model.py 文件源码

def content_extractor(self, images, reuse=False):
        # images: (batch, 32, 32, 3) or (batch, 32, 32, 1)

        if images.get_shape()[3] == 1:
            # For mnist dataset, replicate the gray scale image 3 times.
            images = tf.image.grayscale_to_rgb(images)

        with tf.variable_scope('content_extractor', 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' or self.mode=='pretrain')):

                    net = slim.conv2d(images, 64, [3, 3], scope='conv1')   # (batch_size, 16, 16, 64)
                    net = slim.batch_norm(net, scope='bn1')
                    net = slim.conv2d(net, 128, [3, 3], scope='conv2')     # (batch_size, 8, 8, 128)
                    net = slim.batch_norm(net, scope='bn2')
                    net = slim.conv2d(net, 256, [3, 3], scope='conv3')     # (batch_size, 4, 4, 256)
                    net = slim.batch_norm(net, scope='bn3')
                    net = slim.conv2d(net, 128, [4, 4], padding='VALID', scope='conv4')   # (batch_size, 1, 1, 128)
                    net = slim.batch_norm(net, activation_fn=tf.nn.tanh, scope='bn4')
                    if self.mode == 'pretrain':
                        net = slim.conv2d(net, 10, [1, 1], padding='VALID', scope='out')
                        net = slim.flatten(net)
                    return net