binarized_wgan_mnist_openAI.py 文件源码

python
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项目:WGAN_mnist 作者: rajeswar18 项目源码 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 = nn.weight_norm(dnn.Conv2DDNNLayer(network, 64, (4,4), pad='valid', W=Normal(0.05), nonlinearity=nn.lrelu))

    network = nn.weight_norm(dnn.Conv2DDNNLayer(network, 32, (5,5), stride=2, pad='valid', W=Normal(0.05), nonlinearity=nn.lrelu))
    network = nn.weight_norm(dnn.Conv2DDNNLayer(network, 32, (5,5), pad='valid', W=Normal(0.05), nonlinearity=nn.lrelu))

    network = nn.weight_norm(dnn.Conv2DDNNLayer(network, 32, (5,5), pad='valid', W=Normal(0.05), nonlinearity=nn.lrelu))

    network = nn.weight_norm(dnn.Conv2DDNNLayer(network, 16, (3,3), pad='valid', W=Normal(0.05), nonlinearity=nn.lrelu))

    network =nn.weight_norm(ll.DenseLayer(network, num_units=1, W=Normal(0.05), nonlinearity=None), train_g=True, init_stdv=0.1)




    return network
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