Question

更新：我已修复该错误，因此我只需要回答第二个问题即可！

我是Python的新手，执行任务时出错。我寻找了此错误，但没有找到答案。

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。我在课堂上使用的代码如下

# neural network class definition

类NeuroNetwork：

#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

我显然首先导入了必要的东西：

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

然后我创建了神经网络的一个实例：

#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)

此后，我将读取带有输入和目标的excel文件

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

文件如下：

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：

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。

我现在想在此数据上训练神经网络，并使用了这些代码行；

# 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)

有两个问题：

代码出了什么问题？
我想在训练后对神经网络的猜测在文件中添加一个额外的列。我该如何实现？

在此先感谢您，并感谢您的反馈！

欢呼

史蒂芬

Answer 1

您已经在使用熊猫，因此您只需获取所有输出，并为熊猫df新建一列。

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

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

1 个答案: