From da9ee586091d670781b2dd0c0bb2b3037fe89999 Mon Sep 17 00:00:00 2001
From: Matt Graham <m.m.graham@ed.ac.uk>
Date: Wed, 2 Nov 2016 01:13:22 +0000
Subject: [PATCH] Adding reshape layer.

---
 mlp/layers.py | 56 +++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 56 insertions(+)

diff --git a/mlp/layers.py b/mlp/layers.py
index 8a892a8..7bb1966 100644
--- a/mlp/layers.py
+++ b/mlp/layers.py
@@ -261,6 +261,62 @@ class AffineLayer(LayerWithParameters):
             self.input_dim, self.output_dim)
 
 
+class ReshapeLayer(layers.Layer):
+    """Layer which reshapes dimensions of inputs."""
+
+    def __init__(self, output_shape=None):
+        """Create a new reshape layer object.
+
+        Args:
+            output_shape: Tuple specifying shape each input in batch should
+                be reshaped to in outputs. This **excludes** the batch size
+                so the shape of the final output array will be
+                    (batch_size, ) + output_shape
+                Similarly to numpy.reshape, one shape dimension can be -1. In
+                this case, the value is inferred from the size of the input
+                array and remaining dimensions. The shape specified must be
+                compatible with the input array shape - i.e. the total number
+                of values in the array cannot be changed. If set to `None` the
+                output shape will be set to
+                    (batch_size, -1)
+                which will flatten all the inputs to vectors.
+        """
+        self.output_shape = (-1,) if output_shape is None else output_shape
+
+    def fprop(self, inputs):
+        """Forward propagates activations through the layer transformation.
+
+        Args:
+            inputs: Array of layer inputs of shape (batch_size, input_dim).
+
+        Returns:
+            outputs: Array of layer outputs of shape (batch_size, output_dim).
+        """
+        return inputs.reshape((inputs.shape[0],) + self.output_shape)
+
+    def bprop(self, inputs, outputs, grads_wrt_outputs):
+        """Back propagates gradients through a layer.
+
+        Given gradients with respect to the outputs of the layer calculates the
+        gradients with respect to the layer inputs.
+
+        Args:
+            inputs: Array of layer inputs of shape (batch_size, input_dim).
+            outputs: Array of layer outputs calculated in forward pass of
+                shape (batch_size, output_dim).
+            grads_wrt_outputs: Array of gradients with respect to the layer
+                outputs of shape (batch_size, output_dim).
+
+        Returns:
+            Array of gradients with respect to the layer inputs of shape
+            (batch_size, input_dim).
+        """
+        return grads_wrt_outputs.reshape(inputs.shape)
+
+    def __repr__(self):
+        return 'ReshapeLayer(output_shape={0})'.format(self.output_shape)
+
+
 class SigmoidLayer(Layer):
     """Layer implementing an element-wise logistic sigmoid transformation."""