Tensorflow：ValueError：形状必须具有相同的等级，但为 0 和 2

Question

乘法 (x1,Wo1) 时出现形状错误。但我找不到原因。错误：ValueError：形状必须具有相同的等级，但为 0 和 2
来自将形状 0 与其他形状合并。对于具有输入形状的“add_2/x”（操作：“Pack”）：[], [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你使用的是tf.matmul(a,b)，这就是矩阵乘法运算。此操作要求 和a都是b矩阵（秩 >=2 的张量）。

在你的情况下，你乘以x1它的定义如下

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

Wo1定义如下

Wo1 = weight_variable([20,1])

正如您所看到的，x1不是矩阵，而是标量（形状为 的张量[]）。

也许您正在寻找元素明智的乘法？这就是tf.multiply目的。