.set_ylabel('Error')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Using similar code to exercise 2, we can visualise the joint input-output space for the trained model. If you implemented the required methods correctly you should now see a much improved fit between predicted and target outputs when running the cell below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data_provider = CCPPDataProvider(\n",
-    "    which_set='train',\n",
-    "    input_dims=[0, 1],\n",
-    "    batch_size=5000, \n",
-    "    max_num_batches=1, \n",
-    "    shuffle_order=False\n",
-    ")\n",
-    "\n",
-    "inputs, targets = data_provider.next()\n",
-    "\n",
-    "# Calculate predicted model outputs\n",
-    "outputs = model.fprop(inputs)[-1]\n",
-    "\n",
-    "# Plot target and predicted outputs against inputs on same axis\n",
-    "fig = plt.figure(figsize=(8, 8))\n",
-    "ax = fig.add_subplot(111, projection='3d')\n",
-    "ax.plot(inputs[:, 0], inputs[:, 1], targets[:, 0], 'r.', ms=2)\n",
-    "ax.plot(inputs[:, 0], inputs[:, 1], outputs[:, 0], 'b.', ms=2)\n",
-    "ax.set_xlabel('Input dim 1')\n",
-    "ax.set_ylabel('Input dim 2')\n",
-    "ax.set_zlabel('Output')\n",
-    "ax.legend(['Targets', 'Predictions'], frameon=False)\n",
-    "fig.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercise 6: visualising training trajectories in parameter space"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Running the cell below will display an interactive widget which plots the trajectories of gradient-based training of the single-layer affine model on the CCPP dataset in the three dimensional parameter space (two weights plus bias) from random initialisations. Also shown on the right is a plot of the evolution of the error function (evaluated on the current batch) over training. By moving the sliders you can alter the training hyperparameters to investigate the effect they have on how training procedes. The hyperparameters are as follows:\n",
-    "\n",
-    "- `n_epochs` : number of training epochs,\n",
-    "- `batch_size` : number of data points per batch,\n",
-    "- `log_lr` : logarithm of the learning rate,\n",
-    "- `n_inits` : number of different parameter initializations,\n",
-    "- `w_scale` : min/max initial weight value,\n",
-    "- `b_scale` : min/max initial bias value,\n",
-    "- `elev`/`azim` : spherical coordinates for camera position.\n",
-    "\n",
-    "When adjusting these hyperparameters, keep in mind that the magnitude of each (per batch) update is independent of the batch size. Increasing the batch size may there for necessitate a larger number of epochs to ensure convergence, or a larger learning rate.\n",
-    "\n",
-    "**Your Tasks:**\n",
-    "\n",
-    "No need to implement anything. Run the cell and explore the following questions:\n",
-    "\n",
-    "  * Are there multiple local minima in parameter space here? Why?\n",
-    "  * What are the effects of using very small learning rates? And very large learning ones?\n",
-    "  * How does the batch size affect learning?\n",
-    "  \n",
-    "**Note:** You don't need to understand how the code below works. The idea of this exercise is to help you understand the role of the various hyperparameters involved in gradient-descent based training methods."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7165a9a125964557a81875e8e1d40707",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "interactive(children=(IntSlider(value=1, description='n_epochs', max=50, min=1), IntSlider(value=200, descript…"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "from ipywidgets import interact\n",
-    "%matplotlib inline\n",
-    "\n",
-    "def setup_figure():\n",
-    "    # create figure and axes\n",
-    "    fig = plt.figure(figsize=(12, 6))\n",
-    "    ax1 = fig.add_axes([0., 0., 0.5, 1.], projection='3d')\n",
-    "    ax2 = fig.add_axes([0.6, 0.1, 0.4, 0.8])\n",
-    "    # set axes properties\n",
-    "    ax2.spines['right'].set_visible(False)\n",
-    "    ax2.spines['top'].set_visible(False)\n",
-    "    ax2.yaxis.set_ticks_position('left')\n",
-    "    ax2.xaxis.set_ticks_position('bottom')\n",
-    "    #ax2.set_yscale('log')\n",
-    "    ax1.set_xlim((-2, 2))\n",
-    "    ax1.set_ylim((-2, 2))\n",
-    "    ax1.set_zlim((-2, 2))\n",
-    "    #set axes labels and title\n",
-    "    ax1.set_title('Parameter trajectories over training')\n",
-    "    ax1.set_xlabel('Weight 1')\n",
-    "    ax1.set_ylabel('Weight 2')\n",
-    "    ax1.set_zlabel('Bias')\n",
-    "    ax2.set_title('Batch errors over training')\n",
-    "    ax2.set_xlabel('Batch update number')\n",
-    "    ax2.set_ylabel('Batch error')\n",
-    "    return fig, ax1, ax2\n",
-    "\n",
-    "def visualise_training(n_epochs=1, batch_size=200, log_lr=-1., n_inits=1,\n",
-    "                       w_scale=1., b_scale=1., elev=30., azim=0.):\n",
-    "    fig, ax1, ax2 = setup_figure()\n",
-    "    # create seeded random number generator\n",
-    "    rng = np.random.RandomState(1234)\n",
-    "    # create data provider\n",
-    "    data_provider = CCPPDataProvider(\n",
-    "        input_dims=[0, 1],\n",
-    "        batch_size=batch_size, \n",
-    "        shuffle_order=False,\n",
-    "    )\n",
-    "    learning_rate = 10 ** log_lr\n",
-    "    n_batches = data_provider.num_batches\n",
-    "    weights_traj = np.empty((n_inits, n_epochs * n_batches + 1, 1, 2))\n",
-    "    biases_traj = np.empty((n_inits, n_epochs * n_batches + 1, 1))\n",
-    "    errors_traj = np.empty((n_inits, n_epochs * n_batches))\n",
-    "    # randomly initialise parameters\n",
-    "    weights = rng.uniform(-w_scale, w_scale, (n_inits, 1, 2))\n",
-    "    biases = rng.uniform(-b_scale, b_scale, (n_inits, 1))\n",
-    "    # store initial parameters\n",
-    "    weights_traj[:, 0] = weights\n",
-    "    biases_traj[:, 0] = biases\n",
-    "    # iterate across different initialisations\n",
-    "    for i in range(n_inits):\n",
-    "        # iterate across epochs\n",
-    "        for e in range(n_epochs):\n",
-    "            # iterate across batches\n",
-    "            for b, (inputs, targets) in enumerate(data_provider):\n",
-    "                outputs = fprop(inputs, weights[i], biases[i])\n",
-    "                errors_traj[i, e * n_batches + b] = error(outputs, targets)\n",
-    "                grad_wrt_outputs = error_grad(outputs, targets)\n",
-    "                weights_grad, biases_grad = grads_wrt_params(inputs, grad_wrt_outputs)\n",
-    "                weights[i] -= learning_rate * weights_grad\n",
-    "                biases[i] -= learning_rate * biases_grad\n",
-    "                weights_traj[i, e * n_batches + b + 1] = weights[i]\n",
-    "                biases_traj[i, e * n_batches + b + 1] = biases[i]\n",
-    "    # choose a different color for each trajectory\n",
-    "    colors = plt.cm.jet(np.linspace(0, 1, n_inits))\n",
-    "    # plot all trajectories\n",
-    "    for i in range(n_inits):\n",
-    "        lines_1 = ax1.plot(\n",
-    "            weights_traj[i, :, 0, 0], \n",
-    "            weights_traj[i, :, 0, 1], \n",
-    "            biases_traj[i, :, 0], \n",
-    "            '-', c=colors[i], lw=2)\n",
-    "        lines_2 = ax2.plot(\n",
-    "            np.arange(n_batches * n_epochs),\n",
-    "            errors_traj[i],\n",
-    "            c=colors[i]\n",
-    "        )\n",
-    "    ax1.view_init(elev, azim)\n",
-    "    plt.show()\n",
-    "\n",
-    "w = interact(\n",
-    "    visualise_training,\n",
-    "    elev=(-90, 90, 2),\n",
-    "    azim=(-180, 180, 2), \n",
-    "    n_epochs=(1, 50), \n",
-    "    batch_size=(10, 1000, 100),\n",
-    "    log_lr=(-5., 1.),\n",
-    "    w_scale=(0., 4.),\n",
-    "    b_scale=(0., 4.),\n",
-    "    n_inits=(1, 10)\n",
-    ")\n",
-    "\n",
-    "for child in w.widget.children:\n",
-    "    child.layout.width = '100%'"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Hints:\n",
-    "- Remember an affine single layer model is an linear with respect to it's parameters, such that any given output $y$ can be expressed as $y = w_1 x_1 + w_2 x_2 + b$. Subsituting this into the loss function we have: \n",
-    "\\begin{equation}\n",
-    "    E =  \\sum_{n=1}^K \\frac{1}{2}  \\left( y^{(n)} - t^{(n)} \\right)^2 = \\sum_{n=1}^K \\frac{1}{2}  \\left( w_1 x_1^{(n)} + w_2 x_2^{(n)} + b - t^{(n)} \\right)^2.\n",
-    "\\end{equation}\n",
-    "The loss surface is therefore *quadratic* with respect to parameters $w_1, w_2, b$. What effect does this have on the number of minima?\n",
-    "\n",
-    "- Note that by using batch-wise updates, we are computing gradients of loss surface described by a subset $B < N$ of the training data:\n",
-    "\\begin{equation}\n",
-    "    E =  \\sum_{n=1}^B \\frac{1}{2}  \\left( y^{(n)} - t^{(n)} \\right)^2.\n",
-    "\\end{equation}\n",
-    "Hence, this gradient direction is only an approximation of the optimal update direction dictated by the full dataset. With very small batch sizes, what convergence behaviour would we therefore expect?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# PyTorch\n",
-    "\n",
-    "PyTorch is a deep-learning framework that allows us to easily build and train neural networks. It is based on the concept of [tensors](https://pytorch.org/docs/stable/tensors.html), which are multidimensional arrays. In this section, we will use PyTorch to build a simple neural network and train it on the Combined Cycle Power Plant dataset dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.optim as optim\n",
-    "from torch.utils.data import Dataset, DataLoader"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To ensure [reproducibility](https://pytorch.org/docs/stable/notes/randomness.html), we wil set the seed of the random number generator to a fixed value.\n",
-    "\n",
-    "We will also use the same hyperparameters as in the previous section."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "torch.manual_seed(seed)\n",
-    "\n",
-    "learning_rate = 1e-2\n",
-    "num_epochs = 10\n",
-    "batch_size = 100\n",
-    "input_dim = 2\n",
-    "output_dim = 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To work with data in PyTorch, we need to create a [Dataset](https://pytorch.org/docs/stable/data.html#torch.utils.data.Dataset) object. This object will be used by a [DataLoader](https://pytorch.org/docs/stable/data.html#torch.utils.data.DataLoader) object to load the data in batches. The DataLoader object will also shuffle the data at each epoch. \n",
-    "\n",
-    "*Here, we do not shuffle the data by setting `shuffle=False` as we want to compare the results with the previous section. However, it is strongly advised to shuffle the data in the training set, but not in the validation or the test set. Can you think about why?*\n",
-    "\n",
-    "For a dataset to be used with PyTorch, it need to have the following methods:\n",
-    "- `__len__` : returns the size of the dataset,\n",
-    "- `__getitem__` : returns the $i^{\\textrm{th}}$ sample of the dataset.\n",
-    "\n",
-    "Also, the data needs to be converted to PyTorch tensors. This can be done by using the [TensorDataset](https://pytorch.org/docs/stable/data.html#torch.utils.data.TensorDataset) class or the `torch.from_numpy()` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class CCPPDataProvider(Dataset):\n",
-    "    \"\"\"Combined Cycle Power Plant dataset.\"\"\"\n",
-    "    \n",
-    "    def __init__(self, data_path, which_set='train', x_dims=None):\n",
-    "        super().__init__()\n",
-    "        self.data = np.load(data_path)\n",
-    "\n",
-    "        assert which_set in ['train', 'valid'], (\n",
-    "            'Expected which_set to be either train or valid '\n",
-    "            'Got {0}'.format(which_set)\n",
-    "        )\n",
-    "        self.x = self.data[which_set + '_inputs']\n",
-    "        if x_dims is not None:\n",
-    "            self.x = self.x[:, x_dims]\n",
-    "        self.x = torch.from_numpy(self.x).to(torch.float32)\n",
-    "        self.t = self.data[which_set + '_targets']\n",
-    "        self.t = torch.from_numpy(self.t).to(torch.float32)\n",
-    "\n",
-    "    def __len__(self):\n",
-    "        return len(self.x)\n",
-    "    \n",
-    "    def __getitem__(self, idx):\n",
-    "        return self.x[idx], self.t[idx]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The `Linear` layer, also called a fully-connected layer perform the affine operation described above by combining the input data with the weights and biases."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class SingleLayerModel(nn.Module):\n",
-    "    \"\"\"Single layer model.\"\"\"\n",
-    "    def __init__(self, input_dim, output_dim):\n",
-    "        super().__init__()\n",
-    "        self.layer = nn.Linear(input_dim, output_dim) \n",
-    "        \n",
-    "    def forward(self, x):\n",
-    "        return self.layer(x)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The weights and biases for each layer (neural network parameters) are initialized randomly but we can decide what distribution to sample from using the [`torch.init.module`](https://pytorch.org/docs/stable/nn.init.html). Here, we will use a uniform distribution for the weights and set the biases to 0."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def weights_init(m):\n",
-    "    \"\"\"Reinitialize model weights\"\"\"\n",
-    "    classname = m.__class__.__name__\n",
-    "    if classname.find('Linear') != -1:\n",
-    "        nn.init.uniform_(m.weight.data, -0.1, 0.1)\n",
-    "        nn.init.constant_(m.bias.data, 0)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data_path = os.path.join(os.environ['MLP_DATA_DIR'], 'ccpp_data.npz')\n",
-    "assert os.path.isfile(data_path), ('Data file does not exist at expected path: ' + data_path)\n",
-    "\n",
-    "dataset = CCPPDataProvider(data_path, which_set='train', x_dims=[0, 1])\n",
-    "\n",
-    "dataloader = DataLoader(dataset, batch_size=100, shuffle=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The error between predictions and ground truth values is calculated using the means square error loss function. This function is implemented in PyTorch as [`torch.nn.MSELoss`](https://pytorch.org/docs/stable/generated/torch.nn.MSELoss.html).\n",
-    "\n",
-    "There are many [optimisers](https://pytorch.org/docs/stable/optim.html#module-torch.optim) available in PyTorch. Here, we will use the [Adam](https://pytorch.org/docs/stable/optim.html#torch.optim.Adam) optimiser. This optimiser takes as input the parameters to optimise and the learning rate."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Model structure: SingleLayerModel(\n",
-      "  (layer): Linear(in_features=2, out_features=1, bias=True)\n",
-      ")\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "model = SingleLayerModel(input_dim, output_dim)\n",
-    "model.apply(weights_init)\n",
-    "\n",
-    "print(f\"Model structure: {model}\\n\\n\")\n",
-    "\n",
-    "loss = nn.MSELoss()  # Mean Squared Error loss\n",
-    "optimizer = optim.Adam(model.parameters(), lr=learning_rate) # Adam optimiser"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The training loop will be similar to the one in the previous section. For every epoch, we will iterate through the datase by batches. For each batch, we will compute the predictions, the loss and the gradients. We will then update the parameters using the gradients and the optimiser.\n",
-    "\n",
-    "However, we will use the [`torch.optim.zero_grad()`](https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.zero_grad.html) method to set the gradients to zero before computing the gradients of the loss function with respect to the parameters. *Think about why we need to do this and what would happens if we do not?* \n",
-    "\n",
-    "We will also use the [`torch.optim.step()`](https://pytorch.org/docs/stable/generated/torch.optim.Optimizer.step.html) method to update the parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch [1/10]\tError(train): 0.4644\n",
-      "Epoch [2/10]\tError(train): 0.1591\n",
-      "Epoch [3/10]\tError(train): 0.1532\n",
-      "Epoch [4/10]\tError(train): 0.1532\n",
-      "Epoch [5/10]\tError(train): 0.1533\n",
-      "Epoch [6/10]\tError(train): 0.1533\n",
-      "Epoch [7/10]\tError(train): 0.1533\n",
-      "Epoch [8/10]\tError(train): 0.1533\n",
-      "Epoch [9/10]\tError(train): 0.1533\n",
-      "Epoch [10/10]\tError(train): 0.1533\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Keep track of the loss values over training\n",
-    "train_loss = [] \n",
-    "\n",
-    "for epoch in range(num_epochs):\n",
-    "    model.train()\n",
-    "    epoch_loss = 0\n",
-    "\n",
-    "    for x, t in dataloader:\n",
-    "        y = model(x)\n",
-    "        E_value = loss(y, t)\n",
-    "        optimizer.zero_grad()\n",
-    "        E_value.backward()\n",
-    "        optimizer.step()\n",
-    "        epoch_loss += E_value.item()\n",
-    "    # Calculate average loss for this epoch\n",
-    "    avg_epoch_loss = epoch_loss / len(dataloader)\n",
-    "    print(f\"Epoch [{epoch+1}/{num_epochs}]\\tError(train): {avg_epoch_loss:.4f}\")\n",
-    "    train_loss.append(avg_epoch_loss)\n",
-    "\n",
-    "# Plot the change in the error over training.\n",
-    "fig = plt.figure(figsize=(8, 4))\n",
-    "ax = fig.add_subplot(111)\n",
-    "ax.plot(train_loss)\n",
-    "ax.set_xlabel('Epoch number')\n",
-    "ax.set_ylabel('Error')\n",
-    "fig.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "When the training is finished, we can compute the predictions and plot them along with the the ground truth values. \n",
-    "\n",
-    "Here, we will use the [`torch.no_grad()`](https://pytorch.org/docs/stable/generated/torch.no_grad.html) context manager to disable gradient calculation as we do not need it for the predictions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "predictions = []\n",
-    "inputs = []\n",
-    "targets = []\n",
-    "\n",
-    "with torch.no_grad():\n",
-    "    model.eval()\n",
-    "    for x, t in dataloader:\n",
-    "        inputs.append(x.numpy())\n",
-    "        targets.append(t.numpy())\n",
-    "        y = model(x)\n",
-    "        predictions.append(y.numpy())\n",
-    "        \n",
-    "predictions = np.concatenate(predictions, axis=0)\n",
-    "inputs = np.concatenate(inputs, axis=0)\n",
-    "targets = np.concatenate(targets, axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x800 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot target and predicted outputs against inputs on same axis\n",
-    "fig = plt.figure(figsize=(8, 8))\n",
-    "ax = fig.add_subplot(111, projection='3d')\n",
-    "ax.plot(inputs[:, 0], inputs[:, 1], targets[:, 0], 'r.', ms=2)\n",
-    "ax.plot(inputs[:, 0], inputs[:, 1], predictions[:, 0], 'b.', ms=2)\n",
-    "ax.set_xlabel('Input dim 1')\n",
-    "ax.set_ylabel('Input dim 2')\n",
-    "ax.set_zlabel('Output')\n",
-    "ax.legend(['Targets', 'Predictions'], frameon=False)\n",
-    "fig.tight_layout()\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "anaconda-cloud": {},
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.12.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/03_Multiple_layer_models.ipynb b/notebooks/03_Multiple_layer_models.ipynb
deleted file mode 100644
index f1cb478..0000000
--- a/notebooks/03_Multiple_layer_models.ipynb
+++ /dev/null
@@ -1,3279 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$\n",
-    "\\newcommand{\\vct}[1]{\\boldsymbol{#1}}\n",
-    "\\newcommand{\\mtx}[1]{\\mathbf{#1}}\n",
-    "\\newcommand{\\tr}{^\\mathrm{T}}\n",
-    "\\newcommand{\\reals}{\\mathbb{R}}\n",
-    "\\newcommand{\\lpa}{\\left(}\n",
-    "\\newcommand{\\rpa}{\\right)}\n",
-    "\\newcommand{\\lsb}{\\left[}\n",
-    "\\newcommand{\\rsb}{\\right]}\n",
-    "\\newcommand{\\lbr}{\\left\\lbrace}\n",
-    "\\newcommand{\\rbr}{\\right\\rbrace}\n",
-    "\\newcommand{\\fset}[1]{\\lbr #1 \\rbr}\n",
-    "\\newcommand{\\pd}[2]{\\frac{\\partial #1}{\\partial #2}}\n",
-    "$\n",
-    "\n",
-    "\n",
-    "# Multiple layer models and Activation Functions\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 and third 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",
-    "    \\boldsymbol{y} = \\boldsymbol{f}(\\mathbf{W} \\boldsymbol{x} + \\boldsymbol{b})\n",
-    "    \\qquad\n",
-    "    \\Leftrightarrow\n",
-    "    \\qquad\n",
-    "    y_k = f\\left(\\sum_{d=1}^D \\left( W_{kd} x_d \\right) + b_k \\right)\n",
-    "\\end{equation}\n",
-    "\n",
-    "where $\\mathbf{W}$ and $\\boldsymbol{b}$ parameterise an affine transformation as discussed in the previous notebook, and $f$ is a function applied elementwise to the result of the affine transformation (sometimes called the activation function). 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 activation function $f$ to 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 transformation and the subsequent activation function $f$ to be two separate transformation layers. \n",
-    "\n",
-    "This allows us to combine our already implemented `AffineLayer` class with any non-linear activation function applied to the outputs by simply 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 duplicate 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 values at the input to the layer and forward propagates them to produce activations 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 backward 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 hidden 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 backward 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 forwards pass.\n",
-    "\n",
-    "So far, we 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 $D$, $\\left\\lbrace \\boldsymbol{x}^{(b)} \\right\\rbrace_{b=1}^B$, and produces a batch of $B$ vector outputs each of dimension $K$, $\\left\\lbrace \\boldsymbol{y}^{(b)}\\right\\rbrace_{b=1}^B$,  then we can calculate the gradient with respect to the $d^\\textrm{th}$ dimension of the $b^{\\textrm{th}}$ input using the gradients with respect to the $b^{\\textrm{th}}$ output\n",
-    "\n",
-    "\\begin{equation}\n",
-    "    \\frac{\\partial \\bar{E}}{\\partial x^{(b)}_d} = \n",
-    "    \\sum_{k=1}^K \\left( \n",
-    "        \\frac{\\partial \\bar{E}}{\\partial y^{(b)}_k} \\frac{\\partial y^{(b)}_k}{\\partial x^{(b)}_d} \n",
-    "    \\right).\n",
-    "\\end{equation}\n",
-    "\n",
-    "The `bprop` method takes an array of gradients with respect to the outputs $\\frac{\\partial \\bar{E}}{\\partial y^{(b)}_k}$ and applies a sum-product operation with the partial derivatives of each output with respect to each input $\\frac{\\partial y^{(b)}_k}{\\partial x^{(b)}_d}$, producing gradients with respect to the inputs of the layer $\\frac{\\partial \\bar{E}}{\\partial x^{(b)}_d}$.\n",
-    "\n",
-    "For the affine transformation used in the `AffineLayer` implemented in lab 2, i.e. a forward propagation corresponding to \n",
-    "\n",
-    "\\begin{equation}\n",
-    "    y^{(b)}_k = \\sum_{d=1}^D \\left( W_{kd} x^{(b)}_d \\right) + 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",
-    "    \\frac{\\partial y^{(b)}_k}{\\partial x^{(b)}_d} = W_{kd}\n",
-    "\\end{equation}\n",
-    "\n",
-    "and so the backwards-propagation method for the `AffineLayer` takes the following form\n",
-    "\n",
-    "\\begin{equation}\n",
-    "    \\frac{\\partial \\bar{E}}{\\partial x^{(b)}_d} = \n",
-    "    \\sum_{k=1}^K \\left( \\frac{\\partial \\bar{E}}{\\partial y^{(b)}_k} W_{kd} \\right).\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 $\\frac{\\partial y^{(b)}_k}{\\partial x^{(b)}_d}$ will be zero when $k \\neq d$ and the above summation reduces to a single term,\n",
-    "\n",
-    "\\begin{equation}\n",
-    "    \\frac{\\partial \\bar{E}}{\\partial x^{(b)}_d} = \n",
-    "    \\frac{\\partial \\bar{E}}{\\partial y^{(b)}_d} \\frac{\\partial y^{(b)}_d}{\\partial 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",
-    "    \\frac{\\partial y^{(b)}_d}{\\partial x^{(b)}_d} = \n",
-    "    \\frac{\\exp(-x^{(b)}_d)}{\\left[ 1 + \\exp(-x^{(b)}_d) \\right]^2} =\n",
-    "     y^{(b)}_d \\left[ 1 -  y^{(b)}_d  \\right]\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",
-    "The concept of training, validation, and test data sets was introduced in lecture one, and the concept of generalisation is discussed in more detail in lecture five. 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": 2,
-   "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",
-    "\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": 3,
-   "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 followed 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, the evolution of the error function and also classification accuracy of the model over the training epochs will be plotted.\n",
-    "\n",
-    "**Your Tasks:**\n",
-    "- 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",
-    "First try a few different parameter initialisation scales:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### `init_scale = 0.01`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 5: 0.5s 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: 0.6s 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: 0.6s 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: 0.6s 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: 0.5s 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: 0.5s 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: 0.5s 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: 0.5s 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: 0.7s 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: 0.6s 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: 0.6s 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: 0.6s 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: 0.6s 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: 0.6s 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: 0.6s 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: 0.5s 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: 0.5s 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: 0.6s 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: 0.5s 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: 0.6s 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": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "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)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### `init_scale = 0.1`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 5: 0.5s to complete\n",
-      "    error(train)=3.22e-01, acc(train)=9.11e-01, error(valid)=3.00e-01, acc(valid)=9.16e-01\n",
-      "Epoch 10: 0.5s to complete\n",
-      "    error(train)=2.95e-01, acc(train)=9.18e-01, error(valid)=2.81e-01, acc(valid)=9.21e-01\n",
-      "Epoch 15: 0.5s to complete\n",
-      "    error(train)=2.85e-01, acc(train)=9.21e-01, error(valid)=2.74e-01, acc(valid)=9.23e-01\n",
-      "Epoch 20: 0.5s to complete\n",
-      "    error(train)=2.78e-01, acc(train)=9.22e-01, error(valid)=2.70e-01, acc(valid)=9.25e-01\n",
-      "Epoch 25: 0.5s to complete\n",
-      "    error(train)=2.72e-01, acc(train)=9.24e-01, error(valid)=2.68e-01, acc(valid)=9.25e-01\n",
-      "Epoch 30: 0.5s to complete\n",
-      "    error(train)=2.68e-01, acc(train)=9.25e-01, error(valid)=2.64e-01, acc(valid)=9.27e-01\n",
-      "Epoch 35: 0.5s to complete\n",
-      "    error(train)=2.64e-01, acc(train)=9.27e-01, error(valid)=2.62e-01, acc(valid)=9.28e-01\n",
-      "Epoch 40: 0.5s to complete\n",
-      "    error(train)=2.63e-01, acc(train)=9.27e-01, error(valid)=2.63e-01, acc(valid)=9.27e-01\n",
-      "Epoch 45: 0.5s to complete\n",
-      "    error(train)=2.60e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.29e-01\n",
-      "Epoch 50: 0.5s to complete\n",
-      "    error(train)=2.58e-01, acc(train)=9.28e-01, error(valid)=2.61e-01, acc(valid)=9.29e-01\n",
-      "Epoch 55: 0.5s to complete\n",
-      "    error(train)=2.56e-01, acc(train)=9.29e-01, error(valid)=2.60e-01, acc(valid)=9.29e-01\n",
-      "Epoch 60: 0.5s to complete\n",
-      "    error(train)=2.55e-01, acc(train)=9.28e-01, error(valid)=2.60e-01, acc(valid)=9.29e-01\n",
-      "Epoch 65: 0.5s 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 70: 0.5s 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 75: 0.5s to complete\n",
-      "    error(train)=2.51e-01, acc(train)=9.30e-01, error(valid)=2.58e-01, acc(valid)=9.29e-01\n",
-      "Epoch 80: 0.5s to complete\n",
-      "    error(train)=2.50e-01, acc(train)=9.30e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
-      "Epoch 85: 0.5s to complete\n",
-      "    error(train)=2.49e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.30e-01\n",
-      "Epoch 90: 0.6s to complete\n",
-      "    error(train)=2.48e-01, acc(train)=9.31e-01, error(valid)=2.57e-01, acc(valid)=9.29e-01\n",
-      "Epoch 95: 0.5s to complete\n",
-      "    error(train)=2.47e-01, acc(train)=9.31e-01, error(valid)=2.58e-01, acc(valid)=9.29e-01\n",
-      "Epoch 100: 0.5s to complete\n",
-      "    error(train)=2.47e-01, acc(train)=9.32e-01, error(valid)=2.58e-01, acc(valid)=9.29e-01\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Set training run hyperparameters\n",
-    "batch_size = 128  # 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)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### `init_scale = 0.5`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 5: 0.6s 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: 0.5s 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: 0.5s 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: 0.6s 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: 0.5s 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: 0.6s 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: 0.5s 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: 0.5s 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: 0.5s 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: 0.6s 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: 0.5s 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: 0.5s 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: 0.5s 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: 0.5s 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: 0.5s 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: 0.6s 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: 0.5s 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: 0.6s 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: 0.6s 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: 0.6s 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": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "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)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "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:green\">\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:green\">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": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 5: 0.6s 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: 0.6s 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: 0.6s 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: 0.5s 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: 0.5s 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: 0.5s 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: 0.5s 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: 0.6s 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: 0.6s 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: 0.5s 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: 0.6s 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: 0.5s 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: 0.6s 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: 0.5s 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 75: 0.5s 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: 0.5s 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: 0.5s 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: 0.6s 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: 0.6s 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: 0.6s 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": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "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)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### `learning_rate = 0.1`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 5: 0.5s 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: 0.5s 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: 0.6s 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: 0.6s 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: 0.6s 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: 0.5s 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: 0.5s 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: 0.5s 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: 0.6s 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: 0.6s 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: 0.6s 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: 0.6s 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: 0.6s 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: 0.5s 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: 0.6s 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: 0.6s 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: 0.5s 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: 0.6s 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 95: 0.5s 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: 0.5s 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": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "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)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### `learning_rate = 0.2`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 5: 0.5s 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: 0.5s 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: 0.6s 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: 0.5s 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 25: 0.6s 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: 0.6s 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: 0.5s 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: 0.5s 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: 0.5s 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: 0.5s 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: 0.5s 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: 0.6s 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: 0.6s 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: 0.6s 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: 0.6s 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: 0.6s 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: 0.5s 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: 0.6s 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: 0.6s 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: 0.6s 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": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "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)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### `learning_rate = 0.5`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Epoch 5: 0.5s 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: 0.5s 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: 0.6s 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: 0.5s 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: 0.6s 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: 0.6s 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: 0.6s 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: 0.6s 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: 0.6s 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: 0.5s 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: 0.6s 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: 0.6s 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: 0.6s 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: 0.5s 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: 0.5s 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: 0.6s 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: 0.6s 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: 0.5s 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: 0.6s 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: 0.5s 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": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "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)\n",
