adda_network.py 文件源码

def network_discriminator(self, features):

        network = {}
        network['discriminator/conv2'] = Conv2DLayer(features, num_filters=32, filter_size=3, stride=1, pad='valid', nonlinearity=rectify, name='discriminator/conv2')
        network['discriminator/pool2'] = MaxPool2DLayer(network['discriminator/conv2'], pool_size=2, stride=2, pad=0, name='discriminator/pool2')
        network['discriminator/conv3'] = Conv2DLayer(network['discriminator/pool2'], num_filters=32, filter_size=3, stride=1, pad='valid', nonlinearity=rectify, name='discriminator/conv3')
        network['discriminator/pool3'] = MaxPool2DLayer(network['discriminator/conv3'], pool_size=2, stride=2, pad=0, name='discriminator/pool3')
        network['discriminator/conv4'] = Conv2DLayer(network['discriminator/pool3'], num_filters=32, filter_size=3, stride=1, pad='valid', nonlinearity=rectify, name='discriminator/conv4')
        network['discriminator/pool4'] = MaxPool2DLayer(network['discriminator/conv4'], pool_size=2, stride=2, pad=0, name='discriminator/pool4')
        network['discriminator/dense1'] = DenseLayer(network['discriminator/pool4'], num_units=64, nonlinearity=rectify, name='discriminator/dense1')
        network['discriminator/output'] = DenseLayer(network['discriminator/dense1'], num_units=2, nonlinearity=softmax, name='discriminator/output')

        return network