model.py 文件源码

def discriminator(self, images, reuse=False):
        # images: (batch, 32, 32, 1)
        with tf.variable_scope('discriminator', reuse=reuse):
            with slim.arg_scope([slim.conv2d], padding='SAME', activation_fn=None,
                                 stride=2,  weights_initializer=tf.contrib.layers.xavier_initializer()):
                with slim.arg_scope([slim.batch_norm], decay=0.95, center=True, scale=True, 
                                    activation_fn=tf.nn.relu, is_training=(self.mode=='train')):

                    net = slim.conv2d(images, 128, [3, 3], activation_fn=tf.nn.relu, scope='conv1')   # (batch_size, 16, 16, 128)
                    net = slim.batch_norm(net, scope='bn1')
                    net = slim.conv2d(net, 256, [3, 3], scope='conv2')   # (batch_size, 8, 8, 256)
                    net = slim.batch_norm(net, scope='bn2')
                    net = slim.conv2d(net, 512, [3, 3], scope='conv3')   # (batch_size, 4, 4, 512)
                    net = slim.batch_norm(net, scope='bn3')
                    net = slim.conv2d(net, 1, [4, 4], padding='VALID', scope='conv4')   # (batch_size, 1, 1, 1)
                    net = slim.flatten(net)
                    return net