# HG changeset patch
# User Yoshua Bengio <bengioy@iro.umontreal.ca>
# Date 1210705899 14400
# Node ID 6ee54861134e1ad3088511f721b876e198178038
# Parent  b60cd6745dc210301c8999942d9829f809c43dcc# Parent  e9a95e19e6f84927ab156579e036a2999fa5d573
Automated merge with ssh://p-omega1@lgcm.iro.umontreal.ca/tlearn

diff -r b60cd6745dc2 -r 6ee54861134e misc.py
--- a/misc.py	Tue May 13 15:04:51 2008 -0400
+++ b/misc.py	Tue May 13 15:11:39 2008 -0400
@@ -1,3 +1,24 @@
+
+import theano
+
+class Print(theano.Op):
+    def __init__(self,message=""):
+        self.message=message
+        self.view_map={0:[0]}
+
+    def make_node(self,xin):
+        xout = xin.type.make_result()
+        return theano.Apply(op = self, inputs = [xin], outputs=[xout])
+
+    def perform(self,node,inputs,output_storage):
+        xin, = inputs
+        xout, = output_storage
+        xout[0] = xin
+        print self.message,xin
+
+    def grad(self,input,output_gradients):
+        return output_gradients
+
 
 def unique_elements_list_intersection(list1,list2):
     """
diff -r b60cd6745dc2 -r 6ee54861134e mlp.py
--- a/mlp.py	Tue May 13 15:04:51 2008 -0400
+++ b/mlp.py	Tue May 13 15:11:39 2008 -0400
@@ -9,7 +9,7 @@
 from theano import tensor as t
 from nnet_ops import *
 import math
-
+from misc import *
 
 class OneHiddenLayerNNetClassifier(OnlineGradientTLearner):
     """
@@ -88,7 +88,7 @@
         self._b2 = t.row('b2')
         self._regularization_term = self._L2_regularizer * (t.sum(self._W1*self._W1) + t.sum(self._W2*self._W2))
         self._output_activations =self._b2+t.dot(t.tanh(self._b1+t.dot(self._input,self._W1.T)),self._W2.T)
-        self._nll,self._output = crossentropy_softmax_1hot(self._output_activations,self._target_vector)
+        self._nll,self._output = crossentropy_softmax_1hot(Print("output_activations")(self._output_activations),self._target_vector)
         self._output_class = t.argmax(self._output,1)
         self._class_error = t.neq(self._output_class,self._target_vector)
         self._minibatch_criterion = self._nll + self._regularization_term / t.shape(self._input)[0]