ac_gan.py 文件源码

def discriminator(images, labels, reuse=False):
    with tf.variable_scope("discriminator") as scope:
        if reuse:
            scope.reuse_variables()

        # conv1
        conv1 = ops.conv_2d(images, 64, scope="conv1")

        # leakly ReLu
        h1 = ops.leaky_relu(conv1)

        # conv2
        conv2 = ops.conv_2d(h1, 128, scope="conv2")

        # batch norm
        norm2 = ops.batch_norm(conv2, scope="batch_norm2", is_training=True)

        # leaky ReLU
        h2 = ops.leaky_relu(norm2)

        # conv3
        conv3 = ops.conv_2d(h2, 256, scope="conv3")

        # batch norm
        norm3 = ops.batch_norm(conv3, scope="batch_norm3", is_training=True)

        # leaky ReLU
        h3 = ops.leaky_relu(norm3)

        # conv4
        conv4 = ops.conv_2d(h3, 512, scope="conv4")

        # batch norm
        norm4 = ops.batch_norm(conv4, scope="batch_norm4", is_training=True)

        # leaky ReLU
        h4 = ops.leaky_relu(norm4)

        # reshape
        h4_reshape = tf.reshape(h4, [FLAGS.batch_size, -1])

        # source logits
        source_logits = ops.fc(h4_reshape, 1, scope="source_logits")

        # class logits
        class_logits = ops.fc(
            h4_reshape, FLAGS.n_classes, scope="class_logits")

        return source_logits, class_logits