--- a/mlp_factory_approach.py	Fri Jun 06 17:55:14 2008 -0400
+++ b/mlp_factory_approach.py	Fri Jun 06 17:58:45 2008 -0400
@@ -4,7 +4,8 @@
 import theano
 from theano import tensor as T
 
-from pylearn import dataset, nnet_ops, stopper, LookupList, filetensor
+import dataset, nnet_ops, stopper, filetensor
+from lookup_list import LookupList
 
 
 class AbstractFunction (Exception): pass
@@ -54,7 +55,9 @@
             return d[key]
 
         def update_minibatch(self, minibatch):
-            #assert isinstance(minibatch, LookupList) # why false???
+            if not isinstance(minibatch, LookupList):
+                print type(minibatch)
+            assert isinstance(minibatch, LookupList)
             self.update_fn(minibatch['input'], minibatch['target'], *self.params)
 
         def update(self, dataset, 
@@ -216,6 +219,7 @@
         l2coef = T.constant(l2coef_val)
         input = T.matrix() # n_examples x n_inputs
         target = T.ivector() # len: n_examples
+        #target = T.matrix()
         W2, b2 = T.matrix(), T.vector()
 
         W1, b1 = T.matrix(), T.vector()
@@ -224,7 +228,7 @@
 
         params = [W1, b1, W2, b2] 
         activations = b2 + T.dot(hid, W2)
-        nll, predictions = nnet_ops.crossentropy_softmax_1hot(activations, target)
+        nll, predictions = nnet_ops.crossentropy_softmax_1hot(activations, target )
         regularization = l2coef * T.sum(W2*W2) + hid_regularization
         output_class = T.argmax(activations,1)
         loss_01 = T.neq(output_class, target)