vaegan.py 文件源码

def discriminate(self, x_var, reuse=False):

        with tf.variable_scope("discriminator") as scope:

            if reuse:
                scope.reuse_variables()

            conv1 = tf.nn.relu(conv2d(x_var, output_dim=32, name='dis_conv1'))
            conv2= tf.nn.relu(batch_normal(conv2d(conv1, output_dim=128, name='dis_conv2'), scope='dis_bn1', reuse=reuse))
            conv3= tf.nn.relu(batch_normal(conv2d(conv2, output_dim=256, name='dis_conv3'), scope='dis_bn2', reuse=reuse))
            conv4 = conv2d(conv3, output_dim=256, name='dis_conv4')
            middle_conv = conv4
            conv4= tf.nn.relu(batch_normal(conv4, scope='dis_bn3', reuse=reuse))
            conv4= tf.reshape(conv4, [self.batch_size, -1])

            fl = tf.nn.relu(batch_normal(fully_connect(conv4, output_size=256, scope='dis_fully1'), scope='dis_bn4', reuse=reuse))
            output = fully_connect(fl , output_size=1, scope='dis_fully2')

            return middle_conv, output