d2l.set_figsize()
d2l.plt.scatter(features[:, 1].detach().numpy(), labels.detach().numpy(), 1)
# 读取数据集
def data_iter(batch_size, features, labels):
num_examples len(features)
indices list(range(num_examples))
random.shuffle(indices)
for i in range(0, num_examples, batch_size):
batch_indices torch.tensor(
indices[i: min(i batch_size, num_examples)])
yield features[batch_indices], labels[batch_indices]
batch_size 10
for X, y in data_iter(batch_size, features, labels):
print(X, n , y)
break
# 初始化模型参数
w torch.normal(0, 0.01, size (2, 1), requires_grad True)
b torch.zeros(1, requires_grad True)
# 定义模型
def linreg(X, w, b):
return torch.matmul(X, w) b
# 定义损失函数
def squared_loss(y_hat, y):
MSRE
return (y_hat - y) ** 2/2
# 定义优化算法
def sgd(params, lr, batch_size):
小批量随机下降
with torch.no_grad():
for param in params:
param - lr * param.grad / batch_size
param.grad.zero_()
# training
lr 0.03
num_epochs 5
net linreg
loss squared_loss
for epoch in range(num_epochs):
for X, y in data_iter(batch_size, features, labels):
l loss(net(X, w, b), y)
l.sum().backward()
sgd([w, b], lr, batch_size)
with torch.no_grad():
train_l loss(net(features, w, b), labels)
print(f epoch {epoch 1}, loss {float(train_l.mean()):f} )
