生成 y Xw b 噪声
X torch.normal(0, 1, (num_examples, len(w))) # 正态分布 均值为0 标准差为1
y torch.matmul(X, w) b # 矩阵相乘
y torch.normal(0, 0.01, y.shape) # 加入噪声项
# 得到的y为行向量的形式 为了使其变为一列的形式需要进行reshape
return X, y.reshape((-1, 1))
def data_iter(batch_size, features, labels):
num_examples len(features)
indices list(range(num_examples))
# 这些样本是随机读出的 没有特定的顺序
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]
def linear(X,w,b):
定义模型
return torch.matmul(X,w) b
def squared_loss(y_hat,y):
return (y_hat-y.reshape(y_hat.shape))**2/2
def sgd(params, lr, batch_size):
小批量梯度下降
with torch.no_grad():
for param in params: # 参数b和w
param - lr*param.grad/batch_size
param.grad.zero_()
if __name__ __main__ :
true_w torch.tensor([2, -3.4])
true_b 4.2
features, labels synthetic_data(true_w,true_b,1000) # 生成数据集
# 初始化模型参数
w torch.normal(0,0.01,(2,1),requires_grad True)
b torch.zeros(1,requires_grad True)
# 定义超参数
lr 0.03
num_epochs 3
batch_size 15
net linear
loss squared_loss
for epoch in range(num_epochs):
for X, y in data_iter(batch_size,features,labels):
y_hat linear(X,w,b)
loss squared_loss(y_hat,y)
loss.sum().backward() # 进行反向传播得到梯度
sgd((w,b),lr,batch_size)
with torch.no_grad():
train_l squared_loss(net(features,w,b),labels)
# print(train_l)
print(f epoch{epoch 1},loss{float(train_l.mean()):f} )
