mnist_bin.py 文件源码

def discriminator(input_var):
    network = lasagne.layers.InputLayer(shape=(None, 1, 28, 28),
                                        input_var=input_var)
    network = ll.DropoutLayer(network, p=0.5)


    network = weight_norm(conv_layer(network, 3, 32, 1, 'same', nonlinearity=lrelu), train_g=False)

    network = weight_norm(conv_layer(network, 3, 32, 2, 'same', nonlinearity=lrelu), train_g=False)
    network = weight_norm(conv_layer(network, 3, 64, 2, 'same', nonlinearity=lrelu), train_g=False)

    network = weight_norm(conv_layer(network, 3, 128, 2, 'same', nonlinearity=lrelu), train_g=False)

    network = weight_norm(conv_layer(network, 4, 128, 1, 'valid', nonlinearity=lrelu), train_g=False)

    network =weight_norm(conv_layer(network, 1, 1, 1, 'valid', nonlinearity=None), train_g=True)




    return network