定义网络模型
def line_reg(x, w, b):
return t.mm(x, w) + b
pass
定义损失函数
def loss(true_y, y):
return (true_y - y.view(true_y.size())) ** 2 / 2
pass
定义优化函数
def optimize(params, lr, batch):
for param in params:
param.data -= lr * param.grad / batch
pass
初始化模型
w = t.tensor(np.random.normal(0, 0.1, (2, 1)), dtype=t.float32, requires_grad=True)
b = t.zeros(1, dtype=t.float32, requires_grad=True)
训练和测试
epochs = 4
lr = 0.03
batch = 10
for epoch in range(epochs):
for x, y in get_random_sample(batch, features, labels):
l = loss(line_reg(x, w, b), y).sum()
l.backward()
optimize([w, b], lr, batch)
w.grad.data.zero_()
b.grad.data.zero_()
train_1 = loss(line_reg(features, w, b), labels)
print(‘epoch %d,loss %f’ % (epoch, train_1.mean().item()))
结果
