更新：我修复了错误，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一个错误。我查找了这个错误，但没有找到答案。
所以，这就是我要做的。
我想建立一个能够预测价值的神经网络。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)
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：
文件的快照
列h、p、D、o是输入，列EOQ是神经网络应该学习的数字。
所以，我首先做了这个：
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。
我现在想在这些数据上训练神经网络，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)
所以有两个问题：
- 代码中出了什么问题
- 我想在文件中添加一个额外的列，在训练后对神经网络进行猜测。我该如何做到这一点
非常感谢您的反馈！
干杯
Steven
您已经在使用panda了，所以您可以简单地获得所有输出，并为pandadf创建一个新列。
result = [nn.query(input) for input in df]
df['result'] = result