Tensorflow：ValueError：形状必须是相同的排名，但是是0和2

Question

我越来越形状错误，而乘（x1，Wo1）。但我找不到原因。 错误：ValueError：形状必须等于等级，但为0和2
将形状0与其他形状合并。对于'add_2/x'（op：'Pack'），输入形状为：[]，[20,1]。

import tensorflow as tf 
    import numpy as np 
    import pandas as pd 
    import math 

    df1=pd.read_csv(r'C:\Ocean of knowledge\Acads\7th sem\UGP\datasets\xTrain.csv') 
    df1 = df1.dropna() 
    xTrain = df1.values 


    df2 = pd.read_csv(r'C:\Ocean of knowledge\Acads\7th sem\UGP\datasets\yTrain.csv') 
    df2 = df2.dropna() 
    yTrain = df2.values 

    sess=tf.Session()  
    saver = tf.train.import_meta_graph(r'C:\Ocean of knowledge\Acads\7th sem\UGP\NeuralNet\my_model.meta') 
    saver.restore(sess,tf.train.latest_checkpoint('./')) 


    graph = tf.get_default_graph() 
    w1 = graph.get_tensor_by_name("input:0") 
    feed_dict ={w1:xTrain1} 
    op_to_restore = graph.get_tensor_by_name("hidden:0") 
    h1 = sess.run(op_to_restore,feed_dict) 
    print(h1) 

    n_input1 = 20 
    n_hidden1 = 1 

    def weight_variable(shape): 
     initial = tf.truncated_normal(shape, stddev=0.1) 
     return tf.Variable(initial) 

    def bias_variable(shape): 
     initial = tf.constant(0.1, shape=shape) 
     return tf.Variable(initial) 

    x1 = tf.placeholder(tf.float32, shape=[]) 
    Wo1 = weight_variable([20,1]) 
    bo1 = bias_variable([1]) 
    y1 = tf.nn.tanh(tf.matmul((x1,Wo1)+ bo1),name="op_to_restore2_") 

    y1_ = tf.placeholder("float", [None,n_hidden1], name="check_") 
    meansq1 = tf.reduce_mean(tf.square(y1- y1_), name="hello_") 
    train_step1 = tf.train.AdamOptimizer(0.005).minimize(meansq1) 

    #init = tf.initialize_all_variables() 

    init = tf.global_variables_initializer() 
    sess.run(init) 

    n_rounds1 = 100 
    batch_size1 = 5 
    n_samp1 = 350 

    for i in range(n_rounds1+1):  
     sample1 = np.random.randint(n_samp1, size=batch_size1) 
     batch_xs1 = h1[sample1][:] 
     batch_ys1 = yTrain[sample1][:] 
     sess.run(x1, feed_dict={x1: batch_xs1, y1_:batch_ys1}) 

Answer 1

这里tf.matmul((x1,Wo1)+ bo1你使用tf.matmul(a,b)，这就是矩阵乘法运算。 该操作要求a和b都是矩阵（等级> = 2的张量）。

在你的情况，你乘以x1多数民众赞成这样定义

x1 = tf.placeholder(tf.float32, shape=[]) 

和Wo1多数民众赞成这样定义

Wo1 = weight_variable([20,1]) 

正如你所看到的，x1不是矩阵，而是相反，标量（形状为[]的张量）。

也许你正在寻找元素明智的乘法？这就是tf.multiply的用途。