ValueError：形状（100,784）和（4,6836）未对齐：784（dim 1）！= 4（dim 0）

Question

更新：我修正了错误，所以我只需要第二个问题的答案！

我是 Python 的新手，在执行任务时遇到了错误。我查找了这个错误，但没有找到我的答案。

所以，这就是我想要做的。

我想构建一个能够预测值的神经网络。 我用于该类的代码如下

# neural network class definition

类神经网络：

#Step 1: initialise the neural network: number of input layers, hidden layers and output layers
def __init__(self, inputnodes, hiddennodes, outputnodes, learningrate):
    #set number of nodes in each input, hidden, output layer
    self.inodes = inputnodes
    self.hnodes = hiddennodes
    self.onodes = outputnodes

    #link weight matrices, wih and who (weights in hidden en output layers), we are going to create matrices for the multiplication of it to get an output
    #weights inside the arrays (matrices) are w_i_j, where link is from node i to node j in the next layer
    #w11 w21
    #w12 w22 etc
    self.wih = numpy.random.normal(0.0,pow(self.inodes,-0.5),( self.hnodes, self.inodes))
    self.who = numpy.random.normal(0.0,pow(self.hnodes,-0.5),( self.onodes, self.hnodes))

    # setting the learning rate
    self.lr = learningrate

    # activation function is the sigmoid function
    self.activation_function = lambda x: scipy.special.expit(x)

    pass

#Step 2: training the neural network - adjust the weights based on the error of the network
def train(self, inputs_list, targets_list):
    #convert input lists to 2d array (matrice)
    inputs = numpy.array(inputs_list, ndmin=2).T
    targets = numpy.array(targets_list, ndmin=2).T

    #calculate signals into hidden layer
    hidden_inputs = numpy.dot(self.wih, inputs)
    #calculate signals emerging from hidden layer
    hidden_outputs = self.activation_function(hidden_inputs)

    #calculate signals into final output layer
    final_inputs = numpy.dot(self.who, hidden_outputs)
    #calculate signals emerging from final output layer
    final_outputs = self.activation_function(final_inputs)
    # output layer error is the (target-actual)
    output_errors = targets -final_outputs
    #hidden layer error is the output_errors, split by weights, recombined at hidden nodes
    hidden_errors = numpy.dot(self.who.T, output_errors)

    #update the weights for the links between the hidden and output layers
    self.who += self.lr * numpy.dot((output_errors*final_outputs * (1.0-final_outputs)),numpy.transpose(hidden_outputs))

    # update the weights for the links between the input and hidden layers
    self.wih += self.lr*numpy.dot((hidden_errors*hidden_outputs*(1.0-hidden_outputs)),numpy.transpose(inputs))

    pass

#Seap 3: giving an output- thus making the neural network perform a guess
def query(self, inputs_list):
    #convert input lists to 2d array (matrice)
    inputs = numpy.array(inputs_list, ndmin=2).T

    #calculate signals into hidden layer
    hidden_inputs = numpy.dot(self.wih, inputs)
    #calculate signals emerging from hidden layer
    hidden_outputs = self.activation_function(hidden_inputs)

    #calculate signals into final output layer
    final_inputs = numpy.dot(self.who, hidden_outputs)
    #calculate signals emerging from final output layer
    final_outputs = self.activation_function(final_inputs)

    return final_outputs

我显然先导入了必要的东西：

import numpy 
#scipy.special for the sigmoid function expit()
import scipy.special

然后我创建了一个神经网络实例：

#number of input, hidden and output nodes
input_nodes = 784
hidden_nodes = 100
output_nodes = 10

#learning rate is 0.8
learning_rate = 0.8

#create instance of neural network
n = neuralNetwork(input_nodes, hidden_nodes, output_nodes, learning_rate)

在此之后，我读取了带有输入和目标的 excel 文件

import pandas as pd
df = pd.read_excel("Desktop\\PythonTest.xlsx")

文件如下所示：

snapshot of file

h、P、D、o 列是输入，EOQ 列是神经网络应该学习的数字。

所以，我首先这样做了：

xcol=["h","P","D","o"]
ycol=["EOQ"]
x=df[xcol].values
y=df[ycol].values

定义 x 和 y 列。 x 是输入，y 是目标。

我现在想根据这些数据训练神经网络，我使用了这些代码行；

# train the neural network
# go through all records in the training data set 
for record in df:
inputs = x
targets = y
n.train(inputs, targets)
pass

这给了我以下错误：

---------------------------------------------------------------------------
  ValueError                                Traceback (most recent call 
  last)
  <ipython-input-23-48e0e741e8ec> in <module>()
  4     inputs = x
  5     targets = y
   ----> 6     n.train(inputs, targets)
  7     pass

  <ipython-input-13-12c121f6896b> in train(self, inputs_list, targets_list)
 31 
 32         #calculate signals into hidden layer
  ---> 33         hidden_inputs = numpy.dot(self.wih, inputs)
 34         #calculate signals emerging from hidden layer
 35         hidden_outputs = self.activation_function(hidden_inputs)

 ValueError: shapes (100,784) and (4,6836) not aligned: 784 (dim 1) != 4 
 (dim 0)

那么两个问题：

1. 代码出了什么问题？
2. 我想在文件中添加一个额外的列，其中包含训练后的神经网络的猜测。我该如何做到这一点？

在此先感谢您的任何反馈！

干杯

史蒂文

Answer 1

您已经在使用 pandas，因此您可以简单地获取所有输出，并为 pandas df 创建一个新列。

result = [nn.query(input) for input in df]
df['result'] = result