binarized_wgan_mnist_openAI.py 文件源码

def generator(input_var):
    network = lasagne.layers.InputLayer(shape=(None, NLAT,1,1),
                                        input_var=input_var)

    network = ll.DenseLayer(network, num_units=4*4*64, W=Normal(0.05), nonlinearity=nn.relu)
    #print(input_var.shape[0])
    network = ll.ReshapeLayer(network, (batch_size,64,4,4))
    network = nn.Deconv2DLayer(network, (batch_size,32,7,7), (4,4), stride=(1,1), pad='valid', W=Normal(0.05), nonlinearity=nn.relu)
    network = nn.Deconv2DLayer(network, (batch_size,32,11,11), (5,5), stride=(1,1), pad='valid', W=Normal(0.05), nonlinearity=nn.relu)
    network = nn.Deconv2DLayer(network, (batch_size,32,25,25), (5,5), stride=(2,2), pad='valid', W=Normal(0.05), nonlinearity=nn.relu)
    network = nn.Deconv2DLayer(network, (batch_size,1,28,28), (4,4), stride=(1,1), pad='valid', W=Normal(0.05), nonlinearity=sigmoid)

    #network =lasagne.layers.Conv2DLayer(network, num_filters=1, filter_size=1, stride=1, nonlinearity=sigmoid)
    return network

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