# HG changeset patch
# User Olivier Breuleux <breuleuo@iro.umontreal.ca>
# Date 1231804030 18000
# Node ID 605ab704abc3f1ad1d1612d69efced6f3e08dfe2
# Parent  7ddb286162f66656aa6ca983ece7c38e5d264697# Parent  8d0b73c7d76803d85c876eb2167bac798bed5767
merge

diff -r 7ddb286162f6 -r 605ab704abc3 pylearn/algorithms/aa.py
--- a/pylearn/algorithms/aa.py	Mon Jan 12 18:46:46 2009 -0500
+++ b/pylearn/algorithms/aa.py	Mon Jan 12 18:47:10 2009 -0500
@@ -4,7 +4,7 @@
 from theano.tensor import nnet as NN
 import numpy as N
 
-class AutoEncoder(theano.FancyModule):
+class AutoEncoder(theano.Module):
 
     def __init__(self, input = None, regularize = True, tie_weights = True):
         super(AutoEncoder, self).__init__()
@@ -64,7 +64,7 @@
 
     def _instance_initialize(self, obj, input_size = None, hidden_size = None, seed = None, **init):
         if (input_size is None) ^ (hidden_size is None):
-            raise ValueError("Must specify hidden_size and target_size or neither.")
+            raise ValueError("Must specify hidden_size and input_size or neither.")
         super(AutoEncoder, self)._instance_initialize(obj, **init)
         if seed is not None:
             R = N.random.RandomState(seed)
diff -r 7ddb286162f6 -r 605ab704abc3 pylearn/algorithms/cost.py
--- a/pylearn/algorithms/cost.py	Mon Jan 12 18:46:46 2009 -0500
+++ b/pylearn/algorithms/cost.py	Mon Jan 12 18:47:10 2009 -0500
@@ -10,7 +10,6 @@
 """
 
 import theano.tensor as T
-from xlogx import xlogx
 
 def quadratic(target, output, axis=1):
     return T.mean(T.sqr(target - output), axis=axis)
@@ -28,5 +27,5 @@
     different shapes then the result will be garbled.
     """
     return -(target * T.log(output) + (1 - target) * T.log(1 - output)) \
-            + (xlogx(target) + xlogx(1 - target))
+            + (T.xlogx(target) + T.xlogx(1 - target))
 #    return cross_entropy(target, output, axis) - cross_entropy(target, target, axis)
diff -r 7ddb286162f6 -r 605ab704abc3 pylearn/algorithms/logistic_regression.py
--- a/pylearn/algorithms/logistic_regression.py	Mon Jan 12 18:46:46 2009 -0500
+++ b/pylearn/algorithms/logistic_regression.py	Mon Jan 12 18:47:10 2009 -0500
@@ -48,7 +48,7 @@
         else:
             # TODO: when above is fixed, remove this hack (need an argmax
             # which is independent of targets)
-            self.argmax_standalone = T.argmax(self.linear_output);
+            self.argmax_standalone = T.argmax(self.linear_output)
             (self._xent, self.softmax, self._max_pr, self.argmax) =\
                     nnet.crossentropy_softmax_max_and_argmax_1hot(
                     self.linear_output, self.target)