Handwritten font recognition based on Python neural network

       This article will share the neural network implementation of handwritten font recognition. There are detailed comments in the code and some of my own experience. I hope it can help everyone (≧∇≦)/ 

##############################################手写字体识别#############################################
import numpy
import scipy.special
######################################定义神经网络的类#########################################
class neuralNetwork:
    # 初始化
    def __init__(self, inputnodes, hiddennodes, outputnodes, learningrate):
        # 设置输入层、隐藏层、输出层节点个数
        self.inodes = inputnodes
        self.hnodes = hiddennodes
        self.onodes = outputnodes

        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))

        # 学习率
        self.lr = learningrate

        # 激活函数
        self.activation_function = lambda x: scipy.special.expit(x)

        pass

    # 训练
    def train(self, inputs_list, targets_list):
        # 转化为二维数组
        inputs = numpy.array(inputs_list, ndmin=2).T
        targets = numpy.array(targets_list, ndmin=2).T

        # 计算隐藏层输入
        hidden_inputs = numpy.dot(self.wih, inputs)
        # 计算隐藏层输出
        hidden_outputs = self.activation_function(hidden_inputs)

        # 计算输出层的输入
        final_inputs = numpy.dot(self.who, hidden_outputs)
        # 计算输出层的输出
        final_outputs = self.activation_function(final_inputs)

        # 计算偏差
        output_errors = targets - final_outputs
       
        hidden_errors = numpy.dot(self.who.T, output_errors)

        # 更新权重
        self.who += self.lr * numpy.dot((output_errors * final_outputs * (1.0 - final_outputs)),
                                        numpy.transpose(hidden_outputs))

        self.wih += self.lr * numpy.dot((hidden_errors * hidden_outputs * (1.0 - hidden_outputs)),
                                        numpy.transpose(inputs))

        pass

    # 验证
    def query(self, inputs_list):
        # 转化为二维数组
        inputs = numpy.array(inputs_list, ndmin=2).T

        # 计算隐藏层输入
        hidden_inputs = numpy.dot(self.wih, inputs)
        # 计算隐藏层输出
        hidden_outputs = self.activation_function(hidden_inputs)

        # 计算输出层的输入
        final_inputs = numpy.dot(self.who, hidden_outputs)
        # 计算输出层的输出
        final_outputs = self.activation_function(final_inputs)

        return final_outputs

############################################init###############################################
# 输入、输出、隐藏层节点个数
input_nodes = 784
hidden_nodes = 500
output_nodes = 10
# 学习率
learning_rate = 0.1
# 训练轮次
epochs=5
#####################################创建一个神经网络############################################
n = neuralNetwork(input_nodes, hidden_nodes, output_nodes, learning_rate)
##########################################训练#################################################
train_file = open("mnist_dataset/MNIST_csv/mnist_train.csv",'r')
train_list = train_file.readlines()
train_file.close()

for e in range(epochs):
    for record in train_list:
        value_train = record.split(',')
        inputs = (numpy.asfarray(value_train[1:])/255.0*0.99)+0.01 # 数据预处理，把像素值范围限定到[0.01,1]

        targets=numpy.zeros(output_nodes)+0.01
        targets[int(value_train[0])] = 0.99
        #创建训练时目标序列（除了目标是0.99，其余9个都是0.01）；而根据数据集特征 all_value[0]是每一组数的目标标签

        n.train(inputs,targets) #训练
    print("第 %d 代训练完成" % e)
##########################################测试#################################################
data_file = open("mnist_dataset/MNIST_csv/mnist_test.csv",'r')
test_data_list = data_file.readlines()
data_file.close()

#测试，并统计准确率
scorecard=[]
for record in test_data_list:
    value_test = record.split(',')
    correct_label=int(value_test[0])
    # print(correct_label,"正确答案")
    inputs = (numpy.asfarray(value_test[1:]) / 255.0 * 0.99) + 0.01
    outputs = n.query(inputs)
    label = numpy.argmax(outputs)
    # print(label,"网络给出的答案")
    if (label==correct_label):
        scorecard.append(1)
    else:
        scorecard.append(0)
#print(scorecard)

#打分
scorecard_array=numpy.asarray(scorecard)
print("performance = ",scorecard_array.sum()/scorecard_array.size)

       The .csv format files of the training set and dataset can be obtained from the following links:

Link: https://pan.baidu.com/s/1Wc55qHUQPbPBNz4uYdY8nA 
Extraction code: 2pq6 
--Share from Baidu Netdisk super member V3

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