Tensorboard 显示计算图节点信息

with tf.Session() as sess:
    tf.global_variables_initializer().run()
    for i in range(TRAINING_STEPS):
        xs, ys = mnist.train.next_batch(BATCH_SIZE)

        if i%1000 == 0:
            # 配置运行时需要记录的信息。
            run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
            # 运行时记录运行信息的proto。
            run_metadata = tf.RunMetadata()
            # 将配置信息和记录运行信息的proto传入运行的过程，从而记录运行时每一个节点的时间空间开销信息
            _, loss_value, step = sess.run(
                [train_op, loss, global_step], feed_dict={x: xs, y_: ys},
                options=run_options, run_metadata=run_metadata)
            writer.add_run_metadata(run_metadata=run_metadata, tag=("tag%d"%i), global_step=i)
            print("After %d training step(s), loss on training batch is %g."%(step, loss_value))
        else:
            _, loss_value, step = sess.run([train_op, loss, global_step], feed_dict={x: xs, y_: ys})