如何在张量流中交替训练op？

我认为最简单的方法就是

for i in range(1000):  batch_xs, batch_ys = mnist.train.next_batch(100)  if i % 2 == 0:    sess.run(train_step1, feed_dict={x: batch_xs, y_: batch_ys})  else:    sess.run(train_step2, feed_dict={x: batch_xs, y_: batch_ys})

但是如果需要通过tensorflow条件流进行切换，请按照以下方式进行 *** 作：

optimizer = tf.train.GradientDescentOptimizer(0.5)train_step = tf.cond(tf.equal(tf.mod(global_step, 2), 0),          true_fn=lambda: optimizer.apply_gradients(zip(tf.gradients(cross_entropy, tvars1), tvars1), global_step),          false_fn=lambda: optimizer.apply_gradients(zip(tf.gradients(cross_entropy, tvars2), tvars2), global_step))