基于tensorflow编写迁移学习中的域分类损失函数，要求设置域判别器判断该域来自源域还是目标域，给出代码

以下是基于 TensorFlow 编写的迁移学习中的域分类损失函数，包括分类器和域判别器，并设置了判断数据来自源域还是目标域的逻辑：

import tensorflow as tf

def domain_classifier(x, reuse=False):
    with tf.variable_scope('domain_classifier', reuse=reuse):
        x = tf.layers.dense(x, units=128, activation=tf.nn.relu)
        x = tf.layers.dense(x, units=64, activation=tf.nn.relu)
        x = tf.layers.dense(x, units=1, activation=tf.nn.sigmoid)
    return x

def domain_classification_loss(source_logits, target_logits):
    source_labels = tf.ones_like(source_logits)
    target_labels = tf.zeros_like(target_logits)
    logits = tf.concat([source_logits, target_logits], axis=0)
    labels = tf.concat([source_labels, target_labels], axis=0)
    loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=logits, labels=labels))
    return loss

def train_step(inputs, labels, source_domain, target_domain, lambda_value):
    # 构建分类器
    with tf.variable_scope('classifier'):
        x = tf.layers.dense(inputs, units=128, activation=tf.nn.relu)
        x = tf.layers.dense(x, units=64, activation=tf.nn.relu)
        logits = tf.layers.dense(x, units=10)
        cls_loss = tf.reduce_mean(
            tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels))
    
    # 构建域判别器
    source_logits = domain_classifier(inputs, reuse=False)
    target_logits = domain_classifier(inputs, reuse=True)
    dom_loss = domain_classification_loss(source_logits, target_logits)
    
    # 计算总损失
    loss = cls_loss - lambda_value * dom_loss
    
    # 定义优化器
    optimizer = tf.train.AdamOptimizer(learning_rate=0.001)
    train_op = optimizer.minimize(loss)
    
    # 运行优化器
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for i in range(num_steps):
            inputs_batch, labels_batch = get_batch(train_data, train_labels, batch_size)
            source_domain_batch = get_domain_labels(inputs_batch.shape[0], 1)
            target_domain_batch = get_domain_labels(inputs_batch.shape[0], 0)
            feed_dict = {
                inputs: inputs_batch,
                labels: labels_batch,
                source_domain: source_domain_batch,
                target_domain: target_domain_batch,
                lambda_value: 0.1
            }
            _, loss_val = sess.run([train_op, loss], feed_dict=feed_dict)
            if i % 100 == 0:
                print('Step %d, loss = %.4f' % (i, loss_val))

以上代码中，`train_step` 函数为训练函数，其中包括分类器和域判别器的构建以及损失函数的定义。`domain_classifier` 函数用于构建域判别器，`domain_classification_loss` 函数用于计算二元交叉熵损失函数。在训练过程中，我们需要为输入数据设置源域和目标域标签，并通过 `lambda_value` 超参数控制分类器和域判别器的权重。