李宏毅-深度学习Note（Lecture 0-2 Introduction Regression）

柔情只为你懂 2023-06-01 06:17 133阅读 0赞

#### 李宏毅-深度学习Note ####

视频链接：[机器学习-李宏毅(2019) Machine Learning ][-_2019_ Machine Learning]

参考：[李宏毅 深度学习19（完整版）国语】机器学习 深度学习][_19_]  
   [李宏毅-深度学习Note（目录）][-_Note]

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*  目录
 *  P1ML Lecture 0 - Introduction of Machine Learning
 *  P2ML Lecture 1 - Regression - Case Study
 *  P3ML Lecture 1 - Regression - Demo
 *  P4ML Lecture 2 - Where does the error come from

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##### P1ML Lecture 0 - Introduction of Machine Learning #####

1.  ML是什么？
    
    输入→ML（大量数据训练的程序）→输出
2.  Learning Map
    
    ![Alt][]

##### P2ML Lecture 1 - Regression - Case Study #####

1.  Regression三步骤
    
    Model → Goodness of Function → Gradient Descent
    
    1.1 Model
    
    自定义预测函数
    
    1.2 Goodness of Function
    
    A set of function → Goodness of function f ← Training Data
    
    1.3 Gradient Descent
    
    凡可微分的函数，都可以用梯度下降找到最优解
    
    注：Linear Regression上是没有局部最优解，只可能有全局最优解  
      Because the loss function L is convex.(凸函数)
    
    问：为什么机器学习时需要划分训练与测试阶段？
    
      主流为监督学习
    
    优化模型可以使函数更加复杂，泰勒展开式
    
    一个越复杂的模型，表示能力越强，因为表示的情况越多。（高次包含低次）

##### P3ML Lecture 1 - Regression - Demo #####

import numpy as np
    import matplotlib.pyplot as plt
    
    x_data = [338., 333., 328., 207., 226., 25., 179., 60., 208., 606.]
    y_data = [640., 633., 619., 393., 428., 27., 193., 66., 226., 1591.]
    # y_data = b + w * x_data
    
    # bias
    x = np.arange(-200, -100, 1)
    # weight
    y = np.arange(-5, 5, 0.1)
    Z = np.zeros((len(x), len(y)))
    X, Y = np.meshgrid(x, y)
    for i in range(len(x)):
        for j in range(len(y)):
            b = x[i]
            w = y[j]
            Z[j][i] = 0
            for n in range(len(x_data)):
                Z[j][i] = Z[j][i] + (y_data[n] - b - w * x_data[n])**2
            Z[j][i] = Z[j][i]/len(x_data)
    
    # bias
    x = np.arange(-200, -100, 1)
    # weight
    y = np.arange(-5, 5, 0.1)
    Z = np.zeros((len(x), len(y)))
    X, Y = np.meshgrid(x, y)
    for i in range(len(x)):
        for j in range(len(y)):
            b = x[i]
            w = y[j]
            Z[j][i] = 0
            for n in range(len(x_data)):
                Z[j][i] = Z[j][i] + (y_data[n] - b - w * x_data[n])**2
            Z[j][i] = Z[j][i]/len(x_data)
    
    # y_data = b + w * x_data
    # initial b
    b = -120
    # initial w
    w = -4
    # learning rate
    lr = 0.0000001
    # iteration = 100000
    iteration = 100
    
    # store initial values for plotting
    b_history = [b]
    w_history = [w]
    
    # iterations
    for i in range(iteration):
        b_grad = 0.0
        w_grad = 0.0
        
        for n in range(len(x_data)):
            b_grad = b_grad - 2.0*(y_data[n] - b - w*x_data[n])*1.0
            w_grad = w_grad - 2.0*(y_data[n] - b - w*x_data[n])*x_data[n]
            
        # update parameters
        b = b - lr*b_grad
        w = w - lr*w_grad
        
        # store parameters for plotting
        b_history.append(b)
        w_history.append(w)
        
        # plot for figure
        plt.contourf(x,y,Z, 50, alpha=0.5, cmap=plt.get_cmap('jet'))
        plt.plot([-188.4], [2.67], x, ms=12, markeredgewidth=3, color='orange')
        plt.plot(b_history, w_history, 'o-', ms=3, lw=1.5, color='black')
        plt.xlim(-200, -100)
        plt.ylim(-5, 5)
        plt.xlabel(r'$b$', fontsize=16)
        plt.ylabel(r'$w$', fontsize=16)
        plt.show()

# 优化
    # y_data = b + w * x_data
    # initial b
    b = -120
    # initial w
    w = -4
    # learning rate
    lr = 1
    # iteration = 100000
    iteration = 100
    
    # store initial values for plotting
    b_history = [b]
    w_history = [w]
    
    # iterations
    for i in range(iteration):
        b_grad = 0.0
        w_grad = 0.0
        
        for n in range(len(x_data)):
            b_grad = b_grad - 2.0*(y_data[n] - b - w*x_data[n])*1.0
            w_grad = w_grad - 2.0*(y_data[n] - b - w*x_data[n])*x_data[n]
            
        # update parameters
        b = b - lr*b_grad
        w = w - lr*w_grad
        
        # store parameters for plotting
        b_history.append(b)
        w_history.append(w)
        
        # plot for figure
        plt.contourf(x,y,Z, 50, alpha=0.5, cmap=plt.get_cmap('jet'))
        plt.plot([-188.4], [2.67], x, ms=12, markeredgewidth=3, color='orange')
        plt.plot(b_history, w_history, 'o-', ms=3, lw=1.5, color='black')
        plt.xlim(-200, -100)
        plt.ylim(-5, 5)
        plt.xlabel(r'$b$', fontsize=16)
        plt.ylabel(r'$w$', fontsize=16)
        plt.show()

##### P4ML Lecture 2 - Where does the error come from #####

与标准值的偏差bias + 与其他函数均值的方差variance

simple model → Large bias + Small variance

comple model → Small bias + Large variance

不能适合训练集 + large bias → Underfitting

适合训练集，在测试集上较大错误 + large variance → Overfitting

large bias：

Add more features as input
    A more complex model

large variance：

More data
    Regularization

Corss Validation：

训练集（训练集+验证集） + 测试集（Public） + 测试集（Private）

[-_2019_ Machine Learning]: https://www.bilibili.com/video/av35932863/?p=1
[_19_]: https://www.bilibili.com/video/av48285039?t=525&p=1
[-_Note]: https://blog.csdn.net/qq_39564555/article/details/101974925
[Alt]: /images/20230601/87aa237a151841648b8c03eba945735c.png