unet.py 文件源码

def discriminator(self, image, is_training, reuse=False):
        with tf.variable_scope("discriminator"):
            if reuse:
                tf.get_variable_scope().reuse_variables()
            h0 = lrelu(conv2d(image, self.discriminator_dim, scope="d_h0_conv"))
            h1 = lrelu(batch_norm(conv2d(h0, self.discriminator_dim * 2, scope="d_h1_conv"),
                                  is_training, scope="d_bn_1"))
            h2 = lrelu(batch_norm(conv2d(h1, self.discriminator_dim * 4, scope="d_h2_conv"),
                                  is_training, scope="d_bn_2"))
            h3 = lrelu(batch_norm(conv2d(h2, self.discriminator_dim * 8, sh=1, sw=1, scope="d_h3_conv"),
                                  is_training, scope="d_bn_3"))
            # real or fake binary loss
            fc1 = fc(tf.reshape(h3, [self.batch_size, -1]), 1, scope="d_fc1")
            # category loss
            fc2 = fc(tf.reshape(h3, [self.batch_size, -1]), self.embedding_num, scope="d_fc2")

            return tf.nn.sigmoid(fc1), fc1, fc2