刘二大人《PyTorch深度学习实践》

线性模型

损失函数是针对一个样本的
training set得到的是一个平均平方误差MSE
穷举法绘制损失曲线：numpy和matplotlib

import numpy as np
import matplotlib.pyplot as plt
#数据集
x_data = [1.0,2.0,3.0]
y_data = [2.0,4.0,6.0]
#定义模型
def forward(x):
    return x * w
#定义损失函数
def loss(x,y):
    y_pred = forward(x)
    return (y_pred-y) * (y_pred-y)
#存放权重和权重损失值对应的列表
w_list = []
mse_list = []
#w取值为0-4，间隔为0.1
for w in np.arange(0.0,4.1,0.1):
    print("w=",w)
    l_sum = 0
# 把数据集里的数据取出来拼成x_val和y_val
    for x_val,y_val in zip(x_data,y_data):
        y_pred_val = forward(x_val)
        loss_val = loss(x_val, y_val)
        l_sum += loss_val#先求和
        print('\t',x_val,y_val,y_pred_val,loss_val)
    print("MSE=",l_sum/3)#取平均
    w_list.append(w)
    mse_list.append(l_sum/3)
plt.plot(w_list, mse_list)
plt.ylabel('Loss')
plt.xlabel('w')
plt.show()

梯度下降

x_data = [1.0, 2.0, 3.0]
y_data = [2.0, 4.0, 6.0]

w = 1.0

def forward(x):
    return x * w

def cost(xs, ys):
    cost = 0
    for x, y in zip(xs, ys):
        y_pred = forward(x)
        cost += (y_pred - y) ** 2
    return cost / len(xs)

def gradient(xs, ys):
    grad = 0
    for