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
binarized_wgan_mnist_openAI.py 文件源码
python
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