Update neural network parameters

Update the neural network parameters:

The simplest update rule is stochastic gradient descent.

weight = weight - learning_rate * gradient

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim


class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, 5)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
        x = x.view(-1, self.num_flat_feature(x))
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

    def num_flat_feature(self, x):
        size = x.size()[1:]
        num_features = 1
        for s in size:
            num_features *= s
        return num_features

net = Net()
input = torch.randn(1, 1, 32, 32)

target = torch.randn(10)  # a dummy target, for example
target = target.view(1, -1)
# # 举个栗子
criterion = nn.MSELoss()

optimizer = optim.SGD(net.parameters(), lr=0.01)
optimizer.zero_grad()
output = net(input)
loss = criterion(output,target)
loss.backward()
optimizer.step()