Adding stochastic layer functionality in preparation for dropout implementation.

mlp/layers.py

@@ -14,6 +14,7 @@ respect to the layer parameters.
 
 import numpy as np
 import mlp.initialisers as init
+from mlp import DEFAULT_SEED
 
 
 class Layer(object):
@@ -96,6 +97,57 @@ class LayerWithParameters(Layer):
         raise NotImplementedError()
 
 
+class StochasticLayer(Layer):
+    """Specialised layer which uses a stochastic forward propagation."""
+
+    def __init__(self, rng=None):
+        """Constructs a new StochasticLayer object.
+
+        Args:
+            rng (RandomState): Seeded random number generator object.
+        """
+        if rng is None:
+            rng = np.random.RandomState(DEFAULT_SEED)
+        self.rng = rng
+
+    def fprop(self, inputs, stochastic=True):
+        """Forward propagates activations through the layer transformation.
+
+        Args:
+            inputs: Array of layer inputs of shape (batch_size, input_dim).
+            stochastic: Flag allowing different deterministic
+                forward-propagation mode in addition to default stochastic
+                forward-propagation e.g. for use at test time. If False
+                a deterministic forward-propagation transformation
+                corresponding to the expected output of the stochastic
+                forward-propagation is applied.
+
+        Returns:
+            outputs: Array of layer outputs of shape (batch_size, output_dim).
+        """
+        raise NotImplementedError()
+
+    def bprop(self, inputs, outputs, grads_wrt_outputs):
+        """Back propagates gradients through a layer.
+
+        Given gradients with respect to the outputs of the layer calculates the
+        gradients with respect to the layer inputs. This should correspond to
+        default stochastic forward-propagation.
+
+        Args:
+            inputs: Array of layer inputs of shape (batch_size, input_dim).
+            outputs: Array of layer outputs calculated in forward pass of
+                shape (batch_size, output_dim).
+            grads_wrt_outputs: Array of gradients with respect to the layer
+                outputs of shape (batch_size, output_dim).
+
+        Returns:
+            Array of gradients with respect to the layer inputs of shape
+            (batch_size, input_dim).
+        """
+        raise NotImplementedError()
+
+
 class AffineLayer(LayerWithParameters):
     """Layer implementing an affine tranformation of its inputs.
 

mlp/models.py

@@ -8,7 +8,7 @@ outputs (and intermediate states) and for calculating gradients of scalar
 functions of the outputs with respect to the model parameters.
 """
 
-from mlp.layers import LayerWithParameters
+from mlp.layers import LayerWithParameters, StochasticLayer
 
 
 class SingleLayerModel(object):
@@ -27,11 +27,13 @@ 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, stochastic=True):
         """Calculate the model outputs corresponding to a batch of inputs.
 
         Args:
             inputs: Batch of inputs to the model.
+            stochastic: Whether to use stochastic forward propagation
+                for stochastic layers (True) or deterministic (False).
 
         Returns:
             List which is a concatenation of the model inputs and model
@@ -40,7 +42,10 @@ class SingleLayerModel(object):
             activations through all immediate layers of the model and including
             the inputs and outputs.
         """
-        activations = [inputs, self.layer.fprop(inputs)]
+        outputs = (self.layer.fprop(inputs, stochastic)
+                   if isinstance(self.layer, StochasticLayer) else
+                   self.layer.fprop(inputs))
+        activations = [inputs, outputs]
         return activations
 
     def grads_wrt_params(self, activations, grads_wrt_outputs):
@@ -88,11 +93,13 @@ class MultipleLayerModel(object):
                 params += layer.params
         return params
 
-    def fprop(self, inputs):
+    def fprop(self, inputs, stochastic=True):
         """Forward propagates a batch of inputs through the model.
 
         Args:
             inputs: Batch of inputs to the model.
+            stochastic: Whether to use stochastic forward propagation
+                for stochastic layers (True) or deterministic (False).
 
         Returns:
             List of the activations at the output of all layers of the model
@@ -101,6 +108,10 @@ class MultipleLayerModel(object):
         """
         activations = [inputs]
         for i, layer in enumerate(self.layers):
+            if isinstance(layer, StochasticLayer):
+                activations.append(self.layers[i].fprop(
+                    activations[i], stochastic=stochastic))
+            else:
                 activations.append(self.layers[i].fprop(activations[i]))
         return activations
 

mlp/optimisers.py

@@ -18,7 +18,8 @@ class Optimiser(object):
     """Basic model optimiser."""
 
     def __init__(self, model, error, learning_rule, train_dataset,
-                 valid_dataset=None, data_monitors=None, schedulers=[]):
+                 valid_dataset=None, data_monitors=None, schedulers=[],
+                 use_stochastic_eval=True):
         """Create a new optimiser instance.
 
         Args:
@@ -33,6 +34,10 @@ class Optimiser(object):
                 validation data sets) to monitor during training in addition
                 to the error. Keys should correspond to a string label for
                 the statistic being evaluated.
+            schedulers: List of learning rule scheduler objects for adjusting
+                learning rule hyperparameters over training. Can be empty.
+            use_stochastic_eval: Whether to use `stochastic=True` flag in
+                `model.fprop` for evaluating model performance during training.
         """
         self.model = model
         self.error = error
@@ -44,6 +49,7 @@ class Optimiser(object):
         if data_monitors is not None:
             self.data_monitors.update(data_monitors)
         self.schedulers = schedulers
+        self.use_stochastic_eval = use_stochastic_eval
 
     def do_training_epoch(self):
         """Do a single training epoch.
@@ -73,7 +79,8 @@ class Optimiser(object):
         data_mon_vals = OrderedDict([(key + label, 0.) for key
                                      in self.data_monitors.keys()])
         for inputs_batch, targets_batch in dataset:
-            activations = self.model.fprop(inputs_batch)
+            activations = self.model.fprop(
+                inputs_batch, stochastic=self.use_stochastic_eval)
             for key, data_monitor in self.data_monitors.items():
                 data_mon_vals[key + label] += data_monitor(
                     activations[-1], targets_batch)
@@ -125,6 +132,10 @@ class Optimiser(object):
             being the total time elapsed in seconds during the training run.
         """
         stats = self.get_epoch_stats()
+        logger.info(
+            'Epoch 0:\n  ' +
+            ', '.join(['{0}={1:.2e}'.format(k, v) for (k, v) in stats.items()])
+        )
         run_stats = [stats.values()]
         run_start_time = time.time()
         for epoch in range(1, num_epochs + 1):