-    "\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "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:green\">\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",
-    "\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": 11,
-   "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": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "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:green\">\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:green\">\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",
-    "**Your Tasks:**\n",
-    "- Use the code from the first exercise as a starting point to train models of varying depths, and compare their results. It is a good idea to start with training hyperparameters which gave reasonable performance for the shallow architecture trained previously.\n",
-    "\n",
-    "Some questions to investigate:\n",
-    "\n",
-    "  1. How does increasing the number of layers affect the model's performance on the training data set? And on the validation data set?\n",
-    "  2. 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",
-    "  3. 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": 12,
-   "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": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--------------------------------------------------------------------------------\n",
-      "learning_rate=0.20 init_scale=0.10\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    final error(train) = 1.87e-02\n",
-      "    final error(valid) = 7.73e-02\n",
-      "    final acc(train)   = 9.98e-01\n",
-      "    final acc(valid)   = 9.76e-01\n",
-      "    run time per epoch = 1.64\n",
-      "--------------------------------------------------------------------------------\n",
-      "learning_rate=0.20 init_scale=0.20\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    final error(train) = 1.85e-02\n",
-      "    final error(valid) = 7.47e-02\n",
-      "    final acc(train)   = 9.98e-01\n",
-      "    final acc(valid)   = 9.78e-01\n",
-      "    run time per epoch = 1.68\n",
-      "--------------------------------------------------------------------------------\n",
-      "learning_rate=0.20 init_scale=0.50\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    final error(train) = 2.05e-02\n",
-      "    final error(valid) = 9.07e-02\n",
-      "    final acc(train)   = 9.97e-01\n",
-      "    final acc(valid)   = 9.75e-01\n",
-      "    run time per epoch = 1.63\n",
-      "--------------------------------------------------------------------------------\n",
-      "learning_rate=0.20 init_scale=1.00\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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MGzaMqqoqnnvuOYwxXH311T10mCIiIn2bMQaqj0HpoROn4is9QZSKj6H5NKfS7fbjwTPNs6ZoykCs5DRoD6GRUV3uGmgTaCT4dTug5uXlUV9fzwsvvEB1dTWZmZncdddd3usJqqurqays9LZ3u93k5+dTWlpKSEgIY8aMYenSpT6n7x0OB8888wxHjx4lIiKCCy+8kCVLlhAdHd0DhygiItK3GGM8I5+HPsR89CHm0IeeBe3raz95x/hEz12VUtIgOe1ECE0eCAMSdYcl6TMsE2Rj9RUVFZ1OqqmrqyMuTjMJ+5rTTZJSXfsmy7JIT0/X6cIgo7qeHeN2wcdlx0OoJ5ByqLjzEVGbDQYOhtR0bwhtHw0laSBWePg56aNqG7x6u7ahoaE9P0lKREREzp5xOqG8BPNRsSeMHvoQSg5Aa0vHxnY7DB6GNTQHhuRgDcmBjKFYoWEd24oEGQVUERGRc8A42uDIR54Q+lGx5/Hwwc6XbAoL9yxgPyTHs3TTkBxIz+x0lrxIf6C/+f1cUVER8+bN44033uixhfp/9atfsXbtWl599VUA7rjjDurq6vi///u/LveZN28e559/vveuUldeeSVLlizRRDkR6RNMawuUHDjpNH0xlB3qfOZ8ZNTxEdFsz+PQHBg4CMsW0vsdFwlQCqj93LJly/ja1752Tu8idf/993f7upY77riD+++/n//6r/86R70SEek+YwzUVHlm0h/5CEqKPdeMlh8B08ndD2Nivafn20/VkzxQk5VETkMBtR8rLS3l1Vdf5b777junn3M2k5hmzZrFnXfeyYYNG7j00kvPQa9ERLpmjIHa40G09BCUlngfu1zOKT4RhmRjtZ+iH5IDiclaoknkLPTLgGqModXln1mI4SFWt35ZbdiwgV//+tfs3bsXm83GxIkTuf/++xk2bBjgCZkPPPAAGzdupLW1lREjRvDggw8yYcIEAAoKCnjkkUfYu3cvUVFRXHTRRTz55JMA/OMf/+D8889n0KBBgGdG/IUXXsiTTz7JZz/7WW8f/vnPf3L77bezY8cOoqOjefDBB3nllVcoKysjNTWVuXPn8p3vfKfLO3udeoq/qamJH/7wh7zyyivExMR4b216spCQEGbOnMmLL76ogCoi54wxBupqTgqiJx5p6iKI2myQmg6DhmBlegIpmdlYAxJ7te8iwaxfBtRWl+G6Z/f55bOfvW4kEfYzD6hNTU3ceuutnHfeeTQ1NfHLX/6SBQsWUFBQQHNzM/PmzSMtLY0//elPpKSk8N577+F2e04zFRYWsmDBAm6//XZ+85vf0NbWxmuvveZ9761bt5Kbm+t9HhcX5w2FJwfUVatWccUVV3jXpY2OjuaRRx4hLS2NPXv28IMf/ICYmBgWLVp0Rsf0wAMPsGXLFp566ilSUlL42c9+xs6dOzn//PN92l1wwQX84Q9/OOPvlYhIV4wxUF8DRw5hSktOBNGyEmis73wnqz2IZmINGuIJpIOGwMDBWF38h1xEeka/DKh9yVVXXeXz/Fe/+hW5ubns27ePt99+m2PHjvHyyy+TkJAAQFZWlrftb37zG66++mq+//3ve7eNGTPG+3VJSQnjxo3zef9rrrmGb3/72zQ3NxMZGUl9fT3r16/nj3/8o7fNHXfc4f06MzOTDz/8kDVr1pxRQG1sbOSZZ57h0Ucf9Y6MPvroo0yaNKlD2/T0dA4fPozb7e5wW1wRka6Y9hHRshLfEdGGTwiiKWknAmh7IE0brCWdRPykXwbU8BCLZ68b6bfP7o6DBw/yi1/8gnfeeYeqqirv6OiRI0fYvXs3Y8eO9YbTU+3evZsbbrihy/duaWkhIiLCZ9usWbOw2+0UFBRw9dVX889//pPo6GimT5/ubZOfn8+TTz7JwYMHaWxsxOVynfEkq4MHD9LW1uYTSBMSEsjJyenQNiIiArfbTWtrK5GRkWf0/iLSfxi32xNAiz+AkoMnBdG6znewLE8QTT9lRDRtMFbYuVngXkTOTr8MqJZldes0uz/Nnz+fQYMG8fOf/5y0tDTcbjczZ87E4XB0CJenOt3riYmJ1NTU+GwLCwvjqquuYtWqVVx99dWsWrWKL37xi9iPr8W3fft2Fi1axPe+9z1mzJhBbGwsq1ev9hlh/STdmc1fXV1NVFSUwqmIAGBamuHAPsyHezBFe6B4X+cTlizLc2vPQUOwBmWeFEQzFERF+oh+GVD7iqqqKvbv38+yZcuYOnUqANu2bfO+Pnr0aFauXEl1dXWno6ijR4/mjTfe4Lrrruv0/ceOHcv+/fs7bJ87dy7XX389e/fuZcuWLdx5553e19566y0yMjL49re/7d125MiRMz6mrKwsQkND2b59O4MHDwagpqaG4uJiLrroIp+2e/fu7XAJgoj0D8YYqKrEFL0PH36A+XAPlBzsuJRTeARkj8IaOtw3iJ6jW36KSO9QQA1gAwYMICEhgaeffprU1FSOHDnCT3/6U+/rc+bM4bHHHuOWW27hrrvuIjU1lV27djFw4EAmTZrEd7/7Xa677jqGDh3K1VdfjdPpZMOGDd5rRadPn86dd96Jy+UiJOTEAtEXX3wxycnJLFmyhMzMTCZOnOh9LSsriyNHjrB69WrGjx/Pa6+9xiuvvHLGxxQdHc2Xv/xlli5dSkJCAikpKSxbtqzTa0y3bdvGjBkzzuI7JyJ9jXE6PQvdf7jHE0iL9kDNsY4Nk1Kxcs6D4aM9j4OHYYVogXuRYKOAGsBsNhuPP/44P/rRj5g1axbZ2dk88MADzJs3D/Ccjl+5ciX33XcfX/3qV3E6nYwcOZIHH3wQgLy8PJ544gkeffRRfve73xETE+MzStl+vemmTZt8gqBlWcyZM4ff//73fOc73/Hp0xVXXME3vvEN7r77btra2pg1axZ33HEHDz/88Bkf17333ktjYyM333yzd5mp+nrfyQtlZWW8/fbb/P73v+/ut01E+gDTWH98ZPR4GD24D9rafBuFhHiWb8o5D2v4aMgZjZWQ5J8Oi0ivskx3b/ET4CoqKnA4Ot7nuK6u7qwWjA92y5cvp6CggBUrVvi7Kz4eeOAB6uvreeSRRzqtZzvVtW+yLIv09HTKysq6fZcxCVxd1dUYAx+X+o6OlpV0fIOoGMg573ggPR+GjdCp+gChn9ng1du1DQ0NJSUl5bTtNILaz914443U1tbS0NBwTm932l3Jycl885vf9Hc3ROQsmLZWzIH9ntHR46G005n1aYM9p+lzRntGSAcO1i1ARQRQQO337Ha7z4SnQHHbbbf5uwsicoZMVQUU78VdvJePD32Iq+gDcDl9G4WGwbDhWO1hNPs8rFid/RCRzimgiojIGTOONvioCFO8F/PhXije6zOZyXsVaXyCZ2S0/frRIdlYdt19SUTOjAKqiIh0yhgDx45iPvzg+PqjH0DJgY6jozYbZGRh5ZxHwoSp1CanY5JSsay+sd60iAQeBVQREQHAtLbCR/sxH+7FFO+FA3uhtrpjw7gBnlP0OaOwskfBUM9kJsuyiE5Pp66sDDSRRkQ+hX4VUI0x+h99EGm/7auIdJ8xBirKPbcJbQ+khw/AqT9XJy31RNZIz6NGR0XkHOs3ATU8PJzm5maioqL83RXpAW63m/r6eqKjo/3dFZE+wbQ0w8HjM+sP7PNcO1pf27HhgETf0dEhObo9qIj0un4VUBsbG6mtrdX//PuQsLAw2k5dvPu46Oho7PZ+81dY5Ix51x09eXT0yEcdbxNqt8PQ4VhZo7ByRnluGZp4+vUJRUTOtX71r7tG2/oWLQwtcuZM9THMnh2wZwfmgx1QU9WxUWKKZ1Q0ZxRW9nmeU/ehmlkvIoGnXwVUEZFgYZoaYO8uzJ4dnmBafti3gT3Ucyem7JNGRwfoNqEi0jcooIqI9AHG0QZFezAf7PQE0oNFvqfsLRsMzcEaPR5r9HgYPhorNMx/HRYR+RQUUEVEApBxu6DkAOb9HZg970LRHnCccj122mBPID1vPIwahxUdOLcrFhH5NBRQRUQCgDEGjpZh9ryL2bMTPtgJTQ2+jeITsUbnwvFQaiUm+6ezIiLn2FkF1HXr1rFmzRpqamrIyMhg/vz5jB49usv2a9euZd26dRw9epTk5GSuueYapk+f7tPm5ZdfpqCggMrKSuLi4pg6dSrXX389YWE6RSUiwcnUVntO139w/DrSqkrfBpFRMHIs1ugLPME0PVOrkIhIv9DtgLplyxaWL1/OggULGDVqFIWFhTz00EM88sgjJCd3/N98QUEBK1euZOHCheTk5FBUVMQTTzxBdHQ0kyZNAmDTpk2sWLGC2267jZEjR1JWVsbjjz8OwPz58z/dEYqIBAjT3AT7dh8fJd0BpYd8G9jtnvvXjx6PdV6uZ5JTSIh/Oisi4kfdDqj5+fnMnDmTWbNmAZ4AuWPHDgoKCrj++us7tN+4cSOXXXYZeXl5AAwcOJD9+/ezevVqb0Ddt28fo0aN4pJLLgEgNTWVadOmUVRUdNYHJiLib8blgg8/OBFID+zzvVOTZXmWehqdizX6Ahh+Pla4FsUXEelWQHU6nRQXFzNnzhyf7bm5uezdu7fTfRwOB6GnrLMXFhZGUVERTqcTu93Oeeedx6ZNmygqKmL48OF8/PHH/Oc//+lwGcCp7+twOLzPLcsiMjLS+7X0fe11VD2DTzDX1tRWY3a9g3nvbczu/0Bzo2+DlHSs88d7TtufNw4rJs4/HT0Hgrmu/Z1qG7wCtbbdCqh1dXW43W7i4+N9tsfHx1NTU9PpPuPHj2f9+vVMmTKFrKwsiouL2bBhAy6Xi/r6ehISEpg2bRp1dXXce++9ALhcLj73uc91CMInW7VqFc8//7z3eVZWFsuWLSMlRXdBCTZpaWn+7oKcI8FQW+Ny0bZ/Dy1vb6b57c0497/v87otLp6IC6YSfsEUIi6Ygn3gID/1tPcEQ12lc6pt8Aq02p7VJKnOUnZXyXvevHnU1NRw9913Y4whPj6e6dOns2bNGmw2GwC7d+/mxRdfZMGCBYwYMYLy8nL+9Kc/MWDAAObNm9fp+86dO5fZs2d3+PyKigqcTufZHJYEGMuySEtLo7y8XHeSCjJ9vbamoR6z+/go6a53oKHOt8HQ4VjjJmIbNwmyRtBmC6ENqHcDZWX+6HKv6Ot1la6ptsGrt2trt9vPaDCxWwE1Li4Om83WYbS0tra2w6hqu7CwMBYtWsStt95KbW0tCQkJFBYWEhkZSWxsLADPPvssl156qfe61iFDhtDS0sIf//hHrrnmGm+QPVloaGiHSwfa6YcnuBhjVNMg1Vdqa4yBkmLMe9sx770Nxft8F8mPjMI6/0IYNwlr7ASs+ISO+/cjfaWu0n2qbfAKtNp2K6Da7Xays7PZuXMnU6ZM8W7fuXMnkydPPu2+SUme2+xt3ryZCRMmeINna2trhxFYm80WUN8oEelfTHMT7Hn3eCjdDrWn3Nt+8FCscZOwxk2E7POw7FpWWkSkp3T7N+rs2bN57LHHyM7OZuTIkRQWFlJZWcnll18OwIoVK6iqqmLJkiUAlJaWUlRUxIgRI2hsbCQ/P5+SkhIWL17sfc+JEyfy8ssvk5WV5T3F/+yzzzJp0qROR09FRHqaMQbKSk6Mkha9Dy7XiQZh4Z4F8sdNwho7EStJ17uLiJwr3Q6oeXl51NfX88ILL1BdXU1mZiZ33XWX93qC6upqKitPLDbtdrvJz8+ntLSUkJAQxowZw9KlS0lNTfW2+e///m8sy+KZZ56hqqqKuLg4Jk6cyFe+8pUeOEQRkc6Z1hb44D3Mrrc9o6THjvo2GDgYa9xEzyjpiLFYXVxWJCIiPcsyQXYevaKiwmf5Kem7LMsiPT2dsrIyXe4RZPxZW3O09MQo6d5d4Dzp94U9FM4bhzV2Eta4CVipwT/jvifpZzZ4qbbBq7drGxoa2vOTpERE+iJztAyzuRDz9mY4Wur7YlLqiWtJR+VqoXwRkQCggCoiQcm0tWLeeRPzxquw970TL4SEeO7YNG4SVu4kSMsIuAWqRUT6OwVUEQkq5tCHmDdexWz9FzQdv4uTZcGYC7HyZnkmOEVG+beTIiLyiRRQRaTPM00NmK0bMW8UwKHiEy8kpWJNu8wTTDXrXkSkz1BAFZE+yRgD+3ZhNhVg3nkTHG2eF+x2rAsvxrrkMjhvPJaWqhMR6XMUUEWkTzE1xzBb1nuuLa0oP/HC4KFYl1yOddEMrJg4/3VQREQ+NQVUEQl4xumEXW/jfqMQ3nsb3MdvMxoRiTXlUqxLLodhIzTZSUQkSCigikjAMuVHPMtDbXkN6mpOvDD8fM9o6aRpWOERfuufiIicGwqoIhJQTGsrZvtmzOZXYd/uEy/ExmPlzcSadjlWeob/OigiIuecAqqI+J0xBj4q8iwPtW0jNDd5XrBsMHYCtksuh9zJWHb9yhIR6Q/0215E/MY01OP+9+ue5aEOHzzxQvJAzyn8i2diJSb7rX8iIuIfCqgi0quM243Zt4tjf/0trs3rwenwvGAPxZpwsWfC06hxWh5KRKQfU0AVkXPOGAMHizDbNmLe3gQ1VTS1v5iRdXx5qOlY0bH+7KaIiAQIBVQROWdM6SFPKH1rExwtO/FCZDTRn/08LRMuwQzJ1vJQIiLiQwFVRHqUqfwY89Ymz2Snk68rDQvDGj8Va8pnsMZOInHIEMrKysAYv/VVREQCkwKqiHxqpq4a89ZmzFsb4cMPTrwQEgJjJngW0x8/BSsiEkAjpiIi8okUUEXkrJimBsw7b3pGSj94D8zxuztZFowc6wmlE/N0XamIiHSbAqqInDHT2orZuc0TSndtB6fzxItZIz2n7yddgjUgyX+dFBGRPk8BVUQ+kXE6YPe7nslOO7ZCa8uJFwcN8YyUTv4MVmq6/zopIiJBRQFVRDowbhfs2+0Jpe+8CY31J15MSvWE0imXYmUM81sfRUQkeCmgighw8lql/8K8/QbUVJ14MW6AZ5R08mcge5QmOYmIyDmlgCrSz3nXKt22ESrKT7wQFY01IQ9ryqUwaiyWLcR/nRQRkX5FAVWkHzJOB2bbJsxra+BQ8YkXwsI9y0FNudSzPFRoqP86KSIi/ZYCqkg/YhobMBvXYtbnnziFH2KHsRM8p/BPWqtURETEXxRQRfoBc7QMU7gGs7kQ2lo9G+MTsWZehXXpFVgxcf7toIiIyEkUUEWCmCnag/vVl+A/W08spJ8xDOvyOZ41S+06hS8iIoHnrALqunXrWLNmDTU1NWRkZDB//nxGjx7dZfu1a9eybt06jh49SnJyMtdccw3Tp0/3vv6Tn/yE999/v8N+F154IXfdddfZdFGk3zJuF/zn37gLXoLivSdeGDsB2+VzYPR4zcIXEZGA1u2AumXLFpYvX86CBQsYNWoUhYWFPPTQQzzyyCMkJyd3aF9QUMDKlStZuHAhOTk5FBUV8cQTTxAdHc2kSZMA+P73v4/zpDvS1NfXc+edd3LxxRd/ikMT6V9MSzNm82uYwtVQ+bFno92ONXWGZ8R08BD/dlBEROQMdTug5ufnM3PmTGbNmgXA/Pnz2bFjBwUFBVx//fUd2m/cuJHLLruMvLw8AAYOHMj+/ftZvXq1N6DGxMT47LN582bCw8O56KKLun1AIv2NqTmGWZ+P+ddaaGr0bIyOxZrxBazPXoUVn+DX/omIiHRXtwKq0+mkuLiYOXPm+GzPzc1l7969ne7jcDgIPWWpmrCwMIqKinA6ndjtHbuwfv168vLyiIiI6LIvDocDh8PhfW5ZFpGRkd6vpe9rr6Pq2TlTcgB3wUue9Utdx89ADByE7bKrsfJmYoV3/fPjb6ptcFJdg5dqG7wCtbbdCqh1dXW43W7i4+N9tsfHx1NTU9PpPuPHj2f9+vVMmTKFrKwsiouL2bBhAy6Xi/r6ehISfEd3ioqKKCkp4bbbbvvEvqxatYrnn3/e+zwrK4tly5aRkpLSnUOSPiAtLc3fXQgYxhhatr9J/aqnaX13m3d7+JgLiZ17AxFTL8Wy2fzYw+5RbYOT6hq8VNvgFWi1PatJUp2l7K6S97x586ipqeHuu+/GGEN8fDzTp09nzZo12Dr5h3T9+vVkZmYyfPjwT+zD3LlzmT17dofPr6io8LmeVfouy7JIS0ujvLzccxvOfsw4HJitr3smPpUe8my02bAmTsP2uTm4skZSA/Dxx/7rZDeotsFJdQ1eqm3w6u3a2u32MxpM7FZAjYuLw2azdRgtra2t7TCq2i4sLIxFixZx6623UltbS0JCAoWFhURGRhIbG+vTtrW1lc2bN3Pdddedti+hoaEdLh1opx+e4GKM6bc1NfV1mH+9gtnwMtTVeDZGRGJd8jmsy/4LKynV066Pfn/6c22DmeoavFTb4BVote1WQLXb7WRnZ7Nz506mTJni3b5z504mT5582n2TkpIAzySoCRMmdBhBffPNN3E6nXzmM5/pTrdEgo4pP4IpXI15cz20tXk2JiRjzfovrM98Disq2r8dFBEROYe6fYp/9uzZPPbYY2RnZzNy5EgKCwuprKzk8ssvB2DFihVUVVWxZMkSAEpLSykqKmLEiBE0NjaSn59PSUkJixcv7vDe69evZ/LkyR1GVkX6A2MM7N/tOY2/8y1o/5/skBysz83BmjgNq5NJhSIiIsGm2//a5eXlUV9fzwsvvEB1dTWZmZncdddd3usJqqurqays9LZ3u93k5+dTWlpKSEgIY8aMYenSpaSmpvq8b2lpKR988AH33HPPpzwkkb7H7NqOe/UKOLj/xMbcydg+NwdGjg242ZUiIiLnkmUC6YKDHlBRUeGz/JT0XZZlkZ6eTllZWUBdF9OTTFMD5tmnMFte82wIDcO6+LNYl12NlZ7h386dQ/2htv2R6hq8VNvg1du1DQ0N7flJUiLSc8yu7bj//FuoOQaWhTXri1hXzsOK7XzCoYiISH+hgCrSy0xTI+a5/8O88apnQ+ogbDffjjX8fP92TEREJEAooIr0IrP7P7j//BhUV54YNZ1zI1Z4uL+7JiIiEjAUUEV6gWlu8oyabirwbEhJwzb/21gjx/i3YyIiIgFIAVXkHDPvv+sZNa2qAPCsZTr3q1jhEX7umYiISGBSQBU5R0xLE+a55ZiNaz0bUtKwfe12rFFj/dsxERGRAKeAKnIOmD07PKOmx44CYH32SqxrvoYVEennnomIiAQ+BVSRHmRamjEv/Bnz+j89G5JSsc2/Heu8XP92TEREpA9RQBXpIWbvLtzLfw2VHwNgTf881rz5WBFRfu6ZiIhI36KAKvIpmdYWzIt/wazP92xITMH2tW9hnX+BX/slIiLSVymginwKZt9uz6hpRTkA1qVXYM27GStSo6YiIiJnSwFV5CyY1lbMS3/FvPYPMAYSk7Hd9C2sMRf6u2siIiJ9ngKqSDeZovdx/+k3cLQUAOuSy7G+9HWsqGg/90xERCQ4KKCKnCHT1op56WlM4RrPqOmAJGw3LcEaN9HfXRMREQkqCqgiZ8B8+AHuP/0aPj4CgJU3C+u6W7CiYvzcMxERkTPX7HBT1eykqtlBVZOT6hYXE1vDGRLu7575UkAV+QTG0YZZ/TdMwWowbohPxHbTYqzcyf7umoiIiFer0xM8q5udxwOok6qmk74+/rzZ6e6wb4MJ5atj4vzQ664poIp0wRTv9Yyalh8GwLr4s1jXfQMrWqOmItJ/OFxuKpqctIQ10tzsJCrUIizE5u9u9TiHy01jm5sGh4s2p8EeYhFqs7DbLEJDjj8efx5is3qxX+ak0OnoPHg2O2ls6xg8uxIVaiMx0u75E2VnTFpghVNQQBXpwDgcmH+swKxddXzUNAHbVxdjjZ/i766JiJwTLU435fVtlDU4KKtvo7zeQVlDG+X1bVQ0OjEAFHvbh9ososNsxISFEB1mIzo05KTnIUSH2ogOCyEmzPPY3iYmzEZUWAj2cxDwXG5Do8NNY5vLEzTbXDQ6PF83trloOP7Y3ubU520uc8afZbPoNLh2+fyT2p0UhJvaT7+fFEDrWl1n3K+wEIukKPuJ8Hk8gCZGhnqfJ0TaiQw98R8My7JIT0+nrKysW9/vc00BVeQkpmgP7r/8FspKALCmTsf6yq1Y0bF+7pmIBILqZidFx1ooqmqmpLaNyFAbAyLsDIgIIf74Y/vzmPAQbFbvjbSdTkObyxM869sob2ij7PjXZQ0Oqpudn7hvhN0izB5CfYsnrDrchpoWFzUtZx6eTn0/T2A9Hl7bQ264J9y2b4+022hxuml0HA+cJ4XKhlaXTyDt7NR1d1lAVJiNMJuF03hGL51uz5+TuQ20uUy3Qu2nYbdZpwTOUwOo509UqA0rgP7OfRoKqCKAqa/FvPBnzOZCz4bYeM+o6YUX+bdjIuI3tS1OPqxqOR5IPY/HThPkThZiQVwn4TX+pBDb/jw+wv6pRxWNMdS1urzB89QQWn+akbiYMBvpsWGkx4SRFht6/GvP44BIO4MGDeJIaSlN7UHRcdJIZZurk9HLU56fFCJbnIYWp7Nb388zFWH3BN6Y0Pbwe9LjSeG3s+eRdlunp++NMTjd4HC7PY8uN063weE2OF3HH93GG2g7PO9q+8n7uwyRobZOAmgosWHBEzzPlAKq9GvG7cJsehXz4l+gqQEAa9plWP89Hys28K7JEZFzo6HV5Qmhx4Poh1XNHG3sGJ4sICM+jOGJEQwdEI7DZahpcVLT4qL2+GNNi5OGNjcu4xlx9YxOtp62D7HhIZ2OxJ4abkNsFuX1Dt8AWt9GeYODJscnjyIOiAjxBM/YUNJiwny+jg0P6XK/9nBks6zjgS8ECD3tMZ3K5TY0nTwa6nD5nm4/Jfg2O9xEhp64hKA9bMacdBnByc/P1eUDlmURGgKhIe3fo66/V9IzFFCl3zIfFeF++vdwcL9nQ0YWthu+iTV8tH87JtIHOVyGI/WtHGw5hquxucdGBc+FJoerw8hoeYOj07aDYsMYnhTB8MQIhidFkJ0Q4XP9XlccLkNd6/HA2uykpsVJ7fHwemqYrWt14TZQ3+qivtVFSW3bWR+bBSRF2U8JoZ5R0IExoUSF+jdYhdgsYsNDPjEMi4ACqvRDprHBs+D+v17xLLgfEYk150asGVdiheiXpsjpNDlcHKxu5UB1K8XVLRRXtXCotq3DdXoAsWE27whgfISdAZF2BoSHMCCy46nucHvPzwxvcbopPmlktKiqhSN1nQfAtJhQco4H0eGJEeQkRhwfKey+0BCLpKhQkqJOP8rochsa2lzewFrT7KS2tT3YujqEW7cxpEaHdhlCg3GGvfQ/CqjSbxhjMG+uxzy/HOprAbCmTMf60s1YAxL92zmRAFXT7DweQj1h9EB1C2X1DjqbGhIdaiMtPpKqhhZq20cF29zUt7VxuO70nxVht3W8TjPy1Os2PYG2s8kgrU43B2tavZOYio61cLiujU5yMylR9uNBNJLhSZ4w6q9RvRCbRXyEnfgIO0P55NXSjTG4Db26zJGIPyigSr9gDh/E/bc/QNH7ng3pmdiuX4h1Xq5/OyYSINzG8HGDwxtGDxwfGa3uYpZ2UqSdrIRwshM9p72zE8MZGBPGoEGDKCsrw+X2zLKu6eK0tvfr46OEDrfxLHXU4O7ydPvJQm2WN7TGR4RQ1ezko5rWTsNoYqT9xGn6xAhykiIYENE3//mzLIsQZVPpB/rmT6jIGTItTZg1KzGv/QPcbggLx5r9ZazLv4hl7/4F/iInM8bQ2ObucKcW72Lax5/bbBaxYSHEHb/2rv3x5K9PfjzXp2gdLkNJ7fHT89WtHKhq4UB1a6fL9FjAoLgwshPCyU6IICsxgqyE8E4D3skjmjbLIi7CTlyEnSFnMCrY7HRT03zq6eyOwbam2UWz043DbahsclLZ5DuRKT4ihBHe0/SR5CRFkBipf+pE+hr91EpQMsZg3t6M+fuTUFPl2TjhYmzXLsBKSvFv56RPaHK4urxV4Mm3EjzTdRA/5vSjgu3CQ6xOg6vn0e4bcsNCiIsIITzE6nQZmvbrRU8+TV9S20pnS0aG2iyGDAj3hNHjI6NDB4Sf0aSgT8OyLKJCQ4gKDWFQXNhp27c63d4Q2/4YEx7CiKQIkiLt/W45HpFgdFYBdd26daxZs4aamhoyMjKYP38+o0d3PfN57dq1rFu3jqNHj5KcnMw111zD9OnTfdo0NjaycuVKtm3bRmNjI6mpqXz1q19lwoQJZ9NF6cdM+WHcK56APTs8G1LSsH1lIda4if7tmASE9vtVd3W7wPZtLd1Y9Ds2zEZiZCgJXSygDVDX4qK+zUVdq5P6VvfxRxd1x/+0z+B2GWh1GSqanFQ0nfkakaE231AbbrdxuK6VsvrOg3F0qM07GpqdEEF2QjgZ8eEBOev+VOF2G6kxNlJjdBZEJFh1O6Bu2bKF5cuXs2DBAkaNGkVhYSEPPfQQjzzyCMnJyR3aFxQUsHLlShYuXEhOTg5FRUU88cQTREdHM2nSJACcTidLly4lLi6O7373uyQlJXHs2DEiIiI+/RFKv2FaWzH/fA6z7kVwOcEeivWFeVhf+G+s0NOPykhwqW3xXJP4UU0rB48/lta3nf39qru4g0tCpL3HTskb41kjsj2wdvrY1nF7+4Lfx5o7X/g8KdJOdmI4WQknrhdNjQ7VSKOIBKxuB9T8/HxmzpzJrFmzAJg/fz47duygoKCA66+/vkP7jRs3ctlll5GXlwfAwIED2b9/P6tXr/YG1PXr19PQ0MADDzyA3e7pUkqKTsPKmTPvbsX9zP+DY0c9G8ZOxPaVW7FS0/3bMTnn2lxuDte2eUPoweoWPqpp7XJyD5zd/ap7g3XSIujpZ3h33fbrN08Nss0ON+mxYWQlhBPfRycEiUj/1a3fWk6nk+LiYubMmeOzPTc3l71793a6j8PhIDTU9zRMWFgYRUVFOJ1O7HY727dvZ8SIETz11FO8/fbbxMXFMW3aNObMmYPN1vk/EA6HA4fjxKkry7KIjIz0fi19X3sdP6mepqIc98o/Yna+5dmQmIzty7diXXiR/h4EsDOp7amMMRxtdHCwun1U1BNEj3SxjBB41rUclhDO0AERDBsQTmZ8OElRwXW/ak+otREdZiftDEPtuezLyY8SPFTb4BWote1WQK2rq8PtdhMfH++zPT4+npqamk73GT9+POvXr2fKlClkZWVRXFzMhg0bcLlc1NfXk5CQwMcff0xFRQWXXHIJd911F2VlZTz11FO43W7mzZvX6fuuWrWK559/3vs8KyuLZcuWaeQ1CKWlpXXYZhxt1L3wF+qf/ROmrRVCQoideyNxX1mALSLSD72Us9FZbQHqWxwUVTZSVNFw/E8jH1Y20NjW+ahofISdnJQYRqTEkJMczYiUGLKTo4kK08ihP3RVV+n7VNvgFWi1Pavf3p2l7K6S97x586ipqeHuu+/GGEN8fDzTp09nzZo13tFRYwxxcXEsXLgQm81GdnY21dXVrFmzpsuAOnfuXGbPnt3h8ysqKnA6z3xigQQuy7JIS0ujvLwcY04Mkbl3v+OZBPVxqafdebnYrv8mzYMyaa6uAWr80l85c+21PVxaxuHaFg5Wt/qcoj916aB2dhtkxIWfGBVNCGfYgHASO8zcbqL2WBO1vXM4clxXP7PS96m2wau3a2u3289oMLFbATUuLg6bzdZhtLS2trbDqGq7sLAwFi1axK233kptbS0JCQkUFhYSGRlJbKznfNSAAQOw2+0+p/MHDx5MTU2N9zKAU4WGhna4dKCdfniCizHG86eqEvP3pzDbN3teiE/A+tLXsaZcCpalugcol9tQ2eTgaKODjxs8j+X1DkobD3HgWEOnyx2B504/QweEM+z4UkfDBoQzKC6sy1nmqn/gaP+ZleCj2gavQKtttwKq3W4nOzubnTt3MmXKFO/2nTt3Mnny5NPum5SUBMDmzZuZMGGCN5COGjWKzZs343a7vdvKyspISEjoNJxK/2KcTtyFazD/WAmtLWDZsGZehfXF67Giov3dvX7P5TZUtzg94bPBwceNvo+VTY4urxEFiLTbjgfRcG8QHTIgnJizvAe6iIj0fd1Of7Nnz+axxx4jOzubkSNHUlhYSGVlJZdffjkAK1asoKqqiiVLlgBQWlpKUVERI0aMoLGxkfz8fEpKSli8eLH3PT/3uc+xdu1ali9fzuc//3nKy8tZtWoVX/jCF3roMKWvatn1Dq5fL4XSQ54NOedhu+E2rMws/3asHzHGUNPi8o5+ftzQ5jMaWtHo6HIUtJ3dZpEaHUpqTCgDo0MZGBPK+Kx04kwjKVFaWF1ERHx1O6Dm5eVRX1/PCy+8QHV1NZmZmdx1113e6wmqq6uprKz0tne73eTn51NaWkpISAhjxoxh6dKlpKametskJydzzz338Oc//5k777yTxMREvvCFL3RYLUD6D+N04P7bH6h441XPhpg4rP/+GlbeLKwuVnaQs2OMob7VdWLks8H3dPzRRsdp75YUYkFKdKhPCD35MSHSju2kEGpZFunpyZSVOQLqlJKIiAQGywTZvw4VFRU+y09J32PcbsyfHsX8+3WwLKxLr8Ca+1WsaD+voRNEqpudbPqojjc+quOjmrbT3jXJZnkWe0+N8Yx+pkaHMjAm7PijZ+3QkG7cgcgTUNMpKytTQA0iqmvwUm2DV2/XNjQ0tOcnSYn0BvPiXzzh1GYj+d6HqcnM0S/EHtDkcPHvkgb+daCWnR83dbguNDHSflL49H1Mjg7tE7fAFBGR4KCAKgHFXbjac6tSwPa124mccgk1ZWV+7lXf5XAZ3ilr4F8H6njrSIPPqfpRyRFMHxbP+PQoUqNDe+x2nSIiIp+WAqoEDPe2jZhnnwLAuuYmbNNm+blHfZPbGPZUNPOvA3VsOVRH/Un3nh8cF8b0YXFcOiyO9NgwP/ZSRESkawqoEhDMnh2Y/3sUAGvmbKzP/7d/O9QHfVTTyr8O1LLxYB0VJy10nxBp5zNDY5mRFU92QrhmzIuISMBTQBW/M4eKcT/+ELicMDEP67pbFKLOUEWjg00H6/jXwToO1rR6t0eF2rg4M5bpWXGMTY3q1gQmERERf1NAFb8ylR/j/s190NIMI8diu+W7WDYt0P5JGlpdbCmp518Hatl9tJn2q0rtNpg4KIbpWXFMGhRDuF3XlIqISN+kgCp+Y+rrcD/6E6ithsFDsS3+X6xQXRfZmVanm7ePNPCvg3VsL/W9PejY1EimZ8WTlxlLTLjCvYiI9H0KqOIXprUF92P3w8dHIDEF27d/ghUV4+9uBRSX2/Dex03862Ad/y6pp8lxIpUOGxDO9GFxfGZYHCnRoX7spYiISM9TQJVeZ1wu3E/8HA7sg+hYbHf8BCshyd/dCgjGGD6sauVfB2vZ9FE91c0nJjulRNm5dFgc07PiGTog3I+9FBERObcUUKVXGWMwf/0dvPc2hIZhW3IPVnqmv7vld6V1bWz6yDPZ6Uhdm3d7bJiNvCFxTM+KY3RKpM/tQkVERIKVAqr0KrP6b5jNhWDZsN16J9bw0f7ukl8YYyiubuXfJfX8u6SeQ7UnQmlYiMXkwZ7JThPSYwgNUSgVEZH+RQFVeo17wz8xL/8dAOvG27AumOrnHvUul9vwQUUzb5bUs/VwPUcbT5y+D7EgNy2aS4fFcVFmDFGhmuwkIiL9lwKq9ArzzhbMyicAsL54PbZLr/Bzj3qHw+VmR3kTb5bU89bhBmpbXd7XwkMsJgyK4aLMGCYNjiEmTKFUREQEFFClF5h9u3H/v1+BMViXXoE1+zp/d+mcanK42H6kkTdL6tle2kjLSWtCxYTZmJIRw0UZsVyQHq21SkVERDqhgCrnlDnyEe7fLQWnAy6YinX9N4PyLlE1LU62HW7g3yX17Chvwuk23teSIu1MzYzhosxYxqRGYdddnURERD6RAqqcM6aqwrMQf1Mj5JyH7RvfxwoJntPYRxsc/PuwZ5LTnopmTsqkDIoN46LMGC7OjGV4UoRm34uIiHSDAqqcE6ax3hNOa45Beia2b92LFda31+40xlBS28abx2feF1e3+ryekxjORZmxXJQZS2ZcWFCOFIuIiPQGBVTpcaatFfdvH4SyEhiQ5LlLVHSsv7t1VtzGsP9Yi3c5qNJ6h/c1mwXnp0RyUWYsUzNiSY3RHZ1ERER6ggKq9CjjdnkmRBW9D5HR2L79Y6ykFH93q1ucbsOuj5v4d0k9Ww83UHXS3ZzsNosL0qK4KDOWKRkxxEfoR0hERKSn6V9X6THGGMzfnoB3/w32UGyL78bKGObvbp0Rh8uwo7yRzYfq2Xa4noa2EzPvI+w2Jg2O5qKMWCYOjtYapSIiIueYAqr0GPPys5iNa8GysC34Htaosf7u0idqc7l5t8wTSt863ECj40QojQ8PYXKGZ5LT+LQoQkO0HJSIiEhvUUCVHuHeVIBZvQIA6yu3Yk3M83OPOtfqdPOfska2HKpn2+EGmk9aozQhIoSLh8SSNySW81OiCNFyUCIiIn6hgCqfmtmxDfPXxwGwrvwSts9e5ece+Wp1utle2sCWQ/W8dcR34fykSDsXD4ll2pBYzkuJ1HJQIiIiAUABVT4V8+EHuP/4czBurLxZWHNu9HeXAGhxutl+pIHNh+p5+0gDra4Ti5QmR9nJOz5SOipZoVRERCTQKKDKWTNlh3E/9gC0tcG4SVhfXezXtT+bHW7eOuIZKd1e2kDbSaE0NdpO3pA48obEMkIL54uIiAQ0BVQ5K6bmGO5HfwyN9ZA1EtvCH2DZe/+vU5PDxVuHPSOl/ylr9AmlA2NCmXZ8pHR4YoQWzhcREekjzipRrFu3jjVr1lBTU0NGRgbz589n9OjRXbZfu3Yt69at4+jRoyQnJ3PNNdcwffp07+uvv/46jz/+eIf9nn76acLCws6mi3IOmaZG3L++D6oqIHWQ5y5R4RG99vmNbS62HW5gS0k9/yltxHHSPUbTjofSaUPjyE4IVygVERHpg7odULds2cLy5ctZsGABo0aNorCwkIceeohHHnmE5OTkDu0LCgpYuXIlCxcuJCcnh6KiIp544gmio6OZNGmSt11kZCS//vWvffZVOA08xuHA/fhDcPggxCdgu+MnWLHx5/xzG1pdbDvSwOaP6ni3vJGT5jkxKDbMO1KapVAqIiLS53U7oObn5zNz5kxmzZoFwPz589mxYwcFBQVcf/31Hdpv3LiRyy67jLw8z7JDAwcOZP/+/axevdonoFqWxYABA87yMKQ3GLcb89TDsPc9iIjEdvuPsFLSztnn1be6eOu9Uv65s4Qdp4TSjLgwpg2NJS8zlqEDFEpFRESCSbcCqtPppLi4mDlz5vhsz83NZe/evZ3u43A4CA31vUd5WFgYRUVFOJ1O7MevW2xpaWHRokW43W6GDRvGddddR1ZWVne6J+eQMQbz7JOY7ZshxI5t0f9iDck5J5/V0OZi1ftVrPmgyuea0qHx4Z7Z90NjGRIffk4+W0RERPyvWwG1rq4Ot9tNfLzvKd34+Hhqamo63Wf8+PGsX7+eKVOmkJWVRXFxMRs2bMDlclFfX09CQgKDBg1i0aJFDBkyhObmZv75z39y77338otf/IL09PRO39fhcOBwOLzPLcsiMjLS+7X0LLP2Bcz6fABsX78D2/kX9PhntDrdvLy3mud3V3pvNZqTHM3Fg6PIGxJLhkJp0Gj/GdXPanBRXYOXahu8ArW2ZzVJqrOD6OrA5s2bR01NDXfffTfGGOLj45k+fTpr1qzBZvPcPnLkyJGMHDnSu8+oUaP4n//5H1555RW+/vWvd/q+q1at4vnnn/c+z8rKYtmyZaSkpJzNIcknaFz/MlUv/BmAAQu+Q+ycL/fo+zvdbvJ3lfP/Nh/kaEMrAFlJ0Sz+TDaXDk8OuB8a6TlpaefuEhHxH9U1eKm2wSvQatutgBoXF4fNZuswWlpbW9thVLVdWFgYixYt4tZbb6W2tpaEhAQKCwuJjIwkNja2031sNhs5OTmUl5d32Ze5c+cye/Zs7/P2EFNRUYHT6ezOYcknMPvfx/XoAwBYV8yl4aKZNJSV9cx7G8ObJfX89d0KjtS1AZ5F9G8Yn8KMrHjsIS4sy6K8vBxjzGneTfoSy7JIS0tTbYOM6hq8VNvg1du1tdvtZzSY2K2Aarfbyc7OZufOnUyZMsW7fefOnUyePPm0+yYlJQGwefNmJkyY4B1BPZUxho8++ojMzMwu3y80NLTDta0n7y+fnjl21DNj3+WECXlY13ytx763O8sb+cu7Few/1gJAbHgI145N4vMjBhAW4vl70f5ZxhjVNEiptsFJdQ1eqm3wCrTadvsU/+zZs3nsscfIzs5m5MiRFBYWUllZyeWXXw7AihUrqKqqYsmSJQCUlpZSVFTEiBEjaGxsJD8/n5KSEhYvXux9z+eee44RI0aQnp7uvQb14MGD3HLLLT10mNJdpqUZ92+XQn0tZGZh+/odWF38h6I7io618Nd3j/JueRMAEXaLq0cnMmd0IlGhIZ/6/UVERKTv63ZAzcvLo76+nhdeeIHq6moyMzO56667vMO11dXVVFZWetu73W7y8/MpLS0lJCSEMWPGsHTpUlJTU71tGhsb+eMf/0hNTQ1RUVFkZWVx3333MXz48B44ROku43bjfuoRz1qncQOwLb7nUy/EX1rXxtM7Kth8qB4Auw2uGJHAtWOSGBCpG5qJiIjICZYJpPHcHlBRUeEzu1+6z73qacw//w52O7bvP4SVc95Zv9exJgfPvneMVz+swW3AAqYPi+MrucmkxX7yjRgsyyI9PZ2ysrKAOu0gn55qG5xU1+Cl2gav3q5taGhoz1+DKsHPvfVfnnAKWF9dctbhtKHNxYu7j/GPvdXetUwnDYrmqxekMCyh926LKiIiIn2PAqp4mQP7MX9+DADrimuw5c3s9nu0r2X6wvvHvGuZnpccyU0XpjAmNapH+ysiIiLBSQFVADDVx3D/7kFwtEHuZKxrvtqt/V1uw2vFtazcWUlVs2eZryHxYXz1ghQmD47RWqYiIiJyxhRQBdPW6gmntVUwaAi2Bd/Dsp3ZjHpjDFtK6nn63UpK6z1rmaZG2/lKbgrTh8URYlMwFRERke5RQO3njDGY5b+Bj4ogJhbbknuwIs/sVPyO8kb+8p8Kiqo8a5nGnbSWaWjIp1+SSkRERPonBdR+zvzzOcxbmyAkBNs3f4iVcvpbnRUda+Ev7x5lh3ctUxtzRidwtdYyFRERkR6ggNqPmXfexLz0NADW9QuxRo37xPZH6tr42ylrmX5+RAJfGpvEgAj9VRIREZGeoVTRT5lDxbifehgAa+ZsbJd+vsu21c1OVu6s9FnLdEaWZy3TgTGfvJapiIiISHcpoPZDpq4a9++WQlsrjB6PdW3Xt5T9sKqFBzaUUN3iAmDy4BhuHJ+stUxFRETknFFA7WeMw4H78Z9CVSWkDsK28H+wQjq/bnT7kQZ+/sYRWpyGIfFh3DYljfO1lqmIiIicYwqo/YgxBvP04/DhBxAZje1b92BFx3Tadt3+Gv7wVjluA+PTovifzwwmOkwToEREROTcU0DtR8yrL2G2vAaWDdvCH2ClZXRsYwxP76jk+d3HAJiZHceiKemEhmg9UxEREekdCqj9hHnvbczzywGwrrsFa8yFHdo4XIbH/l3Gvw7WAfDlcUl8eVyy7gIlIiIivUoBtR8wpYdw/79fgjFYn/kc1szZHdo0tLn42cYjvPdxEyEWLJqaxmU5A3q/syIiItLvKaAGOdNQh/u3S6G5CUaO8ax3esqIaEWjg/s2lFBS20aE3cYPLx3MhenRfuqxiIiI9HcKqEHMOJ24/7AMKsohKRXbN+/Csof6tCmuauH+1w9T3ewkMdLOvTMyyE7UElIiIiLiPwqoQcw8+/9g73sQHoltyT1YsXE+r3uWkSqlxelmaHw49342g5To0C7eTURERKR3KKAGKfeGf2JefwUsC9uC72JlDPN5vaCoht9v8ywjlZsWxQ+1jJSIiIgECAXUIGT27MA880cArLk3YV0w9cRrxrBiZyV/3+VZRuqzWXEsnqplpERERCRwKKAGGfNxqee6U7cb66IZWJ+/xvuaw2X47dYyXj/gWUbqunFJfEXLSImIiEiAUUANIqapAfdvH4CmBsgaiXXTEm/4PHkZKZsFi6akcfnwAf7tsIiIiEgnFFCDhHG5cP/xF1B+BBKSsS2+Gys0DPAsI3X/hhIOHV9G6n8+M4gJgzq/xamIiIiIvymgBgnz/HLY/R8IC/OE0/gEwLOM1AOvH6aq2UlCpJ0faRkpERERCXAKqEHA/carmMLVANhuvgNraA4A75Q2sGyTZxmpIfFh/OizmVpGSkRERAKeAmofZ/a/j3n69wBY//UVrEmXAPBqUQ2Pty8jNTCK/7l0MDFaRkpERET6AAXUPsxUfoz79z8FlxNr4jSs2dd1WEZqRlYcS7SMlIiIiPQhCqh9lGlpxv3bpVBfC0NysG6+A6ex+O2/Tywjde3YJK7P1TJSIiIi0recVUBdt24da9asoaamhoyMDObPn8/o0aO7bL927VrWrVvH0aNHSU5O5pprrmH69Omdtt28eTO//vWvmTRpEj/4wQ/OpntBz7jduJ96GI58BHEDsC3+X5osOz97vYSd5Z5lpG6bksbntIyUiIiI9EHdDqhbtmxh+fLlLFiwgFGjRlFYWMhDDz3EI488QnJycof2BQUFrFy5koULF5KTk0NRURFPPPEE0dHRTJo0yadtRUUFf/3rXz8x7AqY1X+Dd7eCPRTbov+lMnwADxQc4qPaVi0jJSIiIn2erbs75OfnM3PmTGbNmuUdPU1OTqagoKDT9hs3buSyyy4jLy+PgQMHMm3aNGbOnMnq1at92rndbn7zm99w7bXXkpqaenZH0w+4t/4L88/nALBuWsLBxGH8YN1HfFTbSkKknZ9ePkThVERERPq0bo2gOp1OiouLmTNnjs/23Nxc9u7d2+k+DoeD0FDfpY3CwsIoKirC6XRit3u68PzzzxMXF8fMmTPZs2fPafvicDhwOBze55ZlERkZ6f06GJlDxZjlvwHA+vx/s3PYFH726iGaHW6GxIfz45nBtYxUex2DtZ79mWobnFTX4KXaBq9ArW23AmpdXR1ut5v4+Hif7fHx8dTU1HS6z/jx41m/fj1TpkwhKyuL4uJiNmzYgMvlor6+noSEBD744APWr1/Pz3/+8zPuy6pVq3j++ee9z7Oysli2bBkpKSndOaQ+5djTj9PkdBAxKY83L72Jhwr34XIbJmYO4BdzxhEbETzh9GRpaWn+7oKcI6ptcFJdg5dqG7wCrbZnNUmqs5TdVfKeN28eNTU13H333RhjiI+PZ/r06axZswabzUZzczOPPfYYCxcuJC4u7oz7MHfuXGbPnt3h8ysqKnA6nd08osBnmptwbXkNA/z5vLk8W+AZsZ4+LI7bLx5IQ3UlDf7tYo+zLIu0tDTKy8sxxvi7O9KDVNvgpLoGL9U2ePV2be12+xkNJnYroMbFxWGz2TqMltbW1nYYVW0XFhbGokWLuPXWW6mtrSUhIYHCwkIiIyOJjY3l0KFDVFRUsGzZMu8+7d+gL3/5yzz66KOdpvrQ0NAOlw6cun8wcW/fjNPh5PcXzmfDYc/xfWlMEjeM9ywjFYzH3M4YE9TH15+ptsFJdQ1eqm3wCrTadiug2u12srOz2blzJ1OmTPFu37lzJ5MnTz7tvklJSYBnKakJEyZgs9kYNGgQv/zlL33aPvPMM7S0tHgnYAmYNzfw/NBZbIg/X8tIiYiISFDr9in+2bNn89hjj5Gdnc3IkSMpLCyksrKSyy+/HIAVK1ZQVVXFkiVLACgtLaWoqIgRI0bQ2NhIfn4+JSUlLF68GPCMsA4ZMsTnM6KjowE6bO+vTOXH1BYXs2bqtQB866J0ZmZ3PmItIiIi0td1O6Dm5eVRX1/PCy+8QHV1NZmZmdx1113e6wmqq6uprKz0tne73eTn51NaWkpISAhjxoxh6dKlWkqqG8y/X2fVkBm02MPJSQzns1lnfq2uiIiISF9jmUC64KAHVFRU+Cw/1dcZYzj6kztZPPxrOGyh/PizGf1mnVPLskhPT6esrCygrouRT0+1DU6qa/BSbYNXb9c2NDT0jCZJdXuhfullxXt5PmYsDlsoo5PCuTA92t89EhERETmnFFADXOmbb/JammcC2lcnDAy4hXRFREREepoCagAzDgfPHovCZQthQpybMalR/u6SiIiIyDmngBrADr21nY1J4wC4/uKhfu6NiIiISO9QQA1gf9vbiLFsXGyrYkSyrj0VERGR/kEBNUDtO1TB1rDBWMbN9ZMH+7s7IiIiIr1GATVA/W3rIQCmNxYxZLhO74uIiEj/oYAagHZ93MS7bdHY3U6+nBXm7+6IiIiI9CoF1ABjjOHpbYcBmFX+NmkX5/m5RyIiIiK9SwE1wGwvbWRPnZswl4MvRVdhxeq2piIiItK/KKAGELcxPP1uBQBXHtlM8kUX+7lHIiIiIr1PATWAvHmongM1rUQ6W5hb+TbkTvJ3l0RERER6nQJqgHC5DX/bWQnAF0s2EjdxEpY91M+9EhEREel9CqgB4vUDtRypayPW0ch/Hd6EdfEsf3dJRERExC8UUAOAw+Xmmfc8o6dzD20gKiUFhg33c69ERERE/EMBNQAUFNVytNFJoquJLxzZgnXxZ7Esy9/dEhEREfELBVQ/a3G6+fsuz+jpvA/XEm5cWBfN8G+nRERERPxIAdXPXt5bTU2Li4FWK7PK3oLzcrESU/zdLRERERG/UUD1o8Y2Fy++fwyAaw9vINS4sC76rJ97JSIiIuJfCqh+9NKeKhra3GREwqX7N0B4BNYELc4vIiIi/ZsCqp/UtjhZ80E1ANc3vkcIBmvCxVgRkX7umYiIiIh/KaD6yQu7j9HidJOTEMbUt18EwLp4pp97JSIiIuJ/Cqh+UNnk4J/7agC4MboSq6kREpNh1Dj/dkxEREQkACig+sHf3zuGw204PyWS3J3rALCmzsCyqRwiIiIiSkS9rKy+jcIPawC4YUQk1q7tgE7vi4iIiLRTQO1lz+ysxGVg4qBozv/wTXC7IWskVnqGv7smIiIiEhAUUHvRRzWt/OtgHQA3jE/BvLkBAOtirX0qIiIi0s5+NjutW7eONWvWUFNTQ0ZGBvPnz2f06NFdtl+7di3r1q3j6NGjJCcnc8011zB9+nTv61u3bmXVqlWUl5fjcrlIS0vjv/7rv7j00kvPpnsB6287KjBA3pBYspvKcR8qhhA71uTP+LtrIiIiIgGj2wF1y5YtLF++nAULFjBq1CgKCwt56KGHeOSRR0hOTu7QvqCggJUrV7Jw4UJycnIoKiriiSeeIDo6mkmTJgEQExPDNddcw6BBg7Db7bzzzjs8/vjjxMXFccEFF3zqgwwE+481s/VwAzYLrs9NxhSs8LyQOwkrJs6/nRMREREJIN0+xZ+fn8/MmTOZNWuWd/Q0OTmZgoKCTttv3LiRyy67jLy8PAYOHMi0adOYOXMmq1ev9rYZM2YMU6ZMISMjg7S0NK688kqGDh3KBx98cPZHFmCefrcCgBlZcWTE2DFbXwfApslRIiIiIj66NYLqdDopLi5mzpw5Pttzc3PZu3dvp/s4HA5CQ0N9toWFhVFUVITT6cRu9+2CMYZdu3ZRWlrKDTfc0GVfHA4HDofD+9yyLCIjI71fB5L3yht5t7wJuw2+nJsCH+yE2mqIicXKnRRw/Q0U7d8XfX+Cj2obnFTX4KXaBq9ArW23AmpdXR1ut5v4+Hif7fHx8dTU1HS6z/jx41m/fj1TpkwhKyuL4uJiNmzYgMvlor6+noSEBACamppYuHAhTqcTm83GLbfcQm5ubpd9WbVqFc8//7z3eVZWFsuWLSMlJaU7h3TOGWO4Z/07AMzJHcyFI4ZybNUfaQJiZnyBhMwh/u1gH5CWlubvLsg5otoGJ9U1eKm2wSvQantWk6Q6S9ldJe958+ZRU1PD3XffjTGG+Ph4pk+fzpo1a7CdtDB9REQEv/jFL2hpaeG9997jL3/5CwMHDmTMmDGdvu/cuXOZPXt2h8+vqKjA6XSezWGdE28drmdnaS1hIRazsyMpLf4Q1xbP7P3m8VNpKSvzcw8Dl2VZpKWlUV5ejjHG392RHqTaBifVNXiptsGrt2trt9vPaDCxWwE1Li4Om83WYbS0tra2w6hqu7CwMBYtWsStt95KbW0tCQkJFBYWEhkZSWxsrLedzWbzpvdhw4Zx5MgRXnrppS4DamhoaIdLB9oFyg+P2xie3uG59nT2qAQSIu2439gAjjZIz8QMHQ4B0tdAZowJmJpKz1Jtg5PqGrxU2+AVaLXt1iQpu91OdnY2O3fu9Nm+c+dORo0addp9k5KSsNlsbN68mQkTJviMoJ7KGONzjWlftOVQPQeqW4kKtTH3/CQAzJvrAc/ap4F2vYeIiIhIIOj2Kf7Zs2fz2GOPkZ2dzciRIyksLKSyspLLL78cgBUrVlBVVcWSJUsAKC0tpaioiBEjRtDY2Eh+fj4lJSUsXrzY+56rVq0iJyeHgQMH4nQ6+c9//sPGjRtZsGBBDx1m73O5DSt2VgJw9ehE4sJDMBXlsG83WBbW1Bn+7aCIiIhIgOp2QM3Ly6O+vp4XXniB6upqMjMzueuuu7zXE1RXV1NZWelt73a7yc/Pp7S0lJCQEMaMGcPSpUtJTU31tmltbeXJJ5/k2LFjhIWFMXjwYL71rW+Rl5fXA4foHxsO1HKkro3Y8BC+eJ5nIlj70lKcl4uV2HHNWBEREREBywTSBQc9oKKiwu+XBjhcbm5bU0xFk5ObJ6QwZ3QSxhjc93wTjpZhff072HR709OyLIv09HTKysoC6roY+fRU2+CkugYv1TZ49XZtQ0NDz2iSVLcX6pfTKyiqpaLJSWKknS+M8Iye8uEHcLQMwiOwJlzs3w6KiIiIBDAF1B7W4nTz912eSxyuHZtEuN3zLTZvepaWsibkYYVH+K1/IiIiIoFOAbWHvby3mpoWF2kxoVyWMwAA42jDvL0J8MzeFxEREZGuKaD2oIY2Fy++fwyAL49LJjTk+DJSO7ZBUyMkJsOocX7soYiIiEjgU0DtQav3VNHQ5iYzPoxLh8V5t7u3HF/79KLPYn3C2q8iIiIiooDaY2panKz5oAqAG8anEGLzjJ6aumrY/Q6g0/siIiIiZ0IBtYe8sPsYLU7D8MQILsqI8W432zaC2w1ZI7HSMvzYQxEREZG+QQG1B1Q2OXhlXw0AN16Q4nMLU9N+ev/imf7omoiIiEifo4DaA/7+3jEcbsOY1EguSIvybjeHD0DJAQixY02+xI89FBEREek7FFA/pbL6Ngo/rAHgxvGnjJ6++brni/GTsWLiOu4sIiIiIh0ooH5KK3dW4jIwcVA056eeNHrqcmG2vg6g25qKiIiIdIMC6qfwUU0rGw/WAZ7RUx973oXaaoiJg7ETe79zIiIiIn2UAuqn8LcdFRhg2pBYshN9b1/qvbXplEux7KF+6J2IiIhI36SAepb2VTaz9XADNguuz032ec00NWL+829Aa5+KiIiIdJcC6ln6244KAGZkxZMRH+7zmtm+GRxtkJ4JQ4f7o3siIiIifZYC6ll47+NG3i1vwm6DL49L6vC6+ffx0/sXz/SZ1S8iIiIip2f3dwf6ouyECK4dm4TTbRgYE+bzmqkoh327wbKwpk73Uw9FRERE+i4F1LMQHRbCDafO2j/O/Pt1zxejx2MlJnfaRkRERES6plP8PcgYg3mz/dammhwlIiIicjYUUHvSh3ugohzCI7EuvNjfvRERERHpkxRQe5B37dOJeVjhEadpLSIiIiKdUUDtIaatFfPWG4BO74uIiIh8GgqoPcTseAuaGyExBUaO9Xd3RERERPosBdQe4p0cddFnsWz6toqIiIicLSWpHmDqqmH3OwBYF8/wb2dERERE+jgF1B5gtm4EtxuyR2GlZfi7OyIiIiJ92lkt1L9u3TrWrFlDTU0NGRkZzJ8/n9GjR3fZfu3ataxbt46jR4+SnJzMNddcw/TpJ+6yVFhYyMaNGykpKQEgOzubr3zlKwwf3jfuY6+1T0VERER6TrcD6pYtW1i+fDkLFixg1KhRFBYW8tBDD/HII4+QnNzxzkkFBQWsXLmShQsXkpOTQ1FREU888QTR0dFMmjQJgPfff59p06YxatQoQkNDWb16NUuXLuXhhx8mMTHx0x/lOWQOH4CSA2C3Y03+jL+7IyIiItLndfsUf35+PjNnzmTWrFne0dPk5GQKCgo6bb9x40Yuu+wy8vLyGDhwINOmTWPmzJmsXr3a2+b222/niiuuYNiwYQwePJhvfvObGGN47733zv7Iekn72qfkTsGKjvVvZ0RERESCQLcCqtPppLi4mPHjx/tsz83NZe/evZ3u43A4CA0N9dkWFhZGUVERTqez031aW1txOp3ExMR0p3u9zrhcmK3/AsCm0/siIiIiPaJbp/jr6upwu93Ex8f7bI+Pj6empqbTfcaPH8/69euZMmUKWVlZFBcXs2HDBlwuF/X19SQkJHTY529/+xuJiYmMGzeuy744HA4cDof3uWVZREZGer/uDWbPu1BbDTFxWOMm9trn9hft3099X4OPahucVNfgpdoGr0Ct7VlNkursILo6sHnz5lFTU8Pdd9+NMYb4+HimT5/OmjVrsHWyXujq1avZvHkzP/nJTwgLC+uyD6tWreL555/3Ps/KymLZsmWkpKScxRGdnWN/eZMmIGbmlSRkDum1z+1v0tLS/N0FOUdU2+CkugYv1TZ4BVptuxVQ4+LisNlsHUZLa2trO4yqtgsLC2PRokXceuut1NbWkpCQQGFhIZGRkcTG+l6zuWbNGlatWsW9997L0KFDP7Evc+fOZfbs2d7n7QG5oqKiy0sHepJpasT15usANOdOpaWs7Jx/Zn9jWRZpaWmUl5djjPF3d6QHqbbBSXUNXqpt8Ort2trt9jMaTOxWQLXb7WRnZ7Nz506mTJni3b5z504mT5582n2TkpIA2Lx5MxMmTPAZQV2zZg0vvPACd999Nzk5OaftS2hoaIdrW9v1xjfY/fYb4GiDQUMwQ7JBP7DnjDFGvxCDlGobnFTX4KXaBq9Aq223T/HPnj2bxx57jOzsbEaOHElhYSGVlZVcfvnlAKxYsYKqqiqWLFkCQGlpKUVFRYwYMYLGxkby8/MpKSlh8eLF3vdcvXo1zz77LLfffjupqaneEdqIiAgiIiJ64DB73slrnwbadRsiIiIifVm3A2peXh719fW88MILVFdXk5mZyV133eUdrq2urqaystLb3u12k5+fT2lpKSEhIYwZM4alS5eSmprqbVNQUIDT6eThhx/2+ax58+Zx7bXXnu2xnTOmohz2vw+WDWvqDH93R0RERCSoWCaQxnN7QEVFhc/s/nPBlJXgfvGv4HYR8q17z+ln9WeWZZGenk5ZWVlAnXaQT0+1DU6qa/BSbYNXb9c2NDS0569BFQ8rPZOQxf+Lcbv93RURERGRoNPtO0nJCVYny2SJiIiIyKejhCUiIiIiAUUBVUREREQCigKqiIiIiAQUBVQRERERCSgKqCIiIiISUBRQRURERCSgKKCKiIiISEBRQBURERGRgKKAKiIiIiIBRQFVRERERAKK3d8d6Gl2e9AdUr+nmgYv1TY4qa7BS7UNXr1V2zP9HMsYY85xX0REREREzphO8UvAam5u5n/+539obm72d1ekh6m2wUl1DV6qbfAK1NoqoErAMsZw4MABNMgffFTb4KS6Bi/VNngFam0VUEVEREQkoCigioiIiEhAUUCVgBUaGsq8efMIDQ31d1ekh6m2wUl1DV6qbfAK1NpqFr+IiIiIBBSNoIqIiIhIQFFAFREREZGAooAqIiIiIgFFAVVEREREAopuqit+tWrVKrZt28aRI0cICwtj5MiR3HjjjQwaNMjbxhjDc889x2uvvUZDQwMjRozglltuITMz0489l+5YtWoVK1eu5Morr2T+/PmA6tqXVVVV8fTTT/Puu+/S1tZGeno6t912G9nZ2YBq21e5XC6ee+45Nm3aRE1NDQkJCcyYMYNrrrkGm80znqXa9g3vv/8+a9as4cCBA1RXV/P973+fKVOmeF8/kzo6HA7++te/snnzZtra2hg7diwLFiwgKSmpV45BI6jiV++//z5XXHEFDz74IPfccw9ut5ulS5fS0tLibbN69Wpefvllvv71r/PTn/6UAQMGsHTp0oC7LZt0rqioiMLCQoYOHeqzXXXtmxoaGrj33nux2+387//+Lw8//DA33XQTUVFR3jaqbd+0evVqXn31VW655RYeeeQRbrzxRtasWcPatWt92qi2ga+1tZVhw4bx9a9/vdPXz6SOy5cvZ9u2bXz729/m/vvvp6WlhZ/97Ge43e5eOQYFVPGru+++mxkzZpCZmcmwYcNYtGgRlZWVFBcXA57/5f3zn/9k7ty5TJ06lSFDhrB48WJaW1t54403/Nx7OZ2WlhYee+wxFi5cSHR0tHe76tp3rV69mqSkJBYtWsTw4cNJTU1l3LhxpKWlAaptX7Zv3z4mTZrEhAkTSE1N5aKLLiI3N5cPP/wQUG37kgsvvJAvf/nLTJ06tcNrZ1LHpqYm1q9fz0033URubi5ZWVl861vf4tChQ+zcubNXjkEBVQJKU1MTADExMQAcPXqUmpoaxo8f720TGhrK+eefz969e/3SRzlzTz75JBdeeCG5ubk+21XXvuvtt98mOzubhx9+mAULFvCDH/yAwsJC7+uqbd913nnnsWvXLkpLSwE4ePAge/fu5cILLwRU22BxJnUsLi7G5XL5/O5OTExkyJAh7Nu3r1f6qWtQJWAYY/jzn//Meeedx5AhQwCoqakBID4+3qdtfHw8lZWVvd1F6YbNmzdz4MABfvrTn3Z4TXXtu44ePcqrr77KVVddxdy5cykqKuJPf/oToaGhTJ8+XbXtw66++mqampr4zne+g81mw+128+Uvf5lLLrkE0M9tsDiTOtbU1GC3272DRSe3ad//XFNAlYDx1FNPcejQIe6///4Or1mW5fNcN0ALbJWVlSxfvpy7776bsLCwLtuprn2P2+0mJyeH66+/HoCsrCxKSkooKChg+vTp3naqbd+zZcsWNm3axO23305mZiYHDx5k+fLl3slS7VTb4HA2dezNWiugSkD4v//7P7Zv3859993nM0NwwIABAN4Zpe3q6uo6/O9PAkdxcTG1tbX88Ic/9G5zu93s2bOHtWvX8uijjwKqa1+UkJBARkaGz7aMjAy2bt0K6Ge2L3v66ae5+uqrmTZtGgBDhgyhoqKCl156iRkzZqi2QeJM6jhgwACcTicNDQ0+o6h1dXWMGjWqV/qpa1DFr4wxPPXUU2zdupUf/ehHpKam+ryemprKgAEDfC7KdjqdvP/++732QyLdN27cOH75y1/y85//3PsnJyeHSy65hJ///OcMHDhQde2jRo0a5b1GsV1paSkpKSmAfmb7stbWVu9yUu1sNpt31Ey1DQ5nUsfs7GxCQkJ82lRXV3Po0CFGjhzZK/3UCKr41VNPPcUbb7zBD37wAyIjI73XtkRFRREWFoZlWVx55ZWsWrWK9PR00tLSWLVqFeHh4d7roiTwREZGeq8jbhceHk5sbKx3u+raN1111VXce++9vPjii+Tl5VFUVMRrr73GrbfeCqCf2T5s4sSJvPjiiyQnJ5ORkcHBgwfJz8/ns5/9LKDa9iUtLS2Ul5d7nx89epSDBw8SExNDcnLyaesYFRXFzJkz+etf/0psbCwxMTH89a9/ZciQIR0mvZ4rltHFI+JH1157bafbFy1a5L3mqX1B4cLCQhobGxk+fDi33HJLhwAkge0nP/kJw4YN67BQv+ra92zfvp0VK1ZQXl5OamoqV111FZdddpn3ddW2b2pububZZ59l27Zt1NbWkpiYyLRp05g3bx52u2c8S7XtG3bv3s19993XYfv06dNZvHjxGdWxra2Np59+mjfeeMNnof7k5OReOQYFVBEREREJKLoGVUREREQCigKqiIiIiAQUBVQRERERCSgKqCIiIiISUBRQRURERCSgKKCKiIiISEBRQBURERGRgKI7SYlIn/L666/z+OOPd/n6j3/8Y8aMGdOLPfJ19OhRlixZwo033sgXv/hFv/WjJwXjMYlIYFNAFZE+adGiRQwaNKjD9oyMDD/0RkREepICqoj0SZmZmeTk5Pi7G9KDnE4nlmUREhLi766IiJ8poIpI0Lr22mu54oorGDJkCPn5+VRUVDBw4EDmzZvHtGnTfNoeOnSIZ555hj179tDW1sagQYO46qqrmDFjhk+7xsZGXnjhBbZt20ZVVRVRUVHk5ORw0003MXjwYJ+2+fn5vPLKK9TV1TFkyBC+9rWvMXLkyE/sc/slDD/60Y948803+fe//40xhvPPP5+vf/3rJCYm+hzfvHnzuPbaa33eY/HixZx//vksXry4w3u+8cYbbNu2DZfLxeTJk1mwYAEtLS383//9Hzt37iQsLIxLLrmE66+/3nv/9XbGGF588UVeffVV6urqyMzM5IYbbmDcuHE+7crKyvj73//Oe++9R1NTEwMHDuSKK67g85//vLdN+73ClyxZwsGDB9m8eTM1NTU8/PDDHb6PItL/KKCKSJ/kdrtxuVw+2yzLwmbznfv59ttvs3v3bq699lrCw8MpKCjg17/+NSEhIVx00UUAlJaWcu+99xIXF8fNN99MTEwMmzZt4vHHH6e2tparr74agObmZn70ox9x9OhRrr76akaMGEFLSwt79uyhurraJ1itW7eOwYMHM3/+fACeffZZfvrTn/K73/2OqKio0x7fE088wYUXXsjtt9/OsWPH+Otf/8pjjz3Gj3/847P+nv3hD39gypQp3HHHHRw4cICVK1ficrkoLS1l6tSpXHbZZbz33nusXr2axMREZs+e7bP/2rVrSUlJYf78+RhjWL16NQ899BD33XefN3gfPnyYe+65h+TkZG666SYGDBjAu+++y5/+9Cfq6+v50pe+5POeK1asYOTIkXzjG9/AZrMRHx9/1scnIsFDAVVE+qS77767wzabzcYzzzzjs62+vp6f/vSnDBgwAIAJEybwve99jxUrVngD6t///necTic//vGPSU5O9rZramri+eef5/LLLycqKoqXX36ZkpIS7rnnHnJzc72fMXXq1A59iYyM5Ic//KE3MCckJPC///u//Oc//+kwetuZ8ePH8/Wvf937vKGhgaeffpqamhrvsXTXhAkTuOmmmwDIzc1l3759bN68mZtuuskbRnNzc9mxYwebNm3qEFDdbjf33HMPYWFh3j4uXryYZ599lnvvvReAP//5z0RGRnL//fd7g3hubi5Op5OXXnqJL3zhC8TExHjfc+DAgXz3u989q+MRkeClgCoifdKSJUs6nAq2LKtDu7Fjx/oEOpvNxsUXX8zzzz/PsWPHSEpKYvfu3YwdO9YbTttNnz6d//znP+zbt48LLriAd999l/T0dJ9w2pUJEyb4jOYOHToUgIqKijM6vkmTJvk8HzJkiHf/sw2oEydO9Hk+ePBg3nrrLSZMmNBh+86dOzvsP3XqVG84BU8InzhxIps3b8btduN0Otm1axeXX3454eHhPiPcF154IWvXrmX//v1ceOGFPu8pInIqBVQR6ZMGDx58RpOkOgtz7dvq6+tJSkqivr6ehISEDu3ar/esr68HoK6urkOI7crJo4QAoaGhALS1tZ3R/rGxsZ9q/zPpU/s1pp1t7+xzuvpeOp1OWlpaaGlpweVysXbtWtauXdtpH9q/l+06+76LiCigikhQq6mp6XJbewiMjY2lurq6Q7uqqiqfdnFxcRw7duzcdPQshIaG4nQ6O2w/NQT2lK6+l3a7nYiICEJCQrDZbFx66aVcccUVnb5Hamqqz/PORr1FRHQnKREJart27fIJVm63mzfffJOBAweSlJQEeC4D2LVrlzeQttu4cSPh4eHeCUAXXHABZWVl7Nq1q9f6/0lSUlL46KOPfLbt2rWLlpaWc/J5W7du9RlZbW5uZvv27YwePRqbzUZ4eDhjxozhwIEDDB06lJycnA5/Th0ZFhHpjEZQRaRPKikp6TCLHyAtLY24uDjv89jYWO6//37++7//2zuL/8iRI9xxxx3eNl/60pd45513uO+++5g3b553Fv8777zDjTfe6J3sc9VVV/Hmm2/y85//nDlz5jB8+HDa2tp4//33mTBhAmPHjj3nx32ySy+9lGeffZZnn32W888/n8OHD7N27dozWiXgbNhsNpYuXcrs2bNxu92sXr2a5uZmn5n5N998M/feey8/+tGP+NznPkdKSgrNzc2Ul5ezffv2T7UKgYj0HwqoItIndXW704ULFzJr1izv80mTJpGZmckzzzxDZWUlaWlp3H777eTl5XnbDBo0iAceeICVK1fy1FNP0dbWxuDBg1m0aJHPOqjts9Ofe+45CgsLee6554iJiSEnJ4fLLrvsnB1rV774xS/S1NTE66+/zj/+8Q+GDx/Od77zHX7xi1+ck8/7/Oc/j8Ph4E9/+hO1tbVkZmbywx/+kPPOO8/bJiMjg2XLlvHCCy/wzDPPUFtbS3R0NOnp6T6To0REPolljDH+7oSIyLnQvlD/Lbfc4u+uiIhIN+gaVBEREREJKAqoIiIiIhJQdIpfRERERAKKRlBFREREJKAooIqIiIhIQFFAFREREZGAooAqIiIiIgFFAVVEREREAooCqoiIiIgEFAVUEREREQkoCqgiIiIiElAUUEVEREQkoPx/Jm8yC4bk4uAAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    final error(train) = 3.41e-02\n",
