我发现一个问题,当你使用Tensorboard进行可视化操作时: 如果你定义了
MERGED = tf.summary.merge_all();
这个操作,之后如果你单独使用SESS.run([MERGED]),那么就会报上面的这个错误;
此时你应该改成和其他的猪op一起进行SESS.run([TRAIN,MERGED]), 改了之后就不会再报这个错误,
具体原因我也很难解释清楚。之前针对这个错误,查了挺长时间,有一些解决方法,但都没有解决我的问题:
https://stackoverflow.com/questions/35114376/error-when-computing-summaries-in-tensorflow
https://blog.csdn.net/lyrassongs/article/details/75012464
后来我是参考了一份Github上一份程序,按它的样子改才改过来了。
# -*- coding: utf-8 -*- """ Created on Wed Oct 31 17:07:38 2018 @author: LiZebin """ from __future__ import print_function import numpy as np import tensorflow as tf tf.reset_default_graph() SESS = tf.Session() LOGDIR = "logs/" X = np.arange(0, 1000, 2, dtype=np.float32) Y = X*2.3+5.6 X_ = tf.placeholder(tf.float32, name="X") Y_ = tf.placeholder(tf.float32, name="Y") W = tf.get_variable(name="Weights", shape=[1], dtype=tf.float32, initializer=tf.random_normal_initializer()) B = tf.get_variable(name="bias", shape=[1], dtype=tf.float32, initializer=tf.random_normal_initializer()) PRED = W*X_+B LOSS = tf.reduce_mean(tf.square(Y_-PRED)) tf.summary.scalar("Loss", LOSS) TRAIN = tf.train.GradientDescentOptimizer(learning_rate=0.0000001).minimize(LOSS) WRITER = tf.summary.FileWriter(LOGDIR, SESS.graph) MERGED = tf.summary.merge_all() SESS.run(tf.global_variables_initializer()) for step in range(20000): c1, c2, loss, RS, _ = SESS.run([W, B, LOSS, MERGED, TRAIN], feed_dict={X_:X, Y_:Y}) ####如果单独在后面写RS=SESS.run(MERGED)就会报之前那个错误 WRITER.add_summary(RS) if step%500 == 0: temp = "c1=%s, c2=%s, loss=%s"%(c1, c2, loss) print(temp) SESS.close()