--- a/mlp.py	Tue May 13 17:00:53 2008 -0400
+++ b/mlp.py	Tue May 13 18:30:08 2008 -0400
@@ -67,7 +67,7 @@
        - 'regularization_term'
 
     """
-    def __init__(self,n_hidden,n_classes,learning_rate,max_n_epochs,L2_regularizer=0,init_range=1.,n_inputs=None,minibatch_size=None):
+    def __init__(self,n_hidden,n_classes,learning_rate,max_n_epochs,L2_regularizer=0,init_range=1.,n_inputs=None,minibatch_size=None,linker='c|py'):
         self._n_inputs = n_inputs
         self._n_outputs = n_classes
         self._n_hidden = n_hidden
@@ -78,7 +78,7 @@
         self.L2_regularizer = L2_regularizer
         self._learning_rate = t.scalar('learning_rate') # this is the symbol
         self._input = t.matrix('input') # n_examples x n_inputs
-        self._target = t.imatrix('target') # n_examples x 1
+        self._target = t.lmatrix('target') # n_examples x 1
         self._target_vector = self._target[:,0]
         self._L2_regularizer = t.scalar('L2_regularizer')
         self._W1 = t.matrix('W1')
@@ -91,7 +91,7 @@
         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]
-        OnlineGradientTLearner.__init__(self)
+        OnlineGradientTLearner.__init__(self, linker = linker)
             
     def attributeNames(self):
         return ["parameters","b1","W2","b2","W2", "L2_regularizer","regularization_term"]
@@ -119,7 +119,7 @@
         
     def updateMinibatch(self,minibatch):
         MinibatchUpdatesTLearner.updateMinibatch(self,minibatch)
-        print self.nll
+        #print self.nll
 
     def allocate(self,minibatch):
         minibatch_n_inputs  = minibatch["input"].shape[1]