# coding:utf-8

# Author:codewithzichao

# Date:2020-1-2

# E-mail:lizichao@pku.edu.cn

'''

数据集：Mnist

准确率：0.1532.

时间：16.96704387664795.

--------------

tips:实现的非常简单，直接将输入数据输入到一个线性层，然后输出10个0-1之间的数，中间没有用任何的隐藏层，

由于是线性变换，所以效果比较差吧，如果加几个隐藏层，效果会好得多！

'''

import numpy as np

import time

def loadData(fileName):

'''

加载数据

:param fileName:数据路径名

:return: 特征向量矩阵、还有标签矩阵

'''

data_list = [];

label_list = []

with open(fileName, "r") as f:

for line in f.readlines():

curline = line.strip().split(",")

label_list.append(int(curline[0]))

data_list.append([int(feature) / 255 for feature in curline[1:]])

data_matrix = np.array(data_list)

label_matrix = np.array(label_list).reshape(1, -1)

return data_matrix, label_matrix

class Softmax:

def __init__(self, train_data, train_label, iter, learning_rate):

'''

构造函数

:param train_data: 训练数据

:param train_label: 训练数据的标签类别

:param iter: epoch的数目

:param learning_rate: 学习率

'''

self.train_data = train_data

self.train_label = train_label

self.iter = iter

self.learning_rate = learning_rate

self.feature_num = self.train_data.shape[1]

self.input_num = self.train_data.shape[0]

self.w, self.b = self.initialize_params(self.feature_num)

def softmax(self, X):

'''

softmax函数

:param X: 输入数据

:return: 返回含有10个元素的np.array

'''

return np.exp(X) / np.sum(np.exp(X))

def initialize_params(self, feature_dim):

'''

初始化参数w,b

:param feature_dim:实例特征数目

:return: 参数w,b

'''

w = np.random.uniform(0, 1, (feature_dim, 10))

b = 0

return w, b

def train(self):

'''

训练

:return:返回参数w,b

'''

for iter in range(self.iter):

for i in range(self.input_num):

x = train_data[i].reshape(-1, 1)

y = np.zeros((10, 1))

y[train_label[0][i]] = 1

y_ = self.softmax(np.dot(self.w.T, x) + self.b)

self.w -= self.learning_rate * (np.dot((y_ - y), x.T))

self.b -= self.learning_rate * (y_ - y)

return self.w, self.b

def predict(self, digit):

'''

预测单个样本的值

:param digit: 严格样本的特征向量

:return: 返回预测的样本类别

'''

# 返回softmax中概率最大的值

return np.argmax(np.dot(self.w.T, digit) + self.b)

def test(self, test_data, test_label):

'''

测试

:param test_data: 测试集的特征向量

:param test_label: 测试集的标签类别

:return: 准确率

'''

error = 0

for i in range(test_data.shape[0]):

if (self.predict(test_data[i]) != test_label[0][i]):

error += 1

print(f"the prediction is {self.predict(test_data[i])},the true is {test_label[0][i]}.")

accuracy = (test_data.shape[0] - error) / test_data.shape[0]

return accuracy

if __name__ == "__main__":

start = time.time()

print("start load data.")

train_data, train_label = loadData("../MnistData/mnist_train.csv")

test_data, test_label = loadData("../MnistData/mnist_test.csv")

print("finished load data.")

a = Softmax(train_data, train_label, 50, 0.005)

accuracy = a.test(test_data, test_label)

end = time.time()

print(f"the accuracy is {accuracy}.")

print(f"the total time is {end - start}.")