gan.py 文件源码

def discriminator(inputs, reuse=False):
    with tf.variable_scope('discriminator'):
        if reuse:
            tf.get_variable_scope().reuse_variables()
        net = lays.conv2d_transpose(inputs, 64, 3, stride=1, scope='conv1', padding='SAME', activation_fn=leaky_relu)
        net = lays.max_pool2d(net, 2, 2, 'SAME', scope='max1')
        net = lays.conv2d_transpose(net, 128, 3, stride=1, scope='conv2', padding='SAME', activation_fn=leaky_relu)
        net = lays.max_pool2d(net, 2, 2, 'SAME', scope='max2')
        net = lays.conv2d_transpose(net, 256, 3, stride=1, scope='conv3', padding='SAME', activation_fn=leaky_relu)
        net = lays.max_pool2d(net, 2, 2, 'SAME', scope='max3')
        net = tf.reshape(net, (batch_size, 4 * 4 * 256))
        net = lays.fully_connected(net, 128, scope='fc1', activation_fn=leaky_relu)
        net = lays.dropout(net, 0.5)
        net = lays.fully_connected(net, 1, scope='fc2', activation_fn=None)
        return net