Adding documentation to models module.

mlp/models.py

@@ -1,35 +1,66 @@
 # -*- coding: utf-8 -*-
-"""Model definitions."""
+"""Model definitions.
+
+This module implements objects encapsulating learnable models of input-output
+relationships. The model objects implement methods for forward propagating
+the inputs through the transformation(s) defined by the model to produce
+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
 
 
 class SingleLayerModel(object):
-    """
-    """
+    """A model consisting of a single transformation layer."""
+
     def __init__(self, layer):
+        """Create a new single layer model instance.
+
+        Args:
+            layer: The layer object defining the model architecture.
+        """
         self.layer = layer
 
     @property
     def params(self):
-        """
-        """
+        """A list of all of the parameters of the model."""
         return self.layer.params
 
     def fprop(self, inputs):
-        """
+        """Calculate the model outputs corresponding to a batch of inputs.
+
+        Args:
+            inputs: Batch of inputs to the model.
+
+        Returns:
+            List which is a concatenation of the model inputs and model
+            outputs, this being done for consistency of the interface with
+            multi-layer models for which `fprop` returns a list of
+            activations through all immediate layers of the model and including
+            the inputs and outputs.
         """
         activations = [inputs, self.layer.fprop(inputs)]
         return activations
 
     def grads_wrt_params(self, activations, grads_wrt_outputs):
-        """
+        """Calculates gradients with respect to the model parameters.
+
+        Args:
+            activations: List of all activations from forward pass through
+                model using `fprop`.
+            grads_wrt_outputs: Gradient with respect to the model outputs of
+               the scalar function parameter gradients are being calculated
+               for.
+
+        Returns:
+            List of gradients of the scalar function with respect to all model
+            parameters.
         """
         return self.layer.grads_wrt_params(activations[0], grads_wrt_outputs)
 
     def params_cost(self):
-        """
-        """
+        """Calculates the parameter dependent cost term of the model."""
         return self.layer.params_cost()
 
     def __repr__(self):
@@ -37,15 +68,20 @@ class SingleLayerModel(object):
 
 
 class MultipleLayerModel(object):
-    """
-    """
+    """A model consisting of multiple layers applied sequentially."""
+
     def __init__(self, layers):
+        """Create a new multiple layer model instance.
+
+        Args:
+            layers: List of the the layer objecst defining the model in the
+                order they should be applied from inputs to outputs.
+        """
         self.layers = layers
 
     @property
     def params(self):
-        """
-        """
+        """A list of all of the parameters of the model."""
         params = []
         for layer in self.layers:
             if isinstance(layer, LayerWithParameters):
@@ -53,7 +89,15 @@ class MultipleLayerModel(object):
         return params
 
     def fprop(self, inputs):
-        """
+        """Forward propagates a batch of inputs through the model.
+
+        Args:
+            inputs: Batch of inputs to the model.
+
+        Returns:
+            List of the activations at the output of all layers of the model
+            plus the inputs (to the first layer) as the first element. The
+            last element of the list corresponds to the model outputs.
         """
         activations = [inputs]
         for i, layer in enumerate(self.layers):
@@ -61,7 +105,18 @@ class MultipleLayerModel(object):
         return activations
 
     def grads_wrt_params(self, activations, grads_wrt_outputs):
-        """
+        """Calculates gradients with respect to the model parameters.
+
+        Args:
+            activations: List of all activations from forward pass through
+                model using `fprop`.
+            grads_wrt_outputs: Gradient with respect to the model outputs of
+               the scalar function parameter gradients are being calculated
+               for.
+
+        Returns:
+            List of gradients of the scalar function with respect to all model
+            parameters.
         """
         grads_wrt_params = []
         for i, layer in enumerate(self.layers[::-1]):
@@ -75,8 +130,7 @@ class MultipleLayerModel(object):
         return grads_wrt_params[::-1]
 
     def params_cost(self):
-        """
-        """
+        """Calculates the parameter dependent cost term of the model."""
         params_cost = 0.
         for layer in self.layers:
             if isinstance(layer, LayerWithParameters):