python程序实例源代码-Python 神经网络手写识别实例源码

深度学习和神经网络对很多初学者来说都是摸不着头脑，今天分享一个完整的手写识别的实例，学习和理解了这个实例代码和过程就基本上掌握了神经网络。

1、构建神经网络类 network_claas.py

#!/usr/bin/env python3

# -*- coding: utf-8 -*-

# neural network class definition

import scipy.special

import numpy

# library for plotting arrays

import matplotlib.pyplot

# helper to load data from PNG image files# helpe

import imageio

# glob helps select multiple files using patterns

import glob

class neuralNetwork :

# initialise the neural network

def __init__(self, inputnodes, hiddennodes, outputnodes, learningrate) :

#set number of nodes in each input , hidden , output

#初始化网络，设置输入层，中间层，和输出层节点数

self.inodes = inputnodes

self.hnodes = hiddennodes

self.onodes = outputnodes

# link weight matrices, wih and who

# weights inside the arrays are w_ i_ j, where link is from node i to node j in the next layer

# w11 w21

# w12 w22 etc

# 初始化权重矩阵，我们有两个权重矩阵，一个是wih表示输入层和中间层节点间链路权重形成的矩阵一个是who,表示中间层和输出层间链路权重形成的矩阵

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))

# learning rate

self.lr = learningrate

# activation function is the sigmoid function

# 设置激活函数的反函数

self.activation_function = lambda x:scipy.special.expit(x)

pass

# train the neural network

def train(self, inputs_list, targets_list) :

# convert inputs list to 2d array

#根据输入的训练数据更新节点链路权重

#把inputs_list, targets_list转换成numpy支持的二维矩阵.T表示做矩阵的转置

inputs = numpy.array(inputs_list, ndmin=2).T

targets = numpy.array(targets_list, ndmin=2).T

# calculate signals into hidden layer

#计算信号经过输入层后产生的信号量

hidden_inputs = numpy.dot(self.wih, inputs)

# calculate the signals emerging from hidden layer

#中间层神经元对输入的信号做激活函数后得到输出信号

hidden_outputs = self.activation_function(hidden_inputs)

# calculate signals into final output layer

#输出层接收来自中间层的信号量

final_inputs = numpy.dot(self.who, hidden_outputs)

# calculate the signals emerging from final output layer

#输出层对信号量进行激活函数后得到最终输出信号

final_outputs = self.activation_function(final_inputs)

# output layer error is the (target - actual)

#计算误差

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)

#根据误差计算链路权重的更新量，然后把更新加到原来链路权重上

# 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

# query the neural network

def query(self, inputs_list) :

#根据输入数据计算并输出答案

# convert inputs list to 2d array

inputs = numpy.array(inputs_list, ndmin=2).T

#计算中间层从输入层接收到的信号量

# calculate signals into hidden layer

hidden_inputs = numpy.dot(self.wih, inputs)

#计算中间层经过激活函数后形成的输出信号量

# calculate the signals emerging from hidden layer

hidden_outputs = self.activation_function(hidden_inputs)

#计算最外层接收到的信号量

# 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

2、初始化及训练测试该网络

#!/usr/bin/env python3

# -*- coding: utf-8 -*-

from network_claas import neuralNetwork

import numpy

import matplotlib

import glob

import imageio

# library for plotting arrays

import matplotlib.pyplot as plt

import pylab

# ensure the plots are inside this notebook, not an external window

#初始化网络

# number of input, hidden and output nodes

input_nodes = 784

hidden_nodes = 100

output_nodes = 10

#初始化学习率

# learning rate is 0.3

learning_rate = 0.3

# create instance of neural network

# 初始化神经网络

n = neuralNetwork(input_nodes,hidden_nodes,output_nodes, learning_rate)

# load the mnist training data CSV file into a list

training_data_file = open("mnist_dataset/mnist_train_100.csv", '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 = 5

for e in range( epochs):

# go through all records in the training data set

for record in training_data_list:

# split the record by the ',' 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 is 0.99)

targets = numpy.zeros(output_nodes) + 0.01

# all_values[0] is 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("mnist_dataset/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 ',' commas

all_values = record.split(',')

# correct answer is first value

correct_label = int( all_values[ 0])

# scale and shift the inputs

inputs = (numpy.asfarray( all_values[ 1:]) / 255.0 * 0.99) + 0.01

# query the network

outputs = n.query( inputs)

# the index of the highest value corresponds to the label

label = numpy.argmax( outputs)

# append correct or incorrect to list

if (label == correct_label):

# network' s answer matches correct answer, add 1 to scorecard

scorecard.append( 1)

else:

# network' s answer doesn' t match correct answer, add 0 to scorecard

scorecard.append( 0)

pass

# calculate the performance score, the fraction of correct answers

scorecard_array = numpy.asarray( scorecard)

print ("performance = ", scorecard_array.sum() / scorecard_array.size)

# our own image test data set# our o

our_own_dataset = []

# load the png image data as test data set

for image_file_name in glob.glob('my_own_images/2828_my_own_?.png'):

# use the filename to set the correct label

label = int(image_file_name[-5:-4])

# load image data from png files into an array

print ("loading ... ", image_file_name)

img_array = imageio.imread(image_file_name, as_gray=True)

# reshape from 28x28 to list of 784 values, invert values

img_data = 255.0 - img_array.reshape(784)

# then scale data to range from 0.01 to 1.0

img_data = (img_data / 255.0 * 0.99) + 0.01

print(numpy.min(img_data))

print(numpy.max(img_data))

# append label and image data to test data set

record = numpy.append(label,img_data)

our_own_dataset.append(record)

pass

# record to test

item = 2

# plot image

plt.imshow(our_own_dataset[item][1:].reshape(28,28), cmap='Greys', interpolation='None')

# correct answer is first value

correct_label = our_own_dataset[item][0]

# data is remaining values

inputs = our_own_dataset[item][1:]

# query the network

outputs = n.query(inputs)

print (outputs)

# the index of the highest value corresponds to the label

label = numpy.argmax(outputs)

print("network says ", label)

# append correct or incorrect to list

if (label == correct_label):

print ("match!")

else:

print ("no match!")

pass

pylab.show()

3，输出如下：

4，完整代码如下：

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python程序实例源代码-Python 神经网络手写识别实例源码

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