diff --git a/mmodel/theano/THEANO.py b/mmodel/theano/THEANO.py
index f2af954..5231128 100644
--- a/mmodel/theano/THEANO.py
+++ b/mmodel/theano/THEANO.py
@@ -48,7 +48,7 @@ class ModelTHEANO(ModelBase):
         X_train, X_test, Y_train, Y_test = cross_validation.train_test_split(X, Y, test_size=0.2, random_state=0)
 
         train_set_x, train_set_y = shared_dataset((X_train, Y_train))
-        valid_set_x, valid_set_y = train_set_x[:1000], train_set_y[:1000]
+        valid_set_x, valid_set_y = shared_dataset((X_train[:1000], Y_train[:1000]))
         test_set_x, test_set_y = shared_dataset((X_test, Y_test))
 
         # compute number of minibatches for training, validation and testing
@@ -89,8 +89,8 @@ class ModelTHEANO(ModelBase):
 
         # Construct the second convolutional pooling layer
         # filtering reduces the image size to (148-5+1, 148-5+1) = (144, 144)
-        # maxpooling reduces this further to (144/4, 144/4) = (38, 38)
-        # 4D output tensor is thus of shape (batch_size, nkerns[1], 38, 38)
+        # maxpooling reduces this further to (144/4, 144/4) = (36, 36)
+        # 4D output tensor is thus of shape (batch_size, nkerns[1], 36, 36)
         layer1 = ConvPoolLayer(
             rng,
             input=layer0.output,
@@ -101,15 +101,15 @@ class ModelTHEANO(ModelBase):
 
         # the HiddenLayer being fully-connected, it operates on 2D matrices of
         # shape (batch_size, num_pixels) (i.e matrix of rasterized images).
-        # This will generate a matrix of shape (batch_size, nkerns[1] * 4 * 4),
-        # or (500, 50 * 4 * 4) = (500, 800) with the default values.
+        # This will generate a matrix of shape (batch_size, nkerns[1] * 36 * 36),
+        # or (500, 50 * 36 * 36) = (500, 800) with the default values.
         layer2_input = layer1.output.flatten(2)
 
         # construct a fully-connected sigmoidal layer
         layer2 = HiddenLayer(
             rng,
             input=layer2_input,
-            n_in=nkerns[1] * 38 * 38,
+            n_in=nkerns[1] * 36 * 36,
             n_out=500,
             activation=T.tanh
         )
@@ -156,8 +156,8 @@ class ModelTHEANO(ModelBase):
         ]
         """
         Total Parameters:
-        >>> 20 * 64 + 1000 * 25 + 50 * 38 * 38 * 500 + 500 * 2
-        36127280
+        >>> 20 * 64 + 1000 * 25 + 50 * 36 * 36 * 500 + 500 * 2
+        32427280
         """
         train_model = theano.function(
             [index],
--
libgit2 0.21.2