InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Y' with dtype float
阿新 • • 發佈:2018-11-01
我發現一個問題,當你使用Tensorboard進行視覺化操作時: 如果你定義了
MERGED = tf.summary.merge_all();
這個操作,之後如果你單獨使用SESS.run([MERGED]),那麼就會報上面的這個錯誤;
此時你應該改成和其他的豬op一起進行SESS.run([TRAIN,MERGED]), 改了之後就不會再報這個錯誤,
具體原因我也很難解釋清楚。之前針對這個錯誤,查了挺長時間,有一些解決方法,但都沒有解決我的問題:
https://stackoverflow.com/questions/35114376/error-when-computing-summaries-in-tensorflow
https://blog.csdn.net/lyrassongs/article/details/75012464
後來我是參考了一份Github上一份程式,按它的樣子改才改過來了。
# -*- 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}) ####如果單獨在後面寫RS=SESS.run(MERGED)就會報之前那個錯誤 WRITER.add_summary(RS) if step%500 == 0: temp = "c1=%s, c2=%s, loss=%s"%(c1, c2, loss) print(temp) SESS.close()