-      "    final error(valid) = 1.26e-01\n",
-      "    final acc(train)   = 9.93e-01\n",
-      "    final acc(valid)   = 9.64e-01\n",
-      "    run time per epoch = 1.66\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 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": 14,
-   "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        | 1.87e-02           | 7.73e-02           |  1.00            | 0.98             |\n",
-      "| 0.2        | 1.85e-02           | 7.47e-02           |  1.00            | 0.98             |\n",
-      "| 0.5        | 2.05e-02           | 9.07e-02           |  1.00            | 0.98             |\n",
-      "| 1.0        | 3.41e-02           | 1.26e-01           |  0.99            | 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 three affine layers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--------------------------------------------------------------------------------\n",
-      "learning_rate=0.20 init_scale=0.10\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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p08zyUjiwF/NoXsNe0MJ873WhpyImDjokQUIiRgdvAD32PAniOvgtjWQYBjGdO1OmXjYROUMKqCIibZhZXgrfZ2F+vxtzXxZ8nwVHj5z8jZFRkOANmkZt8PR7ntARw+5o+RMQEWmEAqqISBvhF0a/z4Lvd0P+4cYPTkqBpBTf0Lu35zPp2PPwxlfnEBEJBgqoIiJByBtGd9eG0V0nDaNG997Qvdexx8jguKZcROR0KKCKiASYWV7m6xn1PmadehhN6+Vd+1NEJIQooIqItCKzvAz2H7te1Ny/G/JyGz84KQUjrRf06K0wKiLtigKqiEgLMUuLvbPp9++uHa7POnkY7d4bo4fCqIi0bwqoIiJnyHQ5Iecg5sF9cHCf9/HQPu8tPRtTP4x27+Udro+KacWKRUSCmwJqO5eVlUVGRgZr165ttoX6n3rqKZYtW8ZHH30EwMyZMykpKeFf//rXCd+TkZHBgAEDmDVrFgCXXnopt99+O5MmTWqWmkSag2ma3tt31oXQusfcg+B2N3yDYUBSZ+jWA6N7H4VREZFTpIDazs2ZM4cbbrihRe8iNWvWrCYv2j1z5kxmzZrFz3/+8xaqSuSHmTXVkHPAG0AP7D3WK1pW2vgbIqMgtQdG1x7ex9Qe0CVNyzmJiJwGBdR2LDs7m48++oiHH364Rb9PbGxsk98zfvx47rnnHlatWsUFF1zQAlWJeJmm6b3D0sF9mAf3HusVPZwNpqfhGwwLpHT1BtCu3TFS0yG1h/d2n4bRytWLiISmdhlQTdOk2h2Y2/CFWY0m/RFbtWoVzz77LDt27MBisTB8+HBmzZpFjx49AG/IfOSRR1i9ejXV1dX06dOHRx99lGHDhgGwYsUK5s6dy44dO4iMjOS8887jpZdeAuC9995jwIABdOnSBYCSkhKGDh3KSy+9xE9+8hNfDR988AF33HEH33zzDVFRUTz66KN8+OGH5OTkkJyczJQpU7jrrruw2+2NnsPxQ/wVFRX84Q9/4MMPPyQ6Oppbb721wXusVivjxo3jnXfeUUCVZmNWVcKh7zEP7as3TP89VJY3/oboGEhN94bR1B7eMNo5VfeWFxFpYe0yoFa7Ta5auDMg33vhVX0Jt516QK2oqOCWW27hrLPOoqKigieffJJp06axYsUKKisrycjIICUlhVdeeYWkpCS2bNmCx+Pt9cnMzGTatGnccccdPPfcc9TU1PDxxx/7Pvvzzz9n8ODBvtexsbG+UFg/oC5atIgJEyYQFRUFQFRUFHPnziUlJYVvv/2We++9l+joaKZPn35K5/TII4+wfv16Xn75ZZKSkvjLX/7C5s2bGTBggN9x55xzDv/4xz9O+WclUp9ZXQ0Hapdz2rfLO4P+cDY0drmJ1Qopqd4g2i3dN0xPXIJ6RUVEAqBdBtS25LLLLvN7/dRTTzF48GB27tzJxo0bOXr0KO+//z4JCQkApKen+4597rnnmDRpEr///e992wYOHOh7fuDAAc4++2y/z7/iiiu48847qaysJCIigtLSUlauXMk///lP3zEzZ870Pe/WrRu7d+9myZIlpxRQy8vLeeONN3jmmWd8PaPPPPMMI0aMaHBs586dOXjwIB6PB4vFctLPlvbLdDq9PaLf7/KG0X1ZkH2g8SH6uATo2gOjW49j14qmpGLYGh8BEBGR1tcuA2qY1WDhVX0D9r2bYt++ffz1r3/lyy+/pKCgwNc7eujQIbZt28agQYN84fR427Zt41e/+tUJP7uqqorw8HC/bePHj8dms7FixQomTZrEBx98QFRUFGPHjvUds3TpUl566SX27dtHeXk5brf7lCdZ7du3j5qaGr9AmpCQQK9evRocGx4ejsfjobq6mogITTQRL9Pthuz9mPt2eRe635cFB/eB29Xw4LgE6NEHo0dvjO59vDPoY+Nbu2QREWmidhlQDcNo0jB7IE2dOpUuXbrwxBNPkJKSgsfjYdy4cTidzgbh8ngn29+hQweKior8tjkcDi677DIWLVrEpEmTWLRoEZdffjk2m/efyqZNm5g+fTq/+93vuPDCC4mJiWHx4sV+Paw/pCmz+QsLC4mMjFQ4bcdMjwcOZ9cLo7vgwB6oqWl4cHRM7SL33kBK9z4YCR1bv2gRETlj7TKgthUFBQXs2rWLOXPmMGrUKAA2bNjg29+/f38WLFhAYWFho72o/fv3Z+3atVx11VWNfv6gQYPYtWtXg+1Tpkzh2muvZceOHaxfv5577rnHt++LL74gNTWVO++807ft0KFDp3xO6enp2O12Nm3aRNeuXQEoKipiz549nHfeeX7H7tixo8ElCBK6TNOE/MPeELqvNozu3w1VlQ0Pjoj03mmphzeQ0r03JHbS9aIiIiFCATWIxcfHk5CQwOuvv05ycjKHDh3i8ccf9+2fPHkyzz//PDfffDP33XcfycnJbN26lU6dOjFixAjuvvturrrqKrp3786kSZNwuVysWrXKd63o2LFjueeee3C73VitVt/n/uhHPyIxMZHbb7+dbt26MXz4cN++9PR0Dh06xOLFixkyZAgff/wxH3744SmfU1RUFFdffTWzZ88mISGBpKQk5syZ0+g1phs2bODCCy88jZ+cBDvTNHHlH8bz1aeYe3f57ktPeSNrjDoc3jDavbdvuJ7kLhi6LllEJGQpoAYxi8XCCy+8wEMPPcT48ePp2bMnjzzyCBkZGYB3OH7BggU8/PDD/PrXv8blctG3b18effRRAEaPHs2LL77IM888w9/+9jeio6P9einrrjdds2aNXxA0DIPJkyfz97//nbvuusuvpgkTJvCb3/yG+++/n5qaGsaPH8/MmTN5+umnT/m8HnzwQcrLy7nxxht9y0yVlvoHk5ycHDZu3Mjf//73pv7YJIiZVZWYH7+H+ckH5BQVNDzAZvMu69SjNox27w2du2HU+x8oEREJfYbZ1Fv8BLm8vDycTmeD7SUlJae1YHyomzdvHitWrGD+/PmBLsXPI488QmlpKXPnzm20PeuoXdsGs6Ya85MPMT98C8pKvBstVuia5t8z2qU7xgnW05XgZxgGnTt3Jicnp8l3j5PgprYNXa3dtna7naSkpJMepx7Udu66666juLiYsrKyFr3daVMlJiby29/+NtBlyBkyXU7MtZmY7y+Euh7T5M5YLr+Wzj+bwuHCQv2xExGRBhRQ2zmbzeY34SlY3HbbbYEuQc6A6XFjfvYJ5ntvQP5h78YOiRgTr8b40TgsdjuWk6wyISIi7ZcCqog0G9PjgS/X41k8H3IPejfGxmNceiXGBRM0fC8iIqdEAVVEzphpmrBlI553X4cDe70bI6MxfvoLjHGXYYSpt1RERE6dAqqInBHz22/wLP437P7OuyE8AuPiSRgXTcKIjApscSIi0ia1q4BqmqYW8g4hdbd9lcAwd3/n7TH9brN3g8OB8ZPLMCb8AiNGKyuIiMjpazcBNSwsjMrKSiIjIwNdijQDj8dDaWkpUVHqoWtt5v493mC6ZaN3g9Xmvb700l9ixHcIbHEiIhIS2lVALS8vp7i4WL2obYjD4aCmsfuu470rlc3Wbv4JB5yZcxBz8b8xN63zbrBYMH40DuPnV2N0TA5scSIiElLa1V939ba1LVoYOjiYebmY772B+dknYHrAMDDO/THGz6/BSOka6PJERCQEtauAKiKnziw8ivn+Qsy1H4Hb7d14zigsk36FkdojoLWJiEhoU0AVET9maTHmh29hfvIhOGsvrxgwFMvk6zDS+wS2OBERaRcUUEUEALOiDHPFu5iZ70F1pXdj7wFYplyH0XdQYIsTEZF2RQFVpJ0zqyoxP34Pc8UiqCj3buzeG8vk62DgUE0qFBGRVqeAKtJOmaUlmOs/xlz+DpQWezd2ScMy6Vcw9DwFUxERCRgFVJF2xPS4YdvXeNZ9BF9vALfLuyMpBePyazFG/hjDYg1skSIi0u4poIq0A+aRbMx1H2OuXwlFR4/t6N4bY+xPveuZak1ZEREJEvqLJBKizOoqzE3rMNdlws5tx3ZEx2Cc9xOMMeMxUtMDV6CIiMgJKKCKhBDTNGHPDsx1mZgb1hybjW9YYOBQLOdfDIPPxbDbA1uoiIjID1BAFQkBZnEh5merMNdmQu7BYzuSUjDGXOQdwu+QGLgCRUREmkABVaSNMl0u2LoRz9pM2LIRPB7vDkcYxvAxGOdfBH0Gaja+iIi0OQqoIm2Mmb3fO+Hp05XHlocC6HWWt7d0xPkYEZGBK1BEROQMKaCKtAFmZQXmF2u8E5727Di2IzYe40c/8QbTzt0CV6CIiEgzUkAVCVKmacLObZjrPsLctA5qarw7LBYYfC6WMRfBoOFaHkpEREKO/rKJBBmzIB/z05Xe3tK83GM7Onfz9pSedyFGXELgChQREWlhCqgiQcB0OuGbz/Gsy4RtX4FpeneER2Cc+2OMMRdBz36a8CQiIu2CAqpIAJlVlZirl2F+tBiKCo7t6DvI21s6fDRGWHjgChQREQkABVSRADDLSjBXLsX8eClUlHk3xnXwhtIx4zCSuwS2QBERkQBSQBVpRWbhUcwV72KuWQ7VVd6NyV0wfnqF9/ajusOTiIiIAqpIazAPZ2Mufwdz/Upwu7wbu6Vj/OyXGMN/hGGxBrZAERGRIHJaAXX58uUsWbKEoqIiUlNTmTp1Kv379z/h8cuWLWP58uUcOXKExMRErrjiCsaOHet3zPvvv8+KFSvIz88nNjaWUaNGce211+JwOE6nRJGgYO7fg7nsbcyN68CsvdNTnwFYLv0lDBymSU8iIiKNaHJAXb9+PfPmzWPatGn069ePzMxMHnvsMebOnUtiYsN7fa9YsYIFCxZw66230qtXL7KysnjxxReJiopixIgRAKxZs4b58+dz22230bdvX3JycnjhhRcAmDp16pmdoUgAmLu24/nwLe8tSOucPQLLpRkYvQcErjAREZE2oMkBdenSpYwbN47x48cD3gD5zTffsGLFCq699toGx69evZqLLrqI0aNHA9CpUyd27drF4sWLfQF1586d9OvXj/PPPx+A5ORkxowZQ1ZW1mmfmEhrM00Ttn6J54M3IWu7d6NhwRgxBuNnGRjd0gNboIiISBvRpIDqcrnYs2cPkydP9ts+ePBgduzY0eh7nE4n9uMmfjgcDrKysnC5XNhsNs466yzWrFlDVlYWvXv35vDhw3z11VcNLgM4/nOdTqfvtWEYRERE+J5L21fXjsHenqbHjblxvbfH9MAe70abDWP0eCwTrsDopBn5x2srbStNo3YNXWrb0BWsbdukgFpSUoLH4yEuLs5ve1xcHEVFRY2+Z8iQIaxcuZKRI0eSnp7Onj17WLVqFW63m9LSUhISEhgzZgwlJSU8+OCDALjdbi655JIGQbi+RYsW8dZbb/lep6enM2fOHJKSkppyStIGpKSkBLqERpnOGspXfkDpW6/izj4AgBEeQdTPriBm8q+wJSYHuMLgF6xtK2dG7Rq61LahK9ja9rQmSTWWsk+UvDMyMigqKuL+++/HNE3i4uIYO3YsS5YswWKxALBt2zbeeecdpk2bRp8+fcjNzeWVV14hPj6ejIyMRj93ypQpTJw4scH3z8vLw+Vync5pSZAxDIOUlBRyc3O9w+dBwru4/nI8KxYdW1w/MhrLRZdjjLuMquhYqpxuyMkJbKFBLFjbVs6M2jV0qW1DV2u3rc1mO6XOxCYF1NjYWCwWS4Pe0uLi4ga9qnUcDgfTp0/nlltuobi4mISEBDIzM4mIiCAmJgaAhQsXcsEFF/iua01LS6Oqqop//vOfXHHFFb4gW5/dbm9w6UAd/fKEFtM0g6JNzfJSzI+XYq5cCuWl3o3xHTAunoxxwQSMcO8lJsFQa1sRLG0rzUvtGrrUtqEr2Nq2SQHVZrPRs2dPNm/ezMiRI33bN2/ezLnnnnvS93bs2BGAdevWMWzYMF/wrK6ubtADa7FYguoHJe2XWXQU86PFmP+3rN7i+p0xJlyB8aNxWlxfRESkmTV5iH/ixIk8//zz9OzZk759+5KZmUl+fj4XX3wxAPPnz6egoIDbb78dgOzsbLKysujTpw/l5eUsXbqUAwcOMGPGDN9nDh8+nPfff5/09HTfEP/ChQsZMWJEo72nIq3BPJJTu7j+x1B32UhqOsalGRjDR2txfRERkRbS5IA6evRoSktLefvttyksLKRbt27cd999vusJCgsLyc/P9x3v8XhYunQp2dnZWK1WBg4cyOzZs0lOPjaB5Be/+AWGYfDGG29QUFBAbGwsw4cP55prrmmGUxRpGvPgXswP3vJfXL/3ACyXZsCg4UE301FERCTUGGaIjaPn5eX5LT8lbZdhGHTu3JmcnJxWudzDdLkw33kV86PFxzYOGo7lZxkYfQe2+PdvT1q7baV1qF1Dl9o2dLV229rt9uafJCUSqsyio3he/KtvgX1j+BjvUH5arwBXJiIi0v4ooEq7Z+7YiuefT0BJEYRHYLnxToxhowNdloiISLulgCrtlmma3klQi14Djwe6dsfy2z9gpHQNdGkiIiItzu0xqXJ5KKsOvvXjFVClXTIryvG88ix8/RkAxnk/wbjuNoyw8ABXJiIircE0TTwmtV+m3yOAzWJgs4DVYmAJssmxbo9JpdNDhdNDhdNNpdNDee3rytptFb79Hiqdbsp9+2q/atxUu70ne+3wSq7uHx3gs/KngCrtjnlgL55//AWO5IDNhnH1Ld6F9oPsP0AiIqHE5TF9waiiXlgqrw1YFfUCV93+SqcHl8cbGk38Q6THA+56wdIEPB6z0cDZ2LamTAeyGt6garcYtcHVwFr7aLcYWC34tvu+rLWPhoHN6t1vNbzb7fWe1/8Mp9v0nXt5bbCs8PvZeLdVuZp3MlOl092sn9ccFFClXfGs/xjz9b+DswY6JHmH9NP7BLosEZGg5fKYVLrceIoq2VdQ5QuYjfXSNRam6nruatxtd/a/2wS32wy6c7BbDCLtFiLsFqIcFiLsViLtFiJtFiIdFiLtViLsFu8235fV/7XDSlpqV3KC7PbcCqjSLpjOGsw3/gdz9XLvhkHDsNx8N0Z0bGALEwkRHtM75JhfVk1ZjRu7xfvHUyMTgVPXY+kLjjUNeyh/qJeu7vmxULarWeoKtxnHglS9r4bbvOHKagGLYWCh9tEAi6X20QALx2/zPhoc22Y1ju0z6h1z7NF/G3iH0Z0eE7fHxOX7ot7zel9uE5d57Lnb9B7ndJu4zdr9p/B5dotRGzZPHCwj7Baiah/t1jO/mVGw/o4qoErIM/Ny8fxjDuzfDYaB8fNrMC67EkN3KWt1JdVu9hRUcai0hvhcN1XlpdgtBg5r3ZcFu9UgzGpgt1r8tjus3iG1YGaa3j9MdX/YXLV/jAzDex5htcN5wfoHwTS9gaasxk15jffR++WhrLre8xo35fWel9X2qHmv3dvp95nHt6Gjtm29bey/ve6599+ApXa/QZjN4v13YjNwWCy1jwYO27F/L3XXCXoDS23YsDQMIgat/wfZY5r1/k1wXEBp7Itjgac22LhNb9ipCz3H92A2Fjabu7cvvLZnLsIXKOuCUuPB8kThKth/j+vYLAZhgS6iHVNAlZBmbtmI56WnoaIMomOw3Pw7jEHDAl1WyDNNkyPlTvYUVLOnsIq9hd7HoxX1Z4oebvLnWg0aBh2bURteLLWh5ViIsVv8w43VMJrWK+KpCxb4nv9gqPCcPBAY4AtejYUzR2PhvF4g853jcWHu+GBnsxhUOo8LmY0Ez/IaN6XV3sBZ7guZp8/A/9q+Gt+wqOfMPrgZNdZj1ljvmvUH9tU9nujfUf3/STnTn+mZCrMavh5K7zBw48Gx0eHfutcOK6ldu2ihfmk1CqgSkkyPG/O9NzCXLvRuSO+L5db/wuh48rtXSNO4PCYHiqu9IbSgir21gbTc2XggSYm20z0+jKjICErKKqhxm1S7TZxujy/M1NR7Xj/0uU2odHmoDL4VUU7IYoBZb0KGSXCGtvocVoMoh5Voh4Xo2kfvaysxDm/Iia59He2wEBVWuy/MSlrXLhw4lEON202Nq2F7Hnt+bJvTbVJd+7ruubP2mGqXN+jVuE50vIdqtzcQnurEl7pJM/6t0noM8J9IY+A/qabRiTj4TcqJdFjrXWfYMFhG1NvWHD2WwdrrL6FLAVVCjllajOelp2D71wAYF16KceXNGHZ7QOsKBRVON3sLq30hdE9BFfuLaxrtObRZIC0ujPSEcHp28D6mJ4QRabc26dZ6dT1UjYZYl0mNX3j54RDjMc1TmknbYDZuI1/W2mssG87mrX+c9w+7WXtt2vEhzT+MNRLk6p+fx6TGZeL0nFpoc3pMImzHgmW0w0J0WL3nDutxIdRau9+C4zSvazMM7+UL3h5cK5EB+JU70dJBZr3XdTO/TbOx2d4nnvXdYBa4yUn/vfj+nVhr/61Zgv9SFZFgoIAqIcXc/R2eF5+AwnxwhGH8egaW8y4MdFltjmmaFFS6fEPzdWE0t8zZ6PFRdgvpCXVh1BtEU2PDsFvP/A+xtfYPepgNwHrGnxcIdaHNboWoQBcT4gzD8C4J5H0V4GpE5HQpoEpIME0Tc9X7mP/5F7hd0Kkrltv+gNG1e6BLC3puj0lOaQ17antG6x6LqxpfF69jpI2edWG0tnc0OcquIUAREWk2CqjS5plVlZiv/Q1zw2rvhuGjsdxwB0ZEZGALCzKmaZJf4eJAcTUHimvYX1zN/qJqvi+q9t1NpD6LAV1jHbVBNMz3GBuu/2yIiEjL0l8aadPMnIN4/v445BwAiwUj40aMiy5v1715JwqiB4prqHQ1PinHYTWODdHXXivaPT6MMJuW4hIRkdangCptlueLtZivPg/VlRDXAcut92L0GRDoslqNaZocrXT5wuf+4mpfKK04wQx6qwGdYxykxYeRFuegW5w3iHaJcWjihoiIBA0FVGlzTJcL8+15mJlLvBv6nY3lN7/HiEsIbGEtpLmCaFpcGJ1jHM0ycUlERKQlKaBKm2IWHsXz4hzY/R0Axk9/gTH5Ogxr25zdXd+ZBtFucQ7SFERFRCQEKKBKm2F++w2e/3kSSoshIgrLTXdinHNeoMs6LeU1br4vqmZf7SSlfYXeMHqixe0VREVEpD1RQJWgZ3o8eJa9jbnodTA9kNrDu4RUcpdAl3ZSLo9JdkmNL4h+X1TFvsJq8ioavxWSxYAuMbVD8vHeINotznuNqIKoiIi0FwqoEtQ8ZaV4XngM8+vPATBGj8e49rcYYWEBrsyfaZoUVrnZV1jl1zN64AR3WQJIjLTRI947SalHQrhvspKCqIiItHcKqBK0zP17yP3nE5i5h8Bmx7j2VozzLw74ElLVLg/7i73D8nVhdF9RNaXVjS9sH26zeEOoL4yG0T0ujOiwtn/drIiISEtQQJWgZH63Gc/zs6CmBhKTsfz2PozuvVq1Bo9pcrjM6e0NLaxmX5G3dzSn1EljfaJ1w/O+MJrgfUyKsmNpx+uyioiINJUCqgQdc8dWXzgNGzoK1w13QlR0i3/fkmo3X+eUs+VwOXtrJy1VuRofno8Ltx7rEa0dok+NdWhhexERkWaggCpBxdy5zRdOjUHDSHroaXKPFmCajQfFM+H2mOzMr+TLnHK+yikn62hVg55Ru8UgLd5B9/hwv0AaH6FfHRERkZaiv7ISNMys7XiemwXVVTBgKJYZ92M4mncy1JEyJ1/llPNVThmbcysaLOvUPT6MoZ2j6NPRG0g76w5LIiIirU4BVYKCufs7PM8+7L1taf8hWGb8EcPuOOPPrXZ52Hq4ojaUlnOwpMZvf4zDwpDOUQyt/eoYaT/j7ykiIiJnRgFVAs7cuxPPs3+GqkrvbUtnPHDaPaemabK/uIavcsr4KrucbUcqcdZb5sliQN+OEQzr4g2kvTqEq4dUREQkyCigSkCZ+3bhmfsnqKyAvoOw/L8Hm7zGaWm1m29yvT2kX2WXc7TSfxH8xEibL5AOToki2qHlnURERIKZAqoEjPn9bjxzH4LKcugzoDachp/0fW6Pya6jVXyVU8aX2eVkFVRRfy18h9VgUHIkQ2tDaWqsI+Brp4qIiMipU0CVgDD378Hz9INQUQ69+2O54yGM8IgTHp9f4eSr7HK+zCnnm9xyymv8JzelxTm815F2iWZAUoSWexIREWnDFFCl1ZkH9+KZ+yBUlEHPflju+BNGeGSD47YfqeCNb3exJuswB4r9JzdFOywMSamd3NQlikRNbhIREQkZCqjSqsxD3+N56kEoK4X0vlju/DNGhH84Lap08T+bDrP2+1LfNosBfTqGM7RzFMO6RNNbk5tERERClgKqtBrz0H48Tz0AZSXQvTeWmX/GiIw6tt80ydxdzCtfHaG8xoPFgJ/2T2FQRyuDO0USo3vXi4iItAsKqNIqzJwDeJ66H0qLIa0XlrtmYUQeu33poZIaXvg8h61HKgHomRDG7ed15seDepKTk9Mid5ISERGR4KSAKi3OzD3o7TktLYZu6VjunoUR5Q2nTrfJou1H+c/Wozg9Jg6rwbWDE7n8rA7YrJroJCIi0h4poEqLMg9n43nyASguhK7dsdz1CEZUDADf5VXyt89z2F87AWpo5yhuG9mJTtFnfgcpERERabsUUKXFmEey8Tx5PxQXeMPp72ZjxMRS4XTzv1/lsWxXESYQG2bl5uHJjO0Rq/VKRURERAFVWoaZl+vtOS06Cp27Ybn7EYyYOD47UMo/vzjsu9vTuJ5x3DgsmVhNgBIREZFaCqjS7Mz8w96e08J8SEnF8vvZFNii+Ofqg3x2oAyAlGg700elMCQl6iSfJiIiIu2NAqo0K/PoEW84LciDTl3h7kdYlgv/+/VeKpwerAZMGdCRKwd11N2eREREpFEKqNJszII8bzg9egSSO3Polj/ztw1lfJfvXTqqT8dwbh+VQo+E8ABXKiIiIsFMAVWahVmQ7w2n+YepSe7Kosv/wNvrCnF5INxmcN2QJC7tm6C7P4mIiMhJKaDKGTOLjnrXOc3LZVvaMP5x9rUcyvL2mp7bNYpbz00hKcoe4CpFRESkrVBAlTNiFhXgefIBygoK+d/B15HZYTCUe4gPt3LLiE6MTovR0lEiIiLSJAqoctrMkkLcTz3Aek9HXh41jSK7d0b+Jb3juOGcZKK1dJSIiIicBgVUOS1mSRGHn/kL/5N4ERsTBwDQNdbBjJEpDOwUGeDqREREpC1TQJUmc5UU8cHLb/Lv7tdSZQvDZkDGoI5kDOyI3aqlo0REROTMKKBKk+w5eJS/Ld9KVvKPAegfZ2X6j9NIiwsLcGUiIiISKhRQ5ZRUuzws/DKbRTtL8IR3ItJdxfUD4pgwvAcWTYISERGRZqSAKif1XV4lc9cdJLfcDYaF8wq/5ZaJw+jYo3ugSxMREZEQpIAqP6ik2s2jnxygpMZDh+oibjmwgvOmTcXomhbo0kRERCREaUaL/KD//eoIJTUeupXn8ty2Fznv5usVTkVERKRFKaDKCW0/UsFHu4sB+G3Wu0Tf+SBGao/AFiUiIiIhTwFVGuV0m/x9Qy4AF2V/zoBzh2Ck9QxwVSIiItIenNY1qMuXL2fJkiUUFRWRmprK1KlT6d+//wmPX7ZsGcuXL+fIkSMkJiZyxRVXMHbsWL9jysvLWbBgARs2bKC8vJzk5GR+/etfM2zYsNMpUc7Qku8K2F9cQ2xNGb/OXYMx/ZlAlyQiIiLtRJMD6vr165k3bx7Tpk2jX79+ZGZm8thjjzF37lwSExMbHL9ixQoWLFjArbfeSq9evcjKyuLFF18kKiqKESNGAOByuZg9ezaxsbHcfffddOzYkaNHjxIeHn7mZyhNdrishje25ANww+6lxP7s5xhR0QGuSkRERNqLJgfUpUuXMm7cOMaPHw/A1KlT+eabb1ixYgXXXnttg+NXr17NRRddxOjRowHo1KkTu3btYvHixb6AunLlSsrKynjkkUew2bwlJSUlnfZJyekzTZMXvzhMjdtkUOFuLvRkY1x4f6DLEhERkXakSQHV5XKxZ88eJk+e7Ld98ODB7Nixo9H3OJ1O7Ha73zaHw0FWVhYulwubzcamTZvo06cPL7/8Mhs3biQ2NpYxY8YwefJkLJbGL5N1Op04nU7fa8MwiIiI8D2X0/PpgVI2ZZdj87i4Zdc7WK+/CYvdEZBa6tpR7Rl61LahSe0autS2oStY27ZJAbWkpASPx0NcXJzf9ri4OIqKihp9z5AhQ1i5ciUjR44kPT2dPXv2sGrVKtxuN6WlpSQkJHD48GHy8vI4//zzue+++8jJyeHll1/G4/GQkZHR6OcuWrSIt956y/c6PT2dOXPmqOf1DJRVu3j53d0ATNn/CT3TUkiemBHwf7QpKSkB/f7SctS2oUntGrrUtqEr2Nr2tCZJNRZYThRiMjIyKCoq4v7778c0TeLi4hg7dixLlizx9Y6apklsbCy33norFouFnj17UlhYyJIlS04YUKdMmcLEiRMbfP+8vDxcLtfpnFa79z9f5JJXVkNKZT6/2L8S972PkZubG7B6DMMgJSWF3NxcTNMMWB3S/NS2oUntGrrUtqGrtdvWZrOdUmdikwJqbGwsFoulQW9pcXFxg17VOg6Hg+nTp3PLLbdQXFxMQkICmZmZREREEBMTA0B8fDw2m81vOL9r164UFRX5LgM4nt1ub3DpQB398jRd1tEq3t9ZCMAtOxfhOGck9DorKH6WpmkGRR3S/NS2oUntGrrUtqEr2Nq2Seug2mw2evbsyebNm/22b968mX79+p30vR07dsRisbBu3TqGDRvmC6T9+vUjNzcXj8fjOz4nJ4eEhIRGw6k0L7fHu+apx4QfH/6Kc0r2YLni+kCXJSIiIu1UkxfqnzhxIh9//DErV67k4MGDzJs3j/z8fC6++GIA5s+fz3//93/7js/Ozmb16tXk5OSQlZXFM888w4EDB7jmmmt8x1xyySWUlpYyb948srOz+fLLL1m0aBETJkxohlOUk1m2q4isgioi3dVM3f0exoWXYnTqEuiyREREpJ1qcvfk6NGjKS0t5e2336awsJBu3bpx3333+a4nKCwsJD8/33e8x+Nh6dKlZGdnY7VaGThwILNnzyY5Odl3TGJiIg888ACvvvoq99xzDx06dOBnP/tZg9UCpPkdrXDy2td5AFy3+30SrB6My64KcFUiIiLSnhlmMF1w0Azy8vL8lp+SH/bEmkOs219Kn/JsHv/iWay/uB7LT38R6LIA74XbnTt3JicnJ6iui5Ezp7YNTWrX0KW2DV2t3bZ2u/2UJkk1eYhfQsemQ2Ws21+KBZPffrsQS4dEjPE/D3RZIiIi0s4poLZT1S4PL248DMBl2Z+SXpaDMeXXGAFalF9ERESkjgJqO/WfrUc5XOakI1VcvfsDSOuFMfKCQJclIiIiooDaHu0vqmbR9qMATNv2HyLcNVh+eSPGCW4rKyIiItKalEjaGY/pXfPUbcK5zmxG5W2FwedinDU40KWJiIiIAAqo7c7KPcVsz6skzGIybeM8sFiwZEwNdFkiIiIiPgqo7UhJlYt5Xx4B4Kr8z0mqLsI4/xKMzt0CXJmIiIjIMQqo7cgrX+VRWuOhh8PJxC3vQlgExuXXnPR9IiIiIq1JAbWd2Hq4gpV7ijGAW7f/B5vpwfjpFIy4hECXJiIiIuJHAbUdcLq9E6MALg4vpN/BbyC+A8bFkwNbmIiIiEgjFFDbgUXfHuVgSQ1xYRauW/dPAIxJv8IICw9wZSIiIiINKaCGuJzSGt7c6l3z9Cbnt0SXHoWu3TFGjwtwZSIiIiKNU0ANYaZp8o8vDlPjNhnSwcb5//cqAJaMGzEs1gBXJyIiItI4BdQQtvb7Ur7OKcduMbjlwHIMtwsGDMUYNCzQpYmIiIickAJqiCqvcfPypsMA/KKLSecvloNhYPnl1MAWJiIiInISCqgh6vVv8iisctMlxs6Uz7xD+8bocRip6QGuTEREROSHKaCGoJ35lXy4swiA3yYUYM/aBg4HxqTrAluYiIiIyClQQA0xbo93zVMTGNs9hkHLXgbAuHgyRkLHwBYnIiIicgoUUEPM+zsL2VNYTbTDwtTKzXAkG2LiMH56RaBLExERETklCqghJL/Cyb+/yQfg+gFxxH3wOgDG5ddihEcGsjQRERGRU6aAGkJe2niYKpeHsxIjGP/dMigrhZRUjB9fEujSRERERE6ZAmqI+OJgGZ8eKMNqwG/7OTAylwBgyZiKYdWi/CIiItJ2KKCGgCqXhxe/yAVgUv8OdP/4DXA5oe8gGHxugKsTERERaRoF1BCwcEs+eRUukqNsXBlXgvnZKgAsv7wRwzACXJ2IiIhI0yigtnH7CqtY/G0BAL8Z0QnHO68AYIwci9GjTyBLExERETktCqhtmMc0eWHDYdwm/KhbNOcWfAc7toDNjjFFi/KLiIhI26SA2oZl7i5mR34l4TYLNw9NxPPWPACM8RMxEjsFtjgRERGR06SA2kYVVbl49asjAPxqSCIdv/oEcg5AVAzGpb8MbHEiIiIiZ0ABtY16ZdMRymo89EwI49K0cMzF8wEwJl6FERkd4OpERERETp8Cahu0ObecT/aVYAC3jUzB8tG7UFIESSkYF/4ssMWJiIiInCEF1DbG6fbw9w2HAfhZ33j62CowVywCwPKLGzBs9kCWJyIiInLGFFDbmLe3FZBdWkNCuJXrhiR5h/ZrqqHXWTBsdKDLExERETljCqhtSHZJDW9uOwrAzcM7EXnkAOa6jwGwZGhRfhEREQkNCqhtyNKdhbg8Jud0juL87jF43p4HpgeGj8bo3T/Q5YmIiIg0CwXUNsLtMVm/vxSAn/dLgG+/hq1fgtWG5YrrA1uciIiISDNSQG0jvsurpLDSRZTdwuDkcDxvzgPAuPBnGMldAlqbiIiISHNSQG0j1u4vAWBUtxhsX/wfHNwLEVEYE68KcGUiIiIizUsBtQ2oP7w/pnM45qLXATAu+yVGdGwgSxMRERFpdgqobcD2vAqKqtxEOyycvTUTio5Cx2SMcRMDXZqIiIhIs1NAbQPWfe/tPR3VKQzb8rcAMKb8GsPuCGRZIiIiIi1CATXIuT0m6w/UDu8f3QpVldC9N8a5Pw5wZSIiIiItQwE1yG07UkFxlZsYh4VB21cBtTP3LWo6ERERCU1KOUFu3f664X0Htn07ATDOHhHIkkRERERalAJqEHN7TD6tm71fc8C7sXtvjLiEAFYlIiIi0rIUUIPY1iMVFFe7iQmzMnDXekC9pyIiIhL6FFCDWN3s/fO6RmLb/iUAxmAFVBEREQltCqhByu0x+bR29v75lqPe2fsxcdC9d4ArExEREWlZCqhBasvhCkqq3cSGWRm47wvAO7yv2fsiIiIS6pR2gtS6/SUA/KhbDJatGwEN74uIiEj7oIAahFwek08PlAEwOtYJuYfAaoX+5wS2MBEREZFWoIAahLYcrqC02k1cmJWBh772buw9ACMyKqB1iYiIiLQGBdQgtPb72uH9tBgsWzS8LyIiIu2LAmqQcXlMPq+dvT8mJQx2bgHAOPvcQJYlIiIi0moUUIPM5txySms8xIVbGZC/A1wuSEqBlK6BLk1ERESkVSigBpl1tbc2HV1/9v7ZIzAMI5BliYiIiLQa2+m8afny5SxZsoSioiJSU1OZOnUq/fv3P+Hxy5YtY/ny5Rw5coTExESuuOIKxo4d2+ix69at49lnn2XEiBHce++9p1Nem+V0m3xWO7w/Oi0G881jAVVERESkvWhyQF2/fj3z5s1j2rRp9OvXj8zMTB577DHmzp1LYmJig+NXrFjBggULuPXWW+nVqxdZWVm8+OKLREVFMWKEf/DKy8vjtdde+8GwG8o255ZTVuMhPtzKgOocKCoARxj0GxTo0kRERERaTZOH+JcuXcq4ceMYP368r/c0MTGRFStWNHr86tWrueiiixg9ejSdOnVizJgxjBs3jsWLF/sd5/F4eO6557jyyitJTk4+vbNp49buP9Z7atmyybux/xAMuyOAVYmIiIi0rib1oLpcLvbs2cPkyZP9tg8ePJgdO3Y0+h6n04ndbvfb5nA4yMrKwuVyYbN5S3jrrbeIjY1l3LhxfPvttyetxel04nQ6fa8NwyAiIsL3vK1xuk0+P+gNqOd3j8PM9A7vWwaf2ybPpznUnXd7Pf9QprYNTWrX0KW2DV3B2rZNCqglJSV4PB7i4uL8tsfFxVFUVNToe4YMGcLKlSsZOXIk6enp7Nmzh1WrVuF2uyktLSUhIYHvvvuOlStX8sQTT5xyLYsWLeKtt97yvU5PT2fOnDkkJSU15ZSCxro9+ZTXeEiMcjC2ewKH9+4EoNNFl2FLbJ89ynVSUlICXYK0ELVtaFK7hi61begKtrY9rUlSjaXsEyXvjIwMioqKuP/++zFNk7i4OMaOHcuSJUuwWCxUVlby/PPPc+uttxIbG3vKNUyZMoWJEyc2+P55eXm4XK4mnlHgLfkqG4BRqVHkrfoQTBO69STP6YacnABXFxiGYZCSkkJubi6maQa6HGlGatvQpHYNXWrb0NXabWuz2U6pM7FJATU2NhaLxdKgt7S4uLhBr2odh8PB9OnTueWWWyguLiYhIYHMzEwiIiKIiYlh//795OXlMWfOHN976n5AV199Nc8880yjqd5utze4dOD497cVTrfn2OL8aTGY7x6bvd/WzqUlmKapn0OIUtuGJrVr6FLbhq5ga9smBVSbzUbPnj3ZvHkzI0eO9G3fvHkz5577w3c6stlsdOzYEfAuJTVs2DAsFgtdunThySef9Dv2jTfeoKqqyjcBK9R9nVNBudNDQoSNszo4MLd9Cej2piIiItI+NXmIf+LEiTz//PP07NmTvn37kpmZSX5+PhdffDEA8+fPp6CggNtvvx2A7OxssrKy6NOnD+Xl5SxdupQDBw4wY8YMwNvDmpaW5vc9oqKiABpsD1Vr95cA3t5Ty57v8FSUQ3QspPcJcGUiIiIira/JAXX06NGUlpby9ttvU1hYSLdu3bjvvvt81xMUFhaSn5/vO97j8bB06VKys7OxWq0MHDiQ2bNnt9ulpI7ndHvYcLAMgPPTYjBXfwiAMWgYhsUayNJEREREAuK0JklNmDCBCRMmNLqvrme0TmpqapNm5zf2GaHsq5xyKpweOkbY6JcUgbn5C+8O3T1KRERE2qkmL9QvzWvd97WL83ePwTh6BHIOgMWCMXBYgCsTERERCQwF1ACqcXv4vHZ4f0xaDOYW7+x9ep2FERUdwMpEREREAkcBNYC+yi6n0uWhY6SNfokRmJvrlpf64RURREREREKZAmoArd1/bO1To6YGdmwBtLyUiIiItG8KqAFS7ao3e797LHy3GZw10CEJurSP5bVEREREGqOAGiBf5ZRT5fKQFGmjb8dwzC3e2fvG4HNPeNtYERERkfZAATVA6mbvj+keC+CbIKXhfREREWnvFFADoNrlYcOhY9efcuh7KMgHhwP6nR3g6kREREQCSwE1AL7MLqfKZZIcZaNPx/Bjy0v1G4zhCAtscSIiIiIBpoAaAGv3lwAwOi0WwzCOLS+l4X0RERERBdTWVu3ysPFQ3ez9GMyyEtj9HaD1T0VERERAAbXVbcouqx3et9O7Qzjmtq/A9EDX7hgdkwJdnoiIiEjAKaC2srW1s/fP7x7jXU7Kd/coDe+LiIiIgAJqq6qqN7w/Ji0W0+PG3PYloIAqIiIiUkcBtRVtOlRGtdukU7SdXh3CYM8OKC+FyGjodVagyxMREREJCgqorWjt/mNrn/rN3h84FMNqDWRpIiIiIkFDAbWVVPnN3ve/exRaXkpERETERwG1lWw8VEaN2yQl2k7PhDDMgjw4uA8MA2Pg8ECXJyIiIhI0FFBbybHZ+7WL82/Z5N3Rsx9GTGwAKxMREREJLgqoraDS6WFTdt3s/Rjg2PC+Zu+LiIiI+FNAbQVf1A7vd4mxk54QhumsgW+/AcAYrLtHiYiIiNSngNoK1u0vAWB0mnd4nx1boKYaEhIhtUdgixMREREJMgqoLazC6ebL7HLAe/coAHPzFwAYZw/3BlYRERER8VFAbWEbD5XXDu876BEfhmmax9Y/1fWnIiIiIg0ooLawtd97h/fP7+5dnJ+cA3D0CNjs0H9IgKsTERERCT4KqC2o/vD+8bP36TcIIyw8UKWJiIiIBC0F1Ba04WAZTo9JaqyD7vFhAPWG9zV7X0RERKQxCqgtaN1+7+L8o9O8w/tmRRlkbQfA0O1NRURERBqlgNpC/Gfve+8UZW77GjweSEnFSEoJYHUiIiIiwUsBtYVsOFiGq3Z4Py3O4d24pXZ5KfWeioiIiJyQAmoLWfu9d3i/bva+6fFgbv0S0N2jRERERH6IAmoLKKtx81VO3ex97/A++3ZBaTFEREGv/gGsTkRERCS4KaC2gLrh/bQ4B2l1s/drl5cyBpyDYbMFsjwRERGRoKaA2gLW1S7OP6Z2chQcu70puv5URERE5AcpoDazsho3X+cetzh/0VHYvwcMA2PQ8ECWJyIiIhL0FFCbmXd4H7rHhdEtrm54f5N3Z48+GLHxgStOREREpA1QQG1ma33D+zG+bcfuHqXhfREREZGTUUBtRmXVbr7OOW543+mEb78GtP6piIiIyKlQQG1Gnx0sxW1Cj/gwUmuH99m1FaqrIC4BuvUMbIEiIiIibYACajNaV7s4f6PD+4OGY1j04xYRERE5GSWmZlJa7eab3OMW56fe+qca3hcRERE5JQqozeTz2uH99IQwusY6ADBzD8GRHLDaYMA5gS1QREREpI1QQG0ma+uG99PqDe/X9p7SdyBGeGQgyhIRERFpcxRQm0GJhvdFREREmo0CajP47EApHhN6JoTRpW54v7ICdm4DwDj73ECWJyIiItKmKKA2g3W+xfmP9Z6y/WtwuyC5C0anLoEpTERERKQNUkA9QyVVLjYfrgCOv/70C0DD+yIiIiJNpYB6hj47WIbHhF4dwugcUzu87/FgbtkE6PamIiIiIk2lgHqG1tYN79ebHMX+3VBSBGER0HdgYAoTERERaaMUUM9AcZWLLY0N79fePYoBQzBs9kCUJiIiItJmKaCegU9rZ+/37hBOSu3wPtRbXkrD+yIiIiJNpoB6BtbVLc7fvV7vaUkh7NsFKKCKiIiInA4F1NNUVOVi65HGZu9/6X2S1gsjvkMgShMRERFp0xRQT9On+73D+306htMpuv7wvpaXEhERETkTCqinad3+2uH9+r2nLpd3gX7AGKy7R4mIiIicDtvpvGn58uUsWbKEoqIiUlNTmTp1Kv379z/h8cuWLWP58uUcOXKExMRErrjiCsaOHevbn5mZyerVqzlw4AAAPXv25JprrqF3796nU16LK6x0sc03vF9veams7VBZATFx0D04axcREREJdk0OqOvXr2fevHlMmzaNfv36kZmZyWOPPcbcuXNJTExscPyKFStYsGABt956K7169SIrK4sXX3yRqKgoRozwDoNv376dMWPG0K9fP+x2O4sXL2b27Nk8/fTTdOgQfNdxHq1w0T0+DLvFIDn62DJSvtn7g4ZjWNQ5LSIiInI6mhxQly5dyrhx4xg/fjwAU6dO5ZtvvmHFihVce+21DY5fvXo1F110EaNHjwagU6dO7Nq1i8WLF/sC6h133OH3nt/+9rd8/vnnbNmyxa+nNVj07hjOM5emU+F0+203N+v6UxEREZEz1aSA6nK52LNnD5MnT/bbPnjwYHbs2NHoe5xOJ3a7/2L1DoeDrKwsXC4XNlvDEqqrq3G5XERHR5+wFqfTidPp9L02DIOIiAjf89YQ5ThWu3kkB3IPgdWKMXBYq9UQyup+hvpZhh61bWhSu4YutW3oCta2bVJALSkpwePxEBcX57c9Li6OoqKiRt8zZMgQVq5cyciRI0lPT2fPnj2sWrUKt9tNaWkpCQkJDd7z73//mw4dOnD22WefsJZFixbx1ltv+V6np6czZ84ckpKSmnJKzab0i/+jCAgbcA7JvXT9aXNKSUkJdAnSQtS2oUntGrrUtqEr2Nr2tCZJNZayT5S8MzIyKCoq4v7778c0TeLi4hg7dixLlizB0sh1mosXL2bdunX8+c9/xuFwNPKJXlOmTGHixIkNvn9eXh4ul6upp3TG3Gs/BsB51mBycnJa/fuHIsMwSElJITc3F9M0A12ONCO1bWhSu4YutW3oau22tdlsp9SZ2KSAGhsbi8ViadBbWlxc3KBXtY7D4WD69OnccsstFBcXk5CQQGZmJhEREcTExPgdu2TJEhYtWsSDDz5I9+7df7AWu93e4NKBOq39y2NWVWLu2OJ9cfYI/fI2M9M09TMNUWrb0KR2DV1q29AVbG3bpKnmNpuNnj17snnzZr/tmzdvpl+/fid9b8eOHbFYLKxbt45hw4b59aAuWbKEt99+mz/+8Y/06tWrKWUF3nffgMsFiZ0gJTXQ1YiIiIi0aU0e4p84cSLPP/88PXv2pG/fvmRmZpKfn8/FF18MwPz58ykoKOD2228HIDs7m6ysLPr06UN5eTlLly7lwIEDzJgxw/eZixcvZuHChdxxxx0kJyf7emjDw8MJDw9vhtNsWebm2uWlzh4RdBcZi4iIiLQ1TQ6oo0ePprS0lLfffpvCwkK6devGfffd57ueoLCwkPz8fN/xHo+HpUuXkp2djdVqZeDAgcyePZvk5GTfMStWrMDlcvH000/7fa+MjAyuvPLK0z23VmGaJuaWTYCWlxIRERFpDoYZTBccNIO8vDy/5adamrl/D55HZoIjDMsz/8awn3hilzSNYRh07tyZnJycoLouRs6c2jY0qV1Dl9o2dLV229rt9lOaJKXbHZ2hurtH0X+IwqmIiIhIM1BAPUO+25tqeF9ERESkWSigngGztBj2eO+gZQxSQBURERFpDgqoZ8Dc+iWYJqSmY3RIDHQ5IiIiIiFBAfVMaHhfREREpNkpoJ4m0+3G3PYl4F3/VERERESahwLq6dr9LVSUQ3QM9Owb6GpEREREQoYC6mny3T1q4DAMizXA1YiIiIiEDgXU0+Rb/1TD+yIiIiLNSgH1NJhHj0D2fjAsGIOGBbocERERkZBiC3QBbVJ1FQwZCW4XRlRMoKsRERERCSkKqKfB6JKG9fYHdD9iERERkRagIf4zYBhGoEsQERERCTkKqCIiIiISVBRQRURERCSoKKCKiIiISFBRQBURERGRoKKAKiIiIiJBRQFVRERERIKKAqqIiIiIBBUFVBEREREJKgqoIiIiIhJUFFBFREREJKjYAl1Ac7PZQu6U2j21aehS24YmtWvoUtuGrtZq21P9PoZpmmYL1yIiIiIicso0xC9Bq7Kykv/6r/+isrIy0KVIM1Pbhia1a+hS24auYG1bBVQJWqZpsnfvXtTJH3rUtqFJ7Rq61LahK1jbVgFVRERERIKKAqqIiIiIBBUFVAladrudjIwM7HZ7oEuRZqa2DU1q19Cltg1dwdq2msUvIiIiIkFFPagiIiIiElQUUEVEREQkqCigioiIiEhQUUAVERERkaCim+pKQC1atIgNGzZw6NAhHA4Hffv25brrrqNLly6+Y0zT5M033+Tjjz+mrKyMPn36cPPNN9OtW7cAVi5NsWjRIhYsWMCll17K1KlTAbVrW1ZQUMDrr7/O119/TU1NDZ07d+a2226jZ8+egNq2rXK73bz55pusWbOGoqIiEhISuPDCC7niiiuwWLz9WWrbtmH79u0sWbKEvXv3UlhYyO9//3tGjhzp238q7eh0OnnttddYt24dNTU1DBo0iGnTptGxY8dWOQf1oEpAbd++nQkTJvDoo4/ywAMP4PF4mD17NlVVVb5jFi9ezPvvv89NN93E448/Tnx8PLNnzw6627JJ47KyssjMzKR79+5+29WubVNZWRkPPvggNpuNP/7xjzz99NNcf/31REZG+o5R27ZNixcv5qOPPuLmm29m7ty5XHfddSxZsoRly5b5HaO2DX7V1dX06NGDm266qdH9p9KO8+bNY8OGDdx5553MmjWLqqoq/vKXv+DxeFrlHBRQJaDuv/9+LrzwQrp160aPHj2YPn06+fn57NmzB/D+X94HH3zAlClTGDVqFGlpacyYMYPq6mrWrl0b4OrlZKqqqnj++ee59dZbiYqK8m1Xu7ZdixcvpmPHjkyfPp3evXuTnJzM2WefTUpKCqC2bct27tzJiBEjGDZsGMnJyZx33nkMHjyY3bt3A2rbtmTo0KFcffXVjBo1qsG+U2nHiooKVq5cyfXXX8/gwYNJT0/n//2//8f+/fvZvHlzq5yDAqoElYqKCgCio6MBOHLkCEVFRQwZMsR3jN1uZ8CAAezYsSMgNcqpe+mllxg6dCiDBw/22652bbs2btxIz549efrpp5k2bRr33nsvmZmZvv1q27brrLPOYuvWrWRnZwOwb98+duzYwdChQwG1bag4lXbcs2cPbrfb77/dHTp0IC0tjZ07d7ZKnboGVYKGaZq8+uqrnHXWWaSlpQFQVFQEQFxcnN+xcXFx5Ofnt3aJ0gTr1q1j7969PP744w32qV3briNHjvDRRx9x2WWXMWXKFLKysnjllVew2+2MHTtWbduGTZo0iYqKCu666y4sFgsej4err76a888/H9Dvbag4lXYsKirCZrP5OovqH1P3/pamgCpB4+WXX2b//v3MmjWrwT7DMPxe6wZowS0/P5958+Zx//3343A4Tnic2rXt8Xg89OrVi2uvvRaA9PR0Dhw4wIoVKxg7dqzvOLVt27N+/XrWrFnDHXfcQbdu3di3bx/z5s3zTZaqo7YNDafTjq3Z1gqoEhT+9a9/sWnTJh5++GG/GYLx8fEAvhmldUpKShr8358Ejz179lBcXMwf/vAH3zaPx8O3337LsmXLeOaZZwC1a1uUkJBAamqq37bU1FQ+//xzQL+zbdnrr7/OpEmTGDNmDABpaWnk5eXx7rvvcuGFF6ptQ8SptGN8fDwul4uysjK/XtSSkhL69evXKnXqGlQJKNM0efnll/n888956KGHSE5O9tufnJxMfHy830XZLpeL7du3t9oviTTd2WefzZNPPskTTzzh++rVqxfnn38+TzzxBJ06dVK7tlH9+vXzXaNYJzs7m6SkJEC/s21ZdXW1bzmpOhaLxddrprYNDafSjj179sRqtfodU1hYyP79++nbt2+r1KkeVAmol19+mbVr13LvvfcSERHhu7YlMjISh8OBYRhceumlLFq0iM6dO5OSksKiRYsICwvzXRclwSciIsJ3HXGdsLAwYmJifNvVrm3TZZddxoMPPsg777zD6NGjycrK4uOPP+aWW24B0O9sGzZ8+HDeeecdEhMTSU1NZd++fSxdupSf/OQngNq2LamqqiI3N9f3+siRI+zbt4/o6GgSExNP2o6RkZGMGzeO1157jZiYGKKjo3nttddIS0trMOm1pRimLh6RALryyisb3T59+nTfNU91CwpnZmZSXl5O7969ufnmmxsEIAluf/7zn+nRo0eDhfrVrm3Ppk2bmD9/Prm5uSQnJ3PZZZdx0UUX+farbdumyspKFi5cyIYNGyguLqZDhw6MGTOGjIwMbDZvf5batm3Ytm0bDz/8cIPtY8eOZcaMGafUjjU1Nbz++uusXbvWb6H+xMTEVjkHBVQRERERCSq6BlVEREREgooCqoiIiIgEFQVUEREREQkqCqgiIiIiElQUUEVEREQkqCigioiIiEhQUUAVERERkaCiO0mJSJvyySef8MILL5xw/5/+9CcGDhzYihX5O3LkCLfffjvXXXcdl19+ecDqaE6heE4iEtwUUEWkTZo+fTpdunRpsD01NTUA1YiISHNSQBWRNqlbt2706tUr0GVIM3K5XBiGgdVqDXQpIhJgCqgiErKuvPJKJkyYQFpaGkuXLiUvL49OnTqRkZHBmDFj/I7dv38/b7zxBt9++y01NTV06dKFyy67jAsvvNDvuPLyct5++202bNhAQUEBkZGR9OrVi+uvv56uXbv6Hbt06VI+/PBDSkpKSEtL44YbbqBv374/WHPdJQwPPfQQn376KZ999hmmaTJgwABuuukmOnTo4Hd+GRkZXHnllX6fMWPGDAYMGMCMGTMafObatWvZsGEDbrebc889l2nTplFVVcW//vUvNm/ejMPh4Pzzz+faa6/13X+9jmmavPPOO3z00UeUlJTQrVs3fvWrX3H22Wf7HZeTk8N//vMftmzZQkVFBZ06dWLChAn89Kc/9R1Td6/w22+/nX379rFu3TqKiop4+umnG/wcRaT9UUAVkTbJ4/Hgdrv9thmGgcXiP/dz48aNbNu2jSuvvJKwsDBWrFjBs88+i9Vq5bzzzgMgOzubBx98kNjYWG688Uaio6NZs2YNL7zwAsXFxUyaNAmAyspKHnroIY4cOcKkSZPo06cPVVVVfPvttxQWFvoFq+XLl9O1a1emTp0KwMKFC3n88cf529/+RmRk5EnP78UXX2To0KHccccdHD16lNdee43nn3+eP/3pT6f9M/vHP/7ByJEjmTlzJnv37mXBggW43W6ys7MZNWoUF110EVu2bGHx4sV06NCBiRMn+r1/2bJlJCUlMXXqVEzTZPHixTz22GM8/PDDvuB98OBBHnjgARITE7n++uuJj4/n66+/5pVXXqG0tJRf/vKXfp85f/58+vbty29+8xssFgtxcXGnfX4iEjoUUEWkTbr//vsbbLNYLLzxxht+20pLS3n88ceJj48HYNiwYfzud79j/vz5voD6n//8B5fLxZ/+9CcSExN9x1VUVPDWW29x8cUXExkZyfvvv8+BAwd44IEHGDx4sO97jBo1qkEtERER/OEPf/AF5oSEBP74xz/y1VdfNei9bcyQIUO46aabfK/Lysp4/fXXKSoq8p1LUw0bNozrr78egMGDB7Nz507WrVvH9ddf7wujgwcP5ptvvmHNmjUNAqrH4+GBBx7A4XD4apwxYwYLFy7kwQcfBODVV18lIiKCWbNm+YL44MGDcblcvPvuu/zsZz8jOjra95mdOnXi7rvvPq3zEZHQpYAqIm3S7bff3mAo2DCMBscNGjTIL9BZLBZ+9KMf8dZbb3H06FE6duzItm3bGDRokC+c1hk7dixfffUVO3fu5JxzzuHrr7+mc+fOfuH0RIYNG+bXm9u9e3cA8vLyTun8RowY4fc6LS3N9/7TDajDhw/3e921a1e++OILhg0b1mD75s2bG7x/1KhRvnAK3hA+fPhw1q1bh8fjweVysXXrVi6++GLCwsL8eriHDh3KsmXL2LVrF0OHDvX7TBGR4ymgikib1LVr11OaJNVYmKvbVlpaSseOHSktLSUhIaHBcXXXe5aWlgJQUlLSIMSeSP1eQgC73Q5ATU3NKb0/JibmjN5/KjXVXWPa2PbGvs+JfpYul4uqqiqqqqpwu90sW7aMZcuWNVpD3c+yTmM/dxERBVQRCWlFRUUn3FYXAmNiYigsLGxwXEFBgd9xsbGxHD16tGUKPQ12ux2Xy9Vg+/EhsLmc6Gdps9kIDw/HarVisVi44IILmDBhQqOfkZyc7Pe6sV5vERHdSUpEQtrWrVv9gpXH4+HTTz+lU6dOdOzYEfBeBrB161ZfIK2zevVqwsLCfBOAzjnnHHJycti6dWur1f9DkpKS+P777/22bd26laqqqhb5fp9//rlfz2plZSWbNm2if//+WCwWwsLCGDhwIHv37qV79+706tWrwdfxPcMiIo1RD6qItEkHDhxoMIsfICUlhdjYWN/rmJgYZs2axS9+8QvfLP5Dhw4xc+ZM3zG//OUv+fLLL3n44YfJyMjwzeL/8ssvue6663yTfS677DI+/fRTnnjiCSZPnkzv3r2pqalh+/btDBs2jEGDBrX4edd3wQUXsHDhQhYuXMiAAQM4ePAgy5YtO6VVAk6HxWJh9uzZTJw4EY/Hw+LFi6msrPSbmX/jjTfy4IMP8tBDD3HJJZeQlJREZWUlubm5bNq06YxWIRCR9kMBVUTapBPd7vTWW29l/PjxvtcjRoygW7duvPHGG+Tn55OSksIdd9zB6NGjfcd06dKFRx55hAULFvDyyy9TU1ND165dmT59ut86qHWz0998800yMzN58803iY6OplevXlx00UUtdq4ncvnll1NRUcEnn3zCe++9R+/evbnrrrv461//2iLf76c//SlOp5NXXnmF4uJiunXrxh/+8AfOOuss3zGpqanMmTOHt99+mzfeeIPi4mKioqLo3Lmz3+QoEZEfYpimaQa6CBGRllC3UP/NN98c6FJERKQJdA2qiIiIiAQVBVQRERERCSoa4hcRERGRoKIeVBEREREJKgqoIiIiIhJUFFBFREREJKgooIqIiIhIUFFAFREREZGgooAqIiIiIkFFAVVEREREgooCqoiIiIgEFQVUEREREQkq/x87oLpDXME9XQAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    final error(train) = 5.52e-03\n",
-      "    final error(valid) = 8.77e-02\n",
-      "    final acc(train)   = 1.00e+00\n",
-      "    final acc(valid)   = 9.77e-01\n",
-      "    run time per epoch = 1.95\n",
-      "--------------------------------------------------------------------------------\n",
-      "learning_rate=0.20 init_scale=0.20\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    final error(train) = 5.21e-03\n",
-      "    final error(valid) = 8.95e-02\n",
-      "    final acc(train)   = 1.00e+00\n",
-      "    final acc(valid)   = 9.76e-01\n",
-      "    run time per epoch = 1.92\n",
-      "--------------------------------------------------------------------------------\n",
-      "learning_rate=0.20 init_scale=0.50\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    final error(train) = 7.20e-03\n",
-      "    final error(valid) = 1.02e-01\n",
-      "    final acc(train)   = 1.00e+00\n",
-      "    final acc(valid)   = 9.74e-01\n",
-      "    run time per epoch = 1.96\n",
-      "--------------------------------------------------------------------------------\n",
-      "learning_rate=0.20 init_scale=1.00\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    final error(train) = 1.73e-02\n",
-      "    final error(valid) = 1.31e-01\n",
-      "    final acc(train)   = 9.98e-01\n",
-      "    final acc(valid)   = 9.66e-01\n",
-      "    run time per epoch = 1.92\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 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": 16,
-   "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        | 5.52e-03           | 8.77e-02           |  1.00            | 0.98             |\n",
-      "| 0.2        | 5.21e-03           | 8.95e-02           |  1.00            | 0.98             |\n",
-      "| 0.5        | 7.20e-03           | 1.02e-01           |  1.00            | 0.97             |\n",
-      "| 1.0        | 1.73e-02           | 1.31e-01           |  1.00            | 0.97             |\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": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "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 = 2.25\n",
-      "--------------------------------------------------------------------------------\n",
-      "learning_rate=0.20 init_scale=0.20\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "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 = 2.21\n",
-      "--------------------------------------------------------------------------------\n",
