@@ -55,30 +55,6 @@ def ReLU(z): return T.maximum(0, z)
5555 except : pass # it's already set
5656 theano .config .floatX = 'float32'
5757
58- def example (mini_batch_size = 10 ):
59- print ("Loading the MNIST data" )
60- training_data , validation_data , test_data = load_data_shared ()
61- print ("Building the network" )
62- net = create_net (10 )
63- print ("Training the network" )
64- try :
65- net .SGD (training_data , 200 , mini_batch_size , 0.1 ,
66- validation_data , test_data , lmbda = 1.0 )
67- except KeyboardInterrupt :
68- pass
69- return net
70-
71- def create_net (mini_batch_size = 10 , activation_fn = tanh ):
72- return Network (
73- [ConvPoolLayer (image_shape = (mini_batch_size , 1 , 28 , 28 ), filter_shape = (20 , 1 , 5 , 5 ), poolsize = (2 , 2 ), activation_fn = activation_fn ),
74- #ConvPoolLayer(image_shape=(mini_batch_size, 20, 12, 12), filter_shape=(40, 20, 5, 5), poolsize=(2, 2), activation_fn=activation_fn),
75- #FullyConnectedLayer(n_in=40*4*4, n_out=100, mini_batch_size=mini_batch_size, activation_fn=activation_fn),
76- #FullyConnectedLayer(n_in=784, n_out=100, mini_batch_size=mini_batch_size, activation_fn=activation_fn),
77- FullyConnectedLayer (n_in = 20 * 12 * 12 , n_out = 100 ),
78- #FullyConnectedLayer(n_in=100, n_out=100, mini_batch_size=mini_batch_size, activation_fn=activation_fn),
79- SoftmaxLayer (n_in = 100 , n_out = 10 )], mini_batch_size )
80- #SoftmaxLayer(n_in=20*12*12, n_out=10)], mini_batch_size)
81-
8258#### Load the MNIST data
8359def load_data_shared (filename = "../data/mnist.pkl.gz" ):
8460 f = gzip .open (filename , 'rb' )
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