Pytorch实践----04Back Propagation反向传播

学习

刘二大人《PyTorch深度学习实践》
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问题1：通过损失的计算对权重进行更新，对于复杂的网络应当怎么去做呢？
通过前馈和反向传播

问题2：在每一层结束引入非线性变换函数的意义是？
为了提高模型的复杂度和泛化程度

import torch

x_data = [1.0, 2.0, 3.0]
y_data = [2.0, 4.0, 6.0]

w = torch.Tensor([1.0])
w.requires_grad = True


def forward(x):
    return x * w


def loss(x, y):
    y_pred = forward(x)
    return (y_pred - y) ** 2


print('predict (before training)', 4, forward(4).item())
for epoch in range(100):
    for x, y in zip(x_data, y_data):
        l = loss(x, y)
        l.backward()
        print('\tgrad:', x, y, w.grad.item())
        w.data = w.data - 0.01 * w.grad.data
        w.grad.data.zero_()

    print('progress:', epoch, l.item())

print('predict (after training)', 4, forward(4).item())