-      "learning_rate=0.20 init_scale=0.50\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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oI/r160fv3vv/XN944w3eeecdsrOzKS8vp6qqin79+jXou2/atAmHw+FTT8+ePYmNja2zbVhYGGVlZQ06blM1OqDOnj2b008/ndGjRwMwYcIEVq1axdy5c7nyyivrbD9hwgSf5SuvvJLly5ezYsUKb0D99NNPSU9PZ/z48YDnXwlr167l008/ZdKkSY0t0a+MMYw9Jp4Xl+zks84nM3b7Amy6zC8iIm2YMaZRl9n9pXZKpzfeeIOkpCSfz0JCQsjKygIgIiKizr7h4eE+y5Zl+YTT2nW/th94xnkWFhb6rOvYsSMjRoxg5syZ3jGgV111lffzWbNm8fDDDzNlyhSGDh1KZGQkL7/8Mj/++OPhvnKd2g6uuT4FBQU+Qx6OhkYFVJfLxebNm33GRYDnXxLr169v0DHcbjdlZWU+d41t2LCBMWPG+Gw3YMAAPvvss0Mep6qqiqqqKu+yMcbbyA35wz2aTu0Wyxsrc8kljmXtj+XEFQux9Rvk15pao9p29Hd7SvNT2wYntWvwaitt27t3b0JDQ9m+fTsnnnhinc9rA2pD9OrVi88++8wnqC5fvpyoqCiSk5MPu2+/fv3YsKHumN3x48fz+OOPM27cOLKyshg3bpz3s6VLlzJkyBCfjsHG1NuzZ09cLherVq1i0CBPZtm0aVOdoAzw888/N6hn9kj+e2lUQC0qKsLtdtfp7o2NjaWgoKBBx5g9ezYVFRU+DV9QUEBcXJzPdnFxcYc95kcffcT777/vXe7WrRtTp04lMTGxQXUcbRcPKue1xVnMTj2ZESvfJenOhzF23ZPWFAf/K1aCh9o2OKldg1dj2rasrAyn03kUq2l+8fHx3HzzzTz88MMYYxg+fDj79u1j6dKlREZG0rlzZ8Az3vPA72aMwW63+6y78cYbefXVV3nwwQe54YYb2LRpE8888wy///3vvZfv69sPYPTo0UyfPr3O+nHjxnHfffdx3333cdJJJ5GWlub9rEePHrz//vt89913dOnShRkzZrBq1SrS0tK8x7HZbBhjvMu15wfo27cvp59+OpMnT+bpp5/G4XDwwAMPEB4eXqfGZcuWcffddx+2fUNCQn41iB9OkxJTfYm4ISl5wYIFzJgxg7vuuqveMQ0Hqq9r/EDjx4/3mR+sdtvc3Nx65yRraSM7OXnDwNq47mxyR8A3X2LTZf5GMcaQlJTEzp07Gz1NiAQ2tW1wUrsGr6a0bWVlpc+VztbijjvuID4+nueff56tW7cSExPDcccdx6233urNFwdfxa2d5unAdQkJCbzxxhs8+uijvPXWW8TFxXH55Zdz6623ererbz/wBNFHHnmEdevW0bNnT+/6sLAw77RXzzzzjM9+V155JRkZGdx0000YYxg3bhzXXnst8+fP927ndnum3Tr4/LXf6emnn+bOO+/kwgsvJCEhgcmTJ7N9+3afGpcvX05RURHnnHPOYdu3srKSnJycOusdDkeDOhON1Yi/RVwuF1dddRW33367zx32r732GpmZmTz88MOH3HfRokW89NJL3H777d67w2r94Q9/YMyYMT6Bc/bs2Xz22We89NJLDS0P8ATUQPmFeHrBDr7NKuK0ncuZmFiA7Zo/+rukVsUYQ3JyMjk5OfqfXZBR2wYntWvwakrbFhUVeadgksZ79NFHKSoq4sknnzzq53I6nQ3OTjfddBP9+/dn4sSJh93uUO3vdDobFFAbNVG/w+Gge/fudZ7BmpGRQZ8+fQ6534IFC3jxxReZOHFinXAKnjEfq1f7PhY0IyPD58601mjsMfEAfNdhIPmrMzRpv4iIiDTIxIkTSU1N9fZwBoKKigqOPfZYfvvb3x71czX6SVJjx47lq6++Yv78+WRnZzNt2jTy8vI488wzAXj77bd54YUXvNvXhtNrrrmG3r17U1BQQEFBAaWlpd5tzjvvPFatWsXMmTPZvn07M2fOZPXq1XVunGpt+iSE06d9GC6bgzmx/WF9xq/vJCIiIm1eTEwMEydO9I4RDQShoaFMmjSp3pkHmlujx6COGDGC4uJiPvjgA/Lz8+ncuTP33nuvt7s2Pz/f56kD8+bNo7q6mldffZVXX33Vu37UqFHccsstAPTp04dJkybx7rvvMn36dJKSkpg0aVKrmwO1PmOPacf6hTv4IuVE/mf5IkKP1d38IiIiIofTqDGorUEgjUEFcLktbnr/Z/ZUGW7d8jGj771Tk/Y3kMazBS+1bXBSuwYvjUENbo0Zg9pQLToGVRrPYTOc29fzhK3ZCUOxftZlfhEREZHDUUBtAWf3bkeIVc2W6BTWrvjJ3+WIiIi0CPWkt021T+Q6EgqoLSAm1M4oTycqnxRGYgXAPK0iIiJHU2ho6FF/XrsEHrfbTXFxcb2Pg20MPdqohYwd1o0vv9jK0vg+7Fq9hqRBA/1dkoiIyFETGhpKSUkJhYWFQf+I1NYuJCSEysrKZjteZGQkDseRRUwF1BbStX0EA6y9rDLt+GzNTq7XzfwiIhLkIiMj/V2C/IpAvblRl/hb0Nie0QDMc3ektKz5/qUiIiIiEkwUUFvQkKF9SS7fS4kjnK+X/uzvckREREQCkgJqC7I7HIwJ8TzE4NNtVbgDqCtdREREJFAooLaw0YO6EOEqY7uJ5IfsIn+XIyIiIhJwFFBbWHjf/oze45msf/aP2X6uRkRERCTwKKC2MGO3c15iNcZy82OxnW2FFf4uSURERCSgKKD6QfLQIQzLWwvA7HV7/FyNiIiISGBRQPWH3v0Zu/dHAL7eUkhxRbWfCxIREREJHAqofmDsdvr3SqHLvh1UuA1fbirwd0kiIiIiAUMB1U9sQ09ibPYCAD7dkE+1W1NOiYiIiIACqv/07s8ppZuJqdxHXqmLxduK/V2RiIiISEBQQPUTY7cTOvB4zt6xGIBP1uf7uSIRERGRwKCA6kdm6EmcveN7HO5q1uWWsXFPmb9LEhEREfE7BVR/6t2fdqE2RuSuAmD2z+pFFREREVFA9SNjt2MGn+i9WWrB1iL2lrn8XJWIiIiIfymg+pkZchI9i7PpU7wNlxs+36BeVBEREWnbFFD9rXd/iI7l/K3fADBnYwGV1W7/1iQiIiLiRwqoflZ7mX943k8kWOUUVlTzXWaRv8sSERER8RsF1ABghpyE3XJzTvZCwDPllGVp4n4RERFpmxRQA0HNZf4zs74lxFhsya/gp92ackpERETaJgXUAFB7mT/aVcap1dsBmPXzXj9XJSIiIuIfCqgBwgw5CYAxP30CwNLsfewsrvRnSSIiIiJ+oYAaKGou83fes4WB0dVYwKeackpERETaIAXUAFF7mR9gbEEGAPN+KaS0qtqfZYmIiIi0OAXUAFJ7mX/gD7PpFO2ktMrN15s15ZSIiIi0LQqogaTmMr+tpJixMSUAzF6/F7emnBIREZE2RAE1gBx4mf/UrAVEOm3sKK7ihx0lfq5MREREpOUooAYYM/RkAMJ+XMgZ3WMATTklIiIibYsCaqDp3Q+iY6GkmPNsOdgMrNpZytaCCn9XJiIiItIiFFADjLHtv8zfYfUChqdGATB7vaacEhERkbZBATUA1V7mt35czNiesQB8vaWQogpNOSUiIiLBTwE1EB1wmf/YPRvpHh9KZbXF3E0F/q5MRERE5KhTQA1AxmbHDBnhWfhhIecf0w6Az9bn43JryikREREJbgqoAap20n7rx8WcnBJBbJidPWUuvt9a7OfKRERERI4uBdRAdcBlfufG1ZzbKw6AT9ZryikREREJbgqoAerAy/zWioWc0ysehw3W55WzPq/Mz9WJiIiIHD0KqAHswMv8cU44pYtn4v6/LthBluZFFRERkSClgBrIDrjMz88Z/GZAIklRTnaXVDF5ThZLsjUeVURERIKPAmoA87nMv3wBiZFOnjqnK8d1jKDc5eaJ/27ng5/2YFm6s19ERESChwJqgPNe5l+5BMvlIibUzkOnd+bcXnFYwBsrc3luUQ6V1W7/FioiIiLSTBRQA91Bl/kBHDbD749P4nfDOmIz8E1mEfd/uZW9ZS4/FysiIiJy5BRQA9zBl/kPdF7veB46vTNRITY27Cnnzi8y+WVvuT/KFBEREWk2CqitgBl6MuC5m99y+faSDkiK5K/ndCU1JoQ9pS7umZvFwqwif5QpIiIi0iwUUFuDXsdCTByU7oOfV9X5ODk6hCfP7sLg5Egqqy2eXLCDdzJycevmKREREWmFFFBbAWOzYwafCIC1fGG920SG2Hng1FTGHRMPwLur9/DUgh2Uu3TzlIiIiLQuCqitxOEu89ey2wzXD+nIrSck4bDBoq3F3Ds3i9ySqpYsVUREROSIKKC2Fr9ymf9AZ/SI439HpxEbamdzfgV3fpGpx6OKiIhIq6GA2kp4LvN77uZ3f/nxr07Of2yHCJ46pwtd4kIpKK/mvi+38vXmwpYoVUREROSIOJqy05w5c5g1axYFBQWkpqYyYcIE+vbtW++2+fn5vPHGG2zevJmdO3dy7rnnMmHCBJ9tvvnmG1566aU6+7711luEhIQ0pcSgZEaPxVo4D9auxPpuLmbk2YfdvmNUCFPP6sKzi3awJHsfz32fw9bCCq4akIjdZlqoahEREZHGaXRAXbRoEdOmTePGG2+kT58+zJs3j8cff5xnn32WhISEOttXVVURExPDRRddxKeffnrI44aHh/P888/7rFM49WWSUjEXXoU1499Y7/0b69iBmISOh90n3GnjnpEp/GdVHu//tIcP1+5lW2EFt5/UiQinvYUqFxEREWm4Rl/inz17NqeffjqjR4/29p4mJCQwd+7cerfv0KED1113HaNGjSIiIuKQxzXGEBcX5/MjdZkzzoeex0JFGe5p/4fl/vW79G3GcPXARG4fkYzTZli2vYS752Sxs7iyBSoWERERaZxG9aC6XC42b97MhRde6LM+PT2d9evXH1Eh5eXl3Hzzzbjdbrp27cpll11Gt27dDrl9VVUVVVX77043xhAeHu59H6yM3YG5fhLVD90K61fDN59hRp/foH1P7R5Hp5hQHv9vNlsLK7lzThb3jkyhf8fIo1x109S2YzC3Z1ultg1OatfgpbYNXoHato0KqEVFRbjdbmJjY33Wx8bGUlBQ0OQiOnXqxM0330xaWhplZWV89tlnTJkyhaeeeork5OR69/noo494//33vcvdunVj6tSpJCYmNrmOViM5meIb/kTBy09iffg6CaedgzMlraG70rdrCnd+lMG6XcVM+Wobk8/ozUUDUo5y0U2XlJTk7xLkKFHbBie1a/BS2wavQGvbJt0kVV/KPpLk3bt3b3r37u1d7tOnD3fffTeff/45119/fb37jB8/nrFjx9Y5f25uLq5DzBMaTKxBJ2GOScf6OYOdU+/HfvcTGFvDx5Q+clon/u/7HL7LKuKJuetZnbWbG4Z0DKibp4wxJCUlsXPnzl+dtUBaF7VtcFK7Bi+1bfBq6bZ1OBwN6kxsVECNiYnBZrPV6S0tLCys06t6JGw2Gz169GDnzp2H3MbpdOJ0Ouv9rE388hiDmTAR66Fb4Zd1uOd+jO3s8Q3ePcRuuOOkZNJiQ/hPRh6z1+eTXVjBXSenEBUaWDdPWZbVNtq0DVLbBie1a/BS2wavQGvbRt0k5XA46N69OxkZGT7rMzIy6NOnT7MVZVkWWVlZulHqV5j2HTCX3gCANfMtrB1bG7e/MVx6XAL3nJJCqN2wcmcpd83JIruo4miUKyIiItIgjb6Lf+zYsXz11VfMnz+f7Oxspk2bRl5eHmeeeSYAb7/9Ni+88ILPPpmZmWRmZlJeXk5RURGZmZlkZ2d7P58xYwYrV65k165dZGZm8vLLL5OZmclZZ511hF8v+JmTz4TjhoKrCve/n8Oqrm70MU5Mi+YvZ3UhIcLBjuJKJn+RxY85JUehWhEREZFf1+gxqCNGjKC4uJgPPviA/Px8OnfuzL333usdT5Cfn09eXp7PPpMnT/a+37x5MwsWLCAxMZEXX3wRgJKSEv75z39SUFBAREQE3bp14+GHH6Znz55H8t3aBGMMtmtuwf3nP0LWJqzP38eMvazRx+neLoynz+nKE99u5+e8Mh75ehvXD+7A2D7xAXdnn4iIiAQ3YwXSgINmkJub6zP9VFvhXvw11qvPgt2B7f6nMZ0PPUXX4VRVu3lp6U7mby4C4Kyesdw0NAmnveVDqjGG5ORkcnJyAmpcjBw5tW1wUrsGL7Vt8GrptnU6nQ26SarRl/glMJnhp8LAE6Dahfvfz2K5mhbSnXYbE09IZsKgRAwwd1Mhf56/laLy4J8ZQURERAKDAmqQMMZgu/oPEBUN2ZlYs6cf0bHGH9ueB05NJdxh46fdZdzxRRaZ+eXNWLGIiIhI/RRQg4iJicf2mz8AYH3+PtaWjUd0vKEpUTx5TheSopzsLqni7rlZLN5W3BylioiIiBySAmqQMUNPxgw7Bdxu3K89h1VVeUTHS4sN5alzunJcxwjKXRZPfLud91bnaQySiIiIHDUKqEHIXPk7iImDnG1YH//niI8XE2rnodM7M6Z3HAD/ycjjqQU7qHC5j/jYIiIiIgdTQA1CJioG2zV/BMCaOxNr09ojPqbDZrhpWBI3H5+E3cDCrcXcMzeL3JK2N2OCiIiIHF0KqEHKDDgec+LpYFm4X3seq6J5bnA6u1cc/zs6jZhQO5vzK7jji0zW5ZY2y7FFREREQAE1qJnLb4S49rA7B+vDN5rtuP06RvDXc7rQNS6UwvJqHpi3lXm/FDTb8UVERKRtU0ANYiYiCtu1twJgzZ+N9XNGsx27Y1QIfzmrCyd2jsLlhr8t3skrK3ZR7dbNUyIiInJkFFCDnOk/GDPybADc0/4Pq7z5LseHO21MPiWFy49rD8AnP+fzyNfb2FdR3WznEBERkbZHAbUNMJdcB+07wJ7dWDNea9Zj24zhivREJp/SiVC7YeXOUu6ck8m2wopmPY+IiIi0HQqobYAJi8A2YSIA1rdzsNb80OznOCkthr+c1YXECAc5xVVMnpPF8u37mv08IiIiEvwc/i5AWoY5Jh1z+lis+bNxv/43bA//DRMR1azn6N4ujL+e25Wp325nbW4Zj36TzTWDEhnftx3GmGY9l4iISDCodluUVLnZV1FNRbWbqmoLl9uiym3tf3/AutrlA997P6s+6NXtpsoNruqaV7e73mOOH1DCJX0i/f1H4UMBtQ0xF13r6T3dvQPr3X9hrr+t2c8RF+bgkdFp/GPZTr78pZDXf8wlK7+CW05IIsSuDnsREQlO1W6Lkspqiivd7KusprjC87Ovspriymr2VVRTXOH2vK/c/1lJpRt/315cUunycwV1KaC2ISY0FNt1f8L95L1Y33+NNXgEZuDwZj+P0264ZXgS3eLDeGXFLr7JLGJ7cSX3jkyhfYSz2c8nIiLSXNyWRVFFTaCsrGZfTaj0hk1v6HQfsE01JVVH9nTFMIeNcIfBaTc4bAanzYbDDg6bDacNHHYbTlvNZ95tfN877MZnm8NuX/MaYrfRo3MnKor2NNOfYPNQQG1jTM++mLPGYc35CPebL2Lr2RcTFdP85zGGMX3iSY0N4cnvtrNxTzl3fJHFfSNT6J0Q3uznExERaYiSympyS6rIK3V5X/NKqsgtrSK3xMXesiqO5EneEU4b0aF2okLsRIfYiAq1Ex1Ssxxqr/nM5llX81lkiB2n3T9D4YwxtIsMIafIL6c/JAXUNsiM+w1WxnLI2Yb19j8wN9111M41ICmSv57Tlcf+m822wkru+3IrfzwhiVO7xR61c4qISNtUVW2xp3R/+MwtrSKvxEVeaZU3jJY2sKczsjZE1gbLEDtRoTbvclSInZia16hQmzdoOmy656I5KKC2QcYZgu36SbifuAtr2XdYg0/EDD35qJ0vOTqEJ8/uwjMLc1i2fR/PLsohq6CCqwYkYtcvsoiINIBlWRSWV3tDp+e1itwDekILylwNGs8ZHWonIcJBYqSTxAgHCRFOEiKdJEZ63rcLd+j/T36mgNpGma69MOdejPXpe7j/8zK23v0wMfFH7XwRTjv3jUrhP6vyeP+nPXy4di9ZBRXccVInIkPsR+28IiIS+CzLYl+l29v7mVdaxZ6a1/1h1EVVA55W6LQZT9CMdJIQ4QmdiRFOEiOdJER41oc5dNNuoFNAbcPM2MuwVi2D7C2433wZ2833HtXpoGzGcPXARLrEhfK3xTms2FHC5DlZ3D8qlU4xIUftvCIi4j+WZVFcGz5LfMOn59XFntIqKqp/PXwaID7c4e3pPDB0JtaE0ZhQu6Y2DAIKqG2YcTg9l/ofuwNWLsZa8g3mhNOO+nlHdo0hOdrJE//dTnZRJXfOyWTyySkMTA6sOdhERAJFtdsip7iSrIIK8stdhNhthNgNoXYboQ7PndjeV7shxFHzarfhsHHUAptlWRRXVB/U67n/fW2PaGUDwidATM2l9/YRzprX2svwThIiHbQLd/rtZiJpWQqobZzp3A1z/uVYM9/CevufWH3SMfHtj/p5e7UP56/nduUv32azPq+ch7/exvWDOzC2T7z+5SsibZZlWeSXV5OZX05WQYX3Z1thZYMub9fHZvAG2NrQ6nlfE3IdB73abYQcEHY96204d1bzS84e9pRUkVfmufN9T2nDLrsDxIYdHD5rej8jnLSvCaOaL1tqKaAK5pz/wVq5BDI34n7jb9gm/rlFQmK7cAePnpHGy0t3Mn9zEa+s2E1mQQW/H9YRp/6SEpEgV1blZmuhJ4BmHhBGiyuq690+1G5IiwulQ6STKrdFhctNZXU9r9We19rc6Lag3OWm/CjOxR4XZj8gcHrCZ/sIz9jP9hEO2il8SiMpoArGbvdc6n9kEqz5AWvBl5hTzmqRc4fYbUw8IZmucWFM+3E3834pZHtRJfeckkK8JvUXkSBQ7bbYXlxJVn5NCK0Jpbv2VdW7vc14Zj/pEhdK17hQutT8dIxyYmtg54FleR5jWXGoAOvaH2QrXBaV1W4qfNa5ffatrHYTHx1JlM1F+3DPmM/2NWG0XbhDnQrS7BRQBQCT3Blz4VVY77+G9d6rWMcOxLTv0DLnNoZxfdvROTaEvy7YwbrcMu74IpP7T+1McnKLlCAicsQsy2JvmatOj+i2wkpch7gMHh/uqBNEU2NCCD3Cu8yNqXmSkB2immGmFGMMycnJ5OTkYFn+fjCntAUKqOJlzrwAa+Vi2LQO97T/w3bbIxhby/2reHCnKJ48pwuPfbOdHcWV3D0nk0mVTga2g0in/nUuIv5nWRZlLjeF5dXkl7m8l+hrf/ZV1j8JfJjDeAPo/p8wYkI1zZ5IfRRQxcvY7Ngm/An3IxPh5wys/36OOW1Mi9aQGhPKU+d04a8LdvBjTglPztuAw2YYlBzJyV2iOT41igin/kIXkebhtixKKt0UVrgoKq+msKK65vXAZZd3fVFF9WFvCrIZ6FTP5fkOjbg8LyIKqHIQ07ET5qIJWO/+E+v9aVj9BmE6dGrRGqJC7Ew5NZVP1ufz360lbM4rYdn2fSzbvg+nzTC4UyQnpUUzTGFVRA5S7bYorqwvZHqWC2tCpvfzimqacnN8qN0QG+YgNSbEp1c0NTZENwOJNAMFVKnDnHYe1o/fw/rVuF/7P2x3PYaxtWwQtNsM449tz82j+7NkXSbfZRWyIKuY7UWVLMnex5LsfYTYDUM6RXJSWgzDUqP0ZBCRNqDC5WbXvip2FFeyo7iSncVV5BRXsrfM08u5r6K6QY+6PFiE00ZMqJ3YMDsxoY6a1wOWQ+3EhNmJrfnsSMeIisjhKaBKHcZmwzZhIu6HJsKmtVjzZmHOGu+3etLiQrkyNpErjksgq6CChVuLWZBVxI7iKr7fto/vt3nC6rCUKE7qEs3QTlH6n4dIK1ZZ7WZnsSeE5hRXknPA+z2lDXzWeoiNmDDfYLk/cNqJDfNd1l3oIoFFAVXqZRI6Yi69HuvNF7E+egvruKGY5M7+rckYusaH0TU+jCvTE9iSvz+s7txXxcKtxSzcWkyo3TAsNYqTu8QwODlSYVUkANWG0JyantCcA97/WgiNcNpIjg6hU7ST5OgQkqNDSIhwEFsTSKND7dhtGu8p0popoMohmVPO8lzqX/MD7n8/h+2eJzH2wBjzaYyhe7swurcL46oBCWzOr2BBVhELsorZXVLFgqxiFmQVE+awcXxqFCenRTOoU6TGhom0oMpqNzv3VZFT5BtCc4oryWtCCE2OdtIpOkTPWhdpAxRQ5ZCMMdiu/iPuh26FzI1Yn8/AjL3c32XVYYyhR7swerQL45qBiWzaW86CrGIWZhWRW+ri28wivs0sIsJZG1ZjGJgc4ZdLetVui8KKavZ4n1Pt8r7HwIi0aIZ0isKh3h9pJdyWRW5JFdmFlWwvriR/dSG/7CpgR5FCqIg0nQKqHJZpl4C58ndYrz6D9cm7WP0GY7r19ndZh2SMoVf7cHq1D2fCoEQ27ClnQVYRC7OK2VPm4pstRXyzpYhIp43hnaM5OS2a9KRInPYj/x9hZbWbvbWhs8xFXmkVe0td5JW62FvmCaF7y1yHvWP4my1FxIbaGdk1htO7x9K9XdgR1yXSHCpcbnYUV3qCaFEl24oq2F7keV9Zfej/qGtDaG3wVAgVkYYwVpA9EiI3N5eqqvofHydNY1kW1r/+irXsO+iQjG3Kc5iw8KN+3uZ8conbslifW8aCmnGq+WX7H0odFWLjhM7RnNwlhuM6RtTpvbQsi9Iqtzd41un9LPO8LzrE87MPZjMQH+Z5NnVChIN2EU4Swh0UlLv4b2YRBeX7j9M1LpTTu8cyqmsMceHB8+9JPZUmMFmWRVFFNdlFniCaXRNCtxVWkltSdcjeUIfNkBIdQkpsCMd0ake0qVQIDTL6nQ1eLd22TqeTxMTEX69LAVUawirZh/vhiZCfhxl5Nrarbznq5zxavzRuy2JdbhkLs4pYuLXYJxBGh9oZlhKJZeETSMtdDTt/iN3QPsJB+3AH7SM8z6r2LO9/HxfmOOQNHNVuix9zSpi/uZAl2fu8j0e0GRjSKZLTu8cyLCWq1d9xrP/Z+Ve122J3zWX57KIKbyDdXlRB8SGehASef8ylxnjm+kyNCfG+7xDpxG4zatcgprYNXgqoLUQB9eix1q3C/cwUAGx/fAAz4Pijer6W+KWpdluszS1lYVYxi7YWU3iYXtCoEJsndIY79gdPn2UnUSG2ZustKq6oZkFWEV9tLmTjnnLv+ugQG6fUDAHo2S6sVfZO6X92LaOsynNZfluhpyc0u6iS7TVjRQ/1bHgDdIhykhoTQsoBITQ15td7Q9WuwUttG7wUUFuIAurR5Z7xb6y5MyE6FttD/4eJiT9q52rpX5pqt8Wa3aVk7Cwlwmmr0/vpz+mqthVW8PXmQr7eUsTeA4YnpMaEMLp7LKO6xdA+wum3+hrLH/+zq3C5ySt14bBBiN2G02Zw2j0/gfgIyqpqN6VVbsqqal5dB7yvclNaVU2Za//ygZ+VVbkprqz2+W/lYCF2Q0pNCO0cE+oJo7EhdIoOafJ/6woxwUttG7wUUFuIAurRZVVV4X7sdtieBccNxXbrlKPWg6e/EOuqdltk7Cpl/i+FLM4u9t6cYjMwMCmS07rHMjw18B9U0BJtu6e0inW5ZfycW8a63DK25JdzqHt57AacdhtOuyGkJrg6bIYQe22IPSDQHvAacqjP7IYQuw1HzXK12zooSB4iXB6w7lA9nI0VG2qv6QGtCaE1QTQxsvmfDa/f2eCltg1egRpQg+euC2kRxunEduMdnpC6ejnWt3Mwo87xd1ltht1mGJQcyaDkSEoqq1m4tZj5mwtZl1vGDzkl/JBTQqTTxsldYjitewzHJIS3yiEAjVXttsgqqDggkJaSW1q39zDMYcNtWVRVWz43/FRbUO1yU37oDke/CbUbIpw2wp12wp22mvc2IhyeV591NduEO2xEhtjoGOW5LC8i0toooEqjmdSumIuuxXrvVaz3XsHq0x+TlOrvstqcyBA7Z/WM46yecewoquTrLYV8vbmQ3FIXczYVMGdTAZ2inZzWPZbTusWSGNl6hgD8mpLKatbneXpGf84rY0NeOeUu35t7bMYzC0LfxHCOSYygb2I4CREOjDFYlkW15ZkazFVtUen2hNaq2tdqy/OZu/b9AZ+53d5tqtz7P3PV7HPgMWo/d9jYHzIdBwbKA0Om72e1QVNPRBKRtkgBVZrEjD4fa/VyWLcK9yvPeJ4y5dB/Tv7SKSaE3wxI5Ir0BNbsKmX+5kIWbS1mR3EV/1mVx9ur8jguKYLTu8VyYlo0YQE+BOBAlmWxc1+V91L9z3llbC2oqDPlUYTTRp+E8JpAGk7v9uGEO+v/nsYYHAYcNjsET24XEQkaShTSJMZmwzbhT7gfvhWyNmF9Oh0z7jf+LqvNsxlDelIk6UmR3DSsmu+3FjN/SxFrdnlu/srYWcrfl+3ipLRoTu8ey7EdwgPuBqGqaje/7K3g57xS7yX7A6cCq5UU5eSYxJpAmhBOWlxowH0XERFpGgVUaTLTLgFz1S1Y/3wS69MZnqdM9ezr77KkRoTTzugecYzuEceufZV8vaWIrzcXsnNfFV9tLuSrzYV0jHLSNzGcMIeNMIeNUIchzG4j1GEjzGFq1h3wmfe953NnzdyXR6Kw3MXPNT2j63LL2LSnnKqDbhBy2DyPs63tHT0mIZz4IHpwgYiI+NLf8HJEbMNOxp2xDGvx17j//Sy2B5/DhEX4uyw5SMeoEC4/LoHL+rdnbW4Z8zcXsjCrmF37qti1r+mzXtgMhNo9YTX0oPB64PtQh40w+/6ga7MZdqzM54esveworqxz3JhQu7dntG9iOD3ahxHSyh9OICIiDaeAKkfMXHET1safIHcn1ruvYCZM9HdJcgjGGPp1iKBfhwhuGtqRZdv3sbukigqXm3KXVfPq+76i2rNc7nJ7t6udAslt4Zmf0wXQsEe91qdzbMgBgTSC5Ghnm5h9QERE6qeAKkfMRERiu34S7r/ej7VwHlb6UMzgEf4uS35FqMMzHVVTuNz1h1lPiLX2v6+uP/i63Bb9UtuTGu6mT/swojQVkoiIHEABVZqF6d0fc85FWJ9/gPuNF7F174OJa+/vsuQocdgMjhA7kSFNC5aa9FtERA5Hg7qk2ZgLroS07lBSjHva/yl4iIiISJMooEqzMQ4nthtuB2cI/PQj1tef+rskERERaYUUUKVZmU5pmIsnAGC9Pw1rx1b/FiQiIiKtjgKqNDtz2hjoPxiqKnG/8jSWq+nTGImIiEjb06SbpObMmcOsWbMoKCggNTWVCRMm0Ldv/RO05+fn88Ybb7B582Z27tzJueeey4QJE+pst3jxYqZPn86uXbvo2LEjV1xxBccff3xTyhM/M8Zgu3ai5ylT27ZgzXobc9G1/i5LREREWolG96AuWrSIadOmcdFFFzF16lT69u3L448/Tl5eXr3bV1VVERMTw0UXXUSXLl3q3WbDhg0899xzjBw5kqeeeoqRI0fy7LPPsnHjxsaWJwHCxLXDdvUtAFhffIi1YY2fKxIREZHWotEBdfbs2Zx++umMHj3a23uakJDA3Llz692+Q4cOXHfddYwaNYqIiPqfMPTpp5+Snp7O+PHjSUlJYfz48fTv359PP9VNNq2ZGTwCc9IZYFm4X30Wq7TE3yWJiIhIK9CoS/wul4vNmzdz4YUX+qxPT09n/fr1TS5iw4YNjBkzxmfdgAED+Oyzzw65T1VVFVVV+8c2GmMIDw/3vpfAYLvit1RvWFPzlKl/eu7yb6DadlR7Bh+1bXBSuwYvtW3wCtS2bVRALSoqwu12Exsb67M+NjaWgoKCJhdRUFBAXFycz7q4uLjDHvOjjz7i/fff9y5369aNqVOnkpiY2OQ65OiomPwYu+/+Ldb3XxM78kwiRp7VqP2TkpKOUmXib2rb4KR2DV5q2+AVaG3bpJuk6kvZzZ28Lcs67DHHjx/P2LFj65w/NzcXl8vVrLXIEYrvgDnvEqzZ09nzt8coaJ+MaZfwq7sZY0hKSmLnzp2a9D/IqG2Dk9o1eKltg1dLt63D4WhQZ2KjAmpMTAw2m61Oz2ZhYWGdXtXGqK+39NeO6XQ6cTqd9X6mX57AY8ZchrXmB8jcSPVrz2Gb9DDG1rAh0JZlqU2DlNo2OKldg5faNngFWts26iYph8NB9+7dycjI8FmfkZFBnz59mlxE7969Wb16dZ1j9u7du8nHlMBiHA7P+NOQUFi3CuurT/xdkoiIiASoRt/FP3bsWL766ivmz59PdnY206ZNIy8vjzPPPBOAt99+mxdeeMFnn8zMTDIzMykvL6eoqIjMzEyys7O9n5933nmsWrWKmTNnsn37dmbOnMnq1avr3DglrZtJSsFcegMA1oevY2Vn+rcgERERCUiNHoM6YsQIiouL+eCDD8jPz6dz587ce++93vEE+fn5deZEnTx5svf95s2bWbBgAYmJibz44osA9OnTh0mTJvHuu+8yffp0kpKSmDRpEr169TqS7yYByIw8GytjGWQsw/3K09jufxrjDPF3WSIiIhJAjBVIAw6aQW5urs/0UxJ4rKJ83A9NhOJCzFkXYrvk+nq3M8aQnJxMTk5OQI2LkSOntg1OatfgpbYNXi3dtk6ns0E3STX6Er/IkTIx8diuvRUAa+5MrHWr/FyRiIiIBBIFVPELM+B4zMhzAHD/+zmskn1+rkhEREQChQKq+I259Hro0AkK9mD952VdNhIRERFAAVX8yISGYbvxdrDZsJZ9h7Xkv/4uSURERAKAAqr4lenWG3P+5QBYb/8da89uP1ckIiIi/qaAKn5nzr0EuveBslLPeFR3tb9LEhERET9SQBW/M3a75ylToWGwYQ3W3Jn+LklERET8SAFVAoLpkIy5/LcAWDP/g7X1Fz9XJCIiIv6igCoBw5x0Bgw6AapduF95Bquywt8liYiIiB8ooErAMMZgu/qPEBsPOdtwf/C6v0sSERERP1BAlYBiomOwTZgIgPXVJ5St+N7PFYmIiEhLU0CVgGP6D8GcNgaAPVPvw73mBz9XJCIiIi1JAVUCkrl4AvToi1VSjPv5h3HP+UhPmhIREWkjFFAlIJmQUOx3PkbkWePAcmO9/xrWK89gVejGKRERkWCngCoByzidxE98ANuVvwe7HWvpf3E/eTfWnlx/lyYiIiJHkQKqBDRjDLbTx2C77X8hKga2bsb92O1YG9b4uzQRERE5ShRQpVUwffpje+AZSOsOxYW4n5mC++vPNC5VREQkCCmgSqth2nfANnkq5viRUF2N9fbfsd58Eauqyt+liYiISDNSQJVWxYSGYm68w3OXvzFY383F/fT9WAV7/V2aiIiINBMFVGl1jDHYzr4I28QHISISfvnZMy51ywZ/lyYiIiLNQAFVWi3Tfwi2+56G5M5QsBf3k/fiXvSVv8sSERGRI6SAKq2a6dgJ231PwcDh4KrCeu153O/+C6u62t+liYiISBMpoEqrZ8IisP3hXsz5lwNgffUJ7uf+jFVc5OfKREREpCkUUCUoGJsN2wVXYvvDvRAaDj9neMalbtvi79JERESkkRRQJaiYwSdiu/cpSEyCPbtx/2Uy1vIF/i5LREREGkEBVYKOSUnDdv/TcOwgqKzA/Y8ncX/0JpZb41JFRERaAwVUCUomMhrbxAcxZ40HwPpsBu4XHsMqLfFzZSIiIvJrFFAlaBm7Hdsl12FuuB2cIbB6Oe7H78TKyfZ3aSIiInIYCqgS9GwnnIrt7r9AuwTYtR33E3dirVrm77JERETkEBRQpU0wXXpiu/8Z6HUslJXifvFR3J++h2VZ/i5NREREDqKAKm2GiYnDdvv/Yk49FywLa+ZbuP8xFau8zN+liYiIyAEUUKVNMQ4ntt/8AXP1LWB3wIpFnqmocnf6uzQRERGpoYAqbZJt5NnY7nwUYuJgexbux+7AWrfK32WJiIgICqjShpmex3rGpXbtBSXFuJ/7M+55H2tcqoiIiJ8poEqbZtolYJv8BObE08Dtxpr+KtZrz2FVlPu7NBERkTZLAVXaPOMMwVw3CXPZjWCzYX3/Ne67b8D94RtY+Xv8XZ6IiEib4/B3ASKBwBiDOeMCrJQuuN98EXJ3Yn3+PtbcjzBDT8acOQ7Tpae/yxQREWkTFFBFDmD6DsD26MuwahnuL2fCxrVYS/6LteS/0LsftjPGwYBhGJvd36WKiIgELQVUkYMYmx0GnYB90AlYWZuwvvwYa/kC2PAT7g0/QWISZvT5mJNGY8Ii/F2uiIhI0NEYVJHDMF16YrvxDmxPvII592KIiPJc/n/3X7gn34B7xr+x9uz2d5kiIiJBRT2oIg1g4ttjLroGa8ylWN/Px5r3CezajjV3Jta8WZjBIzBnXIDpcYy/SxUREWn1FFBFGsGEhmFOPQ9r5DmwZgXuebNg3Sqs5Qs8wwC698GcMQ4z+ESMXeNURUREmkIBVaQJjM0G6cOwpw/D2rYFa94srKX/hc3rsf75JFa7RMzosZiTz8JERPq7XBERkVZFY1BFjpDp3A3bdX/C9pdXMWMvh+hY2JuLNeM13JOvx/3uv7B25/i7TBERkVZDPagizcTExmPGXYl13sVYi7/BmjcLdmzF+uoTrPmzYcBwbGdeAL36YYzxd7kiIiIBSwFVpJkZZwjmlLOwTj4T1q7EPe9jWPMDrFyMe+ViSOuBOfMCzwMAHE5/lysiIhJwFFBFjhJjDPQbhL3fIKzantTvv4atv2C9+izWB69jThuDGXUOJjLa3+WKiIgEDI1BFWkBplMatqtvwTb135gLr4LYeCjYi/XRm7gnX4f7rZewdmb7u0wREZGAoB5UkRZkomMwYy7FOms81rLvsOZ9DNu2YP33C6xv52JOPRdz4W8wEVH+LlVERMRvFFBF/MA4nZgRp2OdeBpsWIN77kzIWIb19adYKxZiLrkOM/xU3UwlIiJtkgKqiB8ZY6DPcdj7HIe1bhXut/8OO7d7xqh+9yW2K3+PSUnzd5kiIiItSmNQRQKE6TsA24P/hxl/NYSEeHpW//dPuGe8hlVe5u/yREREWowCqkgAMU4ntvMuwfbwizBwOFRXY839CPeDt2CtWIhlWf4uUURE5Khr0iX+OXPmMGvWLAoKCkhNTWXChAn07dv3kNuvXbuW119/nezsbOLj47ngggs466yzvJ9/8803vPTSS3X2e+uttwgJCWlKiSKtmknoiP2W+7EyluF+55+Qtwv336dCv0HYrvgdpmMnf5coIiJy1DQ6oC5atIhp06Zx44030qdPH+bNm8fjjz/Os88+S0JCQp3td+/ezRNPPMHo0aO59dZbWb9+Pa+88goxMTGccMIJ3u3Cw8N5/vnnffZVOJW2zqQPw3ZMOtbn72N98QH89CPuh/6IOedizLn/gwkJ9XeJIiIiza7Rl/hnz57N6aefzujRo729pwkJCcydO7fe7efOnUtCQgITJkwgNTWV0aNHc9ppp/HJJ5/4bGeMIS4uzudHRMCEhGIb9xtsD70A/QaBy4U1+13cf/4jVsYyf5cnIiLS7BrVg+pyudi8eTMXXnihz/r09HTWr19f7z4bN24kPT3dZ93AgQP5+uuvcblcOByeEsrLy7n55ptxu9107dqVyy67jG7duh2ylqqqKqqqqrzLxhjCw8O976X1q21HtaeHSUrBTHoYa8Ui3NP/5bns/7f/xQwcju2KmzDtO/i7xAZT2wYntWvwUtsGr0Bt20YF1KKiItxuN7GxsT7rY2NjKSgoqHefgoKCerevrq6muLiY+Ph4OnXqxM0330xaWhplZWV89tlnTJkyhaeeeork5OR6j/vRRx/x/vvve5e7devG1KlTSUxMbMxXklYgKSnJ3yUElk4X4x59LkXvvELxx29jrVyCe91KYi6/kejxV2GcTn9X2GBq2+Ckdg1eatvgFWht26SbpOpL2YdL3gd/Vnsncu363r1707t3b+/nffr04e677+bzzz/n+uuvr/eY48ePZ+zYsXXOkZubi8vlauA3kUBmjCEpKYmdO3fq7vX6nHcp9gHDqf7Py1gbfqLw9RcpnDMT22/+gK3vAH9Xd1hq2+Ckdg1eatvg1dJt63A4GtSZ2KiAGhMTg81mq9NbWlhYWKeXtFZcXFyd7YuKirDb7URF1f84R5vNRo8ePdi5c+cha3E6nTgP0VOkX57gYlmW2vRQOqVhu/NxrMXfYM34N+zcjvvpB7CGnYK59HpMXHt/V3hYatvgpHYNXmrb4BVobduom6QcDgfdu3cnIyPDZ31GRgZ9+vSpd59evXrV2X7VqlV0797dO/70YJZlkZWVpRulRBrAGIPtxNOwPfoy5rQxYGxYy77DPeVm3PM+xqqu9neJIiIijdLou/jHjh3LV199xfz588nOzmbatGnk5eVx5plnAvD222/zwgsveLc/66yzyMvL886DOn/+fObPn8/555/v3WbGjBmsXLmSXbt2kZmZycsvv0xmZqbPXKkicngmIgrblb/Ddv/T0K03lJdhTX8V96O3YW1a6+/yREREGqzRY1BHjBhBcXExH3zwAfn5+XTu3Jl7773XO54gPz+fvLw87/YdOnTg3nvv5fXXX2fOnDnEx8dz3XXX+cyBWlJSwj//+U8KCgqIiIigW7duPPzww/Ts2bMZvqJI22K69MB2z5NYC77E+vANyM7EPfUezEmjMf8zARNd/3AcERGRQGGsQBpw0Axyc3N9pp+S1ssYQ3JyMjk5OQE1LqY1sYqLsD58HWvBl54VEVGYi67BnHImxmb3W11q2+Ckdg1eatvg1dJt63Q6G3STVKMv8YtI62GiY7Bdeyu2u6dCajco3Yf11ku4n5iMlbXJ3+WJiIjUSwFVpA0wPftie+AZzOW/hbBwyNyI+7E7cP/nZaySYn+XJyIi4qNJ86CKSOtj7HbM6POxhpyENeM1rKX/xfrmc6yFX2HSh2GGj4L+Q1rVRP8iIhKcFFBF2hgT1w7z2zuwTjkT9/RXIDsTa8VCrBULITwSM2QE5viR0Ke/X8epiohI26WAKtJGmWPSsT34PGzd7OlNXfotFOz13P2/4EuIbYcZdgpm+Ejo0jPgntMsIiLBSwFVpA0zxkCXHpguPbD+51rYuBZryX89vamFe7HmfYw172PomII5fqTnJynF32WLiEiQU0AVEQDP5fw+x2H6HId1xe/gpx+wln6LtWoJ7NqO9ck7WJ+84+lNHT4KM+zkgH+UqoiItE4KqCJSh3E6YeBwzMDhWOWlWCuXYC35Ftb+CFmbsLI2Yc34tyfQHj8SM3gEJjLK32WLiEiQUEAVkcMyYRGYE06DE07DKi7EWr4Aa8l/4Zef4ecMrJ8zsN7+O/Qfim34SEgfhgkJ9XfZIiLSiimgikiDmehYzGlj4LQxWLk7sZZ95wmrO7bCysW4Vy6GsHDMoBMwx4+CvgMwds0EICIijaOAKiJNYhKTMOddAuddgpWd6ZkJYMm3sDcX6/uvsb7/GqJjPTMBHD8SuvfRTAAiItIgCqgicsRMaldMalesC6+GzT9jLfkWa/kCKC7Emj8ba/5sSOiIOX4UZvhITEoXf5csIiIBTAFVRJqNsdmg57GYnsdiXXYjrFvl6Vn9cTHk7cL67D2sz97DndqVghGn4e7cA3ocg3GG+Lt0EREJIAqoInJUGIcDjhuCOW4IVkU51qqlnocBrFkB2ZkUv/eaZ0OHE3r2xRyTjuk7wDONlcatioi0aQqoInLUmdAwzzjU40di7SuCjGWEZW6g9IclULh3/2wAM9+C8Ajo3X9/YO2UprGrIiJtjAKqiLQoExWDOekM2l98NRU7dmDlbMNatwprXQZsWA2lJbBqqafHFSAmDnNMOtQEVpPQ0d9fQUREjjIFVBHxG2MMJHfGJHeG08diuath62asdRlYP6+CTWuhqMAzNGDpt57Ampi0P7Aek46JifPztxARkeamgCoiAcPY7NC1F6ZrLzj3f7CqqjyzAqxbhfVzBmzZALk7sXJ3wndzPYE1pYunZ/WYdM/QgPAIf38NERE5QgqoIhKwjNPpeZxqn+MAsMpKYeNP+3tYszNhexbW9iysebPAZvME3GMGYPqma4YAEZFWSgFVRFoNEx7heZRq+jAArKICrPWrPdNZ/ZwBuTth83qszeuxPnsPnCEHzRDQw9NLKyIiAU0BVURaLRMThxl2Cgw7BQBrz26sdaugtoe1qMATXtetwvroTQiPhD41MwQcMwA6ddYMASIiAUgBVUSChmnfAXPymXDymViWBTu2Yf1c07u6fg2UlcDKJVgrl9SdIeCYdExikr+/goiIoIAqIkHKGAMpaZiUNBh9PlZ1NWz9xTPf6rpVsGld3RkCEjr6BtbYeH9/DRGRNkkBVUTaBGO3Q7femG694dyL988QUBtYt2zwPI51wZew4EtPYO2UVjNDwHGeGQIiovz9NURE2gQFVBFpk3xmCBj3G6zyUti4dv+UVtu2wI6tWDu2Yn31CRib5yarvp7eVXociwkN9ffXEBEJSgqoIiKACYuA44ZijhsKgFVcBBtW1wTW1bBrO2RuxMrciPX5B+BwQI++mGOO89xw1bUXxqG/UkVEmoP+NhURqYeJjoEhJ2GGnASAtTfXE1R/rnksa8EeWL8aa/1qrI/fhtBw6N2vZoaAdEjtirHZ/PwtRERaJwVUEZEGMO0SMSNOhxGne2YI2LXjgBkCVsO+Yli9HGv1cs/41ahozxCCYwZgevWD5BTNwSoi0kAKqCIijWSMgaQUTFIKnHoeltsN2Zk1gXU1bFjjCawrFmGtWOQJrKFh0Lm75zGuXXtiuvSEDsnqZRURqYcCqojIETI2G6R1x6R1h7PGY7lcnvGqtYE1cyNUlMOmtVib1gJ4Qmt4BKT1wHTthenaE7r09Ex1pYcHiEgbp4AqItLMjMPhecRqz74w9nIsdzXs3I6VuQmyNmFlbvTMElBWun8ca+3OkdHQpadvaI1vr9AqIm2KAqqIyFFmbHbPnKqd0mDE6QCeXtacbZ6wmrXJE16zM6GkGNb+iLX2x/2hNSbON7R27YmJ0UMERCR4KaCKiPiBcTigczdM525wylkAnocHbM/07WndsRWKCnxvwAKIT6gJrZ7gSpcemKgYf30dEZFmpYAqIhIgjNPpmU+1ay/vOquiArK31ITWjZ7XndmQnwf5eVgrF+8PrQkdPTdfdeuFSesBKV0gOlbDA0Sk1VFAFREJYCY0FHocg+lxjHedVV4KWzf79rTuzvE8qjVvF6xYuD+0RkVDUmdMciok738lPkEzCIhIwFJAFRFpZUxYBPTuj+nd37vOKtkHW3/BytyElbURsn6BPbs9010dPHsAeKa9Skr1BNakVExyZ09wTUzSE7FExO/0t5CISBAwkVHQdwCm7wDvOquiAnZlY+Vke27Iqnll9w7PtFdZm7CyNnm2rd3J7oAOyT69rSa5s2fe15DQlv9iItImKaCKiAQpExrqmWc1rYfPesvlgtydNaF1G+zcH2KprPC85mzzhlYLwBho36FucE1OxUREtfRXE5Egp4AqItLGGIcDklM94ZITvestt9tz89UBva3e4FpSDHm7PONcVy/3bF+7Y2y8Z5hAp84U9z4WtzMc2idC+w6Y8IiW/4Ii0uopoIqICFDzRKz2HTzBsv8Q73rLsqC4EHKyD+hx3QY7tkHBHijMh8J8rPWrKfj6M9+DRkRBQs0x23eE9omYhA5Q+169ryJSDwVUERE5LGOM52EBMXGYPv19PrPKSvcH1pxswgr3UrZ9q+cGrZJiKN0HW/d5Zh2o3efAA4RHekJxQgdMbTiueSWhA0REaZoskTZIAVVERJrMhEdAt96Ybr0xxpCQnExOTg6WZXmmw9qTC3m7sfbsgj25nte83TUzDBRBWQlkb/HM9VpzTJ8AGxa+v1f3oCBL+44QFa0AKxKEFFBFROSoMGERnocFpHShvghpVZR7guqe3Vh7dnuDq1WzjqICKC+D7VmwPav+ABsaBu0SPT284RGYsHAIi4Dw2tcICIvA1C6H+a4nJEQBVyQAKaCKiIhfmNAw6JQGndIOEWArYG8u7NmFtcfzSt5urL2eXlkK93qmy6qZdQAOCq8HHutQRdhsBwTWcM9PeIQnXIcfHGjDfcNueIRnjG1UNMYZ0gx/IiJSSwFVREQCkgkNPWC2gbqsqkrPEIK9u7H2FUN5KZSV1byWQnkpVnnZQetq3peXgWWB2+0ZJ1u6z/fYh6mr3s9CQiAiGiKjINLzaiLrWY6oXa75LDRMPbgi9VBAFRGRVsk4QyApxfMQgUbua7ndnjlfGxRqPdtYFWUHLNf8lO7zhNzKSqjc45nVoPYcB5+zvkIcDk9YjTh0sCUy2vMghtrtomIgLFzBVoKaAqqIiLQ5xmbbf0k/7qDPGnEcy7I8vbElxVCyD0qKPY+dLSk+4Gcf1gGfU7rP8wjaahe4XN5purzHrO88B6+wOzxBNToGomIw0bEQFQ1RsTXrYjFR0d73REVjHM5GfDMR/1JAFRERaSJjjGcsangEJHT0rGvAfpZleXpwDwyu3iB7iGBbsg9Kiz29tdUuzxjcwr2e49V3joNXhEd6Qmx0rCfUHhBwiY71LHvXxXrG4qqXVvxEAVVERKSFGWM8MxDUzkJQu74B+1oVFZ4pump+rOJCz/vimuV9vsvsKwbL7ZnSq6zE85hbGjAEwW73BNaaHtq8+HZUu/GMtw0Ng5Cw/e9DwyAk1DNuOMTz3rM+1PM+xLONsduP4E9N2hIFVBERkVbEhIZCaKLncbL8eqi1am8Eqw21xUVY+4o8Twc7cNm7rhgqyqC6ev9TwoCyBtR2uJvLAM/QBJ8QG+oNt4SE1QTcUN8A7HSC44AfZwjG6Tho3cHb+H5ubLYGVC+BRAFVREQkiBmbzdsT6l33K/tYVZUH9MB6gmxsaAiFu3d75q+tLIeKCs80X5UVWJX733tfa99XVHh6cMEzNKHUBaUl9Z+3gd+podt52e2+AdbhqCfU7v/M1G5T+2qvCb32g9fXHMfuwNS7/tDb165XeK6fAqqIiIj4MM4QaJfg+cEzJCEqOZnimqeENYZlWZ6bwSrLDwixBwbZcs+wBW+4PWgbVxWWq8pzjKoqcB3w41121V13oOpqz09FecNqbtQ3bPo+gGcu3toQa3d4wrTd4fveceB633Wmzrra5QOPc+hjGYeDymPTITy6qd/gqGhSQJ0zZw6zZs2ioKCA1NRUJkyYQN++fQ+5/dq1a3n99dfJzs4mPj6eCy64gLPOOstnm8WLFzN9+nR27dpFx44dueKKKzj++OObUp6IiIgECGOMp9fQ6fRMlVXfNs18TsuyamZJqIIq1yFCbZXP59bBn9fOsuANwK4D1nvWWQeG48Nuf8D6apdvsd5pyiqb9l2P9M8K2HfexXDRtUd4pObV6IC6aNEipk2bxo033kifPn2YN28ejz/+OM8++ywJCQl1tt+9ezdPPPEEo0eP5tZbb2X9+vW88sorxMTEcMIJJwCwYcMGnnvuOS677DKOP/54li5dyrPPPssjjzxCr169jvxbioiISJthjNl/6T6sgfsc3ZK8vD3K1VUHBdqqmp7emkBb+7662htsrQPXVVfVu43PunqOZR28nbsaZ6dUKlro+zdUowPq7NmzOf300xk9ejQAEyZMYNWqVcydO5crr7yyzvZz584lISGBCRMmAJCamsovv/zCJ5984g2on376Kenp6YwfPx6A8ePHs3btWj799FMmTZrUxK8mIiIiElh8epQbu+9Rqic6OZl9OTlH4ehN16iRuS6Xi82bNzNgwACf9enp6axfv77efTZu3Eh6errPuoEDB7J582ZcLk8394YNG+psM2DAADZs2NCY8kREREQkCDSqB7WoqAi3201sbKzP+tjYWAoKCurdp6CgoN7tq6urKS4uJj4+noKCAuLi4ny2iYuLO+QxAaqqqqiq2j8I2hhDeHi49720frXtqPYMPmrb4KR2DV5q2+AVqG3bpJuk6vsSh/tiB39Wewfg4faxLOuwn3/00Ue8//773uVu3boxdepUEhMTD7mPtE5JSUn+LkGOErVtcFK7Bi+1bfAKtLZtVECNiYnBZrPV6dksLCys00taq76e0KKiIux2O1FRUYfc5nDHBM841bFjx3qXa8Nsbm6ud+iAtG7GGJKSkti5c2ejpzWRwKa2DU5q1+Cltg1eLd22DoejQZ2JjQqoDoeD7t27k5GR4TMFVEZGBsOGDat3n169erFixQqfdatWraJ79+44HJ7T9+7dm9WrV/sEzoyMDHr37n3IWpxOJ85DDDDWL09wsSxLbRqk1LbBSe0avNS2wSvQ2rbRjy8YO3YsX331FfPnzyc7O5tp06aRl5fHmWeeCcDbb7/NCy+84N3+rLPOIi8vzzsP6vz585k/fz7nn3++d5vzzjuPVatWMXPmTLZv387MmTNZvXo1Y8aMaYavKCIiIiKtSaPHoI4YMYLi4mI++OAD8vPz6dy5M/fee6+3uzY/P5+8vDzv9h06dODee+/l9ddfZ86cOcTHx3Pdddd5p5gC6NOnD5MmTeLdd99l+vTpJCUlMWnSJM2BKiIiItIGGSuQ+nObQW5urs/d/dJ6GWNITk4mpwmP1pPAprYNTmrX4KW2DV4t3bZOp7NBY1AbfYlfRERERORoUkAVERERkYCigCoiIiIiAaVJE/UHstqpqyR4qE2Dl9o2OKldg5faNni1VNs29DxBd5OUiIiIiLRuusQvAausrIy7776bsrIyf5cizUxtG5zUrsFLbRu8ArVtFVAlYFmWxZYtWzSlSRBS2wYntWvwUtsGr0BtWwVUEREREQkoCqgiIiIiElAUUCVgOZ1OLr74YpxOp79LkWamtg1OatfgpbYNXoHatrqLX0REREQCinpQRURERCSgKKCKiIiISEBRQBURERGRgKKAKiIiIiIBRQ/VFb/66KOPWLp0Kdu3byckJITevXtz1VVX0alTJ+82lmUxY8YMvvrqK/bt20evXr244YYb6Ny5sx8rl8b46KOPeOeddzjvvPOYMGECoHZtzfbu3ctbb73FypUrqaysJDk5mT/84Q90794dUNu2VtXV1cyYMYPvvvuOgoIC4uPjOfXUU7nooouw2Tz9WWrb1mHt2rXMmjWLLVu2kJ+fz5133snxxx/v/bwh7VhVVcWbb77JwoULqayspH///tx44420b9++Rb6DelDFr9auXcvZZ5/NY489xgMPPIDb7ebRRx+lvLzcu83HH3/Mp59+yvXXX88TTzxBXFwcjz76aMA9lk3qt2nTJubNm0eXLl181qtdW6d9+/YxZcoUHA4H9913H8888wzXXHMNERER3m3Utq3Txx9/zJdffskNN9zAs88+y1VXXcWsWbP44osvfLZR2wa+iooKunbtyvXXX1/v5w1px2nTprF06VL+9Kc/8cgjj1BeXs5f/vIX3G53i3wHBVTxq/vvv59TTz2Vzp0707VrV26++Wby8vLYvHkz4PlX3meffcb48eMZPnw4aWlp3HLLLVRUVLBgwQI/Vy+/pry8nL/97W/87ne/IzIy0rte7dp6ffzxx7Rv356bb76Znj170qFDB4477jiSkpIAtW1rtmHDBoYOHcrgwYPp0KEDJ5xwAunp6fzyyy+A2rY1GTRoEJdffjnDhw+v81lD2rG0tJT58+dzzTXXkJ6eTrdu3bj11lvZunUrGRkZLfIdFFAloJSWlgIQFRUFwO7duykoKGDAgAHebZxOJ8ceeyzr16/3S43ScK+88gqDBg0iPT3dZ73atfVavnw53bt355lnnuHGG29k8uTJzJs3z/u52rb1OuaYY1izZg07duwAIDMzk/Xr1zNo0CBAbRssGtKOmzdvprq62ufv7nbt2pGWlsaGDRtapE6NQZWAYVkWr7/+OscccwxpaWkAFBQUABAbG+uzbWxsLHl5eS1dojTCwoUL2bJlC0888USdz9Surdfu3bv58ssvGTNmDOPHj2fTpk289tprOJ1ORo0apbZtxcaNG0dpaSm33XYbNpsNt9vN5Zdfzsknnwzo9zZYNKQdCwoKcDgc3s6iA7ep3f9oU0CVgPHqq6+ydetWHnnkkTqfGWN8lvUAtMCWl5fHtGnTuP/++wkJCTnkdmrX1sftdtOjRw+uvPJKALp168a2bduYO3cuo0aN8m6ntm19Fi1axHfffcfEiRPp3LkzmZmZTJs2zXuzVC21bXBoSju2ZFsroEpA+Pe//82KFSt4+OGHfe4QjIuLA/DeUVqrqKiozr/+JHBs3ryZwsJC7rnnHu86t9vNunXr+OKLL3juuecAtWtrFB8fT2pqqs+61NRUlixZAuh3tjV76623GDduHCeddBIAaWlp5ObmMnPmTE499VS1bZBoSDvGxcXhcrnYt2+fTy9qUVERffr0aZE6NQZV/MqyLF599VWWLFnCgw8+SIcOHXw+79ChA3FxcT6Dsl0uF2vXrm2xXxJpvOOOO46//vWvPPnkk96fHj16cPLJJ/Pkk0/SsWNHtWsr1adPH+8YxVo7duwgMTER0O9sa1ZRUeGdTqqWzWbz9pqpbYNDQ9qxe/fu2O12n23y8/PZunUrvXv3bpE61YMqfvXqq6+yYMECJk+eTHh4uHdsS0REBCEhIRhjOO+88/joo49ITk4mKSmJjz76iNDQUO+4KAk84eHh3nHEtUJDQ4mOjvauV7u2TmPGjGHKlCl8+OGHjBgxgk2bNvHVV19x0003Aeh3thUbMmQIH374IQkJCaSmppKZmcns2bM57bTTALVta1JeXs7OnTu9y7t37yYzM5OoqCgSEhJ+tR0jIiI4/fTTefPNN4mOjiYqKoo333yTtLS0Oje9Hi3G0uAR8aNLL7203vU333yzd8xT7YTC8+bNo6SkhJ49e3LDDTfUCUAS2B566CG6du1aZ6J+tWvrs2LFCt5++2127txJhw4dGDNmDGeccYb3c7Vt61RWVsb06dNZunQphYWFtGvXjpNOOomLL74Yh8PTn6W2bR1++uknHn744TrrR40axS233NKgdqysrOStt95iwYIFPhP1JyQktMh3UEAVERERkYCiMagiIiIiElAUUEVEREQkoCigioiIiEhAUUAVERERkYCigCoiIiIiAUUBVUREREQCigKqiIiIiAQUPUlKRFqVb775hpdeeumQn//5z3+mX79+LViRr927d/PHP/6Rq666igsuuMBvdTSnYPxOIhLYFFBFpFW6+eab6dSpU531qampfqhGRESakwKqiLRKnTt3pkePHv4uQ5qRy+XCGIPdbvd3KSLiZwqoIhK0Lr30Us4++2zS0tKYPXs2ubm5dOzYkYsvvpiTTjrJZ9utW7fy7rvvsm7dOiorK+nUqRNjxozh1FNP9dmupKSEDz74gKVLl7J3714iIiLo0aMH11xzDSkpKT7bzp49m88//5yioiLS0tK49tpr6d2792Frrh3C8OCDD/L999+zePFiLMvi2GOP5frrr6ddu3Y+3+/iiy/m0ksv9TnGLbfcwrHHHsstt9xS55gLFixg6dKlVFdXM2zYMG688UbKy8v597//TUZGBiEhIZx88slceeWV3uev17Isiw8//JAvv/ySoqIiOnfuzG9+8xuOO+44n+1ycnJ47733WL16NaWlpXTs2JGzzz6bc845x7tN7bPC//jHP5KZmcnChQspKCjgmWeeqfPnKCJtjwKqiLRKbreb6upqn3XGGGw233s/ly9fzk8//cSll15KaGgoc+fO5fnnn8dut3PCCScAsGPHDqZMmUJMTAzXXXcdUVFRfPfdd7z00ksUFhYybtw4AMrKynjwwQfZvXs348aNo1evXpSXl7Nu3Try8/N9gtWcOXNISUlhwoQJAEyfPp0nnniCF198kYiIiF/9fv/4xz8YNGgQEydOZM+ePbz55pv87W9/489//nOT/8z+/ve/c/zxxzNp0iS2bNnCO++8Q3V1NTt27GD48OGcccYZrF69mo8//ph27doxduxYn/2/+OILEhMTmTBhApZl8fHHH/P444/z8MMPe4N3dnY2DzzwAAkJCVxzzTXExcWxcuVKXnvtNYqLi7nkkkt8jvn222/Tu3dvfvvb32Kz2YiNjW3y9xOR4KGAKiKt0v33319nnc1m49133/VZV1xczBNPPEFcXBwAgwcP5o477uDtt9/2BtT33nsPl8vFn//8ZxISErzblZaW8v7773PmmWcSERHBp59+yrZt23jggQdIT0/3nmP48OF1agkPD+eee+7xBub4+Hjuu+8+fvzxxzq9t/UZMGAA119/vXd53759vPXWWxQUFHi/S2MNHjyYa665BoD09HQ2bNjAwoULueaaa7xhND09nVWrVvHdd9/VCahut5sHHniAkJAQb4233HIL06dPZ8qUKQC8/vrrhIeH88gjj3iDeHp6Oi6Xi5kzZ3LuuecSFRXlPWbHjh25/fbbm/R9RCR4KaCKSKv0xz/+sc6lYGNMne369+/vE+hsNhsnnngi77//Pnv27KF9+/b89NNP9O/f3xtOa40aNYoff/yRDRs2MHDgQFauXElycrJPOD2UwYMH+/TmdunSBYDc3NwGfb+hQ4f6LKelpXn3b2pAHTJkiM9ySkoKy5YtY/DgwXXWZ2Rk1Nl/+PDh3nAKnhA+ZMgQFi5ciNvtxuVysWbNGs4880xCQ0N9ergHDRrEF198wcaNGxk0aJDPMUVEDqaAKiKtUkpKSoNukqovzNWuKy4upn379hQXFxMfH19nu9rxnsXFxQAUFRXVCbGHcmAvIYDT6QSgsrKyQftHR0cf0f4Nqal2jGl96+s7z6H+LF0uF+Xl5ZSXl1NdXc0XX3zBF198UW8NtX+Wter7cxcRUUAVkaBWUFBwyHW1ITA6Opr8/Pw62+3du9dnu5iYGPbs2XN0Cm0Cp9OJy+Wqs/7gENhcDvVn6XA4CAsLw263Y7PZGDlyJGeffXa9x+jQoYPPcn293iIiepKUiAS1NWvW+AQrt9vN999/T8eOHWnfvj3gGQawZs0abyCt9e233xIaGuq9AWjgwIHk5OSwZs2aFqv/cBITE8nKyvJZt2bNGsrLy4/K+ZYsWeLTs1pWVsaKFSvo27cvNpuN0NBQ+vXrx5YtW+jSpQs9evSo83Nwz7CISH3UgyoirdK2bdvq3MUPkJSURExMjHc5OjqaRx55hP/5n//x3sW/fft2Jk2a5N3mkksu4YcffuDhhx/m4osv9t7F/8MPP3DVVVd5b/YZM2YM33//PU8++SQXXnghPXv2pLKykrVr1zJ48GD69+9/1L/3gUaOHMn06dOZPn06xx57LNnZ2XzxxRcNmiWgKWw2G48++ihjx47F7Xbz8ccfU1ZW5nNn/nXXXceUKVN48MEHOeuss0hMTKSsrIydO3eyYsWKI5qFQETaDgVUEWmVDvW409/97neMHj3auzx06FA6d+7Mu+++S15eHklJSUycOJERI0Z4t+nUqRP/+7//yzvvvMOrr75KZWUlKSkp3HzzzT7zoNbenT5jxgzmzZvHjBkziIqKokePHpxxxhlH7bseygUXXEBpaSnffPMNn3zyCT179uS2227jqaeeOirnO+ecc6iqquK1116jsLCQzp07c88993DMMcd4t0lNTWXq1Kl88MEHvPvuuxQWFhIZGUlycrLPzVEiIodjLMuy/F2EiMjRUDtR/w033ODvUkREpBE0BlVEREREAooCqoiIiIgEFF3iFxEREZGAoh5UEREREQkoCqgiIiIiElAUUEVEREQkoCigioiIiEhAUUAVERERkYCigCoiIiIiAUUBVUREREQCigKqiIiIiAQUBVQRERERCSj/Dwf2DR3+tAO5AAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "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 = 2.23\n",
-      "--------------------------------------------------------------------------------\n",
-      "learning_rate=0.20 init_scale=1.00\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "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 = 2.23\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": 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        | 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": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--------------------------------------------------------------------------------\n",
-      "learning_rate=0.20 init_scale=0.10\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    final error(train) = 4.75e-03\n",
-      "    final error(valid) = 2.19e-01\n",
-      "    final acc(train)   = 1.00e+00\n",
-      "    final acc(valid)   = 9.64e-01\n",
-      "    run time per epoch = 1.48\n",
-      "--------------------------------------------------------------------------------\n",
