binarized_wgan_mnist.py 文件源码

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

    network = lasagne.layers.Conv2DLayer(
            network, num_filters=32, filter_size=(3, 3),
            nonlinearity=lasagne.nonlinearities.rectify,
            W=lasagne.init.GlorotUniform())

    network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2, 2))

    network = lasagne.layers.Conv2DLayer(
            network, num_filters=32, filter_size=(4, 4),
            nonlinearity=lasagne.nonlinearities.rectify)
    network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2, 2))

    network = lasagne.layers.Conv2DLayer(
            network, num_filters=16, filter_size=(4, 4),
            nonlinearity=lasagne.nonlinearities.rectify)
    network = lasagne.layers.MaxPool2DLayer(network, pool_size=(2, 2))

    network =lasagne.layers.Conv2DLayer(network, num_filters=1, filter_size=1, stride=1,nonlinearity=linear)


    return network