python类LeakyRectify()的实例源码

def build_critic(input_var=None):
    from lasagne.layers import (InputLayer, Conv2DLayer, ReshapeLayer,
                                DenseLayer)
    try:
        from lasagne.layers.dnn import batch_norm_dnn as batch_norm
    except ImportError:
        from lasagne.layers import batch_norm
    from lasagne.nonlinearities import LeakyRectify
    lrelu = LeakyRectify(0.2)
    # input: (None, 1, 28, 28)
    layer = InputLayer(shape=(None, 1, 28, 28), input_var=input_var)
    # two convolutions
    layer = batch_norm(Conv2DLayer(layer, 64, 5, stride=2, pad='same',
                                   nonlinearity=lrelu))
    layer = batch_norm(Conv2DLayer(layer, 128, 5, stride=2, pad='same',
                                   nonlinearity=lrelu))
    # fully-connected layer
    layer = batch_norm(DenseLayer(layer, 1024, nonlinearity=lrelu))
    # output layer (linear)
    layer = DenseLayer(layer, 1, nonlinearity=None)
    print ("critic output:", layer.output_shape)
    return layer

def build_critic(input_var=None, verbose=False):
    from lasagne.layers import (InputLayer, Conv2DLayer, ReshapeLayer,
                                DenseLayer)
    try:
        from lasagne.layers.dnn import batch_norm_dnn as batch_norm
    except ImportError:
        from lasagne.layers import batch_norm
    from lasagne.nonlinearities import LeakyRectify, sigmoid
    lrelu = LeakyRectify(0.2)
    # input: (None, 1, 28, 28)
    layer = InputLayer(shape=(None, 3, 32, 32), input_var=input_var)
    # two convolutions
    layer = batch_norm(Conv2DLayer(layer, 128, 5, stride=2, pad='same',
                                   nonlinearity=lrelu))
    layer = batch_norm(Conv2DLayer(layer, 256, 5, stride=2, pad='same',
                                   nonlinearity=lrelu))
    layer = batch_norm(Conv2DLayer(layer, 512, 5, stride=2, pad='same',
                                   nonlinearity=lrelu))
    # # fully-connected layer
    # layer = batch_norm(DenseLayer(layer, 1024, nonlinearity=lrelu))
    # output layer (linear)
    layer = DenseLayer(layer, 1, nonlinearity=None)
    if verbose: print ("critic output:", layer.output_shape)
    return layer

def build_critic(input_var=None):
    from lasagne.layers import (InputLayer, Conv2DLayer, ReshapeLayer,
                                DenseLayer)
    try:
        from lasagne.layers.dnn import batch_norm_dnn as batch_norm
    except ImportError:
        from lasagne.layers import batch_norm
    from lasagne.nonlinearities import LeakyRectify
    lrelu = LeakyRectify(0.2)
    # input: (None, 1, 28, 28)
    layer = InputLayer(shape=(None, 1, 28, 28), input_var=input_var)
    # two convolutions
    layer = batch_norm(Conv2DLayer(layer, 64, 5, stride=2, pad='same',
                                   nonlinearity=lrelu))
    layer = batch_norm(Conv2DLayer(layer, 128, 5, stride=2, pad='same',
                                   nonlinearity=lrelu))
    # fully-connected layer
    layer = batch_norm(DenseLayer(layer, 1024, nonlinearity=lrelu))
    # output layer (linear and without bias)
    layer = DenseLayer(layer, 1, nonlinearity=None, b=None)
    print ("critic output:", layer.output_shape)
    return layer

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