CAE.py 文件源码

def build_net(nz=10):
    # nz = size of latent code
    #N.B. using batch_norm applies bn before non-linearity!
    F=32
    enc = InputLayer(shape=(None,1,28,28))
    enc = Conv2DLayer(incoming=enc, num_filters=F*2, filter_size=5,stride=2, nonlinearity=lrelu(0.2),pad=2)
    enc = Conv2DLayer(incoming=enc, num_filters=F*4, filter_size=5,stride=2, nonlinearity=lrelu(0.2),pad=2)
    enc = Conv2DLayer(incoming=enc, num_filters=F*4, filter_size=5,stride=1, nonlinearity=lrelu(0.2),pad=2)
    enc = reshape(incoming=enc, shape=(-1,F*4*7*7))
    enc = DenseLayer(incoming=enc, num_units=nz, nonlinearity=sigmoid)
    #Generator networks
    dec = InputLayer(shape=(None,nz))
    dec = DenseLayer(incoming=dec, num_units=F*4*7*7)
    dec = reshape(incoming=dec, shape=(-1,F*4,7,7))
    dec = Deconv2DLayer(incoming=dec, num_filters=F*4, filter_size=4, stride=2, nonlinearity=relu, crop=1)
    dec = Deconv2DLayer(incoming=dec, num_filters=F*4, filter_size=4, stride=2, nonlinearity=relu, crop=1)
    dec = Deconv2DLayer(incoming=dec, num_filters=1, filter_size=3, stride=1, nonlinearity=sigmoid, crop=1)

    return enc, dec