李沐老师 《动手学深度学习》笔记

 for epoch in range(0, num_epochs):

    
     for X, y in read_data(batch_size, features, labels):
    
     f = loss(net(X, w, b), y)
    
     f.sum().backward()
    
     sgd([w, b], batch_size, lr)  # 使用参数的梯度更新参数
    
     with torch.no_grad():
    
     train_l = loss(net(features, w, b), labels)
    
     print("w {0} \nb {1} \nloss {2:f}".format(w, b, float(train_l.mean())))

 def sgd(params, batch_size, lr):

    
     with torch.no_grad():  # with torch.no_grad() 则主要是用于停止autograd模块的工作，
    
     for param in params:
    
         param -= lr * param.grad / batch_size  ##  这里用param = param - lr * param.grad / batch_size会导致导数丢失， zero_()函数报错
    
         param.grad.zero_()  ## 导数如果丢失了，会报错‘NoneType’ object has no attribute ‘zero_’

 import random

    
 import torch
    
  
    
  
    
 ## 人造数据集
    
 def create_data(w, b, nums_example):
    
     X = torch.normal(0, 1, (nums_example, len(w)))
    
     y = torch.matmul(X, w) + b
    
     print("y_shape:", y.shape)
    
     y += torch.normal(0, 0.01, y.shape)  # 加入噪声
    
     return X, y.reshape(-1, 1)  # y从行向量转为列向量
    
  
    
  
    
 true_w = torch.tensor([2, -3.4])
    
 true_b = 4.2
    
 features, labels = create_data(true_w, true_b, 1000)
    
  
    
  
    
 ## 读数据集
    
 def read_data(batch_size, features, lables):
    
     nums_example = len(features)
    
     indices = list(range(nums_example))  # 生成0-999的元组，然后将range()返回的可迭代对象转为一个列表
    
     random.shuffle(indices)  # 将序列的所有元素随机排序。
    
     for i in range(0, nums_example, batch_size):  # range(start, stop, step)
    
     index_tensor = torch.tensor(indices[i: min(i + batch_size, nums_example)])
    
     yield features[index_tensor], lables[index_tensor]  # yield就是 return 返回一个值，并且记住这个返回的位置，下次迭代就从这个位置后开始。
    
  
    
  
    
 batch_size = 10
    
 for X, y in read_data(batch_size, features, labels):
    
     print("X:", X, "\ny", y)
    
     break;
    
  
    
 ##初始化参数
    
 w = torch.normal(0, 0.01, size=(2, 1), requires_grad=True)
    
 b = torch.zeros(1, requires_grad=True)
    
  
    
  
    
 # 定义模型
    
 def net(X, w, b):
    
     return torch.matmul(X, w) + b
    
  
    
  
    
 # 定义损失函数
    
 def loss(y_hat, y):
    
     # print("y_hat_shape:",y_hat.shape,"\ny_shape:",y.shape)
    
     return (y_hat - y.reshape(y_hat.shape)) ** 2 / 2  # 这里为什么要加 y_hat_shape: torch.Size([10, 1])  y_shape: torch.Size([10])
    
  
    
  
    
 # 定义优化算法
    
 def sgd(params, batch_size, lr):
    
     with torch.no_grad():  # with torch.no_grad() 则主要是用于停止autograd模块的工作，
    
     for param in params:
    
         param -= lr * param.grad / batch_size  ##  这里用param = param - lr * param.grad / batch_size会导致导数丢失， zero_()函数报错
    
         param.grad.zero_()  ## 导数如果丢失了，会报错‘NoneType’ object has no attribute ‘zero_’
    
  
    
  
    
 # 训练模型
    
 lr = 0.03
    
 num_epochs = 3
    
  
    
 for epoch in range(0, num_epochs):
    
     for X, y in read_data(batch_size, features, labels):
    
     f = loss(net(X, w, b), y)
    
     # 因为`f`形状是(`batch_size`, 1)，而不是一个标量。`f`中的所有元素被加到一起，
    
     # 并以此计算关于[`w`, `b`]的梯度
    
     f.sum().backward()
    
     sgd([w, b], batch_size, lr)  # 使用参数的梯度更新参数
    
     with torch.no_grad():
    
     train_l = loss(net(features, w, b), labels)
    
     print("w {0} \nb {1} \nloss {2:f}".format(w, b, float(train_l.mean())))
    
  
    
 print("w误差 ", true_w - w, "\nb误差 ", true_b - b)