discogan_model.py 文件源码

def discriminator(self, x, name, reuse=None):
        with tf.variable_scope(name, reuse=reuse):
            with slim.arg_scope([slim.conv2d, slim.fully_connected],
                                weights_initializer=tf.contrib.layers.variance_scaling_initializer(),
                                weights_regularizer=slim.l2_regularizer(2e-4)):
                with slim.arg_scope([slim.conv2d], padding="SAME", stride=2, kernel_size=4):
                    net = slim.conv2d(x, self.df_dim)
                    net = lrelu(net)

                    mul = 2
                    for bn in self.d_bn:
                        net = slim.conv2d(net, self.df_dim * mul)
                        net = bn(net)
                        net = lrelu(net)
                        mul *= 2

                net = tf.reshape(net, shape=[-1, 2*2*512])
                net = slim.fully_connected(net, 512, activation_fn=lrelu, normalizer_fn=slim.batch_norm)
                net = slim.fully_connected(net, 1, activation_fn=tf.nn.sigmoid)
            return net  # return prob