PyTorch 深度学习实践 第4讲

第4讲 反向传播back propagation

来源：B站 刘二大人

import torch

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

w = torch.Tensor([1.0])  # w的初值为1.0
w.requires_grad = True  # 需要计算梯度


def forward(x):
    return x * w  # w是一个Tensor


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是一个张量，tensor主要是在建立计算图 forward, compute the loss
        l.backward()  # backward,compute grad for Tensor whose requires_grad set to True
        print('\tgrad:', x, y, w.grad.item())
        w.data = w.data - 0.01 * w.grad.data  # 权重更新时，需要用到标量，注意grad也是一个tensor

        w.grad.data.zero_()  # after update, remember set the grad to zero

    print('progress:', epoch, l.item())  # 取出loss使用l.item，不要直接使用l（l是tensor会构建计算图）

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

y=w*x线性模型，用pytorch实现反向传播代码如下：

import numpy as np
import matplotlib.pyplot as plt
import torch

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

w = torch.Tensor([1.0])#初始权值
w.requires_grad = True#计算梯度，默认是不计算的

def forward(x):
    return x * w

def loss(x,y):#构建计算图
    y_pred = forward(x)
    return (y_pred-y) **2

print('Predict (befortraining)',4,forward(4))

for epoch in range(100):
    l = loss(1, 2)#为了在for循环之前定义l,以便之后的输出，无实际意义
    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 #注意这里的grad是一个tensor，所以要取他的data
        w.grad.data.zero_() #释放之前计算的梯度
    print('Epoch:',epoch,l.item())

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