TypeError：'ndim'是此函数的无效关键字参数

Question

我使用numpy 1.13和ahve数组函数的一些麻烦。

数据文件从： https://github.com/makeyourownneuralnetwork/makeyourownneuralnetwork/tree/master/mnist_dataset

在运行python3 neuralNetwork.py结果： 回溯（最近通话最后一个）：文件 “neuralNetwork.py”，线路141，在main（）文件“neuralNetwork.py “，第105行，主要n.train（输入，目标）文件”neuralNetwork.py“，第38行，列车输入= numpy.array（inputs_list，ndim = 2）.T TypeError：'ndim'是一个无效的关键字 参数此功能

代码如下：

#!/bin/usr/python 
# -*- coding: utf-8 -*- 

import numpy 
import scipy.special 
import matplotlib.pyplot 

#neural network class definition 
class neuralNetwork: 

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

     #learning rate 
     self.lr=learningrate 

     #gewichtsmatrizen wih and who 
     #weigths im array w_i_j, wo der link von node i zu node j im 
     nächsten layer geht 
     #w11 w21 w31 etc 
     #w12 w22 w32 etc 
     #w13 w23 w33 etc 
     #initialisierung mit 1/Wurzel(Anzahl der eingehenden Verknüfpungen), 
     -0.5 um sicher zu stellen 
     #das alle zahlen zwischen -1 und 1 sind, 0 darf nicht vorkommen.  
     self.wih=numpy.random.normal(0.0, pow(self.hnodes, -0.5), 
     (self.hnodes, self.inodes)) 
     self.who=numpy.random.normal(0.0, pow(self.onodes, -0.5), 
     (self.onodes, self.hnodes)) 

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

    #train neural netork 
    def train(self, inputs_list, targets_list): 
     #convert inputs into 2D array 
     inputs = numpy.array(inputs_list, ndim=2).T 
     targets = numpy.array(targets_list, ndim=2).T 

     #calculate signals into hidden layer 
     hidden_inputs = numpy.dot(self.wih, inputs) 
     #calculate the 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) 
     #calcualte the signals emerging from final output layer 
     final_outputs = self.activation_function(final_inputs) 

     #error is the (target - actual) 
     output_errors = targets - final_outputs 

     #hidden layer errors 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((ouput_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 


    #query neural network 
    def query(self, inputs_list): 
     #convert inputs list to 2d array 
     inputs = numpy.array(inputs_list, ndmin=2).T 

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

     #calculate the signasl emerging from hidden layout 
     hidden_outputs=self.activation_function(hidden_inputs) 

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

def main(): 
    inputNodes=784 
    hiddenNodes=100 
    outputNodes=10 
    learningRate=0.3 
    n=neuralNetwork(inputNodes, hiddenNodes, outputNodes, learningRate) 

    #load the mnist training data 
    training_data_file = open("/data/mnist_train_100.csv",'r') 
    training_data_list = training_data_file.readlines() 
    training_data_file.close() 

    #train the neural network 
    #go tgrough all records in the training data set 
    for record in training_data_list: 
     #split record by ',' commas 
     all_values = record.split(",") 
     #scale and shift the inputs 
     inputs = (numpy.asfarray(all_values[1:])/255.0 * 0.99) + 0.01 
     #create the target output values (all 0.01, except the desired label 
     which i 0.99) 
     targets = numpy.zeros(outputNodes) + 0.01 
     #all_values[0] us the target label for this record 
     targets[int(all_values[0])] = 0.99 
     n.train(inputs, targets) 
     pass 

    #load the mnist test data csv file into a list 
    test_data_file = open("/data/mnist_test_10.csv",'r') 
    test_data_list = test_data_file.readlines() 
    test_data_file.close() 

    #test the neural_network 
    #scorecard for how well the network performs, initially empty 
    scorecard = [] 

    #go through all the records in the test data set 
    for record in test_data_list: 
     #split the record by the ',' 
     all_values=record.split(",") 
     #correct answer is first values 
     correct_label=int(all_values[0]) 
     print(correct_label, "correct label") 
     #scale and shift the inputs 
     inputs=(numpy.asfarray(all_values[1:])/255.0 * 0.99) + 0.01 
     #query the network 
     label=numpy.argmax(outputs) 
     print(label, "network's label") 
     #append correct or incorrect to list 
     if (label == correct_label): 
      #networks answer matches correct answer, add 0 to scrorecard 
      scrorecard.append(1) 
     else: 
      #networks answer doesn't match correct answer, add 0 to scorcard 
      scorecard.append(0) 
      pass 
     pass  
    print(scorecard) 

if __name__ == '__main__': 
    main() 

Answer 1

np.array需要ndmin参数，但不是ndim之一。