--- a/mlp.py	Tue May 13 15:12:20 2008 -0400
+++ b/mlp.py	Tue May 13 15:14:04 2008 -0400
@@ -9,22 +9,7 @@
 from theano import tensor as t
 from nnet_ops import *
 import math
-
-def sum_l2_cost(*params):
-    p = params[0]
-    rval = t.sum(p*p)
-    for p in params[1:]:
-        rval = rval + t.sum(p*p)
-    return rval
-
-def activation(w, b, v, c, x):
-    return t.dot(t.tanh(t.dot(x, w) + b), v) + c
-def nll(w, b, v, c, x, y):
-    return  crossentropy_softmax_1hot(prediction(w, b, v, c, x), y)[0]
-def output(w, b, v, c, x, y):
-    return  crossentropy_softmax_1hot(prediction(w, b, v, c, x), y)[1]
-
-
+from misc import *
 
 class OneHiddenLayerNNetClassifier(OnlineGradientTLearner):
     """
@@ -102,7 +87,7 @@
         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]