个人理解：就是TF的一种输入语法。
跟C语言的scanf（），C++的 cin>> 意思差不多，只是长相奇怪了点而已。
做完下面几个例子，基本也就适应了。
首先占位符申请空间；使用的时候，通过占位符“喂（feed）”给程序。然后程序就可以run了。。。
理解的不一定对，也不够深入，仅供参考。
import tensorflow as tf
Str = tf.placeholder(tf.string) with tf.Session() as sess: output = sess.run(Str, feed_dict={Str: ‘Hello World !‘}) print(output)
Hello World !
Str1 = tf.placeholder(tf.string) Str2 = tf.placeholder(tf.string) Str3 = tf.placeholder(tf.string) Str = tf.string_join([Str1, Str2, Str3], separator=" ") with tf.Session() as sess: output = sess.run(Str, feed_dict={Str1: ‘I‘, Str2: ‘like‘, Str3: ‘TensorFlow !‘}) print(output.decode())
I like TensorFlow !
Num1 = tf.placeholder(tf.float32) Num2 = tf.placeholder(tf.float32) Result = tf.multiply(Num1, Num2) with tf.Session() as sess: print(sess.run(Result, feed_dict={Num1:[5.],Num2:[6.]}))
[ 30.]
只是验证一下，不推荐使用。初始化时的常量值将会被覆盖。
Num1 = tf.constant(1.0) Num2 = tf.constant(2.0) Result = tf.multiply(Num1, Num2) with tf.Session() as sess: print (sess.run(Result, feed_dict = {Num1: 5., Num2: 6.}))
30.0
顺道学点新东西
Matrix1 = tf.Variable(tf.random_normal([2,3])) Matrix2 = tf.Variable(tf.truncated_normal([3,2])) Result = tf.matmul(Matrix1,Matrix2) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) print (‘Matrix1:‘) print (sess.run(Matrix1)) print (‘Matrix2:‘) print (sess.run(Matrix2)) print (‘Result:‘) print (sess.run(Result))
Matrix1: [[-1.00879586 0.61892986 -0.39552122] [-0.83463311 -0.54309726 -0.31309164]] Matrix2: [[ 1.35596943 0.67712855] [-0.09836598 -0.41533285] [ 0.20601694 -0.14825489]] Result: [[-1.51026201 -0.88150841] [-1.14281678 -0.29317039]]
表达看上去更繁琐。。。对比一下是为了更好地理解feed_dict。。。
Matrix1_Value = tf.random_normal([2,3]) Matrix2_Value = tf.truncated_normal([3,2]) Matrix1 = tf.placeholder(dtype=tf.float32,shape=[2,3]) Matrix2 = tf.placeholder(dtype=tf.float32,shape=[3,2]) Result = tf.matmul(Matrix1,Matrix2) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) print (‘Matrix1:‘) print (sess.run(Matrix1_Value)) print (‘Matrix2:‘) print (sess.run(Matrix2_Value)) print (‘Result:‘) print (sess.run(Result,feed_dict={Matrix1:sess.run(Matrix1_Value),Matrix2:sess.run(Matrix2_Value)}))
Matrix1: [[-0.6228959 0.04135797 -0.76592982] [ 0.04814498 -0.98519218 -0.88335162]] Matrix2: [[-0.73028505 0.62314421] [-0.64763296 -0.18691106] [ 0.0198773 0.68467569]] Result: [[-1.66321826 -2.89716744] [ 1.28906226 2.08242035]]
TensorFlow学习笔记（三）-- feed_dict 使用
原文：https://www.cnblogs.com/itboys/p/8858172.html