李宏毅机器学习 赢家抑或输家预测 学习记录

输家预测 学习记录"/>

李宏毅机器学习 赢家抑或输家预测 学习记录

1.参考博客戳这里.

2.学习过程

2.1学习代码

因为初学机器学习，希望能理解更透彻，在原作者的注释基础上添加自己的理解：

import pandas as pd
import numpy as np# 更新参数，训练模型
def train(x_train, y_train, epoch):num = x_train.shape[0]       #训练集样本数量dim = x_train.shape[1]       #每一个样本的特征数量bias = 0  # 偏置值初始化weights = np.ones(dim)  # 权重初始化learning_rate = 1  # 初始学习率reg_rate = 0.001  # 正则项系数bg2_sum = 0  # 用于存放偏置值的梯度平方和wg2_sum = np.zeros(dim)  # 用于存放权重的梯度平方和for i in range(epoch):b_g = 0w_g = np.zeros(dim)# 在所有数据上计算梯度，梯度计算时针对损失函数求导for j in range(num):y_pre = weights.dot(x_train[j, :]) + biassig = 1 / (1 + np.exp(-y_pre))b_g += (-1) * (y_train[j] - sig)              #更新参数bfor k in range(dim):w_g[k] += (-1) * (y_train[j] - sig) * x_train[j, k] + 2 * reg_rate * weights[k]     #更新每一个w（i）b_g /= numw_g /= num# adagradbg2_sum += b_g ** 2wg2_sum += w_g ** 2# 更新权重和偏置bias -= learning_rate / bg2_sum ** 0.5 * b_gweights -= learning_rate / wg2_sum ** 0.5 * w_g# 每训练3轮，输出一次在训练集上的正确率# 在计算loss时，由于涉及到log()运算，因此可能出现无穷大，计算并打印出来的loss为nan# 有兴趣的同学可以把下面涉及到loss运算的注释去掉，观察一波打印出的lossif i % 3 == 0:# loss = 0acc = 0result = np.zeros(num)for j in range(num):y_pre = weights.dot(x_train[j, :]) + biassig = 1 / (1 + np.exp(-y_pre))if sig >= 0.5:result[j] = 1else:result[j] = 0if result[j] == y_train[j]:acc += 1.0# loss += (-1) * (y_train[j] * np.log(sig) + (1 - y_train[j]) * np.log(1 - sig))# print('after {} epochs, the loss on train data is:'.format(i), loss / num)print('after {} epochs, the acc on train data is:'.format(i), acc / num)return weights, bias# 验证模型效果
def validate(x_val, y_val, weights, bias):num = x_val.shape[0]# loss = 0acc = 0result = np.zeros(num)for j in range(num):y_pre = weights.dot(x_val[j, :]) + biassig = 1 / (1 + np.exp(-y_pre))if sig >= 0.5:result[j] = 1else:result[j] = 0if result[j] == y_val[j]:acc += 1.0# loss += (-1) * (y_val[j] * np.log(sig) + (1 - y_val[j]) * np.log(1 - sig))return acc / numdef main():# 从csv中读取有用的信息df = pd.read_csv('spam_train.csv')# 空值填0df = df.fillna(0)# (4000, 59)array = np.array(df)# (4000, 57)x = array[:, 1:-1]           #取所有行的2-58列，-1表示最后一个元素，这种表示方法取左不取右# scalex[:, -1] /= np.mean(x[:, -1])  #由于最后两列参数较大，对最后两列除平均x[:, -2] /= np.mean(x[:, -2])# (4000, )y = array[:, -1]             #取所有行的最后一个元素# 划分训练集与验证集x_train, x_val = x[0:3500, :], x[3500:4000, :]y_train, y_val = y[0:3500], y[3500:4000]epoch = 30  # 训练轮数# 开始训练w, b = train(x_train, y_train, epoch)# 在验证集上看效果acc = validate(x_val, y_val, w, b)print('The acc on val data is:', acc)if __name__ == '__main__':main()

2.2运行结果

"E:\python program\venv\Scripts\python.exe" "E:/python program/venv/spam.py"
after 0 epochs, the acc on train data is: 0.6134285714285714
after 3 epochs, the acc on train data is: 0.8994285714285715
after 6 epochs, the acc on train data is: 0.914
after 9 epochs, the acc on train data is: 0.9168571428571428
after 12 epochs, the acc on train data is: 0.9225714285714286
after 15 epochs, the acc on train data is: 0.9242857142857143
after 18 epochs, the acc on train data is: 0.9251428571428572
after 21 epochs, the acc on train data is: 0.9242857142857143
after 24 epochs, the acc on train data is: 0.9248571428571428
after 27 epochs, the acc on train data is: 0.9248571428571428
The acc on val data is: 0.94Process finished with exit code 0

3.修改及优化

在此程序基础上增加自己的修改和优化，可以加深理解和运用。随机取3800作为训练集，200作为验证集
在第一份源代码的基础上，只修改了main函数：

def main():# 从csv中读取有用的信息df = pd.read_csv('spam_train.csv')# 空值填0df = df.fillna(0)# (4000, 59)array = np.array(df)# (4000, 57)x = array[:, 1:-1]           #取所有行的2-58列，-1表示最后一个元素，这种表示方法取左不取右# scalex[:, -1] /= np.mean(x[:, -1])  #由于最后两列参数较大，对最后两列除平均x[:, -2] /= np.mean(x[:, -2])# (4000, )y = array[:, -1]             #取所有行的最后一个元素# 随机划分训练集与验证集rand_x = np.arange(x.shape[0])       # rand_X即每个数据帧的编号，用于后面随机打乱np.random.shuffle(rand_x)            # 打乱4000个数据帧x_train, x_val = x[rand_x[0:3800]], x[rand_x[3800:4000]]y_train, y_val = y[rand_x[0:3800]], y[rand_x[3800:4000]]epoch = 30  # 训练轮数# 开始训练w, b = train(x_train, y_train, epoch)# 在验证集上看效果acc = validate(x_val, y_val, w, b)print('The acc on val data is:', acc)

运行结果：

"E:\python program\venv\Scripts\python.exe" "E:/python program/venv/spam.py"
after 0 epochs, the acc on train data is: 0.6128947368421053
after 3 epochs, the acc on train data is: 0.9007894736842105
after 6 epochs, the acc on train data is: 0.9147368421052632
after 9 epochs, the acc on train data is: 0.9184210526315789
after 12 epochs, the acc on train data is: 0.921578947368421
after 15 epochs, the acc on train data is: 0.9263157894736842
after 18 epochs, the acc on train data is: 0.9257894736842105
after 21 epochs, the acc on train data is: 0.9257894736842105
after 24 epochs, the acc on train data is: 0.9265789473684211
after 27 epochs, the acc on train data is: 0.9247368421052632
The acc on val data is: 0.95Process finished with exit code 0