-      "learning_rate=0.20 init_scale=0.20\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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RkQEp6sN4TJqb3W0WVTlHq2csIiID0iGF8bp167j55pv58Y9/zPLlyw+43qZNm/jOd77Du+++G676eq3jJK4t3nyorsRuaY5wRSIiIl31GMaWZbFkyRLuvPNOFi5cyOrVq9mxY//7PFuWxZ/+9CcmTpzYF3UesdGpbkwDSuKzAgvKdEa1iIgMLD2G8aZNm8jKyiIzMxOn08nUqVNZs2bNfuutXLmSKVOmkJyc3CeFHim302REcgxbSAR0j2oRERl4ejybuqamBo/HE5r3eDxs3Lhxv3Xef/99fvGLX/Df//3fB9xXUVERRUVFAMybNw+v13ukdR+W8cOrWbu9HlwxxNXuIqmfPre/OZ3OfmvToUJtGn5q076hdg2//mzTHsPYtu39lhmG0WX+iSee4Morr8Q0D97RLiwspLCwMDRfVVV1qHX2Sk68wcu726jLHgub/0VrP31uf/N6vf3WpkOF2jT81KZ9Q+0afn3RptnZ2d0u7zGMPR4P1dXVofnq6mrS0tK6rLN582YefvhhABoaGvjoo48wTZPJkyf3puawGZPmBmDr8PFM3PBGhKsRERHpqscwzs/Pp6ysjMrKStLT0ykuLuamm27qss7ixYu7TJ988skDJohh77ONt6SOZGJNFXbzHoy4+AhXJSIiEtBjGDscDmbNmsXcuXOxLItp06aRm5vLqlWrAJgxY0afF9lbiW4HGQkuShzBY9+lX0H+MZEtSkREJOiQboc5adIkJk2a1GXZgUL4xhtv7H1VfSAv3U1JtR8InFFtKIxFRGSAiPo7cHXIS4uldI9Fc2wilOpaYxERGTiGVBgDbM09Qdcai4jIgDJ0wjg9cEZ1ScZRuke1iIgMKEMmjNPjnKTEOtiSMBzqqrH3NEW6JBEREWAIhbFhGOSlxVJiBm/XqePGIiIyQAyZMAbIS3PzVauDdsOBXbot0uWIiIgAQy2M02Px2/BVaq56xiIiMmAMrTAOnlFdkn2czqgWEZEBY0iFcVaSizinSUn6aJ1RLSIiA8aQCmPTMBiT5qbEPQzqa7F3N0a6JBERkaEVxgBj0mPZasXhx4Cd6h2LiEjkDbkwzktz02IZlMd5ddxYREQGhCEXxvnpwZO40kbruLGIiAwIQy6Mc1PcOE2DLZlHqWcsIiIDwpALY6dpMCo1hpKkHPWMRURkQBhyYQwwJi2WLY5U7MZ67MaGSJcjIiJD3JAM47y0WBptJ9XuFPWORUQk4oZmGAcfp7glMUfHjUVEJOKGZBiPTo3FAErSRqlnLCIiETckwzjOZZKdHENJep56xiIiEnFDMowhcPOPkrgMKN2GbduRLkdERIawoRvG6bHsMuJobPFDY12kyxERkSFs6IZxx+MUE7N1j2oREYmooRvGwdtibknKxi7dHuFqRERkKBuyYZzsduCNd1KSqjOqRUQksoZsGEOgd7wlOVdnVIuISEQN7TBOc1PqTKGlvExnVIuISMQ4D2WldevWsWzZMizLYvr06Vx88cVd3l+zZg1PP/00hmHgcDiYOXMmxxxzTF/UG1Z5abHYhsE2M4nx9bWQmh7pkkREZAjqMYwty2LJkiXcddddeDwe5syZQ0FBASNGjAitM2HCBAoKCjAMg23btrFw4UIeeuihvqw7LEIncSXmML70K4WxiIhERI/D1Js2bSIrK4vMzEycTidTp05lzZo1XdaJjY3FMAwAWltbQ9MDnTfeSZLLoCQxW8eNRUQkYnrsGdfU1ODxeELzHo+HjRs37rfe+++/z1NPPUV9fT1z5szpdl9FRUUUFRUBMG/ePLxe75HWHTZHZ5VTUpdLbM0WkgdAPb3hdDoHRJtGE7Vp+KlN+4baNfz6s017DOPuTmzqruc7efJkJk+ezOeff87TTz/N3Xffvd86hYWFFBYWhuarqqoOt96wy010sC4+k8YtL9M2AOrpDa/XOyDaNJqoTcNPbdo31K7h1xdtmp2d3e3yHoepPR4P1dXVofnq6mrS0tIOuP6xxx5LeXk5DQ0NR1Bm/8tLj8VnONhR26IzqkVEJCJ6DOP8/HzKysqorKzE5/NRXFxMQUFBl3XKy8tDQbZlyxZ8Ph9JSUl9U3GY5aUFn20ckw51NRGuRkREhqIeh6kdDgezZs1i7ty5WJbFtGnTyM3NZdWqVQDMmDGDd999lzfffBOHw0FMTAy33HLLoDmJa3hSDG7TpiQxJ3AnrjRPzxuJiIiE0SFdZzxp0iQmTZrUZdmMGTNC0xdffPF+1x4PFg7TYHRKTOCM6p3bMI47KdIliYjIEDOk78DVIc+bQElSDpYubxIRkQhQGBM4iavZ4aa8si7SpYiIyBCkMKbTs4132zqjWkRE+p3CGBiVGoMDmy3uYVCj6/RERKR/KYwBl8MkNw5KErP1bGMREel3CuOgPG88JUk5uke1iIj0O4VxUF5GEnUxSdSUlke6FBERGWIUxkEdj1PcXNsa4UpERGSoURgHjQneFrOk1YVtWRGuRkREhhKFcVC8y8FwZzslcRlQsyvS5YiIyBCiMO5kTLJTZ1SLiEi/Uxh3kpeVQkWch6YdOyJdioiIDCEK407yM5MBKKkYHM9iFhGR6KAw7qTjtphbGnUCl4iI9B+FcSepcU7SaWWLP1ZnVIuISL9RGO9jTKxFSXwWVFVEuhQRERkiFMb7yPPEsSMhg9YdOqNaRET6h8J4H3k5HizDwdYdutZYRET6h8J4H/nDUwAoqW6OcCUiIjJUKIz3kZHgIsFqY0uzmkZERPqHEmcfhmEwxtFMiZmMbfkjXY6IiAwBCuNujEk02Rafhb9Cj1MUEZG+pzDuRn5mEm0OFzu37ox0KSIiMgQojLuRNyYLgM3ldZEtREREhgSFcTdGeFOIsdopqW+PdCkiIjIEKIy74TANRvkb2NLujnQpIiIyBDgPZaV169axbNkyLMti+vTpXHzxxV3ef+utt3jhhRcAiI2N5Qc/+AGjR48Od639aoy7ndWGB8vnw3QeUjOJiIgckR57xpZlsWTJEu68804WLlzI6tWr2bHP834zMjL45S9/yQMPPMC3vvUt/ud//qfPCu4veWludjvjqNxeGulSREQkyvUYxps2bSIrK4vMzEycTidTp05lzZo1XdY5+uijSUxMBGDcuHFUV1f3TbX9KC/bA8CWbXpghIiI9K0ew7impgaPxxOa93g81NTUHHD9V199lZNOOik81UXQqLEjMG0/n+5qiXQpIiIS5Xo8GGrb9n7LDMPodt1PP/2U1157jXvvvbfb94uKiigqKgJg3rx5eL3ew6m1353W/Dp/N/LI+WwX3z/zmAN+3wOF0+kc8G062KhNw09t2jfUruHXn23aYxh7PJ4uw87V1dWkpaXtt962bdt47LHHmDNnDklJSd3uq7CwkMLCwtB8VVXVkdTcb26+YAI8+w7/s+4Eyvd8yjUFWZgDOJC9Xu+Ab9PBRm0afmrTvqF2Db++aNPs7Oxul/c4TJ2fn09ZWRmVlZX4fD6Ki4spKCjosk5VVRUPPPAA//Ef/3HADxqMXDm53HJ6LufvWM3fNtTzX++W4bf2HykQERHpjR57xg6Hg1mzZjF37lwsy2LatGnk5uayatUqAGbMmMGzzz5LU1MTv//970PbzJs3r28r7yeOk7/Gdds2kfL5Kv7CDJraLH52WjZupy7RFhGR8DDs7g4K95PS0sFx2ZBt+bEW3cvK+gR+P/YbHJcRx51njSAhxhHp0rrQMFX4qU3DT23aN9Su4TeghqkFDNOBed3POL9lIz/Z+je+2NXMXUVfUdfii3RpIiISBRTGh8hISMK8fg5nlL7PnKoidjS0MWfVNiqa2iJdmoiIDHIK48NgjMzDuOpGJq1fxa9YR32rn9mrvuKrutZIlyYiIoOYwvgwmadOw5h+EUe/8ifmZu7CBub8cxv/qmqOdGkiIjJIKYyPgHHpNTDuWEY+vZB5E0wSYxzcXfQVa0ubIl2aiIgMQgrjI2A4nZg/vAPiExm2bB6/Od1LdnIMc9/YwVtbGyJdnoiIDDIK4yNkpKRh/mg21FSR8r8P8etzcjjKE8eC1aWs3FAb6fJERGQQURj3gpF/DMbl18GnH5Lw8v/xy3NyKchJ4NE1FTyzvqrb+3qLiIjsq8c7cMnBGWedB1s3YK94mphRY5l95mT+690y/vRJFQ2tfmadnDGg72fdH2zbpr7FT1lTG2WN7TgMOH1UMg5zaLeLiEgHhXEvGYYBV16PvWMb1tKFOO5cwM1fyybJ7eDFL2tpbPXz468NxxnlwWPZNtV7fJQ1tlHe1E5ZYyB4y4MB3OKzuqz/ekkDt54WaCcRkaFOYRwGhisG8/o5WPfdgvXI/Zh3zufaSRkkux386eMqmtr83H5GzqC/n3W736ZydzvljW2UNbVR3tgeCt/ypnZ8nR6i4TQNMhNdDE90cVxGPMOTXGQlxjA8KYZPynfz+w8ruXXlVmafmUN+emwEvysRkcjTvanDyP7iY6yFv4BJp2L+8A4Mw2DlhloeW1PB+GFx/PzsEST24f2sw3kfVZ9l89bWBr7Y1Rzq3VbtaafzQ6tinQbDk2LISnQFX2NCoeuJdx50GHpDVTO/fWsn9S1+fjQ5k8L81LDUHW6632/4qU37xmBp13a/hWEYOAwG/DPi+/Pe1ArjMLNefh772WUYl87E/LdLAHh7WwMLi0sZkezml+fkkhbXNwMS4fjB8Vs2r5XU83+fVlPe1E5SjElWUkyX0B0efE2JdfTqP1N9i48Fq0v5uHwP5+an8O+nZBLjGFijB4PlF9xgojbtG5FqV59l09Dqp77FR32Ln7rga0Pr3un6Fl9w3t/lkJXTNHCaBi4zMO0IzjtNA5dj7/S+Xy7TwOmgyzII/P7yWeC3bfyWjd8CX2jaxmeDZdn4LBu/HVjXsoPzlo3fZu+0ZWNh8PuL84h3ha8TdaAw1jB1mBkzLoaSDdh//QP2yHyM8Sdy+qhkEmIc/OaNHcxetY1fnZNLVlJMpEvtwm/ZvF5SzzPBEM5Pd/Pzs3I4JSexz/56TYl18otpuTz1SRXPflbNltpWZp+RQ0aiq08+T0QOzG/ZtPotmtstWn02LT6L3e1+GloCIVrf2hGsweANBnBTm9Xt/hxG4P94SqyDlFhn6A/4jvNEfJaNzx8IQl8wRNs7QrPTV3twvVaf1WV54L3Aftr9NgbgMAkEuhEIdocJDsMIBv3eadM0iDH2hr9pGDiD23ZePyE+Hkc/9d7VM+4Ddksz1m9ug4ZazLsWYngyAPhXVTO/fm07fhvOGp1MYX4q+enusIXdkfxl7Lds3tjawDOfVlHW2E5empvLT/AyuQ9DuDvvbW/koXfKcBhw62nZTMpO7LfPPhj14sJPbXpoOnpsHUG1b0jt+xWbkERldR0tPosWn02zz6LVZ4VeW9ptWvwWLe2dlgXXbfFZtPkPHgUGkOR2hMI1xe0gNdZBcmja2eW9hBhzwA9D90TD1FHArijFmnsrDBuOecc8jBg3ADsaWnlmfTXvbG+kzW8zJs1NYX4KZ41O6fWZxYfzg+O3bN4MhnBpYztj0txcMcHL5BH9G8KdlTa0Me+tnXxV18oVJ3j59vGeiF8WpuAIv2hu03a/TUNHD7KboduO4dr2TiHq79TD6xy4Vhh+MxtArNMk1mkQ6zKD0+beZZ3nXfsvi3eZod5tUoxjyF2OqDCOEvbH72P97j6MqdMxZt7UJeSa2vy8ubWBos11bK5pxWUanJqbSGF+KidkxR9RCB3KD47fsnlrWwNPr6+mtLGN0amBnvCUEYkRDz6AFp/Ff79XzutbGyjITuCWqdkkRvDyp2gOjkgJd5vuafdT2dROm9/GNALDi6ZhYBqdXwm+t/9yxz7rdv5/atk2TW1WKFTrW3xHNGTrNA1SYgM9ySS3E7ej8/HR4PFSw8DpCBwP7Zh2GoFjox1Dpy7H3mHUji9HcPsMTxqtuxu7BGqMwxj0vdNIUhhHEeuFp7BX/AXjyusxzz6/23W21LRQtLmON7Y20NRmkZHgYnp+CtPzUhiWcOjHTw/2g+O3bN7e1sDTn1azs6GNUamBnvCU3IERwp3Zts1LG+pYurYCb7yLO87IIS9Clz8pjMPvcNvUsm1qm32BS+g6XUpXEby8rr7VH9b6Oof3gXqoBpDceci20/Bsx3Sqe+978a6+H7LVz2r4KYyjiG1ZWL+7Dz5fh3nb/Rj5xxxw3Ta/xbvbm/jn5jo+Kd+DAUwcnsC5+SlMHpGIq4czjbv7wfFbNqu/auTp9VXsaGhjVIqby0/wcGpu0oAL4X19uStw+VNTm5/rJ2dxTl5Kv9egX3Dh112btvktKoMhWx4M2fKmtmDotnc5nmka4I13kZXkIisxcCldZqKLWKeJ3w6Ep2UHzqS1Os13fvVb3S/fdzuHaZDaEa6dgncgDtnqZzX8FMZRxt7dFDh+3NaGefdCjJS0HrepaGqjaHM9r2ypp3qPj2S3g7PGJHNufiqjUt3dbtP5B8eybVZva+TpT6vYXt/GyJQYLp/g5WsjB34Id1bX7GP+6lI+rdjDeeNS+cHJGT3+URJO+gXXe/5Ol740tPrxOePYVF5DeWOgd1vW1E7NHh+dfxHFOo1QyHZcVtcx7Y134XIMnp/h/qKf1fBTGEche8fWwBnWI/Mxf3ofhvPQrirzWzYfl+/mn5vreX9HIz4LxnliOTc/lTNGJ3W5/s3r9VK5axfFXzXyl/WBEM4NhvDUQRbCnfktm//9eBfPfV7DOE8sd5yRc1jD972hX3D7a/NboetIOwK2+/nA9IGOo6bFOYM9WxdZwcDNSgy89vYa9qFIP6vhpzCOUtb7b2I//gDGWedhfPdHGObh9fDqW3y8XhI46eur+jbcDoPTRiVRmJ/KMd44Pq83eHx1CdvqWxmRvDeEB9pw2pF656tGHn6nDJfD4GenZ3NiVkKff2a0/YLzW4FLXlp8getJm4OXuXRc7tJ5WbPPYndb56ANvO57n/EODiNwHDU51hl47Th+6naSHOsgxe0gOdbB6KxhxLQ3Dfrbww400fazOhAojKOY9ewy7Jefh2NOwJx1C0aa57D3Yds2G6oDJ329tbWRZp9FvMtkT7vFiOQYvjPBy2lRFMKd7WhoZd6bO9nZ0MaVJwzjkuPSw9bj91s2tS0+KoLHKSt3t2M7Ymhubu5x2yP9T9S58n17gl3f63m7jmtKm9s7he0+rz1dS9p5/7FOk4QYk5RYB8nuwMlJScFQTQkGbop773Wmh3pdqUKjb6hdw09hHMVs28YufgX7z/8DThfm9/8DY9LXjnh/LT6L4q8aWVvaxDnHDOfEdCMqQ7iz5naLxe+V8da2RqaMSOTmrw0n4RDu+W3bNvWt/r1hGwzciqY2Kna3s2t3O/t2+uJcJof6P+RwW93uZq7js7r7yO7f27ud22kSF7xeNM5pEucKfMU6u853THdcR9rdNjEOo88Oayg0+obaNfwUxkOAXVGK9fgDsG0Txpn/hnHZtRju3l2+M5T+M9q2zYp/1bJsbSUZiS5mn5HD6LRYmlr9wYBtp2J3Wyh0K3YHXlv36Rkmux1kJrrISHB1fQ1OZ2dmDJk27S9D6ee0P6ldw68/w1j3po4QIzMbc/ZvsV94Cvvl57A3fIp53c8wRuZHurRBwTAMLjomnfz0WP7z7VJ+9o9txDgMdrd37drGu0wyE11kJ8Vw0vCEUNhmJsaQkeAizqXjliISeQrjCDKcLoxvXY197ESspQux7r8N45KrMAq/edgndw1Vx2bEs/D80fxlfRWmQafebQyZCa6I3r1LRORQHVIYr1u3jmXLlmFZFtOnT+fiiy/u8v7OnTt55JFHKCkp4fLLL+cb3/hGX9QatYzxJ2L+YhHWH36H/X/LsD/7CPOan2Ckpke6tEEhLc7J9ZOzIl2GiMgR67H7ZVkWS5Ys4c4772ThwoWsXr2aHTt2dFknMTGRa665hosuuqjPCo12RmIy5vVzMK66ATZ9jvWrm7A/fj/SZYmISD/oMYw3bdpEVlYWmZmZOJ1Opk6dypo1a7qsk5KSwtixY3E4NCTYG4ZhYJ55HuZdD0GaB+t392H96b+xW1sjXZqIiPShHoepa2pq8Hj2Xgvr8XjYuHHjEX1YUVERRUVFAMybNw+v13tE+4l6Xi/2gmU0/ekx9rzwZ8zNX5Jy669wjR570M2cTqfaNMzUpuGnNu0batfw68827TGMu7vy6UhvU1dYWEhhYWFoXqfh9+DrV2COOQb/soeouW0WxrdmYky/6IDtr0sbwk9tGn5q076hdg2//ry0qcdhao/HQ3V1dWi+urqatLSeH3Qg4WEcdxLmLxbBcZOwn/491qJfYTfURrosEREJox7DOD8/n7KyMiorK/H5fBQXF1NQUNAftUmQkZSCeePPMb77I/jXp1i/vAl7/QeRLktERMKkx2Fqh8PBrFmzmDt3LpZlMW3aNHJzc1m1ahUAM2bMoK6ujtmzZ9Pc3IxhGLz00ks8+OCDxMfH9/k3MFQYhoEx7QLso47Henw+1qJ7Mc75OsalMzFcMZEuT0REekG3wxyE7PY27L8+if3Ki5AzKnDnrpxRh3V8w7YsaNkDu5tgz27Y0wR7mrB3N4Wm2bMbnC5I80BKeuChFinpkObp9a07Bwsdhws/tWnfULuGn26HKQdluGIwLr8O+7hJWMsewrrvVoxvfZ/W8Sdgl+3E3tMEu/cGLLubAss6Qnd3EzTvAbv7R+EBYJoQnwjtbdDaAuzzgIK4eEj1QGp64OYkqemQ6glOB5aTnHbIz20WERnK9JtyEDMmnIz5y0VYyxZhP72Eun1XcDghPgESEgPBmpSCkZkDCQkQlxh4jU/EiE/cu058cLk7LnTWtt28B+pqoK4aO/hKXQ12x+u/PoX6GvD7uwa2YUBSSqfQDr7mjIKTTtXD40VEghTGg5yRnIZ50z2w8TNSUlKob/cHAzURYtxhCTwjLj7QEx4+4oCPCbQtC5oa9gvqwHQN1FRhl2yAxvpAYE88FXPmTRgJib2uT0RksFMYRwHDMOCo44nxejEidMzIME1ITg18jcw/cGj72rFfewn7r09i/fongePd+cf0Y6UiIgOPHg0k/cpwujDP/SbmHfMAsObPwXr5+UDPWkRkiFIYS0QYY47CvOchOHEy9rPLsH53H3ZTQ6TLEhGJCIWxRIwRn4j5o9kY3/0hfLEO696fYG/8PNJliYj0O4WxRJRhGJjTLsScMx9cLqwH7sR66f80bC0iQ4rCWAYEY2Q+5l0LMU4+Dfv5P2I9/CvshrpIlyUi0i8UxjJgGHHxGNf9DOOqG2DjZ1j33oz95SeRLktEpM8pjGVAMQwD88zzMO+cD3HxWA/eg/W3P2Nb/kiXJiLSZxTGMiAZI8Zg/vxBjClnYb/4Z6wH7wncPEREJAopjGXAMmLjMK+9BWPmzVCyITBs/flHkS5LRCTsFMYy4JmnTcf8+QJISsF66JdYz/8vtl/D1iISPRTGMigY2SMx71yAcVoh9kvPYC34OXaNHhcnItFBYSyDhuF2Y179Y4xrb4WvtmD9+mbs9R9EuiwRkV5TGMugY556NuZdCyHVi7XoXqxnl2H7fJEuS0TkiOmpTTIoGVk5mHfOx35mCfbLz2Nv/Bzj9HMxMoZDRjakpAWeJCUiMggojGXQMlwxGFdej330BKw/PoL9h98FnpUMEBMDw4ZDxvBQQAdeh0OqR0EtIgOKwlgGPaPgdMxJX4OaKqgsw64sDb6WQfnOwHFln29vULtiYFhWl4AO9ajTFNQi0v8UxhIVDNMB3kzwZmIcO7HLe7blh9rqvQFdWbr39dMPwde+N6idrmBQD8fIzGZP7mgshwsjKQWSUiE5FRISFdgiElYKY4l6hukATwZ4MjDGn9jlPduyoK4aKkqxd5UFAruiDHaVYX++jsb2tsB6XXcIScmQlALJqRhJqZCccuD5GHd/fasiMkgpjGVIM0wT0odB+rBug9oTG0P11i3QWI/dUA8NddBYF5wPvpb8CxrroaU5sN2+H+KO2xvOSSkYKWmBz/RmYngywJsByTrhTGQoUxiLHIBhmpjJqRjZIwPzPaxvt7ZCUz0EQ9sOhnaX+epK7C3B8KZTcDudgYD2ZGB4M4NhnYHhyQz06lPTAj18EYlKCmORMDHcbnAHhsPh4OFtt7ZAzS6oqsSuroCqSqjZhV1Vgf3x+4EeOJ3C2uGEdG8grDt6055MDM8w8GRCWrrCWmQQUxiLRIDhjoXhuTA8t9vQtttaO4V1JQQD267Zhf3pWqgPPMFqb1g7IDEZEpICr4lJGKH5wDIjITk0TWISxCVoaFxkgFAYiwxARowbskZA1ojuw7q9Dap3QU0ldlUlVFcGjl83NcDuRijbgb27MTAdfKjGfseyDRMSEveGc2IyRqfwJiEJIz4hcClYx1dMDLjc4HJ1nXY4MYyeBvJF5EAOKYzXrVvHsmXLsCyL6dOnc/HFF3d537Ztli1bxkcffYTb7eaGG24gLy+vL+oVEQI3PCErB7JyDj4cbtvQvCcQyk0N0NSIvbshNL13WSNUVWBv3RRY5msPbH/IBZnBcHYFAzo4HePeG+LOGIyYGOoTk7D8/sBlZE5n968OZzDkXRguJzg6r3OQ7ZxOcDg0ZC+DTo9hbFkWS5Ys4a677sLj8TBnzhwKCgoYMWJEaJ2PPvqI8vJyFi1axMaNG/n973/P/fff36eFi0jPDMOA+ITA17CswLIetrFtG9paA2HdvBva2wJfbYFXe5/5vdOt0N4eeG1rw+6Ybm+D3U3Q1ord3kab34fd1gY+XyD0fe1gHzj2D/kPgs5MMxjMHQHtDAZ2x/Q+y4OvRscfAg7H3uWGEdifYXadNo19Xs0Drxua7rx9x7wjcLig472OL6PzdOftHPt8ZuDV17oHu64usG7HKIVpBv7FO5YZBOsJ/iR0TIe26byucQg/LUcoVIcR+F6CnzuUR1d6DONNmzaRlZVFZmYmAFOnTmXNmjVdwviDDz7gzDPPxDAMjjrqKHbv3k1tbS1paWl9V7mI9AnDMMAdG/hi2P7v93L/Xq+Xqqquj7+0/f694exvh3bfPq/t4O8I78Cr7es6j88XXCf45e9umQ9733VbW/buc5918fsCfyhYNtj+4KsNlgW21cuW6PT9h2Ef1WHYR8SF/hAwu/5REPqjJxjcZud1Oq0LnX5AOy3r2Hfn1y7T+24fmK5yOrHvmIcRG98n325nPYZxTU0NHo8nNO/xeNi4ceN+63i93i7r1NTU7BfGRUVFFBUVATBv3rwu20jvOZ1OtWmYqU3DL5ra1LZtsDpC2gLLwratwHxHYFtW4OYydqd1/P5gqPuD2+ydxt+xDyuwzLYD21t+8Af2GZjvvH8/DsPE7/MBwT8eCP7hYNuB/dtWIPU7T9NRpx2YtazgdgTXsbuGV/gabu/3FarHDrWjbe+tq/P79r7LOta3rL37hb3fQ2iZ3fX9ziMxHe3TzXamYZA0LCNwwmUf6zGM7W6Gj/YdSjiUdQAKCwspLCwMze/717H0Tnc9DukdtWn4Dd02NQBHcJjZFfa9D9127TuhNm1sCts+s7Ozu13e43UNHo+H6uq9AyDV1dX79Xg9Hk+XH4Lu1hEREZHu9RjG+fn5lJWVUVlZic/no7i4mIKCgi7rFBQU8Oabb2LbNhs2bCA+Pl5hLCIicoh6HKZ2OBzMmjWLuXPnYlkW06ZNIzc3l1WrVgEwY8YMTjrpJNauXctNN91ETEwMN9xwQ58XLiIiEi0Mu7sDvv2ktLQ0Uh8dlXTMKPzUpuGnNu0batfw64s2PeJjxiIiItK3FMYiIiIRpjAWERGJMIWxiIhIhEX0BC4RERFRzziqzJ49O9IlRB21afipTfuG2jX8+rNNFcYiIiIRpjAWERGJMIVxFOn8EA4JD7Vp+KlN+4baNfz6s011ApeIiEiEqWcsIiISYQpjERGRCOvxqU0y8FRVVbF48WLq6uowDIPCwkIuuOACmpqaWLhwIbt27WLYsGHccsstJCYmRrrcQcWyLGbPnk16ejqzZ89Wm4bB7t27efTRR9m+fTuGYXD99deTnZ2tdu2FFStW8Oqrr2IYBrm5udxwww20tbWpTQ/TI488wtq1a0lJSWHBggUAB/0///zzz/Pqq69imibXXHMNEydODFst6hkPQg6Hg6uuuoqFCxcyd+5cXn75ZXbs2MHy5cuZMGECixYtYsKECSxfvjzSpQ46L730Ejk5OaF5tWnvLVu2jIkTJ/LQQw8xf/58cnJy1K69UFNTw8qVK5k3bx4LFizAsiyKi4vVpkfg7LPP5s477+yy7EDtuGPHDoqLi3nwwQf5+c9/zpIlS7AsK2y1KIwHobS0NPLy8gCIi4sjJyeHmpoa1qxZw1lnnQXAWWedxZo1ayJZ5qBTXV3N2rVrmT59emiZ2rR39uzZwxdffME555wDgNPpJCEhQe3aS5Zl0dbWht/vp62tjbS0NLXpETj22GP3Gz04UDuuWbOGqVOn4nK5yMjIICsri02bNoWtFg1TD3KVlZWUlJQwduxY6uvrSUtLAwKB3dDQEOHqBpcnnniC733vezQ3N4eWqU17p7KykuTkZB555BG2bdtGXl4eM2fOVLv2Qnp6OhdddBHXX389MTExnHjiiZx44olq0zA5UDvW1NQwbty40Hrp6enU1NSE7XPVMx7EWlpaWLBgATNnziQ+Pj7S5QxqH374ISkpKaERBwkPv99PSUkJM2bM4D//8z9xu90aPu2lpqYm1qxZw+LFi3nsscdoaWnhzTffjHRZUa+vrwJWz3iQ8vl8LFiwgDPOOIMpU6YAkJKSQm1tLWlpadTW1pKcnBzhKgePf/3rX3zwwQd89NFHtLW10dzczKJFi9SmveTxePB4PKEexamnnsry5cvVrr2wfv16MjIyQm02ZcoUNmzYoDYNkwO1o8fjobq6OrReTU0N6enpYftc9YwHIdu2efTRR8nJyeHrX/96aHlBQQFvvPEGAG+88QannHJKpEocdL773e/y6KOPsnjxYn7yk59w/PHHc9NNN6lNeyk1NRWPx0NpaSkQCJIRI0aoXXvB6/WyceNGWltbsW2b9evXk5OTozYNkwO1Y0FBAcXFxbS3t1NZWUlZWRljx44N2+fqDlyD0Jdffsk999zDyJEjMQwDgCuuuIJx48axcOFCqqqq8Hq93Hrrrbq04Qh89tlnvPjii8yePZvGxka1aS9t3bqVRx99FJ/PR0ZGBjfccAO2batde+GZZ56huLgYh8PB6NGj+dGPfkRLS4va9DA99NBDfP755zQ2NpKSksJll13GKaeccsB2fO6553jttdcwTZOZM2dy0kknha0WhbGIiEiEaZhaREQkwhTGIiIiEaYwFhERiTCFsYiISIQpjEVERCJMYSwyiF122WWUl5dHuoz9PPPMMyxatCjSZYgMGroDl0iY3HjjjdTV1WGae//GPfvss7n22msjWJWIDAYKY5EwuuOOOzjhhBMiXUZU8fv9OByOSJch0qcUxiL94PXXX+eVV15hzJgxvPHGG6SlpXHttdcyYcIEIHCf28cff5wvv/ySxMREvvnNb1JYWAgEHpe3fPlyXnvtNerr6xk+fDi33XYbXq8XgE8++YT777+fxsZGTjvtNK699trQndk6e+aZZ9ixYwcxMTG8//77eL1ebrzxRvLz84HAkPeiRYvIysoCYPHixXg8Hi6//HI+++wz/uu//ovzzz+fF198EdM0+cEPfoDT6eTJJ5+koaGBiy66iEsuuST0ee3t7SxcuJCPPvqI4cOHc/311zN69OjQ97t06VK++OILYmNjufDCC7ngggtCdW7fvh2Xy8WHH37I97///S6PtRSJRjpmLNJPNm7cSEZGBkuWLOGyyy7jgQceoKmpCYCHH34Yj8fDY489xk9/+lP+/Oc/s379egBWrFjB6tWrmTNnDk8++STXX389brc7tN+1a9fym9/8hvnz5/POO+/w8ccfH7CGDz/8kKlTp/LEE09QUFDA0qVLD7n+uro62tvbefTRR7nssst47LHHeOutt5g3bx733nsvf/3rX6moqAit/8EHH/C1r32NpUuXctpppzF//nx8Ph+WZfHb3/6W0aNH89hjj3HPPffw0ksvsW7dui7bnnrqqSxbtowzzjjjkGsUGawUxiJhNH/+fGbOnBn6KioqCr2XkpLChRdeiNPpZOrUqWRnZ7N27Vqqqqr48ssvufLKK4mJiWH06NFMnz499Fi8V155hcsvv5zs7GwMw2D06NEkJSWF9nvxxReTkJCA1+vluOOOY+vWrQes75hjjmHSpEmYpsmZZ5550HX35XA4uOSSS3A6nZx22mk0NjZywQUXEBcXR25uLiNGjGDbtm2h9fPy8jj11FNxOp18/etfp729nY0bN7J582YaGhq49NJLcTqdZGZmMn36dIqLi0PbHnXUUUyePBnTNImJiTnkGkUGKw1Ti4TRbbfddsBjxunp6V2Gj4cNG0ZNTQ21tbUkJiYSFxcXes/r9bJ582YAqquryczMPOBnpqamhqbdbjctLS0HXDclJSU0HRMTQ3t7+yEfk01KSgqdnNYRkPvur/Nnezye0LRpmng8HmprawGora1l5syZofcty2L8+PHdbisyFCiMRfpJTU0Ntm2HArmqqoqCggLS0tJoamqiubk5FMhVVVWhZ6V6PB4qKioYOXJkn9bndrtpbW0NzdfV1fUqFDs/+9WyLKqrq0lLS8PhcJCRkaFLn0Q60TC1SD+pr69n5cqV+Hw+3nnnHXbu3MlJJ52E1+vl6KOP5qmnnqKtrY1t27bx2muvhY6VTp8+naeffpqysjJs22bbtm00NjaGvb7Ro0fz9ttvY1kW69at4/PPP+/V/rZs2cJ7772H3+/npZdewuVyMW7cOMaOHUtcXBzLly+nra0Ny7L46quv2LRpU5i+E5HBRz1jkTD67W9/2+U64xNOOIHbbrsNgHHjxlFWVsa1115Lamoqt956a+jY780338zjjz/OD3/4QxITE/n2t78dGu7uON5633330djYSE5ODj/72c/CXvvMmTNZvHgxL7/8MqecckqvH07f8TD2xYsXk5WVxU9/+lOczsCvnDvuuIM//OEP3Hjjjfh8PrKzs/nOd74Tjm9DZFDS84xF+kHHpU2//vWvI12KiAxAGqYWERGJMIWxiIhIhGmYWkREJMLUMxYREYkwhbGIiEiEKYxFREQiTGEsIiISYQpjERGRCPt/LHJBVF1hDsEAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 576x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    final error(train) = 1.14e-03\n",
-      "    final error(valid) = 1.49e-01\n",
-      "    final acc(train)   = 1.00e+00\n",
-      "    final acc(valid)   = 9.71e-01\n",
-      "    run time per epoch = 1.47\n",
-      "--------------------------------------------------------------------------------\n",
-      "learning_rate=0.20 init_scale=0.50\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    final error(train) = 1.81e-03\n",
-      "    final error(valid) = 1.47e-01\n",
-      "    final acc(train)   = 1.00e+00\n",
-      "    final acc(valid)   = 9.71e-01\n",
-      "    run time per epoch = 1.49\n",
-      "--------------------------------------------------------------------------------\n",
-      "learning_rate=0.20 init_scale=1.00\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "    final error(train) = 4.95e-03\n",
-      "    final error(valid) = 1.88e-01\n",
-      "    final acc(train)   = 1.00e+00\n",
-      "    final acc(valid)   = 9.60e-01\n",
-      "    run time per epoch = 1.58\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 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": 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        | 4.75e-03           | 2.19e-01           |  1.00            | 0.96             |\n",
-      "| 0.2        | 1.14e-03           | 1.49e-01           |  1.00            | 0.97             |\n",
-      "| 0.5        | 1.81e-03           | 1.47e-01           |  1.00            | 0.97             |\n",
-      "| 1.0        | 4.95e-03           | 1.88e-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": [
-    "> 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: green;'>\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: green;'>\n",
-    "This makes sense as adding more layers increases the total number of free parameters in the model and so we would expect the model to be able to fit too the training data better.\n",
-    "</span>\n",
-    "\n",
-    "<span style='color: green;'>\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",
-    "\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 **grow** 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",
-    "\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",
-    "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",
-    "\n",
-    "\n",
-    "In general the models with more layers also take longer to train, which can be accounted for by their increased complexity, 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",
-    "\n",
-    "\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 is expected to grow as the sum will contain more terms. 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",
-    "\n",
-    "\n",
-    "If we wish to keep the typical magnitude of the activations and backpropagated gradients at a given layer roughly constant through the network, we may wish to initialise a layer parameters according to its size. 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"
-   ]
-  },
-  {
-   "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 discussed in lecture 3, although logistic sigmoid has some favourable properties in terms of interpretability, there are also disadvantages from a computational perspective. In particular:\n",
-    "1. the gradients of the sigmoid become close to zero (and may actually become zero because of finite numerical precision) for large positive or negative inputs, \n",
-    "2. 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 (tanh)  and the rectified linear function (ReLU).\n",
-    "\n",
-    "For tanh (`TanhLayer`) 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 ReLU (`ReluLayer`) 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",
-    "**Your Tasks:**\n",
-    "- Using these definitions implement the `fprop` and `bprop` methods for the skeleton `TanhLayer` and `ReluLayer` class definitions below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "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": 20,
-   "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": 21,
-   "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": "markdown",
-   "metadata": {},
-   "source": [
-    "# PyTorch\n",
-    "\n",
-    "In this section we will builld on we learned in the previous lab and will use PyTorch to build a multi-layer model for the MNIST classification task. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<torch._C.Generator at 0x7f0a02286d70>"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.optim as optim\n",
-    "from torchvision import datasets,transforms\n",
-    "from torch.utils.data.sampler import SubsetRandomSampler\n",
-    "\n",
-    "torch.manual_seed(seed)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Neural networks are typically take a long time to converge. This process can be sped up by using a GPU. If you have a GPU available, you can use it by setting the `device` variable below to `cuda`. If you do not have a GPU available, you can still run the code on the CPU by setting `device` to `cpu`.\n",
-    "\n",
-    "When training, both the model and the data should be on the same device. The `to` method can be used to move a tensor to a device. For example, `x = x.to(device)` will move the tensor `x` to the device specified by `device`. Look through the code to see where we put the model and the data on the CPU or GPU device."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "device(type='cpu')"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Device configuration\n",
-    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
-    "device"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set training run hyperparameters\n",
-    "batch_size = 128  # number of data points in a batch\n",
-    "learning_rate = 0.001  # learning rate for gradient descent\n",
-    "num_epochs = 50  # number of training epochs to perform\n",
-    "stats_interval = 5  # epoch interval between recording and printing stats"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The [transforms](https://pytorch.org/vision/0.9/transforms.html) are common transformations for the image datasets. We will use the `Compose` transform to combine the `ToTensor` and `Normalize` transforms. The `ToTensor` transform converts the image to a tensor and the `Normalize` transform normalizes the image by subtracting the mean and dividing by the standard deviation. The `Normalize` transform takes two arguments: the mean and the standard deviation. The mean and standard deviation are calculated for each channel. The mean and standard deviation for the MNIST dataset are $0.1307$ and $0.3081$ respectively. `Normalize` transform is particularly useful when there is a big discrepancy between the values of the pixels in an image.\n",
-    "\n",
-    "When working with images, transforms are used to augment the dataset (i.e. create artificial images based on existing ones). This is done to increase the size of the dataset and to make the model more robust to changes in the input images. An illustration of how transforms affect an image is shown [here](https://pytorch.org/vision/0.11/auto_examples/plot_transforms.html#sphx-glr-download-auto-examples-plot-transforms-py)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transform=transforms.Compose([\n",
-    "        transforms.ToTensor(),\n",
-    "        transforms.Normalize((0.1307,), (0.3081,))\n",
-    "        ])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Popular machine learning datasets are available in the [`torchvision.datasets`](https://pytorch.org/vision/0.15/datasets.html) module. The `MNIST` dataset is available in the [`torchvision.datasets.MNIST`](https://pytorch.org/vision/0.15/generated/torchvision.datasets.MNIST.html) class. This way, we can download the dataset directly from the PyTorch library into the `data` folder of our repository. \n",
-    "\n",
-    "The `MNIST` class takes the following arguments:\n",
-    "- `root`: the path where the dataset will be stored\n",
-    "- `train`: if `True`, the training set is returned, otherwise the test set is returned\n",
-    "- `download`: if `True`, the dataset is downloaded from the internet and put in `root`. If the dataset is already downloaded, it is not downloaded again\n",
-    "- `transform`: the transform to be applied to the dataset"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
-      "Failed to download (trying next):\n",
-      "HTTP Error 403: Forbidden\n",
-      "\n",
-      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\n",
-      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to ../data/MNIST/raw/train-images-idx3-ubyte.gz\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100.0%\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting ../data/MNIST/raw/train-images-idx3-ubyte.gz to ../data/MNIST/raw\n",
-      "\n",
-      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
-      "Failed to download (trying next):\n",
-      "HTTP Error 403: Forbidden\n",
-      "\n",
-      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz\n",
-      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to ../data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100.0%\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting ../data/MNIST/raw/train-labels-idx1-ubyte.gz to ../data/MNIST/raw\n",
-      "\n",
-      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n",
-      "Failed to download (trying next):\n",
-      "HTTP Error 403: Forbidden\n",
-      "\n",
-      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz\n",
-      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100.0%\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting ../data/MNIST/raw/t10k-images-idx3-ubyte.gz to ../data/MNIST/raw\n",
-      "\n",
-      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
-      "Failed to download (trying next):\n",
-      "HTTP Error 403: Forbidden\n",
-      "\n",
-      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\n",
-      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "100.0%"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Extracting ../data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ../data/MNIST/raw\n",
-      "\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "train_dataset = datasets.MNIST('../data', train=True, download=True, transform=transform)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test_dataset = datasets.MNIST('../data', train=False, download=True, transform=transform)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that the training set has $60,000$ images and the test set has $10,000$ images. Each image is a $28 \\times 28$ grayscale image. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train dataset: \n",
-      " Dataset MNIST\n",
-      "    Number of datapoints: 60000\n",
-      "    Root location: ../data\n",
-      "    Split: Train\n",
-      "    StandardTransform\n",
-      "Transform: Compose(\n",
-      "               ToTensor()\n",
-      "               Normalize(mean=(0.1307,), std=(0.3081,))\n",
-      "           )\n",
-      "torch.Size([60000, 28, 28])\n",
-      "torch.Size([60000])\n",
-      "\n",
-      "Test dataset: \n",
-      " Dataset MNIST\n",
-      "    Number of datapoints: 10000\n",
-      "    Root location: ../data\n",
-      "    Split: Test\n",
-      "    StandardTransform\n",
-      "Transform: Compose(\n",
-      "               ToTensor()\n",
-      "               Normalize(mean=(0.1307,), std=(0.3081,))\n",
-      "           )\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Train dataset: \\n\", train_dataset)\n",
-    "print(train_dataset.data.size())\n",
-    "print(train_dataset.targets.size())\n",
-    "print(\"\\nTest dataset: \\n\", test_dataset)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.imshow(train_dataset.data[42], cmap='gray')\n",
-    "plt.title('%i' % train_dataset.targets[42])\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "However, since we want to evaluate the performance of our model during training, we need a validation set. We can create a validation set by splitting the training set into two parts. As a general rule, the validation set should be $10-20\\%$ of the training set. The `SubsetRandomSampler` class can be used to create a subset of the training set. The `SubsetRandomSampler` class takes a list of randomly shuffled indices as an argument and selects a subset of the training set based on these indices.\n",
-    "\n",
-    "*Why would we want to randomly shuffle the data when creating the separate training and validation set?*\n",
-    "\n",
-    "*We could just take the first 80% of data points and assign them to the training set and the last 20% of data points to the validation set. When and why would this be a bad practice?*\n",
-    "\n",
-    "*Why do we want to shuffle the training and valisation sets but not the test set (see `shuffle=False`)?*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "valid_size=0.2 # Leave 20% of training set as validation set\n",
-    "num_train = len(train_dataset)\n",
-    "indices = list(range(num_train))\n",
-    "split = int(np.floor(valid_size * num_train))\n",
-    "np.random.shuffle(indices) # Shuffle indices in-place\n",
-    "train_idx, valid_idx = indices[split:], indices[:split] # Split indices into training and validation sets\n",
-    "train_sampler = SubsetRandomSampler(train_idx)\n",
-    "valid_sampler = SubsetRandomSampler(valid_idx)\n",
-    "\n",
-    "# Create the dataloaders\n",
-    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, sampler=train_sampler, pin_memory=True)\n",
-    "valid_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, sampler=valid_sampler, pin_memory=True)\n",
-    "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, pin_memory=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To create a multy-layer model, we will use the [`nn.Sequential`](https://pytorch.org/docs/stable/generated/torch.nn.Sequential.html) container. The `nn.Sequential` container takes a list of layers as an argument and applies them sequentially. The `nn.Sequential` container is a convenient way to create a model with multiple layers. However, it is not very flexible. For example, we cannot have skip connections in a model created using the `nn.Sequential` container.\n",
-    "\n",
-    "Since we are working with images, we will have to flatten the images before passing them to the model. We can do this using the [`nn.Flatten`](https://pytorch.org/docs/stable/generated/torch.nn.Flatten.html) layer. The `nn.Flatten` layer takes a tensor of shape `(N, C, H, W)` and flattens it to a tensor of shape `(N, C*H*W)`.\n",
-    "\n",
-    "In between the affine layers, we will use the [`nn.ReLU`](https://pytorch.org/docs/stable/generated/torch.nn.ReLU.html) activation function. The `nn.ReLU` activation function applies the ReLU function elementwise to the input tensor. There are other acrtivation functions available in PyTorch, such as the [`nn.Tanh`](https://pytorch.org/docs/stable/generated/torch.nn.Tanh.html) activation function, the [`nn.Sigmoid`](https://pytorch.org/docs/stable/generated/torch.nn.Sigmoid.html) activation function, and the [`nn.LeakyReLU`](https://pytorch.org/docs/stable/generated/torch.nn.LeakyReLU.html) activation function. The `nn.LeakyReLU` activation function is similar to the `nn.ReLU` activation function, but it allows a small gradient when the input is negative. This can help with training."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class MultipleLayerModel(nn.Module):\n",
-    "    \"\"\"Multiple layer model.\"\"\"\n",
-    "    def __init__(self, input_dim, output_dim, hidden_dim):\n",
-    "        super().__init__()\n",
-    "        self.flatten = nn.Flatten()\n",
-    "        self.linear_relu_stack = nn.Sequential(\n",
-    "            nn.Linear(input_dim, hidden_dim),\n",
-    "            nn.ReLU(),\n",
-    "            nn.Linear(hidden_dim, hidden_dim),\n",
-    "            nn.ReLU(),\n",
-    "            nn.Linear(hidden_dim, output_dim),\n",
-    "        )\n",
-    "        \n",
-    "    def forward(self, x):\n",
-    "        x = self.flatten(x)\n",
-    "        logits = self.linear_relu_stack(x)\n",
-    "        return logits"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Since our image size is $1 \\times 28 \\times 28$, this will be the output size of the `nn.Flatten` layer. The input size of the first affine layer that will take in $x_i$ datapoints will be $1 \\times 28 \\times 28$ and the output size will be $100$. The input size of the second affine layer will be $100$ and the output size will be $10$. The output size of the second affine layer is 10 because we have $10$ classes in the MNIST dataset. Therefore, the last layer will out put the a vector $y = (y_1, \\dots, y_{K})^{\\top}$ where $y_k$ is the probability that the image belongs to class $k$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "MultipleLayerModel(\n",
-      "  (flatten): Flatten(start_dim=1, end_dim=-1)\n",
-      "  (linear_relu_stack): Sequential(\n",
-      "    (0): Linear(in_features=784, out_features=100, bias=True)\n",
-      "    (1): ReLU()\n",
-      "    (2): Linear(in_features=100, out_features=100, bias=True)\n",
-      "    (3): ReLU()\n",
-      "    (4): Linear(in_features=100, out_features=10, bias=True)\n",
-      "  )\n",
-      ")\n"
-     ]
-    }
-   ],
-   "source": [
-    "input_dim = 1*28*28\n",
-    "output_dim = 10\n",
-    "hidden_dim = 100\n",
-    "\n",
-    "model = MultipleLayerModel(input_dim, output_dim, hidden_dim).to(device)\n",
-    "print(model)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "As we want to calissify images by their labels, we will use the [`nn.CrossEntropyLoss`](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html) loss function. The `nn.CrossEntropyLoss` loss function combines the softmax function and the cross entropy loss function. The `nn.CrossEntropyLoss` loss function takes the logits as an input and returns the loss. The logits are the outputs of the last affine layer before the softmax function is applied. The `nn.CrossEntropyLoss` loss function is equivalent to applying the softmax function to the logits and then applying the cross entropy loss function to the softmax outputs and the labels."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loss = nn.CrossEntropyLoss()\n",
-    "optimizer = optim.Adam(model.parameters(), lr=learning_rate) # Adam optimiser"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, our training loop will combine a training and an evaluation pass per epoch. During the training pass, we propagate the training data through the model and calculate the loss. Then, we calculate the gradients of the loss with respect to the parameters of the model and update the parameters of the model. During the evaluation pass, we propagate the validation data through the model and calculate the loss and the accuracy. We do not calculate the gradients of the loss with respect to the parameters of the model and we do not update the parameters of the model. \n",
-    "\n",
-    "*What would happen if we do?*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Epoch: 0 \tError(train): 0.358887 \tAccuracy(train): 0.896062 \tError(valid): 0.185471 \tAccuracy(valid): 0.946836\n",
-      "Epoch: 5 \tError(train): 0.052651 \tAccuracy(train): 0.983104 \tError(valid): 0.095970 \tAccuracy(valid): 0.971687\n",
-      "Epoch: 10 \tError(train): 0.022297 \tAccuracy(train): 0.992771 \tError(valid): 0.090072 \tAccuracy(valid): 0.974374\n",
-      "Epoch: 15 \tError(train): 0.014676 \tAccuracy(train): 0.995188 \tError(valid): 0.104987 \tAccuracy(valid): 0.975731\n",
-      "Epoch: 20 \tError(train): 0.009700 \tAccuracy(train): 0.996771 \tError(valid): 0.109034 \tAccuracy(valid): 0.977089\n",
-      "Epoch: 25 \tError(train): 0.010248 \tAccuracy(train): 0.996354 \tError(valid): 0.122468 \tAccuracy(valid): 0.978363\n",
-      "Epoch: 30 \tError(train): 0.005657 \tAccuracy(train): 0.998208 \tError(valid): 0.166619 \tAccuracy(valid): 0.970855\n",
-      "Epoch: 35 \tError(train): 0.010991 \tAccuracy(train): 0.996333 \tError(valid): 0.143561 \tAccuracy(valid): 0.976978\n",
-      "Epoch: 40 \tError(train): 0.011190 \tAccuracy(train): 0.996521 \tError(valid): 0.152304 \tAccuracy(valid): 0.975150\n",
-      "Epoch: 45 \tError(train): 0.002502 \tAccuracy(train): 0.999458 \tError(valid): 0.152308 \tAccuracy(valid): 0.977255\n",
-      "Epoch: 50 \tError(train): 0.002459 \tAccuracy(train): 0.999167 \tError(valid): 0.227140 \tAccuracy(valid): 0.971493\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Keep track of the loss values over training\n",
-    "train_loss = [] \n",
-    "valid_loss = []\n",
-    "\n",
-    "# Keep track of the accuracy values over training\n",
-    "train_acc = []\n",
-    "valid_acc = []\n",
-    "\n",
-    "for i in range(num_epochs+1):\n",
-    "    # Training\n",
-    "    model.train()\n",
-    "    batch_loss = []\n",
-    "    batch_acc = []\n",
-    "    for batch_idx, (x, t) in enumerate(train_loader):\n",
-    "        x = x.to(device)\n",
-    "        t = t.to(device)\n",
-    "        \n",
-    "        # Forward pass\n",
-    "        y = model(x)\n",
-    "        E_value = loss(y, t)\n",
-    "        \n",
-    "        # Backward pass\n",
-    "        optimizer.zero_grad()\n",
-    "        E_value.backward()\n",
-    "        optimizer.step()\n",
-    "        \n",
-    "        # Calculate accuracy\n",
-    "        _, argmax = torch.max(y, 1)\n",
-    "        acc = (t == argmax.squeeze()).float().mean()\n",
-    "        \n",
-    "        # Logging\n",
-    "        batch_loss.append(E_value.item())\n",
-    "        batch_acc.append(acc.item())\n",
-    "    \n",
-    "    train_loss.append(np.mean(batch_loss))\n",
-    "    train_acc.append(np.mean(batch_acc))\n",
-    "\n",
-    "    # Validation\n",
-    "    model.eval()\n",
-    "    batch_loss = []\n",
-    "    batch_acc = []\n",
-    "    for batch_idx, (x, t) in enumerate(valid_loader):\n",
-    "        x = x.to(device)\n",
-    "        t = t.to(device)\n",
-    "        \n",
-    "        # Forward pass\n",
-    "        y = model(x)\n",
-    "        E_value = loss(y, t)\n",
-    "        \n",
-    "        # Calculate accuracy\n",
-    "        _, argmax = torch.max(y, 1)\n",
-    "        acc = (t == argmax.squeeze()).float().mean()\n",
-    "        \n",
-    "        # Logging\n",
-    "        batch_loss.append(E_value.item())\n",
-    "        batch_acc.append(acc.item())\n",
-    "    \n",
-    "    valid_loss.append(np.mean(batch_loss))\n",
-    "    valid_acc.append(np.mean(batch_acc))\n",
-    "\n",
-    "    if i % stats_interval == 0:\n",
-    "        print('Epoch: {} \\tError(train): {:.6f} \\tAccuracy(train): {:.6f} \\tError(valid): {:.6f} \\tAccuracy(valid): {:.6f}'.format(\n",
-    "            i, train_loss[-1], train_acc[-1], valid_loss[-1], valid_acc[-1]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see below the evolution of our training and validation losses, as well as, the respective accuracies. We can see that the training loss decreases and the training accuracy increases with each epoch. However, the validation loss starts increasing after $10$ epochs and the validation accuracy increases only up to a certain point. \n",
-    "\n",
-    "*What could be happening here?*\n",
-    "\n",
-    "*Is training for 50 epoch a sensible choice?* \n",
-    "\n",
-    "*What number of epochs would be a better choice and why?* "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# 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",
-    "ax_1.plot(train_loss, label='Error(train)')\n",
-    "ax_1.plot(valid_loss, label='Error(valid)')\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",
