pytorch梯度更新原理以及不同实现方式

• 使用数学计算进行参数更新.

import torch
from torch.autograd import Variable

N,D_in,H,D_out=64,1000,100,10
x=Variable(torch.randn(N,D_in),requires_grad=False)
y=Variable(torch.randn(N,D_out),requires_grad=False)
w1=Variable(torch.randn(D_in,H),requires_grad=True)
w2=Variable(torch.randn(H,D_out),requires_grad=True)

learning_rate=1e-6
for t in range(500):
    y_pred=x.mm(w1).clamp(min=0).mm(w2)
    loss=(y_pred-y).pow(2).sum()

    if w1.grad is not None: w1.grad.data.zero_()
    if w2.grad is not None: w2.grad.data.zero_()
    loss.backward()

    w1.data-=learning_rate*w1.grad.data
    w2.data-=learning_rate*w2.grad.data
    print('loss:',loss)

• 使用torch内部的Sequntial()方法和grad方法进行更新

import torch
from torch.autograd import Variable

N,D_in,H,D_out=64,1000,100,10
x=Variable(torch.randn(N,D_in),requires_grad=False)
y=Variable(torch.randn(N,D_out),requires_grad=False)
w1=Variable(torch.randn(D_in,H),requires_grad=True)
w2=Variable(torch.randn(H,D_out),requires_grad=True)

learning_rate=1e-6
for t in range(10):
    y_pred=x.mm(w1).clamp(min=0).mm(w2)
    loss=(y_pred-y).pow(2).sum()

    if w1.grad is not None: w1.grad.data.zero_()
    if w2.grad is not None: w2.grad.data.zero_()
    loss.backward()

    w1.data-=learning_rate*w1.grad.data
    w2.data-=learning_rate*w2.grad.data
    print('w1:',w1,'nw2:',w2)

• 根据模型的构建获取参数,进而更新参数

import torch
from torch.autograd import Variable

N,D_in,H,D_out=64,100,100,10
x=Variable(torch.randn(N,D_in))
y=Variable(torch.randn(N,D_out),requires_grad=False)

model=torch.nn.Sequential(torch.nn.Linear(D_in,H),
                          torch.nn.ReLU(),
                          torch.nn.Linear(H,D_out))
loss_fn=torch.nn.MSELoss(size_average=False)

learning_rate=1e-4
for t in range(500):
    y_pred=model(x)
    loss=loss_fn(y_pred,y)
    model.zero_grad()
    loss.backward()
    for param in model.parameters():
        param.data-=learning_rate*param.grad.data
    print('loss:',loss)

• 根据自己编写的模型和优化器进行梯度更新

import torch
from torch.autograd import Variable

class TwoLayerNet(torch.nn.Module):
    def __init__(self,D_in,H,D_out):
        super(TwoLayerNet,self).__init__()
        self.linear1=torch.nn.Linear(D_in,H)
        self.linear2=torch.nn.Linear(H,D_out)

    def forward(self,x):
        h_relu=self.linear1(x)
        y_pred=self.linear2(h_relu)
        return y_pred

N,D_in,H,D_out=64,1000,100,10
x=Variable(torch.randn(N,D_in))
y=Variable(torch.randn(N,D_out),requires_grad=False)

model=TwoLayerNet(D_in,H,D_out)

criterion=torch.nn.MSELoss(size_average=False)
optimizer=torch.optim.SGD(model.parameters(),lr=1e-4)
for t in range(500):
    y_pred=model(x)
    loss=criterion(y_pred,y)

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
    print(loss.detach().numpy())

本文有助于读者对梯度更新原理的理解以及pytorch模型的构建.更多的详细信息可以参考斯坦福公开课.