用PyCharm实现MNIST手写数字识别。

本人几乎没有编程基础，完全按照《Python神经网络》一书学习，在此过程中困惑很多。希望可以相互学习，下面是我最终代码。

数据链接：

https://pjreddie.com/media/files/mnist_test.csv测试集

https://pjreddie.com/media/files/mnist_train.csv训练集

import numpy
# scipy.special for the sigmoid function expit()
import scipy.special
# library for plotting arrays
import matplotlib.pyplot# 初始化函数--设定输入层节点、隐藏节点、和输出层节点数量
# 训练——训练给定样本，优化权重。（使用训练数据集）
# 查询函数——给定输入，从输出节点给出答案（最后导入测试数据集，使用查询函数获得输出，与正确结果比较，获得该神经网络的准确度）# neural network class definition
class neuralNetwork:# initialise the neural networkdef __init__(self, inputnodes, hiddennodes, outputnodes, learningrate):# set number of nodes in each input, hidden, output playerself.inodes = inputnodesself.hnodes = hiddennodesself.onodes = outputnodes# link weight matrices,wih and who# weight inside the arrays are w_i_j,where link is from node i to node j in the next layer# w11 w21# w12 w22 etcselfwih = 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))# learning rateself.lr = learningrate# activation function is the sigmoid functionself.activation_function = lambda x: scipy.special.expit(x)pass# train the neural workdef train(self, inputs_list, targets_list):# convert inputs list to 2d arrayinputs = numpy.array(inputs_list, ndmin=2).Ttargets = numpy.array(targets_list, ndmin=2).T# calculate signals into hidden layerhidden_inputs = numpy.dot(self.wih, inputs)# calculate the signals emerging from hidden layerhidden_outputs = self.activation_function(hidden_inputs)# calculate signals into final output layerfinal_inputs = numpy.dot(self.who, hidden_outputs)# calculate the signals emerging from final output layerfinal_outputs = self.activation_function(final_inputs)# output layer error is the (target-actual)output_errors = targets - final_outputs# hidden layer error is the output_error,split by weights,recombined at hidden nodeshidden_errors = numpy.dot(self.who.T, output_errors)# update the weights for the links between the hidden and output layersself.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 layersself.wih += self.lr * numpy.dot((hidden_errors * hidden_outputs * (1.0 - hidden_outputs)),numpy.transpose(inputs))pass# query the neural networkdef query(self, inputs_list):# convert inputs list to 2d arrayinputs = numpy.array(inputs_list, ndmin=2).T# calculate signals into hidden layerhidden_inputs = numpy.dot(self.wih, inputs)# calculate the signals emerging from hidden layerhidden_outputs = self.activation_function(hidden_inputs)# calculate signals into final output layerfinal_inputs = numpy.dot(self.who, hidden_outputs)# calculate the signals emerging from final output layerfinal_outputs = self.activation_function(final_inputs)return final_outputs# number of nodesinput_nodes = 784
hidden_nodes = 100
output_nodes = 10# learning rate is 0.3
learning_rate = 0.1# create instance of neural network
n = neuralNetwork(input_nodes, hidden_nodes, output_nodes, learning_rate)# 读入手写字体训练数据集
training_data_file = open("训练集数据存储路径", 'r')
training_data_list = training_data_file.readlines()
training_data_file.close()# train the neural network
# epochs is the number of times the training data set is used for training
epochs = 6
for e in range(epochs):# go through all record in the training data setfor record in training_data_list:# split the record by the','commasall_values = record.split(',')# scale and shift the inputsinputs = (numpy.asfarray(all_values[1:]) / 255.0 * 0.99) + 0.01# create the target output values(all 0.01,except the desired label which is 0.99）targets = numpy.zeros(output_nodes) + 0.01# all_values[0] is the target label for this recordtargets[int(all_values[0])] = 0.99n.train(inputs, targets)passpass# 读入手写字体测试数据集test_data_file = open("测试集数据存储路径", 'r')test_data_list = test_data_file.readlines()test_data_file.close()# test the neural networkscorecard = []
# go through all record in the test data set
for record in test_data_list:# split the record by the','commasall_values = record.split(',')# correct answer is first valuecorrect_label = int(all_values[0])# scale and shift the inputsinputs = (numpy.asfarray(all_values[1:]) / 255.0 * 0.99) + 0.01# query the networkoutputs = n.query(inputs)# the index of the highest value corresponds to the labellabel = numpy.argmax(outputs)if label == correct_label:scorecard.append(1)else:scorecard.append(0)passpass
scorecard_array = numpy.asfarray(scorecard)
print("performance=", scorecard_array.sum() / scorecard_array.size)

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