-    "ax_2.plot(train_acc, label='Accuracy(train)')\n",
-    "ax_2.plot(valid_acc, label='Accuracy(valid)')\n",
-    "ax_2.legend(loc=0)\n",
-    "ax_2.set_xlabel('Epoch number')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Once our model is training and we are satisfied with the results, we can test its performance on unseen data. We can do this by propagating the test data through the model and calculating the accuracy. We can see that the test accuracy is similar to the validation accuracy.\n",
-    "\n",
-    "*Altought using a test set is not necessary for training, why is it important to have a one?*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Accuracy(test): 0.971915\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Testing\n",
-    "test_acc = []\n",
-    "model.eval()\n",
-    "for batch_idx, (x, t) in enumerate(test_loader):\n",
-    "    x = x.to(device)\n",
-    "    t = t.to(device)\n",
-    "\n",
-    "    # Forward pass\n",
-    "    y = model(x)\n",
-    "    \n",
-    "    # Calculate accuracy\n",
-    "    _, argmax = torch.max(y, 1)\n",
-    "    acc = (t == argmax.squeeze()).float().mean()\n",
-    "    \n",
-    "    test_acc.append(acc.item())\n",
-    "test_acc = np.mean(test_acc)\n",
-    "print('Accuracy(test): {:.6f}'.format(test_acc))"
-   ]
-  }
- ],
- "metadata": {
-  "anaconda-cloud": {},
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.12.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/04_Generalisation_and_overfitting.ipynb b/notebooks/04_Generalisation_and_overfitting.ipynb
deleted file mode 100644
index ca12442..0000000
--- a/notebooks/04_Generalisation_and_overfitting.ipynb
+++ /dev/null
@@ -1,614 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Generalisation and overfitting\n",
-    "\n",
-    "In this notebook we will explore the issue of overfitting and how we can measure how well the models we train generalise their predictions to unseen data. This will build upon the introduction to generalisation given in the fourth lecture."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Exercise 1: overfitting and model complexity in a 1D regression problem\n",
-    "\n",
-    "As an exercise we will consider a regression problem. In particular, given a fixed set of (noisy) observations of the underlying functional relationship between inputs and outputs, we will attempt to use a multiple layer network model to learn to predict output values from inputs. The aim of the exercise will be to visualise how increasing the complexity of the model we fit to the training data effects the ability of the model to make predictions across the input space.\n",
-    "\n",
-    "### Function\n",
-    "\n",
-    "To keep things simple we will consider a single input-output function defined by a fourth degree polynomial (quartic)\n",
-    "\n",
-    "$$ f(x) = 10 x^4 - 17 x^3 + 8 x^2 - x $$\n",
-    "\n",
-    "with the observed values being the function values plus zero-mean Gaussian noise\n",
-    "\n",
-    "$$ y = f(x) + 0.01 \\epsilon \\qquad \\epsilon \\sim \\mathcal{N}\\left(\\cdot;\\,0,\\,1\\right) $$\n",
-    "\n",
-    "The inputs will be drawn from the uniform distribution on $[0, 1]$.\n",
-    "\n",
-    "First import the necessary modules and seed the random number generator by running the cell below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "plt.style.use('ggplot')\n",
-    "seed = 17102016 \n",
-    "rng = np.random.RandomState(seed)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Your Tasks:**\n",
-    "\n",
-    "Write code in the cell below to calculate a polynomial function of one dimensional inputs. \n",
-    "\n",
-    "If $\\boldsymbol{c}$ is a length $P$ vector of coefficients corresponding to increasing powers in the polynomial (starting from the constant zero power term up to the $P-1^{\\textrm{th}}$ power) the function should correspond to the following\n",
-    "\n",
-    "\\begin{equation}\n",
-    "  f_{\\textrm{polynomial}}(x,\\ \\boldsymbol{c}) = \\sum_{p=0}^{P-1} \\left( c_p x^p \\right)\n",
-    "\\end{equation}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def polynomial_function(inputs, coefficients):\n",
-    "    \"\"\"Calculates polynomial with given coefficients of an array of inputs.\n",
-    "    \n",
-    "    Args:\n",
-    "        inputs: One-dimensional array of input values of shape (num_inputs,)\n",
-    "        coefficients: One-dimensional array of polynomial coefficient terms\n",
-    "           with `coefficients[0]` corresponding to the coefficient for the\n",
-    "           zero order term in the polynomial (constant) and `coefficients[-1]`\n",
-    "           corresponding to the highest order term.\n",
-    "           \n",
-    "    Returns:\n",
-    "        One dimensional array of output values of shape (num_inputs,)\n",
-    "    \n",
-    "    \"\"\"\n",
-    "    raise NotImplementedError(\"TODO Implement this function\") "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Run the cell below to test your implementation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test_coefficients = np.array([-1., 3., 4.])\n",
-    "test_inputs = np.array([0., 0.5, 1., 2.])\n",
-    "test_outputs = np.array([-1., 1.5, 6., 21.])\n",
-    "assert polynomial_function(test_inputs, test_coefficients).shape == (4,), (\n",
-    "    'Function gives wrong shape output.'\n",
-    ")\n",
-    "assert np.allclose(polynomial_function(test_inputs, test_coefficients), test_outputs), (\n",
-    "    'Function gives incorrect output values.'\n",
-    ")\n",
-    "print(\"Function is correct!\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now need to use the random number generator to sample input values and calculate the corresponding target outputs using your polynomial implementation with the relevant coefficients for our function. Do this by running the cell below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "coefficients = np.array([0, -1., 8., -17., 10.])\n",
-    "input_dim, output_dim = 1, 1\n",
-    "noise_std = 0.01\n",
-    "num_data = 80\n",
-    "inputs = rng.uniform(size=(num_data, input_dim))\n",
-    "epsilons = rng.normal(size=num_data)\n",
-    "targets = (polynomial_function(inputs[:, 0], coefficients) + \n",
-    "           epsilons * noise_std)[:, None]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will split the generated data points in to equal sized training and validation data sets and use these to create data provider objects which we can use to train models in our framework. As the dataset is small here we will use a batch size equal to the size of the data set. Run the cell below to split the data and set up the data provider objects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from mlp.data_providers import DataProvider\n",
-    "num_train = num_data // 2\n",
-    "batch_size = num_train\n",
-    "inputs_train, targets_train = inputs[:num_train], targets[:num_train]\n",
-    "inputs_valid, targets_valid = inputs[num_train:], targets[num_train:]\n",
-    "train_data = DataProvider(inputs_train, targets_train, batch_size=batch_size, rng=rng)\n",
-    "valid_data = DataProvider(inputs_valid, targets_valid, batch_size=batch_size, rng=rng)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can now visualise the data we will be modelling. Run the cell below to plot the target outputs against inputs for both the training and validation sets. Note the clear underlying smooth functional relationship evident in the noisy data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "fig = plt.figure(figsize=(8, 4))\n",
-    "ax = fig.add_subplot(111)\n",
-    "ax.plot(inputs_train[:, 0], targets_train[:, 0], '.', label='training data')\n",
-    "ax.plot(inputs_valid[:, 0], targets_valid[:, 0], '.', label='validation data')\n",
-    "ax.set_xlabel('Inputs $x$', fontsize=14)\n",
-    "ax.set_ylabel('Ouputs $y$', fontsize=14)\n",
-    "ax.legend(loc='best')\n",
-    "fig.tight_layout()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Model\n",
-    "\n",
-    "We will fit models with a varying number of parameters to the training data. As multi-layer logistic sigmoid models tend to perform poorly in regressions tasks like this we will instead use a [radial basis function (RBF) network](https://en.wikipedia.org/wiki/Radial_basis_function_network).\n",
-    "\n",
-    "This model predicts the output as the weighted sum of basis functions (here Gaussian like bumps) tiled across the input space. The cell below generates a random set of weights and bias for a RBF network and plots the modelled input-output function across inputs $[0, 1]$. Run the cell below for several different number of weight parameters (specified with `num_weights` variable) to get a feel for the sort of predictions the RBF network models produce."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "num_weights = 15\n",
-    "weights_scale = 1.\n",
-    "bias_scale = 1.\n",
-    "\n",
-    "def basis_function(x, centre, scale):\n",
-    "    return np.exp(-(x - centre)**2 / scale**2)\n",
-    "\n",
-    "weights = rng.normal(size=num_weights) * weights_scale\n",
-    "bias = rng.normal() * bias_scale\n",
-    "\n",
-    "centres = np.linspace(0, 1, weights.shape[0])\n",
-    "scale = 1. / weights.shape[0]\n",
-    "\n",
-    "xs = np.linspace(0, 1, 200)\n",
-    "ys = np.zeros(xs.shape[0])\n",
-    "\n",
-    "fig = plt.figure(figsize=(12, 4))\n",
-    "ax = fig.add_subplot(1, 1, 1)\n",
-    "for weight, centre in zip(weights, centres):\n",
-    "    ys += weight * basis_function(xs, centre, scale)\n",
-    "ys += bias\n",
-    "ax.plot(xs, ys)\n",
-    "ax.set_xlabel('Input', fontsize=14)\n",
-    "ax.set_ylabel('Output', fontsize=14)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You do not need to study the details of how to implement this model. All of the additional code you need to fit RBF networks is provided in the `RadialBasisFunctionLayer` in the `mlp.layers` module. The `RadialBasisFunctionLayer` class has the same interface as the layer classes we encountered in the previous lab, defining both `fprop` and `bprop` methods, and we can therefore include it as a layer in a `MultipleLayerModel` as with any other layer. \n",
-    "\n",
-    "Here we will use the `RadialBasisFunctionLayer` as the first layer in a two layer model. This first layer calculates the basis function terms which are then be weighted and summed together in an `AffineLayer`, the second and final layer. This illustrates the advantage of using a modular framework - we can reuse the code we previously implemented to train a quite different model architecture just by defining a new layer class. \n",
-    "\n",
-    "Run the cell below which contains some necessary setup code."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from mlp.models import MultipleLayerModel\n",
-    "from mlp.layers import AffineLayer, RadialBasisFunctionLayer\n",
-    "from mlp.errors import SumOfSquaredDiffsError\n",
-    "from mlp.initialisers import ConstantInit, UniformInit\n",
-    "from mlp.learning_rules import GradientDescentLearningRule\n",
-    "from mlp.optimisers import Optimiser\n",
-    "\n",
-    "# Regression problem therefore use sum of squared differences error\n",
-    "error = SumOfSquaredDiffsError()\n",
-    "# Use basic gradient descent learning rule with fixed learning rate\n",
-    "learning_rule = GradientDescentLearningRule(0.1)\n",
-    "# Initialise weights from uniform distribution and zero bias\n",
-    "weights_init = UniformInit(-0.1, 0.1)\n",
-    "biases_init = ConstantInit(0.)\n",
-    "# Train all models for 2000 epochs\n",
-    "num_epoch = 2000"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The next cell defines RBF network models with varying number of weight parameters (equal to the number of basis functions) and fits each to the training set, recording the final training and validation set errors for the fitted models. Run it now to fit the models and calculate the error values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "num_weight_list = [2, 5, 10, 25, 50, 100]\n",
-    "models = []\n",
-    "train_errors = []\n",
-    "valid_errors = []\n",
-    "for num_weight in num_weight_list:\n",
-    "    model = MultipleLayerModel([\n",
-    "        RadialBasisFunctionLayer(num_weight),\n",
-    "        AffineLayer(input_dim * num_weight, output_dim, \n",
-    "                    weights_init, biases_init)\n",
-    "    ])\n",
-    "    optimiser = Optimiser(model, error, learning_rule, \n",
-    "                            train_data, valid_data)\n",
-    "    print('-' * 80)\n",
-    "    print('Training model with {0} weights'.format(num_weight))\n",
-    "    print('-' * 80)\n",
-    "    _ = optimiser.train(num_epoch, -1)\n",
-    "    outputs_train = model.fprop(inputs_train)[-1]\n",
-    "    outputs_valid = model.fprop(inputs_valid)[-1]\n",
-    "    models.append(model)\n",
-    "    train_errors.append(error(outputs_train, targets_train))\n",
-    "    valid_errors.append(error(outputs_valid, targets_valid))\n",
-    "    print('  Final training set error: {0:.1e}'.format(train_errors[-1]))\n",
-    "    print('  Final validation set error: {0:.1e}'.format(valid_errors[-1]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Your Tasks**\n",
-    "\n",
-    "In the cell below write code to [plot bar charts](http://matplotlib.org/examples/api/barchart_demo.html) of the training and validation set errors for the different fitted models.\n",
-    "\n",
-    "Some questions to think about from the plots:\n",
-    "\n",
-    "  * Do the models with more free parameters fit the training data better or worse?\n",
-    "  * What does the validation set error value tell us about the models?\n",
-    "  * Of the models fitted here which would you say seems like it is most likely to generalise well to unseen data? \n",
-    "  * Do any of the models seem to be overfitting?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#TODO plot the bar charts here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now let's visualise what the fitted model's predictions look like across the whole input space compared to the 'true' function we were trying to fit. \n",
-    "\n",
-    "**Your Tasks:**  \n",
-    "\n",
-    "In the cell below, for each of the fitted models stored in the `models` list above:\n",
-    "  * Compute output predictions for the model across a linearly spaced series of 500 input points between 0 and 1 in the input space.\n",
-    "  * Plot the computed predicted outputs and true function values at the corresponding inputs as line plots on the same axis (use a new axis for each model).\n",
-    "  * On the same axis plot the training data sets input-target pairs as points."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [],
-   "source": [
-    "#TODO plot the graphs here"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You should be able to relate your answers to the questions above to what you see in these plots - ask a demonstrator if you are unsure what is going on. In particular for the models which appeared to be overfitting and generalising poorly you should now have an idea how this looks in terms of the model's predictions and how these relate to the training data points and true function values.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# PyTorch\n",
-    "\n",
-    "As we have seen in the [previous lab](https://github.com/VICO-UoE/mlpractical/tree/mlp2023-24/lab3/notebooks/03_Multiple_layer_models.ipynb), our model shows signs of overfitting after $15$ epochs. \n",
-    "\n",
-    "Overfitting occurs when the model learns the training data too well, and fails to generalise to unseen data. In this case, the model learns the noise in the training data, and fails to learn the underlying function. \n",
-    "\n",
-    "The model may be too complex, and we can reduce the complexity by reducing the number of parameters. However, this may not be the best solution, as we may not be able to learn the underlying function with a simple model.\n",
-    "\n",
-    "In practice, a model that overfits the training data will have a low training error, but a high validation error.\n",
-    "\n",
-    "*What can we deduce if we observe a high training error and a high validation error?*\n",
-    "\n",
-    "Overfitting is a common problem in machine learning, and there are many techniques to avoid it. In this lab, we will explore one of them: *early stopping*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import torch\n",
-    "import torch.nn as nn\n",
-    "import torch.optim as optim\n",
-    "from torchvision import datasets,transforms\n",
-    "from torch.utils.data.sampler import SubsetRandomSampler\n",
-    "\n",
-    "torch.manual_seed(seed)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Device configuration\n",
-    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
-    "\n",
-    "# Set training run hyperparameters\n",
-    "batch_size = 128  # number of data points in a batch\n",
-    "learning_rate = 0.001  # learning rate for gradient descent\n",
-    "num_epochs = 50  # number of training epochs to perform\n",
-    "stats_interval = 1  # epoch interval between recording and printing stats"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transform=transforms.Compose([\n",
-    "        transforms.ToTensor(),\n",
-    "        transforms.Normalize((0.1307,), (0.3081,))\n",
-    "        ])\n",
-    "\n",
-    "train_dataset = datasets.MNIST('../data', train=True, download=True, transform=transform)\n",
-    "test_dataset = datasets.MNIST('../data', train=False, download=True, transform=transform)\n",
-    "\n",
-    "valid_size=0.2 # Leave 20% of training set as validation set\n",
-    "num_train = len(train_dataset)\n",
-    "indices = list(range(num_train))\n",
-    "split = int(np.floor(valid_size * num_train))\n",
-    "np.random.shuffle(indices) # Shuffle indices in-place\n",
-    "train_idx, valid_idx = indices[split:], indices[:split] # Split indices into training and validation sets\n",
-    "train_sampler = SubsetRandomSampler(train_idx)\n",
-    "valid_sampler = SubsetRandomSampler(valid_idx)\n",
-    "\n",
-    "# Create the dataloaders\n",
-    "train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, sampler=train_sampler, pin_memory=True)\n",
-    "valid_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, sampler=valid_sampler, pin_memory=True)\n",
-    "test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False, pin_memory=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class MultipleLayerModel(nn.Module):\n",
-    "    \"\"\"Multiple layer model.\"\"\"\n",
-    "    def __init__(self, input_dim, output_dim, hidden_dim):\n",
-    "        super().__init__()\n",
-    "        self.flatten = nn.Flatten()\n",
-    "        self.linear_relu_stack = nn.Sequential(\n",
-    "            nn.Linear(input_dim, hidden_dim),\n",
-    "            nn.ReLU(),\n",
-    "            nn.Linear(hidden_dim, hidden_dim),\n",
-    "            nn.ReLU(),\n",
-    "            nn.Linear(hidden_dim, output_dim),\n",
-    "        )\n",
-    "        \n",
-    "    def forward(self, x):\n",
-    "        x = self.flatten(x)\n",
-    "        logits = self.linear_relu_stack(x)\n",
-    "        return logits\n",
-    "    \n",
-    "input_dim = 1*28*28\n",
-    "output_dim = 10\n",
-    "hidden_dim = 100\n",
-    "\n",
-    "model = MultipleLayerModel(input_dim, output_dim, hidden_dim).to(device)\n",
-    "\n",
-    "loss = nn.CrossEntropyLoss() # Cross-entropy loss function\n",
-    "optimizer = optim.Adam(model.parameters(), lr=learning_rate) # Adam optimiser"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Early stopping is a simple technique to avoid overfitting. The idea is to stop training when the validation error starts to increase. This is usually done by monitoring the validation error during training, and stopping when it has not decreased for a certain number of epochs.\n",
-    "\n",
-    "*Can we state that overfitting is ultimatelly inevitable given training over a very large number of epochs?*\n",
-    "\n",
-    "In this section, we will implement early stopping in PyTorch. We will use the same model as in the previous lab, but we will train it for $50$ epochs with an early stopping."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class EarlyStopping:\n",
-    "    \"\"\"Early stops the training if validation loss doesn't improve after a given patience.\"\"\"\n",
-    "    def __init__(self, patience=5, min_delta=0):\n",
-    "\n",
-    "        self.patience = patience # Number of epochs with no improvement after which training will be stopped\n",
-    "        self.min_delta = min_delta # Minimum change in the monitored quantity to qualify as an improvement\n",
-    "        self.counter = 0\n",
-    "        self.min_validation_loss = float('inf')\n",
-    "        self.early_stop = False\n",
-    "\n",
-    "    def __call__(self, validation_loss):\n",
-    "        # If validation loss is lower than minimum validation loss so far,\n",
-    "        # reset counter and set minimum validation loss to current validation loss\n",
-    "        if validation_loss < self.min_validation_loss:\n",
-    "            self.min_validation_loss = validation_loss\n",
-    "            self.counter = 0\n",
-    "        # If validation loss hasn't improved since minimum validation loss,\n",
-    "        # increment counter\n",
-    "        elif validation_loss > (self.min_validation_loss + self.min_delta):\n",
-    "            self.counter += 1\n",
-    "            # If counter has reached patience, set early_stop flag to True\n",
-    "            if self.counter >= self.patience: \n",
-    "                self.early_stop = True"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Initialise early stopping object\n",
-    "early_stopping = EarlyStopping(patience=5, min_delta=0.01)\n",
-    "\n",
-    "# Keep track of the loss values over training\n",
-    "train_loss = [] \n",
-    "valid_loss = []\n",
-    "\n",
-    "# Train model\n",
-    "for i in range(num_epochs+1):\n",
-    "    # Training\n",
-    "    model.train()\n",
-    "    batch_loss = []\n",
-    "    for batch_idx, (x, t) in enumerate(train_loader):\n",
-    "        x = x.to(device)\n",
-    "        t = t.to(device)\n",
-    "        \n",
-    "        # Forward pass\n",
-    "        y = model(x)\n",
-    "        E_value = loss(y, t)\n",
-    "        \n",
-    "        # Backward pass\n",
-    "        optimizer.zero_grad()\n",
-    "        E_value.backward()\n",
-    "        optimizer.step()\n",
-    "        \n",
-    "        # Logging\n",
-    "        batch_loss.append(E_value.item())\n",
-    "    \n",
-    "    train_loss.append(np.mean(batch_loss))\n",
-    "\n",
-    "    # Validation\n",
-    "    model.eval()\n",
-    "    batch_loss = []\n",
-    "    for batch_idx, (x, t) in enumerate(valid_loader):\n",
-    "        x = x.to(device)\n",
-    "        t = t.to(device)\n",
-    "        \n",
-    "        # Forward pass\n",
-    "        y = model(x)\n",
-    "        E_value = loss(y, t)\n",
-    "        \n",
-    "        # Logging\n",
-    "        batch_loss.append(E_value.item())\n",
-    "    \n",
-    "    valid_loss.append(np.mean(batch_loss))\n",
-    "\n",
-    "    if i % stats_interval == 0:\n",
-    "            print('Epoch: {} \\tError(train): {:.6f} \\tError(valid): {:.6f} '.format(\n",
-    "                i, train_loss[-1], valid_loss[-1]))\n",
-    "            \n",
-    "    # Check for early stopping\n",
-    "    early_stopping(valid_loss[-1])\n",
-    "\n",
-    "    if early_stopping.early_stop:\n",
-    "        print(\"Early stopping\")\n",
-    "        break # Stop training"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 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",
-    "ax_1.plot(train_loss, label='Error(train)')\n",
-    "ax_1.plot(valid_loss, label='Error(valid)')\n",
-    "ax_1.legend(loc=0)\n",
-    "ax_1.set_xlabel('Epoch number')\n",
-    "plt.show()"
-   ]
-  }
- ],
- "metadata": {
-  "anaconda-cloud": {},
-  "kernelspec": {
-   "display_name": "mlp",
-   "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": "3.12.5"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/notebooks/Coursework_1.ipynb b/notebooks/Coursework_1.ipynb
deleted file mode 100644
index 3a9693d..0000000
--- a/notebooks/Coursework_1.ipynb
+++ /dev/null
@@ -1,182 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Coursework 1\n",
-    "\n",
-    "This notebook is intended to be used as a starting point for your experiments. The instructions can be found in the MLP2024_25_CW1_Spec.pdf (see Learn,  Assignment Submission, Coursework 1). 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 num_epochs 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",
-    "    ax_1.set_ylabel('Error')\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",
-    "    ax_2.set_xlabel('Accuracy')\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",
-    "import sys\n",
-    "# sys.path.append('/path/to/mlpractical')\n",
-    "from mlp.data_providers import MNISTDataProvider, EMNISTDataProvider\n",
-    "\n",
-    "# Seed a random number generator\n",
-    "seed = 11102019 \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",
-    "%pip install tqdm\n",
-    "\n",
-    "from mlp.layers import AffineLayer, SoftmaxLayer, SigmoidLayer, ReluLayer\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 AdamLearningRule\n",
-    "from mlp.optimisers import Optimiser\n",
-    "\n",
-    "# Setup hyperparameters\n",
-    "learning_rate = 0.001\n",
-    "num_epochs = 100\n",
-    "stats_interval = 1\n",
-    "input_dim, output_dim, hidden_dim = 784, 47, 128\n",
-    "\n",
-    "weights_init = GlorotUniformInit(rng=rng)\n",
-    "biases_init = ConstantInit(0.)\n",
-    "\n",
-    "# Create model with ONE hidden layer\n",
-    "model = MultipleLayerModel([\n",
-    "    AffineLayer(input_dim, hidden_dim, weights_init, biases_init), # hidden layer\n",
-    "    ReluLayer(),\n",
-    "    AffineLayer(hidden_dim, output_dim, weights_init, biases_init) # output layer\n",
-    "])\n",
-    "\n",
-    "error = CrossEntropySoftmaxError()\n",
-    "# Use a Adam learning rule\n",
-    "learning_rule = AdamLearningRule(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": [
-    "# Create model with TWO hidden layers\n",
-    "model = MultipleLayerModel([\n",
-    "    AffineLayer(input_dim, hidden_dim, weights_init, biases_init), # first hidden layer\n",
-    "    ReluLayer(),\n",
-    "    AffineLayer(hidden_dim, hidden_dim, weights_init, biases_init), # second hidden layer\n",
-    "    ReluLayer(),\n",
-    "    AffineLayer(hidden_dim, output_dim, weights_init, biases_init) # output layer\n",
-    "])\n",
-    "\n",
-    "error = CrossEntropySoftmaxError()\n",
-    "# Use a Adam learning rule\n",
-    "learning_rule = AdamLearningRule(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)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "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": "3.8.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/Coursework_2_Pytorch_Introduction.ipynb b/notebooks/Coursework_2_Pytorch_Introduction.ipynb
new file mode 100644
index 0000000..7929287
--- /dev/null
+++ b/notebooks/Coursework_2_Pytorch_Introduction.ipynb
@@ -0,0 +1,665 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Introduction to PyTorch \n",
+    "\n",
+    "## Introduction\n",
+    "Pytorch is a  modern, intuitive, Pythonic and fast framework for building differentiable graphs. Neural networks, as it happens, are a type of acyclic differentiable graph, making PyTorch a convenient framework to use, should you wish to build (potentially) complicated deep neural networks fairly easily.\n",
+    "\n",
+    "## MLP package vs Pytorch\n",
+    "**Student**: Why do I have to learn to use PyTorch now? I've spent all this time working on the MLP framework. Was that a waste of time?\n",
+    "\n",
+    "**TA**: Pytorch is everything the MLP package is, and more. It's faster, cleaner and far more up to date with modern deep learning advances, meaning it is easy to tailor to experiments you may wish to run. Since it is one of the main deep learning frameworks being used by industry and research alike, it conforms to the expectation of real users like researchers and engineers. The result is that PyTorch is (and continues to become) a robust and flexible package. Coming to grips with PyTorch now means that you'll be able to apply it to any future project that uses deep learning. \n",
+    "\n",
+    "Furthermore, the MLP framework was written in NumPy and your time developing this has taught you some fundamental implementation details of NNs: this could (and should) make future research directions more easy to think of and will also enable your debugging prowess. PyTorch was written to emulate NumPy as much as possible, so it will feel very familiar to you. The skills you have acquired are highly transferable (they generalize well, so not much overfitting there!).\n",
+    "\n",
+    "The devleopers of PyTorch try to make sure that the \"latest and greatest\" state-of-the-art research is included and implemented. If this is not the case, you will often find other people reproducing . If you can't wait, you can reproduce it yourself and open source it (a great way to showcase your skills and get github likes).\n",
+    "\n",
+    "PyTorch has Autograd! Automatic differentiation. \"What is this?\" you may ask. Remember having to write all those backprop functions? Forget about it. Automatic differentiation allows you to backprop through any PyTorch operation you have used in your graph, by simply calling backward(). This [blog-post](https://jdhao.github.io/2017/11/12/pytorch-computation-graph/) explains how Pytorch's autograd works at an intuitive level.\n",
+    "\n",
+    "**Student**: Why did we even have to use the MLP package? Why did we even bother if such awesome frameworks are available?\n",
+    "\n",
+    "**TA**: The purpose of the MLP package was not to allow you to build fast deep learning systems. Instead, it was to help teach you the low level mechanics and sensitivities of building a deep learning system. Building this enabled you to dive deep into how to go about building a deep learning framework from scratch. The intuitions you have gained from going through your assignments and courseworks allow you to see deeper in what makes or breaks a deep learning system, at a level few people actually care to explore. You are no longer restricted to the higher level modules provided by Pytorch/TensorFlow. \n",
+    "\n",
+    "If, for example, a new project required you to build something that does not exist in PyTorch/TensorFlow, or otherwise modify existing modules in a way that requires understanding and intuitions on backpropagation and layer/optimizer/component implementation, you would be able to do it much more easily than others who did not. You are now equipped to understand differentiable graphs, the chain rule, numerical errors, debugging at the lowest level and deep learning system architecture. \n",
+    "\n",
+    "By trying to implement your modules in an efficient way, you have also become aware of how to optimize a system for efficiency, and gave you intuitions on how one could further improve such a system (parallelization of implementations). \n",
+    "\n",
+    "Finally, the slowness of CPU training has allowed you to understand just how important modern GPU acceleration is, for deep learning research and applications. By coming across a large breadth of problems and understanding their origins, you will now be able to both anticipate and solve future problems in a more comprehensive way than someone who did not go through the trouble of implementing the basics from scratch. \n",
+    "<!-- \n",
+    "**Student**: If we are switching to Pytorch, then why bother implementing convolutions in the MLP package for the coursework?\n",
+    "\n",
+    "**TA**: All your instructors, myself included, have found it greatly beneficial to implement convolutional networks from scratch. Once you implement convolutional layers, you will have a much deeper insight and understanding into how and why they work, as well as how they break. This way, you know what to do and what to avoid in the future. You might even be able to come with the next great network type yourself.  -->\n",
+    "\n",
+    "\n",
+    "## Getting Started\n",
+    "\n",
+    "**Student**: So, how is the learning curve of Pytorch? How do I start?\n",
+    "\n",
+    "**TA**: You can start by using this notebook on your experiments, it should teach you quite a lot on how to properly use PyTorch for basic conv net training. You should be aware of the [official pytorch github](https://github.com/pytorch/pytorch), the [pytorch official documentation page](https://pytorch.org/docs/stable/nn.html) and the [pytorch tutorials page](https://pytorch.org/tutorials/). \n",
+    "\n",
+    "Over the past year, nearly all students using PyTorch and Tensorflow on MLP and on projects found it easier and faster to get up to speed with PyTorch. In fact, I was a TensorFlow user myself, and learning TensorFlow was much more challenging than PyTorch. Mainly because TensorFlow has its own way of 'thinking' about how you build a graph and execute operations - whereas PyTorch is dynamic and works like NumPy, hence is more intuitive. If you were able to work well with the MLP package, you'll be up and running in no time. \n",
+    "\n",
+    "**Student**: OK, so how fast is pytorch compared to MLP?\n",
+    "\n",
+    "**TA**: On the CPU side of things, you'll find pytorch at least 5x faster than the MLP framework (about equal for fully connected networks, but much faster for more complicated things like convolutions - unless you write extremely efficient convolutional layer code), and if you choose to use GPUs, either using MS Azure, Google Cloud or our very own MLP Cluster (available for next semester), you can expect, depending on implementation and hardware an approximate 25-70x speed ups, compared to the CPU performance of pytorch. Yes, that means an experiment that would run overnight, now would only require about 15 minutes.\n",
+    "\n",
+    "**Student**: Ahh, where should I go to ask more questions?\n",
+    "\n",
