--- a/deep/convolutional_dae/stacked_convolutional_dae.py	Tue Mar 09 10:15:19 2010 -0500
+++ b/deep/convolutional_dae/stacked_convolutional_dae.py	Wed Mar 10 13:48:16 2010 -0500
@@ -56,7 +56,7 @@
         self.b = theano.shared(value = initial_b, name = "b")
     
  
-    initial_b_prime= numpy.zeros((filter_shape[1],))
+    initial_b_prime= numpy.zeros((filter_shape[1],),dtype=theano.config.floatX)
         
     self.W_prime=T.dtensor4('W_prime')
 
@@ -64,7 +64,7 @@
  
     self.x = input
 
-    self.tilde_x = theano_rng.binomial( self.x.shape, 1, 1 - corruption_level) * self.x
+    self.tilde_x = theano_rng.binomial( self.x.shape, 1, 1 - corruption_level,dtype=theano.config.floatX) * self.x
 
     conv1_out = conv.conv2d(self.tilde_x, self.W, filter_shape=filter_shape,
                             image_shape=image_shape, border_mode='valid')
@@ -135,7 +135,7 @@
         self.conv_n_layers = len(conv_hidden_layers_sizes)
         self.mlp_n_layers = len(mlp_hidden_layers_sizes)
         
-        self.x = T.dmatrix('x') # the data is presented as rasterized images
+        self.x = T.matrix('x') # the data is presented as rasterized images
         self.y = T.ivector('y') # the labels are presented as 1D vector of
         
         for i in xrange( self.conv_n_layers ):
@@ -156,7 +156,7 @@
             
             self.layers += [layer]
             self.params += layer.params
-            
+
             da_layer = dA_conv(corruption_level = corruption_levels[0],
                                input = layer_input,
                                shared_W = layer.W, shared_b = layer.b,