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())