--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/pylearn/formulas/nnet.py	Wed Jul 27 10:37:41 2011 -0400
@@ -0,0 +1,39 @@
+import theano
+from theano import tensor
+from theano.sandbox import neighbours
+
+import tags
+
+
+@tags.tags('nnet', 'max pooling', 'inverse')
+def inverse_max_pooling(max_pool_out,output_shape,pooling_shape=(2,2),
+                        ignore_borders = True):
+    """
+    Return a symbolic variable representing the inverse of a max pooling
+    on a given tensor.
+
+    Parameters
+    ----------
+    max_pool_out : 4D tensor
+        A Theano variable representing the output of a max pooling
+    output_shape : 4D shape
+        The shape of the input before pooling
+    pooling_shape : 2D shape
+        The shape of the pooling windows
+    ignore_borders : boolean
+        Will pad borders with zeros if true
+    """
+    # flatten the input and repeat it 
+    repeated_input = [max_pool_out.flatten()]*(pooling_shape[0]*pooling_shape[1])
+
+    # concatenate the repeated vectors into
+    # a 2D matrix in the format neibs2images wants
+    stacked_conv_neibs = T.stack(*repeated_input).T
+    
+    # then get back a stretched version of the stacked neighbours
+    stretch_unpooling_out = \
+        neighbours.neibs2images(stacked_conv_neibs,
+                                pooling_shape,
+                                output_shape,
+                                'ignore_borders' if ignore_borders else 'valid')
+    return stretch_unpooling_out