+    "**TA**: As always, start with a Google/DuckDuckGo search, then have a look at the PyTorch Github and PyTorch docs, and if you can't find the answer come to Piazza and the lab sessions. We will be there to support you.\n",
+    "\n",
+    "\n",
+    "#### Note: The code in this jupyter notebook is to introduce you to pytorch and allow you to play around with it in an interactive manner. However, to run your experiments, you should use the Pytorch experiment framework located in ```pytorch_mlp_framework/```. Instructions on how to use it can be found in ```notes/pytorch-experiment-framework.md``` along with the comments and documentation included in the code itself."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Imports and helper functions\n",
+    "\n",
+    "First, let's import the packages necessary for our tutorial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from torch import nn\n",
+    "from copy import deepcopy\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "import torch.nn.functional as F\n",
+    "import torch.backends.cudnn as cudnn\n",
+    "import torchvision\n",
+    "import tqdm\n",
+    "import os\n",
+    "import mlp.data_providers as data_providers\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's write a helper function for plotting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "plt.style.use('ggplot')\n",
+    "\n",
+    "def plot_stats_in_graph(total_losses, y_axis_label, x_axis_label):\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 total_losses.keys():\n",
+    "        if \"loss\" in k:\n",
+    "            ax_1.plot(np.arange(len(total_losses[k])), total_losses[k], label=k)\n",
+    "    ax_1.legend(loc=0)\n",
+    "    ax_1.set_xlabel(x_axis_label)\n",
+    "    ax_1.set_ylabel(y_axis_label)\n",
+    "    \n",
+    "\n",
+    "    plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Basics: What is a tensor?\n",
+    "\n",
+    "In numpy we used arrays, whereas in pytorch we use tensors. Tensors are basically multi-dimensional arrays, that can also automatically compute backward passes, and thus gradients, as well as store data to be used at any point in our pytorch pipelines."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([ 5.,  1., 10.]) tensor(5.3333) tensor(3.6818) \n",
+      " [ 5.  1. 10.] 5.3333335 3.6817868\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_pytorch = torch.Tensor([5., 1., 10.]).float()\n",
+    "data_numpy = np.array([5., 1., 10]).astype(np.float32)\n",
+    "\n",
+    "print(data_pytorch, data_pytorch.mean(), data_pytorch.std(unbiased=False), '\\n',\n",
+    "      data_numpy, data_numpy.mean(), data_numpy.std())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Tensors have a rich support for a variety of operations, for more information look at the official pytorch [documentation page](https://pytorch.org/docs/stable/torch.html#torch.std)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Basics: A simple pytorch graph of operations\n",
+    "\n",
+    "Pytorch automatically tracks the flow of data through operations without requiring explicit instruction to do so. \n",
+    "For example, we can easily compute the grads wrt to a variable **a** (which is initialized with requires grad = True to let the framework know that we'll be requiring the grads of that variable) by simple calling .backward() followed by .grad:\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[[[0.0019, 0.0018, 0.0016,  ..., 0.0024, 0.0022, 0.0021],\n",
+      "          [0.0019, 0.0025, 0.0017,  ..., 0.0028, 0.0024, 0.0023],\n",
+      "          [0.0024, 0.0019, 0.0025,  ..., 0.0023, 0.0012, 0.0027],\n",
+      "          ...,\n",
+      "          [0.0023, 0.0021, 0.0025,  ..., 0.0017, 0.0027, 0.0019],\n",
+      "          [0.0026, 0.0023, 0.0015,  ..., 0.0028, 0.0024, 0.0028],\n",
+      "          [0.0019, 0.0010, 0.0024,  ..., 0.0021, 0.0014, 0.0019]],\n",
+      "\n",
+      "         [[0.0027, 0.0026, 0.0022,  ..., 0.0025, 0.0027, 0.0022],\n",
+      "          [0.0025, 0.0023, 0.0025,  ..., 0.0020, 0.0024, 0.0030],\n",
+      "          [0.0024, 0.0027, 0.0024,  ..., 0.0014, 0.0019, 0.0023],\n",
+      "          ...,\n",
+      "          [0.0025, 0.0024, 0.0012,  ..., 0.0027, 0.0022, 0.0024],\n",
+      "          [0.0021, 0.0023, 0.0026,  ..., 0.0024, 0.0020, 0.0022],\n",
+      "          [0.0022, 0.0019, 0.0026,  ..., 0.0013, 0.0025, 0.0018]],\n",
+      "\n",
+      "         [[0.0021, 0.0018, 0.0017,  ..., 0.0023, 0.0021, 0.0015],\n",
+      "          [0.0026, 0.0012, 0.0023,  ..., 0.0022, 0.0018, 0.0022],\n",
+      "          [0.0018, 0.0023, 0.0024,  ..., 0.0020, 0.0020, 0.0021],\n",
+      "          ...,\n",
+      "          [0.0023, 0.0017, 0.0025,  ..., 0.0025, 0.0023, 0.0026],\n",
+      "          [0.0023, 0.0023, 0.0025,  ..., 0.0019, 0.0020, 0.0016],\n",
+      "          [0.0025, 0.0022, 0.0021,  ..., 0.0023, 0.0023, 0.0021]]],\n",
+      "\n",
+      "\n",
+      "        [[[0.0022, 0.0025, 0.0026,  ..., 0.0025, 0.0021, 0.0016],\n",
+      "          [0.0010, 0.0021, 0.0029,  ..., 0.0025, 0.0021, 0.0026],\n",
+      "          [0.0016, 0.0023, 0.0020,  ..., 0.0025, 0.0020, 0.0026],\n",
+      "          ...,\n",
+      "          [0.0024, 0.0016, 0.0025,  ..., 0.0024, 0.0027, 0.0020],\n",
+      "          [0.0016, 0.0017, 0.0023,  ..., 0.0017, 0.0023, 0.0020],\n",
+      "          [0.0015, 0.0031, 0.0018,  ..., 0.0020, 0.0022, 0.0013]],\n",
+      "\n",
+      "         [[0.0023, 0.0022, 0.0029,  ..., 0.0017, 0.0019, 0.0026],\n",
+      "          [0.0022, 0.0018, 0.0023,  ..., 0.0023, 0.0011, 0.0025],\n",
+      "          [0.0024, 0.0022, 0.0022,  ..., 0.0028, 0.0025, 0.0020],\n",
+      "          ...,\n",
+      "          [0.0021, 0.0025, 0.0021,  ..., 0.0021, 0.0023, 0.0026],\n",
+      "          [0.0021, 0.0026, 0.0014,  ..., 0.0031, 0.0024, 0.0025],\n",
+      "          [0.0025, 0.0025, 0.0020,  ..., 0.0021, 0.0024, 0.0021]],\n",
+      "\n",
+      "         [[0.0016, 0.0019, 0.0025,  ..., 0.0021, 0.0019, 0.0029],\n",
+      "          [0.0019, 0.0023, 0.0021,  ..., 0.0026, 0.0017, 0.0026],\n",
+      "          [0.0016, 0.0026, 0.0020,  ..., 0.0027, 0.0022, 0.0028],\n",
+      "          ...,\n",
+      "          [0.0015, 0.0026, 0.0015,  ..., 0.0015, 0.0021, 0.0027],\n",
+      "          [0.0019, 0.0018, 0.0022,  ..., 0.0020, 0.0016, 0.0021],\n",
+      "          [0.0018, 0.0021, 0.0020,  ..., 0.0018, 0.0025, 0.0019]]],\n",
+      "\n",
+      "\n",
+      "        [[[0.0015, 0.0021, 0.0028,  ..., 0.0023, 0.0013, 0.0017],\n",
+      "          [0.0019, 0.0023, 0.0021,  ..., 0.0022, 0.0014, 0.0020],\n",
+      "          [0.0027, 0.0017, 0.0019,  ..., 0.0022, 0.0018, 0.0015],\n",
+      "          ...,\n",
+      "          [0.0017, 0.0027, 0.0022,  ..., 0.0019, 0.0024, 0.0026],\n",
+      "          [0.0018, 0.0023, 0.0016,  ..., 0.0018, 0.0013, 0.0028],\n",
+      "          [0.0018, 0.0021, 0.0017,  ..., 0.0028, 0.0022, 0.0020]],\n",
+      "\n",
+      "         [[0.0018, 0.0023, 0.0020,  ..., 0.0020, 0.0021, 0.0019],\n",
+      "          [0.0019, 0.0016, 0.0016,  ..., 0.0026, 0.0021, 0.0025],\n",
+      "          [0.0020, 0.0027, 0.0012,  ..., 0.0020, 0.0016, 0.0025],\n",
+      "          ...,\n",
+      "          [0.0023, 0.0019, 0.0023,  ..., 0.0025, 0.0026, 0.0030],\n",
+      "          [0.0026, 0.0017, 0.0017,  ..., 0.0018, 0.0018, 0.0023],\n",
+      "          [0.0024, 0.0025, 0.0031,  ..., 0.0028, 0.0024, 0.0024]],\n",
+      "\n",
+      "         [[0.0020, 0.0023, 0.0029,  ..., 0.0030, 0.0020, 0.0022],\n",
+      "          [0.0023, 0.0014, 0.0024,  ..., 0.0018, 0.0019, 0.0027],\n",
+      "          [0.0022, 0.0013, 0.0019,  ..., 0.0021, 0.0025, 0.0015],\n",
+      "          ...,\n",
+      "          [0.0022, 0.0019, 0.0019,  ..., 0.0015, 0.0026, 0.0020],\n",
+      "          [0.0017, 0.0016, 0.0025,  ..., 0.0021, 0.0023, 0.0019],\n",
+      "          [0.0019, 0.0027, 0.0020,  ..., 0.0021, 0.0022, 0.0022]]],\n",
+      "\n",
+      "\n",
+      "        ...,\n",
+      "\n",
+      "\n",
+      "        [[[0.0017, 0.0022, 0.0020,  ..., 0.0021, 0.0025, 0.0025],\n",
+      "          [0.0024, 0.0021, 0.0024,  ..., 0.0019, 0.0023, 0.0020],\n",
+      "          [0.0021, 0.0027, 0.0019,  ..., 0.0024, 0.0014, 0.0018],\n",
+      "          ...,\n",
+      "          [0.0020, 0.0022, 0.0016,  ..., 0.0030, 0.0028, 0.0021],\n",
+      "          [0.0015, 0.0024, 0.0020,  ..., 0.0018, 0.0026, 0.0025],\n",
+      "          [0.0028, 0.0025, 0.0030,  ..., 0.0015, 0.0022, 0.0023]],\n",
+      "\n",
+      "         [[0.0029, 0.0020, 0.0021,  ..., 0.0026, 0.0019, 0.0021],\n",
+      "          [0.0027, 0.0023, 0.0024,  ..., 0.0017, 0.0021, 0.0024],\n",
+      "          [0.0021, 0.0027, 0.0015,  ..., 0.0017, 0.0019, 0.0025],\n",
+      "          ...,\n",
+      "          [0.0018, 0.0023, 0.0015,  ..., 0.0026, 0.0021, 0.0019],\n",
+      "          [0.0016, 0.0018, 0.0027,  ..., 0.0009, 0.0013, 0.0023],\n",
+      "          [0.0013, 0.0026, 0.0022,  ..., 0.0021, 0.0020, 0.0022]],\n",
+      "\n",
+      "         [[0.0028, 0.0020, 0.0014,  ..., 0.0019, 0.0025, 0.0026],\n",
+      "          [0.0024, 0.0018, 0.0017,  ..., 0.0013, 0.0023, 0.0025],\n",
+      "          [0.0023, 0.0017, 0.0024,  ..., 0.0018, 0.0023, 0.0025],\n",
+      "          ...,\n",
+      "          [0.0006, 0.0027, 0.0023,  ..., 0.0022, 0.0022, 0.0017],\n",
+      "          [0.0015, 0.0024, 0.0018,  ..., 0.0023, 0.0021, 0.0019],\n",
+      "          [0.0015, 0.0014, 0.0025,  ..., 0.0020, 0.0017, 0.0026]]],\n",
+      "\n",
+      "\n",
+      "        [[[0.0020, 0.0024, 0.0019,  ..., 0.0023, 0.0020, 0.0024],\n",
+      "          [0.0022, 0.0020, 0.0021,  ..., 0.0017, 0.0019, 0.0019],\n",
+      "          [0.0022, 0.0020, 0.0028,  ..., 0.0020, 0.0025, 0.0024],\n",
+      "          ...,\n",
+      "          [0.0018, 0.0024, 0.0017,  ..., 0.0018, 0.0019, 0.0019],\n",
+      "          [0.0021, 0.0015, 0.0012,  ..., 0.0016, 0.0022, 0.0015],\n",
+      "          [0.0020, 0.0019, 0.0016,  ..., 0.0019, 0.0025, 0.0023]],\n",
+      "\n",
+      "         [[0.0020, 0.0017, 0.0020,  ..., 0.0016, 0.0017, 0.0020],\n",
+      "          [0.0020, 0.0014, 0.0021,  ..., 0.0022, 0.0021, 0.0026],\n",
+      "          [0.0021, 0.0018, 0.0016,  ..., 0.0025, 0.0029, 0.0016],\n",
+      "          ...,\n",
+      "          [0.0018, 0.0016, 0.0018,  ..., 0.0018, 0.0020, 0.0015],\n",
+      "          [0.0017, 0.0015, 0.0018,  ..., 0.0024, 0.0020, 0.0022],\n",
+      "          [0.0019, 0.0022, 0.0017,  ..., 0.0014, 0.0026, 0.0020]],\n",
+      "\n",
+      "         [[0.0019, 0.0024, 0.0022,  ..., 0.0023, 0.0024, 0.0022],\n",
+      "          [0.0023, 0.0032, 0.0018,  ..., 0.0013, 0.0030, 0.0020],\n",
+      "          [0.0022, 0.0018, 0.0025,  ..., 0.0024, 0.0021, 0.0014],\n",
+      "          ...,\n",
+      "          [0.0020, 0.0018, 0.0025,  ..., 0.0025, 0.0020, 0.0023],\n",
+      "          [0.0021, 0.0027, 0.0019,  ..., 0.0021, 0.0015, 0.0020],\n",
+      "          [0.0019, 0.0018, 0.0028,  ..., 0.0024, 0.0018, 0.0026]]],\n",
+      "\n",
+      "\n",
+      "        [[[0.0024, 0.0024, 0.0030,  ..., 0.0029, 0.0023, 0.0018],\n",
+      "          [0.0024, 0.0028, 0.0016,  ..., 0.0019, 0.0020, 0.0022],\n",
+      "          [0.0014, 0.0022, 0.0019,  ..., 0.0025, 0.0021, 0.0023],\n",
+      "          ...,\n",
+      "          [0.0022, 0.0024, 0.0016,  ..., 0.0017, 0.0019, 0.0029],\n",
+      "          [0.0025, 0.0027, 0.0022,  ..., 0.0018, 0.0028, 0.0019],\n",
+      "          [0.0029, 0.0020, 0.0027,  ..., 0.0016, 0.0024, 0.0025]],\n",
+      "\n",
+      "         [[0.0026, 0.0019, 0.0024,  ..., 0.0029, 0.0025, 0.0010],\n",
+      "          [0.0021, 0.0020, 0.0027,  ..., 0.0023, 0.0023, 0.0021],\n",
+      "          [0.0022, 0.0027, 0.0023,  ..., 0.0012, 0.0019, 0.0015],\n",
+      "          ...,\n",
+      "          [0.0026, 0.0023, 0.0020,  ..., 0.0024, 0.0019, 0.0017],\n",
+      "          [0.0027, 0.0025, 0.0021,  ..., 0.0030, 0.0026, 0.0025],\n",
+      "          [0.0015, 0.0012, 0.0027,  ..., 0.0030, 0.0023, 0.0018]],\n",
+      "\n",
+      "         [[0.0026, 0.0028, 0.0025,  ..., 0.0025, 0.0023, 0.0017],\n",
+      "          [0.0017, 0.0033, 0.0028,  ..., 0.0022, 0.0013, 0.0021],\n",
+      "          [0.0020, 0.0019, 0.0019,  ..., 0.0024, 0.0024, 0.0020],\n",
+      "          ...,\n",
+      "          [0.0022, 0.0014, 0.0017,  ..., 0.0019, 0.0022, 0.0019],\n",
+      "          [0.0020, 0.0021, 0.0027,  ..., 0.0021, 0.0021, 0.0019],\n",
+      "          [0.0020, 0.0014, 0.0020,  ..., 0.0029, 0.0021, 0.0026]]]])\n"
+     ]
+    }
+   ],
+   "source": [
+    "a = torch.randn((32, 3, 14, 14), requires_grad=True)\n",
+    "b = torch.ones((32, 3, 14, 14)) * 5\n",
+    "\n",
+    "result_addition = a + b\n",
+    "result_double = result_addition * 2\n",
+    "result_square = result_double ** 2\n",
+    "result_mean = result_square.mean()\n",
+    "\n",
+    "loss = result_mean\n",
+    "\n",
+    "loss.backward()\n",
+    "\n",
+    "print(a.grad)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Student**: Ok, so we can build graphs, what about neural networks? Are there any pre-built layers? How do we train things? How do we define parameters and biases for our models? \n",
+    "\n",
+    "**TA**: Don't rush. Let's take it step by step. Let's look at nn.Parameters first.\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**TA**: In Pytorch all learnable components are created using the nn.Parameter class. That class, automatically tracks all gradients, and allows quick and easy updates in a given graph.\n",
+    "\n",
+    "**Note**: np.dot for a single batch going to a single 2D weight matrix is called using F.linear in Pytorch.\n",
+    "\n",
+    "**Further Note**: There also exist ParameterDicts for dictionaries of parameters, and ParameterLists when you define a list of parameters for part of your model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "torch.Size([16, 32])\n",
+      "current loss tensor(0.3759, grad_fn=<MeanBackward0>)\n",
+      "current loss tensor(0.3666, grad_fn=<MeanBackward0>)\n",
+      "current loss tensor(0.3480, grad_fn=<MeanBackward0>)\n",
+      "current loss tensor(0.3201, grad_fn=<MeanBackward0>)\n",
+      "current loss tensor(0.2830, grad_fn=<MeanBackward0>)\n",
+      "current loss tensor(0.2365, grad_fn=<MeanBackward0>)\n",
+      "current loss tensor(0.1808, grad_fn=<MeanBackward0>)\n",
+      "current loss tensor(0.1157, grad_fn=<MeanBackward0>)\n",
+      "current loss tensor(0.0414, grad_fn=<MeanBackward0>)\n",
+      "current loss tensor(-0.0422, grad_fn=<MeanBackward0>)\n"
+     ]
+    }
+   ],
+   "source": [
+    "weights = nn.Parameter(torch.randn(32, 32), requires_grad=True)\n",
+    "inputs = torch.randn(16, 32)\n",
+    "outputs = F.linear(inputs, weights)\n",
+    "learning_rate = 0.1\n",
+    "\n",
+    "print(outputs.shape)\n",
+    "\n",
+    "for i in range(10):\n",
+    "    outputs = F.linear(inputs, weights)\n",
+    "    loss = torch.mean(outputs)\n",
+    "    loss.backward()\n",
+    "    weights.data = weights.data - learning_rate * weights.grad\n",
+    "    print('current loss', loss)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## nn.Modules and why they are important\n",
+    "\n",
+    "Pytorch implements a class called the nn.Module class. The nn.Module class automatically detects any nn.Parameter, nn.ParameterList or nn.ParameterDict and adds it to a collection of parameters which can be easily accessed using .parameters and/or .named_parameters().\n",
+    "\n",
+    "Let's look at an example:\n",
+    "\n",
+    "Let's build a fully connected layer followed by an activation function that can be preselected, similar to coursework 1. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class LinearLayerWithActivation(nn.Module):\n",
+    "    def __init__(self, input_shape, num_units, bias=False, activation_type=nn.ReLU()):\n",
+    "        super(LinearLayerWithActivation, self).__init__()\n",
+    "        self.activation_type = activation_type\n",
+    "        self.weights = nn.Parameter(torch.empty(size=(num_units, input_shape[1]), requires_grad=True))\n",
+    "        \n",
+    "        nn.init.normal_(self.weights)\n",
+    "        \n",
+    "        if bias:\n",
+    "            self.bias = nn.Parameter(torch.zeros(num_units), requires_grad=True)\n",
+    "        else:\n",
+    "            self.bias = None\n",
+    "        \n",
+    "    def forward(self, x):\n",
+    "        out = F.linear(x, self.weights, self.bias)\n",
+    "        out = self.activation_type.forward(out)\n",
+    "        return out\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parameters with name weights and shape torch.Size([512, 128])\n",
+      "Parameters with name bias and shape torch.Size([512])\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = torch.arange(16*128).view(16, 128).float()\n",
+    "y = torch.arange((16))\n",
+    "\n",
+    "fcc_net = LinearLayerWithActivation(input_shape=x.shape, num_units=512, bias=True, activation_type=nn.Identity())\n",
+    "optimizer = optim.Adam(fcc_net.parameters(), amsgrad=False, weight_decay=0.0)\n",
+    "\n",
+    "\n",
+    "for name, params in fcc_net.named_parameters():\n",
+    "    print('Parameters with name', name, 'and shape', params.shape)\n",
+    "\n",
+    "metric_dict = {'losses': []}    \n",
+    "    \n",
+    "for i in range(50):\n",
+    "\n",
+    "    out = fcc_net.forward(x)\n",
+    "    loss = F.cross_entropy(out, y)\n",
+    "    fcc_net.zero_grad() #removes grads of previous step\n",
+    "    optimizer.zero_grad() #removes grads of previous step\n",
+    "    loss.backward() #compute gradients of current step\n",
+    "    optimizer.step() #update step\n",
+    "    metric_dict['losses'].append(loss.detach().cpu().numpy()) #.detach: Copies the value of the loss \n",
+    "#                                                               and removes it from the graph, \n",
+    "#                                                             .cpu() sends to cpu, and \n",
+    "#                                                              numpy(), converts it to numpy format."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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kIyPD26UUeQEBAWRlZf3lmDOF5POhoCyXhDEG6jfFUfc6WP+NOzDv+RE75yPswtmYdt3cfwIVmEVE5MrldDopVUrn81yMgvztgIKyXFLG4YAGzXDUawrffeNew7znR+zsqdgFfwzMJb1dqoiIiMhfUlCWy8I4HNCwGY76TeG7le4Z5r2p2NlTsAtnYW7q7l7HrMAsIiIihZSCslxW7sDcHEf967FrV2BnT4F9P2FnTsZ+eTIwt+2KKaFL8ImIiEjhoqAsBcI4HJjGLbANm2HXLsfOnnoyMH+I/XKm+5Jybbtgiiswi4iISOGgoCwFyh2YW7oD85qTgXn/buyMD34PzG06KzCLiIiI1ykoi1cYhx/muhuwjZpjv12GnTMV9u/BJiZgv5zhvi32jZ0xxUt4u1QRERG5Qikoi1cZhx+mSSts4xbY1V9j53wEP+/BTp+EnZ+IaX8r5sZOCswiIiJS4BSUpVAwDj9M09bYxi2x3y7Fzv4IDuzFTn/fHZhvvhXTuhOmWHFvlyoiIiJXCAVlKVSMnx+m6Y3YxjdgVy3BfvYRHNiHnfYe9otETIdbMa07KjCLiIjIZacbjUuhZPz8cDRrg2P4/zD3DYXyYXD0CHbaRFxPDsA1PxF74oS3yxQREREfpqAshZrx88PRvO3JwPwIlLvKHZg/mYjryf645s9QYBYREZHLQkFZigTjdOJo3g7Hv9/E3DsEylY4GZjfxRX/AK4FM7FZCswiIiJy6SgoS5FinE4cLW7C8cJbmLiH3YH5yGHsRxNwPfUArgWzFJhFRETkklBQliLJOJ04WrbH8cKbmHsegtDyJwPzeFxPDcS1cDY2O8vbZYqIiEgRpqAsRZpx+uO4oQOOEW9h+gyG0HJw5Ffs1HfcM8yL5igwi4iIyAVRUBafYJz+OFrdjOOFcZjeD0KZcpD2K3bK2+4Z5sWfYbOzvV2miIiIFCEKyuJTjL8/jtYdcYwYh+k96GRg/gU7eRyu+IG4vpqrwCwiIiLnREFZfJI7MHdyB+a7B0JIWTh8CPvhW7ieHojrq8+xOQrMIiIicmYKyuLTjL8/jhs74xg5DnPXAxASCr8ewn74Jq74QbiWzlNgFhERkXwpKMsVwfgH4GjTBcfItzF3DoDSofDrQWzC/3A9/SCupV9gc3K8XaaIiIgUIgrKckUx/gE42nZ1zzDf0R9Kl4FfDmATxuJ6ehCur+crMIuIiAigoCxXKBNQDEe7bu4Z5jv6QakQd2Ce9AauZx7EtexLBWYREZErnIKyXNHcgbk7jpHvYHqdDMyHfsa+PwbXsMG4li/A5uZ6u0wRERHxAgVlEcAUK4bjppOB+fb7Ibg0HNyPfe919wzzioUKzCIiIlcYBWWRPzDFiuFo3wPHqHcwPf8QmCf+n3uGecUiBWYREZErhIKySD5MseI4OpwKzPdBUCk4sA878TVcwx7C9c1irEuBWURExJcpKIv8BXdgvtUdmG+7F4KC4cBe7IRXcQ17GNc3Xykwi4iI+CgFZZFzYIqXwHHzbThGjcfcGgclg+HnPdgJr+B69mFcq5YoMIuIiPgYBWWR82CKl8DRsSeOF9/B9LjHHZj378GO/y+u5x7BtXqpArOIiIiPUFAWuQCmeCCOTre7l2Tc0gcCg2DfT9h3/uMOzN9+jXW5vF2miIiIXAQFZZGLYEoE4ujcC8eL4zHde0NgSXdgfvtlXM8NwfXtMgVmERGRIsrp7QLmz5/P/PnzOXjwIAARERH07NmT+vXrk5OTw9SpU/nuu+84cOAAgYGB1KlTh7vvvpvQ0FDPPrKzs0lISGD58uVkZWURExND//79KVu2rGdMRkYGEydOZM2aNQA0atSIvn37UrJkSc+YQ4cOMX78eDZv3kxAQADNmzcnLi4Op9Prb5MUcqZEIKbLHdg2XbALZ2O/nHkyML+Evboyjq53Qv3rMQ59NxURESkqjLXWerOANWvW4HA4CAsLA2DJkiXMmjWLl156ibJly/Lf//6Xtm3bUqVKFTIyMnj//ffJzc3lxRdf9OzjnXfeYe3atQwePJjg4GAmTZpERkYGo0ePxnEymIwcOZJffvmFgQMHAjBu3DjKly/PE088AYDL5eKxxx6jVKlSxMXFcfToUcaOHUuTJk3o27fveb+ugwcPkp2dfbFvz1kZY6hYsSL79u3Dy62UP7CZGdgFs7ELZsFvx9wbr66Mo+tdUL9pvoFZvfQd6qXvUC99h3rpOy5FL/39/SlfvvxZx3l9eqtRo0Y0aNCA8PBwwsPDueuuuyhevDjbtm0jMDCQZ555hmbNmhEeHk50dDT3338/KSkpHDp0CIDMzEwWLVpEXFwcsbGxREVFMWTIEFJTU0lKSgJg9+7drF+/nkGDBhEdHU10dDQDBw5k3bp17N27F4ANGzawe/duhgwZQlRUFLGxscTFxbFw4UIyMzO99v5I0WQCg3B0u8t90l/XO6FEIOz5EddbL+L699+w61ZoSYaIiEghV6jWFLhcLlauXMmJEyeIjo7Od0xmZibGGAIDAwFISUkhNzeX2NhYz5jQ0FAiIyNJTk6mXr16JCcnExgYSI0aNTxjoqOjCQwMZOvWrYSHh5OcnExkZGSeJR1169YlOzublJQUYmJi8q0nOzs7z8yxMYYSJUp4/n65nTpGQRxLzp8pGQzde2Pbdcf15Qz3DPPuXbjefBEqReHoehemflOMMeqlD1EvfYd66TvUS99RkL0sFEE5NTWV+Ph4srOzKV68OI8++igRERGnjcvKymLy5Mk0b97cE5TT0tJwOp0EBQXlGVu6dGnS0tI8Y0qXLn3a/s42JigoCKfT6RmTn8TERKZNm+b5OSoqitGjR5/TdP6ldGrpihRi1R8jt88DZMyYzNGZU7E/7cT1v5H4V42m9N0PULxpK0C99CXqpe9QL32Heuk7CqKXhSIoh4eH8/LLL3Ps2DFWrVrF2LFjef755/OE5ZycHF577TWstfTv3/+s+zyXNSvW2jzfRvL7ZvLnMX/Wo0cPunTpcto+Dh48SE5OzllruFjGGMLCwti/f7/WXBUV7W7B0bQNrvkzsAvnkJ2SzKEXHoXIapS7dzBplfP/bYoUHfpc+g710neol77jUvTS6XSe06RmoQjKTqfT862gWrVq7Nixg7lz5/LAAw8A7pD86quvcvDgQYYNG+aZTQYICQkhJyeHjIyMPLPK6enp1KxZ0zPmyJEjpx03PT3dM4scEhLC9u3b8zyekZFBbm5uvrPRp/j7++Pv75/vYwX5QbTW6oNflJQMxtHjHuxN3bHzZ2AXzYHUHRz69z8hshqObndBbGP9irCI0+fSd6iXvkO99B0F0Uuvn8yXH2utZ93vqZC8f/9+nnnmGYKDg/OMrVq1Kn5+fp4T9wAOHz5MamqqZ51zdHQ0mZmZeYLwtm3byMzM9ITp6OhoUlNTOXz4sGdMUlIS/v7+VK1a9bK9VrmymaBSOG6Nc98a++bbMMVLQOoOXG+8gGvEP7FJ3+ofdBERES/xelCePHky33//PQcOHCA1NZUpU6awefNmWrZsSW5uLq+88gopKSkMGTIEl8tFWloaaWlpnmUNgYGBtGnThoSEBDZu3MjOnTsZM2YMkZGRnhP8IiIiqFevHuPGjSM5OZnk5GTGjRvnudoGuE/ci4iI4I033mDnzp1s3LiRhIQE2rZtm2cGW+RyMMGl8Ot5HxXfnYW5+TYIKAY/bsc15t+4Rj6K3bhGgVlERKSAef06ym+++SabNm3i8OHDBAYGUrlyZbp3705sbCwHDhzg4Ycfzvd5zz77LNdeey3gPsnvgw8+YNmyZXluOFKuXDnP+IyMDN59913Wrl0LQMOGDenXr1++NxzZtGkTAQEBtGjRgnvuueeMSyv+iq6jLOfrj710padhv5iOXTwXsk64B0RFu5dkXNtASzIKOX0ufYd66TvUS99RkNdR9npQ9lUKynK+8uulPRWYv5oLWVnugVVrum9ccm19BeZCSp9L36Fe+g710ndcUTccEZEzM6VCcNzeF8eodzA3dYeAAEjZiuv/nsM1+l/Yzd/pH3wREZHLREFZpAgwpcrg6NUPx8h3MO26g38A7PgB12vPugPzlvUKzCIiIpeYgrJIEWJKl8FxRz8cI9/GtO0KTn93YH51GK6XnsR+v0GBWURE5BJRUBYpgkxIKI47B+AY9YfAvH0LrleewfXyk9gfks6+ExEREflLCsoiRZgJKft7YG7TxR2Yt23B9d+nyX35KezWjd4uUUREpMhSUBbxASakLI67HnAvybixEzidkLwJ13/iyf1PPDZ5k7dLFBERKXIUlEV8iClTFsfdg3CMeBvT+mRg3roR18tPkfvfp7HJm71dooiISJHh9HYBInLpmdBymN6DsB1vw34+Dfv1l/BDEq4fkqBWXRzd7sJUr+3tMkVERAo1BWURH2ZCy2N6P4i9uSd27ifY5Qvg+w24vt9wMjDfjaley9tlioiIFEoKyiJXAFO2POaewdhO+QTm2vXdM8zVrvF2mSIiIoWKgrLIFcSUrYC55yFsx5OBecVC2PIdri3fwbX1cXRVYBYRETlFQVnkCmTKXYWJezhvYN78Ha7N30FMA3dgrlrT22WKiIh4lYKyyBXMlA/D3DsE2+l27GcfY1cugk3rcG1aB3Ua4eh6JyYq2ttlioiIeIWCsoi4A/N9j2A798J+9hF25WLYuAbXxjXuwNztLkyVGt4uU0REpEApKIuIhzswD3XPMM/5GPvNV78H5tjG7sBcubq3yxQRESkQCsoichpTIRzT92+/zzB/swSSvsWV9C3Uvc69hrlyNW+XKSIiclkpKIvIGZmrwjF9/47tdDIwr1oKG1bj2rAa6jVxB+bIqt4uU0RE5LJQUBaRszJhV2P6/cM9wzznI+zqpbB+Fa71q6BeU/eSjEpR3i5TRETkklJQFpFzZsIiMP3/ie18B3bOVOy3X8P6b3Ct/wYaXO++SkaEArOIiPgGh7cLEJGix1SMwDHgURzPv4Fp3BKMgXUrcT0/lNw3X8Tu3uXtEkVERC6agrKIXDBTsRKOBx7D8eyYPwTmFbief4Tct17E7vnR2yWKiIhcMAVlEblo5urIk4H5dUzD5u6Na92B2TXuJeyeVO8WKCIicgG0RllELhlzdWXMoH9hd+/CNWcqrF2BXbMMu3Y5plELTJc7MOGR3i5TRETknCgoi8glZyKq4DfoCezunbhmfwTrVmC//Rq7Zpk7MHe9E1OxkrfLFBER+UsKyiJy2ZiIKPwefAL70073DPO6lb8H5sY3uGeYK0Z4u0wREZF8KSiLyGVnKkXh9+CT2NQUXLOnwvpvsKuXYL/9GnNdS0yXOzFhV3u7TBERkTwUlEWkwJjIqvg99BQ2dcfJwLwKu2oJdvXXmCat3DPMV4V7u0wRERFAQVlEvMBEVsPvoXjsjztwzZ4CG1Zjv1mMXbUE0/RkYK6gwCwiIt6loCwiXmMqV8Pv4aexP27HNWsKJH2LXXkqMN+I6dwLU6Git8sUEZErlIKyiHidqVwdvyHPYHduc88wb1yDXbEQ+81izPU3YjrfgSkf5u0yRUTkCqOgLCKFhomqgd8jw7A7k90zzJvWYpcvxK5cjLm+jXuGWYFZREQKiIKyiBQ6Jioav6HPYlO2umeYN63DLl/gnmFu1hbT6XZMuau8XaaIiPg4BWURKbRM1Zr4DX0Ou+MHd2De/B326/nYFQsVmEVE5LLzelCeP38+8+fP5+DBgwBERETQs2dP6tevD4C1lk8++YSFCxeSkZFBjRo16NevH5Uq/X5Xr+zsbBISEli+fDlZWVnExMTQv39/ypYt6xmTkZHBxIkTWbNmDQCNGjWib9++lCxZ0jPm0KFDjB8/ns2bNxMQEEDz5s2Ji4vD6fT62yRyRTPVrsHvb89jt3/vDsxb1p8MzIswzdtiOvXClC3v7TJFRMTHGGut9WYBa9asweFwEBbmXne4ZMkSZs2axUsvvUSlSpWYMWMGiYmJDB48mIoVKzJ9+nS+//57XnvtNUqUKAHAO++8w9q1axk8eDDBwcFMmjSJjIwMRo8ejcPhAGDkyJH88ssvDBw4EIBx48ZRvnx5nnjiCQBcLhePPfYYpUqVIi4ujqNHjzJ27FiaNGlC3759z/t1HTx4kOzs7EvxFv0lYwwVK1Zk3759eLmVcpHUy3Nnt29xr2H+foN7g58T06IdpuPthSIwq5e+Q730Heql77gUvfT396d8+bP//8JxQXu/hBo1akSDBg0IDw8nPDycu+66i+LFi7Nt2zastcydO5cePXrQpEkTIiMjeeihhzhx4gTLli0DIDMzk0WLFhEXF0dsbCxRUVEMGTKE1NRUkpKSANi9ezfr169n0KBBREdHEx0dzcCBA1m3bh179+4FYMOGDezevZshQ4YQFRVFbGwscXFxLFy4kMzMTK+9PyJyOlO9Nn7/+DeOx1+EWnUhNwe7ZB6u+IG4PnwT++tBb5coIiI+oFCtKXC5XKxcuZITJ04QHR3NgQMHSEtLo27dup4x/v7+1K5dm61bt3LTTTeRkpJCbm4usbGxnjGhoaFERkaSnJxMvXr1SE5OJjAwkBo1anjGREdHExgYyNatWwkPDyc5OZnIyEhCQ0M9Y+rWrUt2djYpKSnExMTkW3N2dnaemWNjjGem2xhzyd6bMzl1jII4llxe6uX5M9HX4vjnC9jkTbhmTcH+kIT96nPssi8xLdvj6Hg7JrRcwdelXvoM9dJ3qJe+oyB7WSiCcmpqKvHx8WRnZ1O8eHEeffRRIiIi2Lp1KwClS5fOM7506dIcOnQIgLS0NJxOJ0FBQaeNSUtL84z58z7OZUxQUBBOp9MzJj+JiYlMmzbN83NUVBSjR48+p+n8S+nU0hUp+tTLC1CxIrS6ieMb15L+4ThObFyHXTyX3K+/JOjmHgTffh/OchUKvCz10neol75DvfQdBdHLQhGUw8PDefnllzl27BirVq1i7NixPP/8857H//yN4VzWo5zrmD/uO79vJn8e82c9evSgS5cup+3j4MGD5OTknLWGi2WMISwsjP3792vNVRGnXl4C5cJh6PM4ftiIa9aHkLyZjDkfkzEvEXNDBxydemJCyp59PxdJvfQd6qXvUC99x6XopdPpPKdJzUIRlJ1Op+dbQbVq1dixYwdz586le/fugHu2t0yZMp7x6enpntnfkJAQcnJyyMjIyDOrnJ6eTs2aNT1jjhw5ctpx/7yf7du353k8IyOD3NzcfGejT/H398ff3z/fxwryg2it1QffR6iXF8/UjMHx6Ej4Icl90t/2LdhFc8hd+gWm1c2Ym2/DhISefUcXSb30Heql71AvfUdB9NLrJ/Plx1pLdnY2FSpUICQkxHNSHkBOTg5btmzxhOCqVavi5+eXZ8zhw4dJTU0lOjoacK9HzszMzBOEt23bRmZmpmc/0dHRpKamcvjwYc+YpKQk/P39qVq16mV9vSJy6RljMLXq4nh8FI5//Buq14KcbOzC2bieegDXR+OxRw6ffUciInLF8vqM8uTJk6lfvz5ly5bl+PHjLF++nM2bNxMfH48xhk6dOpGYmEjFihUJCwsjMTGRYsWK0aJFCwACAwNp06YNCQkJBAcHExQUREJCApGRkZ4T/CIiIqhXrx7jxo1jwIABALz99tueq22A+8S9iIgI3njjDfr06UNGRgYJCQm0bduWwMBA77w5InLRjDFQqy6Oa2Lh+w24Zk2GHT9gF8zCLpmHadUR0/FWTKkyZ9+ZiIhcUbx+HeU333yTTZs2cfjwYQIDA6lcuTLdu3f3hNxTNxxZsGABx44do3r16vTr14/IyEjPPrKysvjggw9YtmxZnhuOlCv3+9nuGRkZvPvuu6xduxaAhg0b0q9fv3xvOLJp0yYCAgJo0aIF99xzzxmXVvwVXUdZzpd6WTCstbBlvTswp7hPGCYgANO6E6bDrZhSIRd9DPXSd6iXvkO99B0FeR1lrwdlX6WgLOdLvSxY1lrY/J07MO9Mdm8MKIY7MPe4qMCsXvoO9dJ3qJe+oyCDsteXXoiIeIMxBmIa4Li2Pmxa5w7Mu7Zh5ydiv5qLubGzOzAHn/lkXhER8W0KyiJyRTPGQJ2GOGIawKa1uGZOhh+3Y7+Y/ntgbt8DE1zK26WKiEgBU1AWEeFUYG6EI6YhJK3BNXuKOzDP+xS7eC6mTWdM+1swQQrMIiJXCgVlEZE/MMZA3cY4YhtB0rfu6zCn7sB+Pg276DNM2y7uwFwy2NuliojIZaagLCKSD3dgvg5HbGPYsArX7KmQmoKd+wl20RxMm66Y9t0VmEVEfJiCsojIXzDGQL2mOOo2gfWr3DPMu3di536MXTwH07Yrpl13TMmgs+9MRESKFAVlEZFzYIyB+k1x1L3OHZhnT4Hdu7BzPsIunI1p2w1zUzdMoAKziIivUFAWETkPxuGABtfjqNcE1n/jnmHe8yN2zlR3YG7XFcdN3b1dpoiIXAIKyiIiF8AdmJvhqNcUvlvpXsO850fs7KnkLpjNkVt7Y5u0gRKB3i5VREQukIKyiMhFMA4HNGyOo/71sG6FOzDvTSX9w7ch8UPMTd3dyzIUmEVEihwFZRGRS8A4HNCoBY4GzWDdChyfTyMnNQU7czL2y1knA3NXBWYRkSLE4e0CRER8iXE4cDRuSdgbU3A88BiERUBmBnbmh7ieHIDrs4+xxzO9XaaIiJwDzSiLiFwGxs8Px3U3QMNm2G+XYed8BPt3Y2d8gP1ypvumJW06Y4prhllEpLBSUBYRuYyMww/TpBW2cYuTgXkq7N+DTUzAfjkD074H5sbOmOIlvF2qiIj8iYKyiEgByBOYVy/Fzv4IDuzFTp+EnT8D0+FkYC5W3NuliojISQrKIiIFyDj8ME1vxDa+wR2Y50yFA/uwn77/e2Bu3UmBWUSkEFBQFhHxAuPnh7n+Rux1N2BXfeVew3xwP3bae9gvEjEdbsUdmIt5u1QRkSuWrnohIuJFxs8PR7O2OP79Jua+oVA+DI4ewU6biOvJ/rjmz8CeOOHtMkVErkgKyiIihYDx88PRvC2O4f/D3PcIlLvKHZg/eRfXUwNwfTkTm6XALCJSkC546cWPP/7IsWPHqF27NgDHjx/ngw8+YOfOncTGxtKrVy+MMZesUBGRK4FxOjHN22GbtMauXIT97GP45QD24wnYL6Zjbr4Vc8PNmAAtyRARudwueEZ50qRJrFu3zvPzlClTWLhwITk5OcyYMYN58+ZdkgJFRK5ExunE0bI9jhfexMQ9DGUrwJHD2I8m4HrqAVwLZmmGWUTkMrvgoJyamkp0dDQA1lqWLVvG7bffzujRo+nevTuLFy++ZEWKiFypjNP/98B8z0MQWv5kYB6P66mBuBbOwWZnebtMERGfdMFBOTMzk1KlSgHuZRgZGRk0a9YMgJiYGH7++edLU6GIiLgD8w0dcIx4C9NnMISWgyO/Yqe+7Z5hXqTALCJyqV1wUA4KCuLQoUMAbNq0iZCQEMLCwgDIycm5NNWJiEgexumPo9XNOF4Yh+n9IJQpB2m/Yqe87Z5hXjwXm53t7TJFRHzCBZ/MV6tWLT755BOOHj3KZ599Rv369T2P7d+/n7Jly16SAkVE5HTG3x/TuiO2eTvs8i+xc6fB4UPYyW9h503DdLwd07wdxt/f26WKiBRZFzyjfPfdd2OM4b333sPf35+ePXt6Hlu5ciU1atS4JAWKiMiZGX9/HK074RgxDnP3QAgJhV8PYT98E9fTA3EtmYfN0QyziMiFMNZaezE7yMjIICgoKM+21NRUQkJCPGuYr0QHDx4kuwB+/WmMoWLFiuzbt4+LbKV4mXrpO7zZS5udhV06H/v5NDjyq3tj2QqYTrdjmrXBODXDfD70ufQd6qXvuBS99Pf3p3z58mcdd9E3HPlzSM7KyiIyMvKKDskiIt5i/ANwtO2CY9TbmDsHQOlQ93WYE8bievpBXF/Px+o8EhGRc3LBQXnFihV88cUXnp/379/P3//+d+655x6GDRtGRkbGJSlQRETOnzswd8Uxchzmjv5Quow7ME96A9fTgxSYRUTOwQUH5dmzZ3PixO8Xu09ISODYsWN06tSJPXv2kJiYeEkKFBGRC2cCiuFo1w3HyLcxvfpBqZDfA/MzD+Ja9qUCs4jIGVxwUP7555+pVKkS4F5usWHDBnr37s29997LnXfeybfffnvJihQRkYtjAorhuKk7jpHvYG7vC8Gl4dDP2PfH4Bo2GNfyhdjcXG+XKSJSqFxwUD5x4gTFihUDYPv27WRnZ3suERcREcGvv/56aSoUEZFLxhQrhqP9LThGjcfcfr87MB/cj33v/9wzzCsUmEVETrngoFymTBl27doFwPr16wkPD/ecwHfs2DFPiBYRkcLHHZh74Bj1DqbnHwLzxP9zzzCvWKTALCJXvAu+4ch1113H1KlT2bJlC+vXr6d79+6ex3788Ueuuuqqc9pPYmIiq1evZs+ePQQEBBAdHU2fPn0IDw/3jDl+/Dgffvgh3377LUePHqVChQp07NiR9u3be8ZkZ2eTkJDA8uXLycrKIiYmhv79++e58UlGRgYTJ05kzZo1ADRq1Ii+fftSsmRJz5hDhw4xfvx4Nm/eTEBAAM2bNycuLg6n84LfKhGRQssUK47p0APbuiN28WfYLxLhwD7sxNewn32M6XoH5robMA4/b5cqIlLgLjj93XnnnRw/fpzk5GRatGiRJyivW7eOOnXqnNN+tmzZQocOHahWrRq5ublMnTqVF154gVdeeYXixYsD8N5777F582aGDBlC+fLlSUpKYvz48ZQpU4bGjRt7xqxdu5ahQ4cSHBzMpEmTePHFFxk9ejQOh3vi/PXXX+eXX34hPj4egHHjxjFmzBieeOIJAFwuF6NGjaJUqVIMHz6co0ePMnbsWAD69u17oW+ViEihZ4oVx9x8G7Z1J+ziudj50+HAXuyEV7FzPsZ0uQNzXUsFZhG5olzw0ouAgAAeeOAB/vOf/zBo0CACAgI8j40YMYK77rrrnPYTHx9P69atqVSpElWqVGHw4MEcOnSIlJQUz5ht27bRqlUrrr32WipUqEC7du2oXLkyO3bsACAzM5NFixYRFxdHbGwsUVFRDBkyhNTUVJKSkgDYvXs369evZ9CgQURHRxMdHc3AgQNZt24de/fuBWDDhg3s3r2bIUOGEBUVRWxsLHFxcSxcuJDMzMwLfatERIoMU7wEjo63uZdk9LgHSgbDz3uwE17B9ewQXKuWYF1akiEiV4ZLsp5g7969ZGRkEBwcTMWKFS9qX6cC6R9vZFKzZk3Wrl1LmzZtKFOmDJs3b2bfvn3cf//9AKSkpJCbm0tsbKznOaGhoURGRpKcnEy9evVITk4mMDAwz621o6OjCQwMZOvWrYSHh5OcnExkZCShoaGeMXXr1iU7O5uUlBRiYmJOqzc7OzvPHfiMMZQoUcLz98vt1DEK4lhyeamXvsMXemlKlITOvbBtu2AXzsH1RSLs340d/1/sZx/j6HonplFzn59h9oVeipt66TsKspcXFZRXrlxJQkICv/zyi2db2bJliYuLo2nTpue9P2st77//Ptdccw2RkZGe7X379uWtt95i0KBB+Pn5YYxh0KBBXHPNNQCkpaXhdDpPu0tg6dKlSUtL84wpXbr0acc825igoCCcTqdnzJ8lJiYybdo0z89RUVGMHj36nG6LeCmFhYUV6PHk8lEvfYfP9LL/UFx39+PorKkcnf4hdt9PuN5+Gee8Tyl9V39KtGiHcVz0jV4LNZ/ppaiXPqQgennBQXndunW89tprVKpUiZtvvpkyZcrw66+/8vXXX/Paa6/xr3/9y3O5uHM1YcIEUlNTGT58eJ7tc+fOZdu2bTz++OOUL1+e77//nvHjxxMSEpJnFvnPzuX+39baPN9I8vt28ucxf9SjRw+6dOly2vMPHjxITgFcxN8YQ1hYGPv379e964s49dJ3+GwvW3XG0bg1duFsXF/OICc1hV9GPwUJb+HodhemQTOfC8w+28srkHrpOy5FL51O5zlNal5wUE5MTKRu3bo88cQTnpPlALp168bIkSOZPn36eQXld999l7Vr1/L888/nuVJFVlYWU6ZM4bHHHqNBgwYAVK5cmV27djF79mxiY2MJCQkhJyeHjIyMPLPK6enp1KxZE4CQkBCOHDly2nHT09M9s8ghISFs3749z+MZGRnk5ubmOxsN4O/vj7+/f76PFeQH0VqrD76PUC99h0/2skQgpssdONp0xi6YjV0wC/am4nprNFxdGUfXO6H+9T4XmH2yl1co9dJ3FEQvL/hfsl27dtG+ffs8IRncKb9Dhw6eayyfjbWWCRMmsGrVKoYNG0aFChXyPJ6Tk0Nubu5pM7oOh8Pz5lStWhU/Pz/PiXsAhw8fJjU1lejoaMC9HjkzMzNPEN62bRuZmZmeMB0dHU1qaiqHDx/2jElKSsLf35+qVaue0+sREbkSmMAgHN3uwvHiO5iud0KJQNjzI663RuMaPhS7dgXW5fJ2mSIiF+WCZ5QdDscZlxbk5OScFqDPZMKECSxbtozHH3+cEiVKeNYCBwYGEhAQQGBgILVr1+aDDz4gICCA8uXLs2XLFpYsWcK9997rGdumTRsSEhIIDg4mKCiIhIQEIiMjPUszIiIiqFevHuPGjWPAgAEAvP322zRo0MBzzea6desSERHBG2+8QZ8+fcjIyCAhIYG2bdsSGBh4oW+ViIjPMoFBmG53Y9t2wy6YhV0462RgfhEiquDoehfUa+JzM8wicmUw9gLnrIcPH87x48d57rnn8lwaLjs7m+eee47ixYvzzDPPnHU/vXr1ynf74MGDad26NeA+yW7y5Mls2LCBjIwMypcvT7t27ejcubNnpjkrK4sPPviAZcuW5bnhSLly5Tz7zMjI8CzxAGjYsCH9+vXL94YjmzZtIiAggBYtWnDPPfeccXnFmRw8eDDP1TAuF2MMFStWZN++ffpVUhGnXvqOK7mX9thR7JczsQtnw/Hf3BsrRf0emIvYFQeu5F76GvXSd1yKXvr7+5/TGuULDso//PADw4cPJygoiKZNmxISEkJaWhqrVq0iIyODYcOGeZY0XIkUlOV8qZe+Q708GZjnnwzMJ/4QmLvdBXWLTmBWL32Heuk7CjIoX/DSi2uuuYann36aDz/8kC+++AJwF16jRg2GDh2a54Q8ERG5spiSwZgefbA3dTs5wzwHftqJa+xIiKzmDsyxjYtMYBaRK9MFzyj/0YkTJzh27BglS5akWLFifPPNN7z66qt89NFHl6LGIkkzynK+1EvfoV6ezh5Nx36ZiF30GZw47t5Yubp7SUZso0IbmNVL36Fe+o4iMaP8R8WKFaNYsWKXYlciIuKDTHApzK33Ym/qgZ2fiF38Gfy4Hdcb/4YqNdwzzDENC21gFpErk05DFhGRAmOCS+G47V4co97BdLgVAorBrm24Xh+Oa9Rj2I1rNdsnIoWGgrKIiBQ4E1waR8/73IG5fQ8ICICdybhef94dmDetU2AWEa9TUBYREa8xpUJw3H7/ycB8y++B+f+ewzX6X9jN3ykwi4jXnNca5ZSUlHMad+DAgQsqRkRErkymVBnM7X2xHXpg503HfvU57PgB12vPQrVr3GuYa9XTGmYRKVDnFZSffPLJy1WHiIiIOzD36oftcCt23qfYJfPcgfnVZ6F6LRzd7oZrYhWYRaRAnFdQfvDBBy9XHSIiIh6mdBnMHf3zBubt3+N65RmoUdt9WTkFZhG5zM4rKJ+6pbSIiEhBMCGhmDsHYG++1b0kY8k82LbFHZijr8XR7W5MzTreLlNEfJRO5hMRkULPhJTFcecAHCPfxrTpAk5/SN6M6z/x5P4nHrt1k7dLFBEfdEluOCIiIlIQTJmymLsewN58G/bzT7Bfz4etG3Ft3Qg16+DodhcmOsbbZYqIj9CMsoiIFDmmTFkcdw/CMeJtTOtO4HS6A/PLT5H736ex27Z4u0QR8QGaURYRkSLLhJbD9B6E7Xgbdu4n2GUL4IckXD8kQa267hnm6rW9XaaIFFEKyiIiUuSZ0PKYPoOxHXti507DLl8A32/A9f0GqF0PR9e7MNVrebtMESliFJRFRMRnmLIVMPcMxnbqif3sY+yKhbBlPa4t66F2ffcMc7VrvF2miBQRCsoiIuJzTNkKmLiH3TPMn087GZi/w7XlO7i2vvuyclVrertMESnkFJRFRMRnmfJhvwfmzz7GrlwEm7/Dtfk7iGnonmGOivZ2mSJSSCkoi4iIzzPlwzD3PYLtdDt27sfYlYth01pcm9ZCnUbuNcxRNbxdpogUMgrKIiJyxTAVKmLuG+oOzJ99gv1mMWxcg2vjGndg7nYXpooCs4i4KSiLiMgVx1QIx9w/FNv5duycj7DfLPk9MNe9DkfXOzGVq3u7TBHxMgVlERG5YpkK4Zi+f8d26uVew7xqCWxYjWvDaqh7HX7d7oKKFb1dpoh4iYKyiIhc8UzY1Zh+f3fPMH/2MXbVUtiwmtwNqznUtBW2/a1QKcrbZYpIAdMtrEVERE4yYRE4+v0Dx/NvYK5rBcbw2zdLyB0+lNz/jcT+tNPbJYpIAdKMsoiIyJ+YihGYAf+ELndQbOFMMpfOh+++wfXdN9Dgevca5gjNMIv4OgVlERGRMzDhlSj7+AhOtO2Ga/ZU7JplsG4lrnUroWEzHF3uxERU8XaZInKZaOmFiIjIWZjwSBwPPIbj2TGYRi3AGFi7Atfzj+B6azR2z4/eLlFELgPNKIuIiJwjc3UkZuDj2D0/YmdPxa5d7v6zbgWmYXNM1zsx4ZHeLlNELhEFZRERkfNkrq6MGfQv7O5duOZMhbUrsGuWYdcuxzRq4Q7MFSt5u0wRuUgKyiIiIhfIRFTBb9AT2N07cc2eCutWYr/9GrtmGaZxS0yXOzEVI7xdpohcIAVlERGRi2QiovB78EnsTztxzZ4C332DXb0U++0yzHUtMV3uwIQpMIsUNQrKIiIil4ipFIXf4KewqSnuGeb132BXLcGu/hrT5AZM5zswYVd7u0wROUcKyiIiIpeYiayK30NPYVN34Jo1BTasxn7zFXbVUkyTVu4Z5qvCvV2miJyF14NyYmIiq1evZs+ePQQEBBAdHU2fPn0ID8/7D8ju3bv58MMP2bJlC9ZaKlWqxN///nfKlSsHQHZ2NgkJCSxfvpysrCxiYmLo378/ZcuW9ewjIyODiRMnsmbNGgAaNWpE3759KVmypGfMoUOHGD9+PJs3byYgIIDmzZsTFxeH0+n1t0pERIoYE1kNv4efxv643T3DvGE19pvF2NVLME1aY7r0wlRQYBYprIy11nqzgBEjRtC8eXOqVatGbm4uU6dOJTU1lVdeeYXixYsDsH//fp566inatGlD8+bNCQwMZM+ePVSrVo3SpUsD8M4777B27VoGDx5McHAwkyZNIiMjg9GjR+NwuC8XPXLkSH755RcGDhwIwLhx4yhfvjxPPPEEAC6Xi8cee4xSpUoRFxfH0aNHGTt2LE2aNKFv377n9boOHjxIdnb2pXqbzsgYQ8WKFdm3bx9ebqVcJPXSd6iXvuNS99Lu2uYOzEnfujc4HJimN2I698JUqHjR+5cz0+fSd1yKXvr7+1O+fPmzjvP6DUfi4+Np3bo1lSpVokqVKgwePJhDhw6RkpLiGTN16lTq169Pnz59iIqK4qqrrqJBgwaekJyZmcmiRYuIi4sjNjaWqKgohgwZQmpqKklJSYB7Rnr9+vUMGjSI6OhooqOjGThwIOvWrWPv3r0AbNiwgd27dzNkyBCioqKIjY0lLi6OhQsXkpmZWfBvjoiI+BRTpQZ+Q57B8dR/oU4jcLmwKxbieuZBXO+9jj2439slisgfFLr1BKcCaVBQEOCe5V23bh3dunVjxIgR7Ny5kwoVKnDLLbdw3XXXAZCSkkJubi6xsbGe/YSGhhIZGUlycjL16tUjOTmZwMBAatSo4RkTHR1NYGAgW7duJTw8nOTkZCIjIwkNDfWMqVu3LtnZ2aSkpBATE3NavdnZ2Xlmjo0xlChRwvP3y+3UMQriWHJ5qZe+Q730HZerl6ZqNI6hz2JTknHNmozdtBa7fAH2m8WY69vg6NwLUz7skh7zSqfPpe8oyF4WqqBsreX999/nmmuuITLSfWej9PR0jh8/zsyZM7njjjvo3bs369ev57///S/PPvsstWvXJi0tDafT6QnXp5QuXZq0tDQA0tLSPDPQ5zMmKCgIp9PpGfNniYmJTJs2zfNzVFQUo0ePPqfp/EspLEz/oPoK9dJ3qJe+47L1smJFaN6KEz9sJP3Dtzm+biV22ZfkrlxEyXZdKXVHX5w66e+S0ufSdxRELwtVUJ4wYQKpqakMHz7cs83lcgHuE++6dOkCQJUqVdi6dSvz58+ndu3aZ9zfuaxbsdbm+UaS37eTP4/5ox49enjq+uPzDx48SE5OzlmPf7GMMYSFhbF//36tuSri1EvfoV76jgLrZelyMPgp/Hb84J5h3vwdx76YwbEFszHN27lnmMtWuHzHvwLoc+k7LkUvnU7nOU1qFpqg/O6777J27Vqef/75PFeqKFWqFH5+fkRE5L1Q+9VXX83WrVsBCAkJIScnh4yMjDyzyunp6dSsWdMz5siRI6cdNz093TOLHBISwvbt2/M8npGRQW5ubr6z0eBeDO7v75/vYwX5QbTW6oPvI9RL36Fe+o4C62XVmjj+9jx2+xb3ZeW+34Bd+gW5yxdimrfDdLodU7Zgf2Ppa/S59B0F0Uuvn8xnrWXChAmsWrWKYcOGUaFC3m/MTqeTatWqeU64O2Xfvn2eS8NVrVoVPz8/z4l7AIcPHyY1NZXo6GjAvR45MzMzTxDetm0bmZmZnjAdHR1Namoqhw8f9oxJSkrC39+fqlWrXtoXLiIicgamem38/vFvHI+/CLXqQm4Oduk8XPEDcX3wP+yvB71dosgVwetBecKECXz99dcMHTqUEiVKkJaWRlpaGllZWZ4x3bp1Y8WKFSxYsID9+/czb9481q5dS4cOHQAIDAykTZs2JCQksHHjRnbu3MmYMWOIjIz0nOAXERFBvXr1GDduHMnJySQnJzNu3DgaNGjguWZz3bp1iYiI4I033mDnzp1s3LiRhIQE2rZtS2BgYMG/OSIickUzNU4G5sdGQs067sC8ZB6upwbi+vBN7K+HvF2iiE/z+nWUe/Xqle/2wYMH07p1a8/PixYtYsaMGfzyyy+Eh4fTq1cvGjdu7Hk8KyuLDz74gGXLluW54cipWWdwL6M4tcQDoGHDhvTr1y/fG45s2rSJgIAAWrRowT333HPG5RVnousoy/lSL32Heuk7Clsv7dZNuGZPga0b3RucTkzL9pibe2JCy/31k69wha2XcuEK8jrKXg/KvkpBWc6Xeuk71EvfUVh7abduxDVrMiRvdm9wOjEtO2A69sSUKfvXT75CFdZeyvm7om44IiIiIufH1KyD49GROP75AtSoDTk52MWf4XrqAVxT3sam/eLtEkV8QqG56oWIiIicO2MMXBOLo2Yd+CHJPcO8/XvsojnYpV9gWt2Mufk2TEjo2XcmIvlSUBYRESnCjDFQqy6Oa2Lh+w3uwLzjB+zC2XkDc+ky3i5VpMhRUBYREfEBxhioXQ9Hrbrw/Xr3dZh3/IBdMAu7dB6mVUfMzbdiSikwi5wrBWUREREf4g7M9XHUqgdb1rtnmFO2Yr+ciV3yOaZ1J0yHWzGlQrxcqUjhp6AsIiLig4wxcG19HLXrweZ17hnmncnY+TOwX32OufFkYA7O/86zIqKgLCIi4tOMMRDTEMe1DWDTOvcM865t2C8SsYvnYm7sfDIwl/J2qSKFjoKyiIjIFcAYA3Ua4ohpAJvW4po5GX7cjv1iOvaruZg2nTE39VBgFvkDBWUREZEriDswN8IR0xCS1rjv9Pfjduznn2IXnQzM7W/BBCkwiygoi4iIXIGMMVC3MY7YRpD0rXsNc+oO7OfTsIs+w7Tt4g7MJYO9XaqI1ygoi4iIXMHcgfk6HLGNYcNq9wxzagp27ifYRXMwbbtibuquwCxXJAVlERERcQfmek1w1L0O1q9yzzDv3on97OPfA3O77piSQd4uVaTAKCiLiIiIhzEG6jf9PTDPngK7d2HnfIRdOBvTthvmpm6YQAVm8X0KyiIiInIa43BAg+tx1GsC679xzzDv+RE7Z6o7MLfrhmnXVYFZfJqCsoiIiJyROzA3w1GvKXz3jXuGec+P2NlTsAtnuZdjtO2KCSzp7VJFLjkFZRERETkr43BAw2Y46jeFdStwzZ4Ke1OxsyZjF8x0n/DXthumRKC3SxW5ZBSURURE5JwZhwMatcDRoBl27Qrs7Cmw7yfszMnYL2edDMxdFZjFJygoi4iIyHkzDgemcQtsw+uxa5ZjZ0+F/buxMz/ELjgVmLtgiiswS9GloCwiIiIXzDj8MNfdgG3UHPvtMuycqbB/D3bGB9gvZ7pvWtKmswKzFEkKyiIiInLRjMMP06QVtnEL7OqvsXM+gp/3YBMTsF/OwLTvgbmxM6Z4CW+XKnLOFJRFRETkkjEOP0zT1tjGLbGrl7oD84G92OmTsPNnYDqcDMzFinu7VJGzUlAWERGRS874+WGuvxF73Q3YVUuwn30EB/ZhP33/98DcupMCsxRqDm8XICIiIr7L+PnhaNYGx/D/Ye4fCuXD4OgR7LT3cD05ANf8ROyJE94uUyRfCsoiIiJy2bkDc1sc/34Tc98fAvMnE3E92R/X/BkKzFLoKCiLiIhIgTF+fjiat3XPMN87BMpddTIwv4vrqQG4vpypwCyFhoKyiIiIFDjjdOJocZN7hjnuYShbAdLTsB9PcM8wf5GIPf6bt8uUK5yCsoiIiHiNcTpxtGyP44WTgfnUDPO0ie41zJ9Pwx7P9HaZcoXSVS9ERETE64zTH9OyPfb6Nu6rZMz92H2VjOmTsF8kuu/0d2NnTGBJb5cqVxAFZRERESk0jNOJad4W27Q19tul2M8+/v1Of/MTMW27Ydp2xZQM8napcgVQUBYREZFCx/j5YZqevA7zt8vcgXnfT9jZU7ALZmLadMG064YJKuXtUsWHKSiLiIhIofX7rbFbwroVuOZ8BHt+xH72MXbBbEzrjpj23TGlyni7VPFBCsoiIiJS6BmHAxq1wNGgGaxfhWvOVPhpJ/aL6dhFczAt27vv9hda3tulig9RUBYREZEiwzgc0OB6HPWbQtIaXJ99BDuTsYvmYJfMw1x/I6bjbZgK4d4uVXyA14NyYmIiq1evZs+ePQQEBBAdHU2fPn0ID8//P/C3336bBQsWcO+999K5c2fP9uzsbBISEli+fDlZWVnExMTQv39/ypYt6xmTkZHBxIkTWbNmDQCNGjWib9++lCz5+xm0hw4dYvz48WzevJmAgACaN29OXFwcTqfX3yoRERE5yRgDdRvjiG0EPyTh+uxj2LoRu+xL7PKFmMYtMZ1ux1wd6e1SpQjz+nWUt2zZQocOHRgxYgRPP/00LpeLF154gePHj582dvXq1Wzbto0yZU5fh/Tee++xevVqhg4dyvDhwzl+/DgvvvgiLpfLM+b1119n165dxMfHEx8fz65duxgzZozncZfLxahRozhx4gTDhw9n6NChrFq1ikmTJl2eFy8iIiIXxRiDqVUXv0dH4HjiJajTCKwLu3oJruceJnfsSOyubd4uU4oorwfl+Ph4WrduTaVKlahSpQqDBw/m0KFDpKSk5Bn366+/8u677/LII4+cNrubmZnJokWLiIuLIzY2lqioKIYMGUJqaipJSUkA7N69m/Xr1zNo0CCio6OJjo5m4MCBrFu3jr179wKwYcMGdu/ezZAhQ4iKiiI2Npa4uDgWLlxIZqYudi4iIlKYmWrX4PfIMBzPvAoNm4ExsP4bXCP+Se6rz3Ji03feLlGKmEK3nuBUIA0K+v36iC6XizFjxtCtWzcqVap02nNSUlLIzc0lNjbWsy00NJTIyEiSk5OpV68eycnJBAYGUqNGDc+Y6OhoAgMD2bp1K+Hh4SQnJxMZGUloaKhnTN26dcnOziYlJYWYmJjTjp2dnU12drbnZ2MMJUqU8Pz9cjt1jII4llxe6qXvUC99h3pZNJnK1XE8+CR270+4Pv/EfQOTzes48K8BmJoxmC53Yq6JVV+LqIL8XBaqoGyt5f333+eaa64hMvL3NUUzZ87Ez8+Pjh075vu8tLQ0nE5nnnANULp0adLS0jxjSpcufdpzzzYmKCgIp9PpGfNniYmJTJs2zfNzVFQUo0ePpnz5gj3rNiwsrECPJ5ePeuk71EvfoV4WURUrQsPryNm3m/RPJ3Hsy1nYrZuwW58m4JpYSt3Zl+KNmiswF1EF8bksVEF5woQJpKamMnz4cM+2lJQU5s6dy+jRo8/7P2Rr7TmN+eN+8zvGn8f8UY8ePejSpctpzz948CA5OTnnVe+FMMYQFhbG/v37z+n1SuGlXvoO9dJ3qJe+wg/Tsy8V7+zHz5PewrX0C7J+SOLQc3+DytVxdOmFqdvEfUUNKfQuxefS6XSe06RmoQnK7777LmvXruX555/Pc6WK77//nvT0dAYPHuzZ5nK5mDRpEnPnzmXs2LGEhISQk5NDRkZGnlnl9PR0atasCUBISAhHjhw57bjp6emeWeSQkBC2b9+e5/GMjAxyc3PznY0G8Pf3x9/fP9/HCvIfVWut/hH3Eeql71AvfYd66Ruc5cJw3PUAdOyJnZ+I/epz+HE7rrEj4erKmM53YBpej3H4ebtUOQcF8bn0elC21vLuu++yevVqnnvuOSpUqJDn8RtuuIE6derk2TZixAhuuOEGbrzxRgCqVq2Kn58fSUlJNGvWDIDDhw+TmppK7969Afd65MzMTLZv30716tUB2LZtG5mZmZ4wHR0dzfTp0zl8+LDnyhpJSUn4+/tTtWrVy/cmiIiISIExpctgbu+Lvfk27JczsYs/c9/t7+2XsGERmM63YxrfgPFTYL7SeT0oT5gwgWXLlvH4449TokQJz1rgwMBAAgICCA4OJjg4OM9znE4nISEhnmstBwYG0qZNGxISEggODiYoKIiEhAQiIyM9J/hFRERQr149xo0bx4ABAwD3NZkbNGjg2U/dunWJiIjgjTfeoE+fPmRkZJCQkEDbtm0JDAwsoHdERERECoIJLo25NQ7b4VbswtnYhbNg/27shFexs6a4r8Pc9EaM7qVwxTLWy79L6tWrV77bBw8eTOvWrfN97KGHHqJTp055bjiSlZXFBx98wLJly/LccKRcuXKeMRkZGZ4lHgANGzakX79++d5wZNOmTQQEBNCiRQvuueeeMy6vOJODBw/muRrG5WKMoWLFiuzbt0+/Fizi1EvfoV76DvXSd5xLL+1vmdjFn2G/nAkZ6e6NZSu4A3OzNhjn+WUBuTwuxefS39//nNYoez0o+yoFZTlf6qXvUC99h3rpO86nl/b4b9il87DzpsPRk+c3hZbHdOyJad4Oc56TZ3JpFWRQ1umdIiIiIn9gipfA0b4HjlHjMXf0g9Jl4NeD2A/fxPXUA7gWzcFmZ3m7TCkACsoiIiIi+TDFiuFo1x3HyLcxdz0AIWUh7RfslLdxPfkArgWzsFknvF2mXEYKyiIiIiJ/wQQUw9Gmizsw9x4EoeXgyK/Yj8bjenIArvmJ2BPHvV2mXAYKyiIiIiLnwPj742jdCceIcZh7HoKyFSA9DfvJRFxP9Mc15yPssQxvlymXkK53IiIiInIejNMfc0MHbLO22FVfYT/7GA7ux878EDtvOqb1zZh23TEhod4uVS6SgrKIiIjIBTBOJ6Z5O2zTG7FrlmHnfQq7d2G/SMQunINp1hbToQemQkVvlyoXSEFZRERE5CIYPz9Mk1bY626AjWtwfT4Ntn/vvsTc1/MxjVtgOt6GiYjydqlynhSURURERC4BYwzENsYvtjE2eTOuzz+BTeuwq5diVy+FOo1wdOqJqV7b26XKOVJQFhEREbnETPS1+EVfi03dgf38U+zaFe7Z5o1roEZtHB1ugzoNMQ5dV6EwU1AWERERuUxMZDXMwMexP+/FfjEdu2IRbNuCa9sWCIvAtL8F07Q1xj/A26VKPvQ1RkREROQyM1eF44h7GMeodzDte0CJQNi/GzvpDVz/6ue+tFxGurfLlD9RUBYREREpIKZMWRy3349j9LuY2/u6b15y9Ah25oe4/tUX1+S3sAf2ebtMOUlLL0REREQKmCkRiGl/C7ZNF+za5dgvpsNPO7GL52K/+hzqX4+j/S2Yatd4u9QrmoKyiIiIiJcYp/P3S8v9kIRr/gzYtBbWrcC1bgVUr4WjfQ+oe51O/PMCBWURERERLzPGQK26+NWqi93zI/bLGdhvlsD273Ft/x7Crsa074FpeiPG39/b5V4x9NVEREREpBAxV1fGcd9QHC++g+l4G5QoCfv3uE/8e3IArnmfYjOPebvMK4KCsoiIiEghZELK4rj1XhwvTcDcfj+ElIUjv2I/fR/XE/1wTXsPm/aLt8v0aVp6ISIiIlKImeKBmPY93Cf+rV6KnTcd9v3kvi7zglnu6zB36IGpWMnbpfocBWURERGRIsA4/THN2mKb3ggb1+Ka9yls34JdvgC7fAHUa4Kjw62Y6rW8XarPUFAWERERKUKMwwF1G+NXtzF2+/e4vkiE9d/A+lW41q+C6GtxdLwdrq3vPklQLpiCsoiIiEgRZarXwq96Ley+3dj5idiViyF5M67kzRBZDUen26F+U11a7gLpXRMREREp4kzFCBz3DsEx8m1Mu+4QUAxSd+B660Vczz6Ma8UibE6Ot8sschSURURERHyECS2H445+OF6cgOnc6+Sl5XZjJ76G6+lBuBbPxWad8HaZRYaCsoiIiIiPMcGlcNzSB8foCZhb74Xg0vDLAezkt36/FvPxTG+XWegpKIuIiIj4KFMiEEfH23C8OB5z90AILQ/pae5rMf+rH66ZH2Iz0r1dZqGlk/lEREREfJwJKIa5sTO2ZQfs6iXYz6e57/Y35yPslzMxrW7G3HQLJiTU26UWKgrKIiIiIlcI43SevBZza/juG1xzP4HUFOz8GdhFn2FatMN0uBVT7ipvl1ooKCiLiIiIXGGMww8aNsfRoBlsWodr7sew/XvsV59jl36BadIK0/F2TMUIb5fqVQrKIiIiIlcoYwzUaYgjpoH7+stzP4Et32FXLsZ+8xU0uB5Hp9sxkdW8XapXKCiLiIiIXOGMMVAzBr+aMdid29yBef03sHYFrrUrIKYhjs63Y6rX9napBUpBWUREREQ8TFQN/B56CrvnR+zcadhvv4ZNa3FtWgs16+Do3Auuib0ibo+toCwiIiIipzFXV8YM+Ce2+93YeZ9iVyyCrRtxbd0IVWvi6NQLYhv5dGDWdZRFRERE5IxMhYo44h7GMXIcpm1X8A+AlK243vg3rn//Dbt2Odbl8naZl4XXZ5QTExNZvXo1e/bsISAggOjoaPr06UN4eDgAOTk5TJ06le+++44DBw4QGBhInTp1uPvuuwkN/f1af9nZ2SQkJLB8+XKysrKIiYmhf//+lC1b1jMmIyODiRMnsmbNGgAaNWpE3759KVmypGfMoUOHGD9+PJs3byYgIIDmzZsTFxeH0+n1t0pERETEa0xoecydA7CdemLnz8R+9Tn8tBPXW6OhYiVMp56Yxjdg/Py8Xeol4/UZ5S1bttChQwdGjBjB008/jcvl4oUXXuD48eMAZGVlsXPnTm677TZGjx7NP//5T/bt28dLL72UZz/vvfceq1evZujQoQwfPpzjx4/z4osv4vrDN5zXX3+dXbt2ER8fT3x8PLt27WLMmDGex10uF6NGjeLEiRMMHz6coUOHsmrVKiZNmlQwb4aIiIhIIWdKlcHR8z4cL76D6XInBJaEfT9hJ7yK65kHcX09H5uT7e0yLwmvB+X4+Hhat25NpUqVqFKlCoMHD+bQoUOkpKQAEBgYyDPPPEOzZs0IDw8nOjqa+++/n5SUFA4dOgRAZmYmixYtIi4ujtjYWKKiohgyZAipqakkJSUBsHv3btavX8+gQYOIjo4mOjqagQMHsm7dOvbu3QvAhg0b2L17N0OGDCEqKorY2Fji4uJYuHAhmZm6H7qIiIjIKSaoFI7ud+MYNR7T4x4IKgUH92MnvYErfiCuhbOxJ457u8yL4vWg/GenAmlQUNBfjjHGEBgYCEBKSgq5ubnExsZ6xoSGhhIZGUlycjIAycnJBAYGUqNGDc+Y6OhoAgMD2bp1q2dMZGRkniUddevWJTs72xPcRUREROR3JrAkjk6343hxPOaOflA6FH49hJ36Dq7H++JK/ACbftjbZV6QQrXw1lrL+++/zzXXXENkZGS+Y7Kyspg8eTLNmzf3BOW0tDScTudp4bp06dKkpaV5xpQuXfq0/Z1tTFBQEE6n0zPmz7Kzs8nO/v3XC8YYSpQo4fn75XbqGL58xumVQr30Heql71AvfYd6efmZ4iXgpluwrTthly/ENT8RDuzDzv0YOz8R06wNjpu6YypWurjjFGAvC1VQnjBhAqmpqQwfPjzfx3Nycnjttdew1tK/f/+z7s9ae05j/vhG5/em/3nMHyUmJjJt2jTPz1FRUYwePZry5cuf9diXUlhYWIEeTy4f9dJ3qJe+Q730HeplAYnsi+11L799s4Sj0xPI+mEjdukX5C79guJNbqDUbXEE1K57UWG3IHpZaILyu+++y9q1a3n++efzXKnilJycHF599VUOHjzIsGHDPLPJACEhIeTk5JCRkZFnVjk9PZ2aNWt6xhw5cuS0/aanp3tmkUNCQti+fXuexzMyMsjNzc13NhqgR48edOnSxfPzqYYfPHiQnJycc335F8wYQ1hYGPv37z+nLwZSeKmXvkO99B3qpe9QL72kai14dCR+27bg+mI6dsNqjq9ayvFVS93XYu7QA1O/KcZx7lfKuBS9dDqd5zSp6fWgbK3l3XffZfXq1Tz33HNUqFDhtDGnQvL+/ft59tlnCQ4OzvN41apV8fPzIykpiWbNmgFw+PBhUlNT6d27N+Bej5yZmcn27dupXr06ANu2bSMzM9MTpqOjo5k+fTqHDx+mTJkyACQlJeHv70/VqlXzrd/f3x9/f/8zvraCYq3VB99HqJe+Q730Heql71AvvaR6LRzV47H7d2O/nOm+eUnKVlxvvggVKmJu6o65vi2mWLFz3mVB9NLrJ/NNmDCBr7/+mqFDh1KiRAnS0tJIS0sjKysLgNzcXF555RVSUlIYMmQILpfLM+bUjG1gYCBt2rQhISGBjRs3snPnTsaMGUNkZKTnBL+IiAjq1avHuHHjSE5OJjk5mXHjxtGgQQPPNZvr1q1LREQEb7zxBjt37mTjxo0kJCTQtm3bPDPYIiIiInL+TFgEjnsewjF6PKZzLygZ7F7H/OFb2Imvebu80xjr5a9VvXr1ynf74MGDad26NQcOHODhhx/Od8yzzz7LtddeC7hP8vvggw9YtmxZnhuOlCtXzjM+IyPDs8QDoGHDhvTr1y/fG45s2rSJgIAAWrRowT333HPGWeMzOXjwYJ6T/C4XYwwVK1Zk3759+oZcxKmXvkO99B3qpe9QLwsne+I4dtkC7JczcNw7BFOr7lmfcyl66e/vf05LL7welH2VgrKcL/XSd6iXvkO99B3qZeFmc3PB4Tink/sKMih7fY2yiIiIiFzZCuttr72+RllEREREpDBSUBYRERERyYeCsoiIiIhIPhSURURERETyoaAsIiIiIpIPBWURERERkXwoKIuIiIiI5ENBWUREREQkHwrKIiIiIiL5UFAWEREREcmHbmF9mTidBfvWFvTx5PJRL32Heuk71EvfoV76jovp5bk+11hr7QUfRURERETER2npRRH322+/8a9//YvffvvN26XIRVIvfYd66TvUS9+hXvqOguylgnIRZ61l586d6BcDRZ966TvUS9+hXvoO9dJ3FGQvFZRFRERERPKhoCwiIiIikg8F5SLO39+fnj174u/v7+1S5CKpl75DvfQd6qXvUC99R0H2Ule9EBERERHJh2aURURERETyoaAsIiIiIpIPBWURERERkXwoKIuIiIiI5EM3PC/CvvjiC2bNmkVaWhoRERHcd9991KpVy9tlyVls2bKFWbNmsXPnTg4fPsyjjz7Kdddd53ncWssnn3zCwoULycjIoEaNGvTr149KlSp5sWr5s8TERFavXs2ePXsICAggOjqaPn36EB4e7hmjXhYN8+fPZ/78+Rw8eBCAiIgIevbsSf369QH1sShLTExkypQpdOrUifvuuw9QP4uKjz/+mGnTpuXZVrp0ad555x2g4PqoGeUiasWKFbz33nvceuutjB49mlq1ajFy5EgOHTrk7dLkLE6cOEGVKlXo27dvvo/PnDmTzz77jL59+zJq1ChCQkJ44YUXdNvVQmbLli106NCBESNG8PTTT+NyuXjhhRc4fvy4Z4x6WTSEhoZy9913M2rUKEaNGkVMTAwvvfQSP/30E6A+FlXbt29nwYIFVK5cOc929bPoqFSpEm+//bbnz3//+1/PYwXVRwXlImrOnDm0adOGtm3bemaTy5Urx/z5871dmpxF/fr1ufPOO2nSpMlpj1lrmTt3Lj169KBJkyZERkby0EMPceLECZYtW+aFauVM4uPjad26NZUqVaJKlSoMHjyYQ4cOkZKSAqiXRUmjRo1o0KAB4eHhhIeHc9ddd1G8eHG2bdumPhZRx48fZ8yYMQwcOJCSJUt6tqufRYvD4SAkJMTzp1SpUkDB9lFBuQjKyckhJSWFunXr5tkeGxvL1q1bvVSVXAoHDhwgLS0tT2/9/f2pXbu2elvIZWZmAhAUFASol0WVy+Vi+fLlnDhxgujoaPWxiBo/fjz169cnNjY2z3b1s2jZv38/AwcO5KGHHuK1117j559/Bgq2j1qjXASlp6fjcrkoXbp0nu2lS5cmLS3NO0XJJXGqf/n1VstqCi9rLe+//z7XXHMNkZGRgHpZ1KSmphIfH092djbFixfn0UcfJSIiwvM/XfWx6Fi+fDk7d+5k1KhRpz2mz2XRUaNGDR566CHCw8NJS0tj+vTpPP3007zyyisF2kcF5SLMGHNO26To+XMfdQPNwm3ChAmkpqYyfPjw0x5TL4uG8PBwXn75ZY4dO8aqVasYO3Yszz//vOdx9bFoOHToEO+99x7x8fEEBASccZz6WfidOpkWIDIykujoaIYMGcKSJUuoUaMGUDB9VFAugkqVKoXD4Tht9vjIkSOnfbuSoiUkJARwz3qUKVPGsz09PV29LaTeffdd1q5dy/PPP0/ZsmU929XLosXpdBIWFgZAtWrV2LFjB3PnzqV79+6A+lhUpKSkcOTIEZ544gnPNpfLxffff8+8efN47bXXAPWzKCpevDiRkZHs27ePxo0bAwXTR61RLoKcTidVq1YlKSkpz/akpCRq1qzpparkUqhQoQIhISF5epuTk8OWLVvU20LGWsuECRNYtWoVw4YNo0KFCnkeVy+LNmst2dnZ6mMRU6dOHf7zn//w0ksvef5Uq1aNFi1a8NJLL3HVVVepn0VUdnY2e/bsoUyZMgX6udSMchHVpUsXxowZQ9WqVYmOjmbBggUcOnSIm266ydulyVkcP36c/fv3e34+cOAAu3btIigoiHLlytGpUycSExOpWLEiYWFhJCYmUqxYMVq0aOHFquXPJkyYwLJly3j88ccpUaKE5zc8gYGBBAQEYIxRL4uIyZMnU79+fcqWLcvx48dZvnw5mzdvJj4+Xn0sYkqUKOE5T+CUYsWKERwc7NmufhYNkyZNolGjRpQrV44jR47w6aef8ttvv9GqVasC/Vwaq4U5RdapG44cPnyYSpUqce+991K7dm1vlyVnsXnz5jxrH09p1aoVDz30kOci6gsWLODYsWNUr16dfv36nfaPv3hXr1698t0+ePBgWrduDaBeFhFvvvkmmzZt4vDhwwQGBlK5cmW6d+/uuWKC+li0Pffcc1SpUuW0G46on4Xba6+9xvfff096ejqlSpWiRo0a3HnnnURERAAF10cFZRERERGRfGiNsoiIiIhIPhSURURERETyoaAsIiIiIpIPBWURERERkXwoKIuIiIiI5ENBWUREREQkHwrKIiIiIiL50J35RET+4KuvvuJ///sf/v7+vPbaa5QvXz7P48899xxHjx7lv//9b4HXdupmNf/4xz9o2rRpgR//fB04cIAJEyaQnJzMsWPH6NSpk+emD392/Phx5s2bx/Llyzlw4ADWWkqXLk1UVBQ333yz52ZKu3fvZsWKFbRu3fq024aLiFxqCsoiIvnIzs5m6tSpDBkyxNulFFnvv/8+27dv58EHHyQkJIQyZcrkO87lcjFixAhSU1Pp2rUr1atXB2D//v2sXbuW77//Pk9QnjZtGtdee62CsohcdgrKIiL5qFevHsuWLaNr165UqVLF2+UUqKysLPz9/THGXNR+fvrpJ6pXr8511133l+O2bNnC1q1befDBB7nxxhvzPHbzzTfjcrkuqg4RkQuloCwiko9u3bqRkpLChx9+SHx8/BnHHThwgIcffpjBgwfTunXrPI/16tWLnj170qtXLwA+/vhjpk2bxssvv8ynn37Khg0bcDgctG7dmj59+vDzzz8zceJEtm7dSnBwMO3bt6d79+6nHTMrK4v333+fZcuWkZmZSfXq1bnvvvuIiorKM27Hjh1MmzaNH374gaysLK6++mpuueUWmjVr5hlzaqlJfHw8y5cvZ+3atRw9epQPPviAgICAfF/zoUOHmDx5MklJSWRmZnLVVVfRpk0bOnfujMPh8CwRAfes8KnX/8Ybb+Q7C5yRkQFwxhlnh8ORp1bAs38gz3uflJTEjBkz2LFjB7m5uURFRdGrVy/q1KnjGX+qD6NHj+bTTz9l48aNADRs2JD77ruPUqVKecZu2rSJadOmkZqayokTJyhVqhTVqlVjyJAhFCtWLN96RcR3KCiLiOSjRIkS3Hrrrbz33nts2rSJmJiYS7bvV199lZYtW9KuXTuSkpKYNWsWubm5bNy4kfbt29O1a1eWLVvGhx9+SFhYGE2aNMnz/ClTphAVFcWgQYPIzMzkk08+4bnnnuOll17iqquuAtwBb+TIkdSoUYMBAwYQGBjIihUreO2118jKyjot1L/55ps0aNCAIUOGcPz4cZzO/P/3kJ6eztNPP01OTg533HEH5cuXZ926dSQkJPDzzz/Tv39/oqKieOGFF/jPf/7DVVddxT333AOcOQhXrVoVPz8/Jk6cSM+ePYmJicl3bIMGDbjrrruYMmUK/fr183wxCAsLA2Dp0qWMHTuWRo0a8dBDD+Hn58eXX37JiBEjiI+PzxOWAf7zn/9w/fXXc9NNN/HTTz/x0UcfsWfPHkaMGIHT6eTAgQOMGjWKWrVq8eCDD1KyZEl+/fVX1q9fT05OjoKyyBVAQVlE5Azat2/P559/zocffsjIkSMveinCKe3ataNLly4AxMbGkpSUxLx583j00Uc9yxSuvfZa1q1bx9dff31aUC5VqhSPPfaYp55rrrmGRx55hMTERAYNGgTAhAkTqFSpEsOGDcPPzw9wLydJT09nypQp3HDDDZ6ZWoCYmBgeeOCBs9Y+Z84cfv31V0aOHOlZS1yvXj1cLhdffvklnTp1Ijw8nOjoaPz9/SlZsiTR0dF/uc8KFSowYMAA3nvvPcaMGQO4Q3WdOnVo27YttWrV8rzuihUrAhAREZFnvydOnOC9996jQYMGPPbYY57t9evX51//+hdTpkw5LShfd9119OnTB4C6desSEhLC66+/zsqVK2nZsiUpKSlkZ2fTp0+fPMtvWrRocdb3SUR8gy4PJyJyBk6nkzvuuIMdO3awcuXKS7bfBg0a5Pn56quvxhhDvXr1PNv8/PwICwvj0KFDpz2/RYsWeUJ7+fLlqVmzJps3bwbcyx327NnjCXS5ubmePw0aNODw4cPs3bs3zz7P9SoamzZtIiIiwhOST2ndujXWWjZt2nRO+/mzNm3a8NZbb/HII4/QsWNHypYty9dff81zzz3HrFmzzvr8rVu3kpGRQatWrfK8Xmst9erVY8eOHRw/fjzPc1q2bJnn5+uvvx4/Pz/P+1ilShWcTidvv/02X331FT///PMFvTYRKbo0oywi8heaN2/O7NmzmTJlyllPSjtXQUFBeX52Op0EBASctibY6XTy22+/nfb8kJCQfLf9+OOPAKSlpQGQkJBAQkJCvjUcPXr0rPs80/PyW2d8aqnEqfXGFyIwMJAWLVp4Av5PP/3Ev//9b6ZMmULbtm0pWbLkGZ975MgRAF555ZUzjsnIyKB48eKen//8mv38/AgKCvK8N2FhYTzzzDPMnDmTCRMmcOLECa666io6duxIp06dLvRlikgRoqAsIvIXjDH07t2bF154gQULFpz2+Klwm52dnWf7n4PopXQqCP9526kAfupktFtuueW0ZRunhIeH5/n5XJeVBAcHc/jw4dO2n9oWHBx8Tvs5F5UqVaJZs2bMnTuXffv2nTaL/ee6APr27UuNGjXyHfPnYJyWlkZoaKjn59zcXDIyMvK8hlq1alGrVi1cLhc7duzg888/57333qN06dI0b978Il6diBQFWnohInIWsbGxxMbG8umnn5726/vSpUvj7+/vmc095dtvv71s9SxfvhxrrefngwcPsnXrVq699lrAHYIrVqzIjz/+SLVq1fL9U6JEiQs6dkxMDLt37yYlJSXP9iVLlmCM8dRwPo4ePUpOTk6+j51aInJqxvrUSYZZWVl5xl1zzTWULFmS3bt3n/E1//kExa+//jrPzytXriQ3N9dzzeY/cjgc1KhRg/79+wOwc+fO836dIlL0aEZZROQc9O7dmyeeeIIjR45QqVIlz3ZjDC1btmTx4sWEhYVRuXJltm/fzrJlyy5bLUeOHOHll1+mXbt2ZGZm8vHHHxMQEMAtt9ziGTNgwABGjRrFiBEjaNWqFaGhoWRkZLBnzx527tzJP/7xjws6dpcuXVi6dCkvvvgivXr18lz1Yv78+dx0002nzVSfi82bNzNx4kRatmxJdHQ0wcHBHDlyhOXLl7N+/XpuuOEGypYtC0BkZCQACxYsoESJEvj7+1OhQgWCg4O5//77GTt2LBkZGTRt2pRSpUqRnp7Ojz/+SHp6OgMGDMhz3NWrV+Pn50dsbKznqheVK1f2XD5v/vz5bNq0iQYNGlCuXDmys7NZvHgxwGknBoqIb1JQFhE5B1FRUTRv3jzfABwXFwfAzJkzOX78ODExMTzxxBM89NBDl6WWu+66ix07dvC///2P3377jerVq/O3v/3Nc5k0cM/8jhw5kunTp/P+++97lhRERERw/fXXX/CxS5UqxQsvvMDkyZOZMmWK5zrKvXv39lzJ43zVqFGDG2+8kc2bN7N06VKOHj1KQEAAERER9O3bl/bt23vGVqhQgfvuu4+5c+fy3HPP4XK5PNdRvuGGGyhXrhyzZs3i7bff5rfffqN06dJUqVLltMvhAfzzn//kk08+Yf78+RhjPNdRPjXzXKVKFZKSkvjkk09IS0ujePHiVKpUiccff5y6dete0GsVkaLF2D/+/k5ERMTHnbrhyPjx4/PcXERE5M+0RllEREREJB8KyiIiIiIi+dDSCxERERGRfGhGWUREREQkHwrKIiIiIiL5UFAWEREREcmHgrKIiIiISD4UlEVERERE8qGgLCIiIiKSDwVlEREREZF8KCiLiIiIiORDQVlEREREJB//D0XDRt/8SYzQAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_stats_in_graph(metric_dict, y_axis_label='Loss', x_axis_label='Number of Steps')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**TA**: Does that make sense now?\n",
+    "\n",
+    "**Student**: Yeah, somewhat. What about more complicated systems? Will I have to implement everything using barebone components like F.linear etc.?\n",
+    "\n",
+    "**TA**: You can use existing nn.Modules as components of new nn.Modules therefore, you are able of modularizing your network blocks, and then combining them at the end in one big network with very few lines of code. Pytorch already provides almost every kind of layer out there in their torch.nn package. Look at the [documentation](https://pytorch.org/docs/stable/nn.html) for more information. Now, let's see how we can combine modules to build a larger module. Let's build a multi layer fully connected module.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class MultiLayerFCCNetwork(nn.Module):\n",
+    "    def __init__(self, input_shape, num_hidden_units, num_output_units, num_hidden_layers):\n",
+    "        super(MultiLayerFCCNetwork, self).__init__()\n",
+    "        self.input_shape = input_shape\n",
+    "        self.num_hidden_units = num_hidden_units\n",
+    "        self.num_output_units = num_output_units\n",
+    "        self.num_hidden_layers = num_hidden_layers\n",
+    "        \n",
+    "        x_dummy = torch.zeros(input_shape)\n",
+    "        \n",
+    "        self.layer_dict = nn.ModuleDict() # Allows us to initialize modules within a dictionary structure.\n",
+    "        out = x_dummy\n",
+    "        for i in range(self.num_hidden_layers):\n",
+    "            self.layer_dict['layer_{}'.format(i)] = LinearLayerWithActivation(input_shape=out.shape, \n",
+    "                                                             num_units=self.num_hidden_units, bias=True,\n",
+    "                                                                       activation_type=nn.PReLU())\n",
+    "            \n",
+    "            out = self.layer_dict['layer_{}'.format(i)].forward(out)\n",
+    "        \n",
+    "        self.layer_dict['output_layer'] = LinearLayerWithActivation(input_shape=out.shape, \n",
+    "                                                             num_units=self.num_output_units, \n",
+    "                                                             bias=True, activation_type=nn.Identity())\n",
+    "        out = self.layer_dict['output_layer'].forward(out)\n",
+    "    \n",
+    "    def forward(self, x):\n",
+    "        out = x\n",
+    "        for i in range(self.num_hidden_layers):\n",
+    "            out = self.layer_dict['layer_{}'.format(i)].forward(out)\n",
+    "\n",
+    "        out = self.layer_dict['output_layer'].forward(out)\n",
+    "        return out\n",
+    "            \n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Parameters with name layer_dict.layer_0.weights and shape torch.Size([64, 128])\n",
+      "Parameters with name layer_dict.layer_0.bias and shape torch.Size([64])\n",
+      "Parameters with name layer_dict.layer_0.activation_type.weight and shape torch.Size([1])\n",
+      "Parameters with name layer_dict.layer_1.weights and shape torch.Size([64, 64])\n",
+      "Parameters with name layer_dict.layer_1.bias and shape torch.Size([64])\n",
+      "Parameters with name layer_dict.layer_1.activation_type.weight and shape torch.Size([1])\n",
+      "Parameters with name layer_dict.layer_2.weights and shape torch.Size([64, 64])\n",
+      "Parameters with name layer_dict.layer_2.bias and shape torch.Size([64])\n",
+      "Parameters with name layer_dict.layer_2.activation_type.weight and shape torch.Size([1])\n",
+      "Parameters with name layer_dict.layer_3.weights and shape torch.Size([64, 64])\n",
+      "Parameters with name layer_dict.layer_3.bias and shape torch.Size([64])\n",
+      "Parameters with name layer_dict.layer_3.activation_type.weight and shape torch.Size([1])\n",
+      "Parameters with name layer_dict.output_layer.weights and shape torch.Size([512, 64])\n",
+      "Parameters with name layer_dict.output_layer.bias and shape torch.Size([512])\n"
+     ]
+    }
+   ],
+   "source": [
+    "fcc_net = MultiLayerFCCNetwork(input_shape=x.shape, num_hidden_units=64, num_output_units=512, \n",
+    "                               num_hidden_layers=4)\n",
+    "optimizer = optim.Adam(fcc_net.parameters(), amsgrad=False, weight_decay=0.0)\n",
+    "\n",
+    "\n",
+    "for name, params in fcc_net.named_parameters():\n",
+    "    print('Parameters with name', name, 'and shape', params.shape)\n",
+    "\n",
+    "metric_dict = {'losses': []}    \n",
+    "    \n",
+    "for i in range(100):\n",
+    "\n",
+    "    out = fcc_net.forward(x)\n",
+    "    loss = F.cross_entropy(out, y)\n",
+    "    fcc_net.zero_grad() #removes grads of previous step\n",
+    "    optimizer.zero_grad() #removes grads of previous step\n",
+    "    loss.backward() #compute gradients of current step\n",
+    "    optimizer.step() #update step\n",
+    "\n",
+    "    metric_dict['losses'].append(loss.detach().cpu().numpy()) #.detach: Copies the value of the loss \n",
+    "#                                                               and removes it from the graph, \n",
+    "#                                                             .cpu() sends to cpu, and \n",
+    "#                                                              numpy(), converts it to numpy format."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_stats_in_graph(metric_dict, y_axis_label='Loss', x_axis_label='Number of Steps')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**TA**: There we go, the network is doing much better during training with a multi-layer neural network. :)\n",
+    "\n",
+    "**Student**: Hmm.. I am weirdly excited even though I have not digested this completely yet. Where do I go to learn more? \n",
+    "\n",
+    "**TA**: Firstly, I think you should go and have a look at the MLP Pytorch Framework, so you can learn how Pytorch can be used with more complicated architectures, as well as to learn some good coding practices for research and industry alike. When you are working on your coursework, make sure to have the [pytorch official documentation page](https://pytorch.org/docs/stable/nn.html) open in your browser, as it is extremely well written most of the times. Then, when you have some spare time, perhaps in preparation for next term, I would recommend going through some of the Pytorch tutorials at the [pytorch tutorials page](https://pytorch.org/tutorials/). Finally, the best way to learn, in my opinion, is by engaging with Pytorch through a project that interests you."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "mlp",
+   "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": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/DropoutandPenalty_tests.ipynb b/notebooks/DropoutandPenalty_tests.ipynb
deleted file mode 100644
index be5adbf..0000000
--- a/notebooks/DropoutandPenalty_tests.ipynb
+++ /dev/null
@@ -1,122 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "# sys.path.append('/path/to/mlpractical')\n",
-    "from mlp.test_methods import test_dropout_layer\n",
-    "import numpy as np\n",
-    "\n",
-    "fprop_test, fprop_output, fprop_correct, \\\n",
-    "bprop_test, bprop_output, bprop_correct = test_dropout_layer()\n",
-    "\n",
-    "assert fprop_test == 1.0, (\n",
-    "'The dropout layer fprop functionality test failed'\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(fprop_correct, fprop_output, fprop_output-fprop_correct)\n",
-    ")\n",
-    "\n",
-    "print(\"Dropout Layer Fprop Functionality Test Passed\")\n",
-    "\n",
-    "assert bprop_test == 1.0, (\n",
-    "'The dropout layer bprop functionality test failed'\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(bprop_correct, bprop_output, bprop_output-bprop_correct)\n",
-    ")\n",
-    "\n",
-    "print(\"Dropout Layer Bprop Test Passed\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from mlp.test_methods import test_L1_Penalty\n",
-    "\n",
-    "\n",
-    "call_test, call_output, call_correct, \\\n",
-    "grad_test, grad_output, grad_correct = test_L1_Penalty()\n",
-    "\n",
-    "\n",
-    "assert call_test == 1.0, (\n",
-    "'The call function for L1 Penalty test failed'\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(call_correct, call_output, call_output-call_correct)\n",
-    ")\n",
-    "\n",
-    "print(\"L1 Penalty Call Functionality Test Passed\")\n",
-    "\n",
-    "assert grad_test == 1.0, (\n",
-    "'The grad function for L1 Penalty test failed'\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(grad_correct, grad_output, grad_output-grad_correct, grad_output/grad_correct)\n",
-    ")\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"L1 Penalty Grad Function Test Passed\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from mlp.test_methods import test_L2_Penalty\n",
-    "\n",
-    "\n",
-    "call_test, call_output, call_correct, \\\n",
-    "grad_test, grad_output, grad_correct = test_L2_Penalty()\n",
-    "\n",
-    "\n",
-    "assert call_test == 1.0, (\n",
-    "'The call function for L2 Penalty test failed'\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(call_correct, call_output, call_output-call_correct)\n",
-    ")\n",
-    "\n",
-    "print(\"L2 Penalty Call Functionality Test Passed\")\n",
-    "\n",
-    "assert grad_test == 1.0, (\n",
-    "'The grad function for L2 Penalty test failed'\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(grad_correct, grad_output, grad_output-grad_correct, grad_output/grad_correct)\n",
-    ")\n",
-    "\n",
-    "\n",
-    "\n",
-    "print(\"L2 Penalty Grad Function Test Passed\")"
-   ]
-  }
- ],
- "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": "3.8.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
diff --git a/notebooks/Plot_Results.ipynb b/notebooks/Plot_Results.ipynb
new file mode 100644
index 0000000..cbba96e
--- /dev/null
+++ b/notebooks/Plot_Results.ipynb
@@ -0,0 +1,200 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import sys\n",
+    "import matplotlib\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "%matplotlib inline\n",
+    "plt.style.use('ggplot')\n",
+    "experiment_dir = 'path/to/mlpractical_directory' #Replace this with your path to the mlpractical directory"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def collect_experiment_dicts(target_dir, test_flag=False):\n",
+    "    experiment_dicts = dict()\n",
+    "    for subdir, dir, files in os.walk(target_dir):\n",
+    "        for file in files:\n",
+    "            filepath = None\n",
+    "            if not test_flag:\n",
+    "                if file == 'summary.csv':\n",
+    "                    filepath = os.path.join(subdir, file)\n",
+    "            \n",
+    "            elif test_flag:\n",
+    "                if file == 'test_summary.csv':\n",
+    "                    filepath = os.path.join(subdir, file)\n",
+    "            \n",
+    "            if filepath is not None:\n",
+    "                \n",
+    "                with open(filepath, 'r') as read_file:\n",
+    "                    lines = read_file.readlines()\n",
+    "                    \n",
+    "                current_experiment_dict = {key: [] for key in lines[0].replace('\\n', '').split(',')}\n",
+    "                idx_to_key = {idx: key for idx, key in enumerate(lines[0].replace('\\n', '').split(','))}\n",
+    "                \n",
+    "                for line in lines[1:]:\n",
+    "                    for idx, value in enumerate(line.replace('\\n', '').split(',')):\n",
+    "                        current_experiment_dict[idx_to_key[idx]].append(float(value))\n",
+    "                \n",
+    "                experiment_dicts[subdir.split('/')[-2]] = current_experiment_dict\n",
+    "                \n",
+    "    return experiment_dicts\n",
+    "                \n",
+    "            "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "VGG_08 ['train_acc', 'train_loss', 'val_acc', 'val_loss']\n",
+      "VGG_38 ['train_acc', 'train_loss', 'val_acc', 'val_loss']\n"
+     ]
+    }
+   ],
+   "source": [
+    "result_dict = collect_experiment_dicts(target_dir=experiment_dir)\n",
+    "for key, value in result_dict.items():\n",
+    "    print(key, list(value.keys()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "plt.style.use('ggplot')\n",
+    "\n",
+    "def plot_result_graphs(plot_name, stats, keys_to_plot, notebook=True):\n",
+    "    \n",
+    "    fig_1 = plt.figure(figsize=(8, 4))\n",
+    "    ax_1 = fig_1.add_subplot(111)\n",
+    "    for name in keys_to_plot:\n",
+    "        for k in ['train_loss', 'val_loss']:\n",
+    "            item = stats[name][k]\n",
+    "            ax_1.plot(np.arange(0, len(item)), \n",
+    "                      item, label='{}_{}'.format(name, k))\n",
+    "            \n",
+    "    ax_1.legend(loc=0)\n",
+    "    ax_1.set_ylabel('Loss')\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 name in keys_to_plot:\n",
+    "        for k in ['train_acc', 'val_acc']:\n",
+    "            item = stats[name][k]\n",
+    "            ax_2.plot(np.arange(0, len(item)), \n",
+    "                      item, label='{}_{}'.format(name, k))\n",
+    "            \n",
+    "    ax_2.legend(loc=0)\n",
+    "    ax_2.set_ylabel('Accuracy')\n",
+    "    ax_2.set_xlabel('Epoch number')\n",
+    "    \n",
+    "    fig_1.savefig('../data/{}_loss_performance.pdf'.format(plot_name), dpi=None, facecolor='w', edgecolor='w',\n",
+    "        orientation='portrait', papertype=None, format='pdf',\n",
+    "        transparent=False, bbox_inches=None, pad_inches=0.1,\n",
+    "        frameon=None, metadata=None)\n",
+    "    \n",
+    "    fig_2.savefig('../data/{}_accuracy_performance.pdf'.format(plot_name), dpi=None, facecolor='w', edgecolor='w',\n",
+    "        orientation='portrait', papertype=None, format='pdf',\n",
+    "        transparent=False, bbox_inches=None, pad_inches=0.1,\n",
+    "        frameon=None, metadata=None)\n",
+    "    \n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-23-0810a62e0421>:32: MatplotlibDeprecationWarning: \n",
+      "The frameon kwarg was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use facecolor instead.\n",
+      "  fig_1.savefig('../data/{}_loss_performance.pdf'.format(plot_name), dpi=None, facecolor='w', edgecolor='w',\n",
+      "<ipython-input-23-0810a62e0421>:37: MatplotlibDeprecationWarning: \n",
+      "The frameon kwarg was deprecated in Matplotlib 3.1 and will be removed in 3.3. Use facecolor instead.\n",
+      "  fig_2.savefig('../data/{}_accuracy_performance.pdf'.format(plot_name), dpi=None, facecolor='w', edgecolor='w',\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 576x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_result_graphs('problem_model', result_dict, keys_to_plot=['VGG_38', 'VGG_08'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "mlp",
+   "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": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/res/._fprop-bprop-block-diagram.png b/notebooks/res/._fprop-bprop-block-diagram.png
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diff --git a/scripts/generate_regularization_layer_test_outputs.py b/scripts/generate_regularization_layer_test_outputs.py
deleted file mode 100644
index c71f252..0000000
--- a/scripts/generate_regularization_layer_test_outputs.py
+++ /dev/null
@@ -1,105 +0,0 @@
-import argparse
-import os
-import numpy as np
-
-import sys
-# sys.path.append('/path/to/mlpractical')
-
-from mlp.layers import DropoutLayer
-from mlp.penalties import L1Penalty, L2Penalty
-parser = argparse.ArgumentParser(description='Welcome to regularization test 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 fprop_bprop_layer(inputs, dropout_layer, grads_wrt_outputs, weights, params=False):
-    if params:
-        dropout_layer.params = [weights]
-
-    fprop = dropout_layer.fprop(inputs)
-    bprop = dropout_layer.bprop(inputs, fprop, grads_wrt_outputs)
-
-    outputs = [fprop, bprop]
-    if params:
-        grads_wrt_weights = dropout_layer.grads_wrt_params(
-            inputs, grads_wrt_outputs)
-        outputs.append(grads_wrt_weights)
-
-    return outputs
-
-
-def call_grad_layer(inputs, penalty_layer, grads_wrt_outputs, weights, params=False):
-    if params:
-        penalty_layer.params = [weights]
-
-    call = penalty_layer(inputs)
-    grad = penalty_layer.grad(inputs)
-
-    outputs = [call, grad]
-    if params:
-        grads_wrt_weights = penalty_layer.grads_wrt_params(
-            inputs, grads_wrt_outputs)
-        outputs.append(grads_wrt_weights)
-
-    return outputs
-
-def get_student_seed(student_id):
-    student_seed_number = int(student_id[1:])
-    return student_seed_number
-
-
-seed = get_student_seed(student_id)
-rng = np.random.RandomState(seed)
-
-reg_output_dict = dict()
-
-inputs = rng.normal(loc=0.0, scale=1.0, size=(32, 3, 8, 8))
-grads_wrt_outputs = rng.normal(loc=0.0, scale=1.0, size=(32, 3, 8, 8))
-weights = rng.normal(loc=0.0, scale=1.0, size=(1))
-
-reg_output_dict['inputs'] = inputs
-reg_output_dict['weights'] = weights
-reg_output_dict['grads_wrt_outputs'] = grads_wrt_outputs
-
-for dropout_layer, params_flag in zip(
-        [DropoutLayer],
-        [False]):
-    if isinstance(dropout_layer(), DropoutLayer):
-        rng = np.random.RandomState(92019)
-        print(True)
-        outputs = fprop_bprop_layer(inputs, dropout_layer(
-            rng=rng), grads_wrt_outputs, weights, params_flag)
-    else:
-        outputs = fprop_bprop_layer(
-            inputs, dropout_layer(), grads_wrt_outputs, weights, params_flag)
-    reg_output_dict['{}_{}'.format(
-        dropout_layer.__name__, 'fprop')] = outputs[0]
-    reg_output_dict['{}_{}'.format(
-        dropout_layer.__name__, 'bprop')] = outputs[1]
-    if params_flag:
-        reg_output_dict['{}_{}'.format(
-            dropout_layer.__name__, 'grads_wrt_outputs')] = outputs[2]
-
-for penalty_layer, params_flag in zip(
-        [L1Penalty, L2Penalty], [False, False]):
-    outputs = call_grad_layer(inputs, penalty_layer(
-        1e-4), grads_wrt_outputs, weights, params_flag)
-    reg_output_dict['{}_{}'.format(
-        penalty_layer.__name__, '__call__correct')] = outputs[0]
-    reg_output_dict['{}_{}'.format(
-        penalty_layer.__name__, 'grad_correct')] = outputs[1]
-    if params_flag:
-        reg_output_dict['{}_{}'.format(
-            penalty_layer.__name__, 'grads_wrt_outputs')] = outputs[2]
-
-np.save(os.path.join(os.environ['MLP_DATA_DIR'],
-        '{}_regularization_test_pack.npy'.format(seed)), reg_output_dict)
-
-test_data = np.load(os.path.join(
-    os.environ['MLP_DATA_DIR'], '{}_regularization_test_pack.npy'.format(seed)), allow_pickle=True)
-
-for key, value in test_data.item().items():
-    print(key, value)