Python 类型错误：'；ndim&x27；此函数的关键字参数无效

我使用的是NUMPY1.13，数组函数有一些问题

数据文件来自：

运行python3 neuralNetwork.py会导致： 回溯（最后一次调用）：文件“neuralNetwork.py”，第141行，在main（）文件“neuralNetwork.py”，第105行，在main.train（输入，目标）文件“neuralNetwork.py”，第38行，在train input=numpy.array（输入列表，ndim=2）中。T TypeError:“ndim”是无效的关键字 此函数的参数

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()

---

np.array

接受一个

ndmin

参数，但不是一个

ndim

参数。

请提供一个感谢信息……没有看到那里的打字错误。