线性模型第一课
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import random import torch import matplotlib as pl #生成样本,特征和标签 def synthetic_data(w,b,num_examples): x=torch.normal(0,1,(num_examples,len(w))) #均值为0方差为1,w列 y=torch.matmul(x,w)+b #x*w+b y+=torch.normal(0,0.01,y.shape) #均值为0,方差为0.01的随机噪音 return x,y.reshape((-1,1)) true_w=torch.tensor([2,-3.4]) true_b=4.2 features,lables=synthetic_data(true_w,true_b,1000) #定义函数,接受批量大小,特征矩阵和标签向量作为输入,生成大小为batch_size的小批量 def data_iter(batch_size,features,lables): num_examples=len(features)#多个样本 indices=list(range(num_examples))#每个样本的index random.shuffle(indices)#打乱顺序 for i in range(0,num_examples,batch_size):#从0开始,每次跳batch_size个大小 batch_indics=torch.tensor( indices[i:min(i+batch_size,num_examples)] )#每次拿b个index。如果不够拿一个最小值 yield features[batch_indics],lables[batch_indics] batch_size=10 for x,y in data_iter(batch_size,features,lables): print(x,'\n',y) break w=torch.normal(0,0.01,size=(2,1),requires_grad=True) b=torch.zeros(1,requires_grad=True) #定义线性模型 def linreg(x,w,b): return torch.matmul(x,w)+b #定义损失函数 #1/2(y-y_hat)(y-y_hat) def squared_loss(y_hat,y): return (y_hat-y.reshape(y_hat.shape))**2/2 #定义最优化算法 #给定所有参数,学习率和批量 def sgd(params,lr,batch_size): with torch.no_grad(): for param in params: param-=lr*param.grad/batch_size param.grad.zero_() lr=0.03 num_epochs=3#整个模型扫3遍 net=linreg#线性模型 loss=squared_loss for epoch in range(num_epochs): for x,y in data_iter(batch_size,features,lables): l=loss(net(x,w,b),y)#求y_hat,再求y_hat和y的损失 l.sum().backward() sgd([w,b],lr,batch_size) with torch.no_grad(): train_1=loss(net(features,w,b),lables) print(f'epoch{epoch+1} ,loss{float(train_1.mean()):f}')