【基于PyTorch的简单多层感知机（MLP）神经网络（深度学习经典代码实现）】

import torch
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.optim as optim
#准备数据集
batch_size=64
transform=transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307, ),(0.3081, ))
])
train_dataset=datasets.MNIST(root=r'D:\Python-Code\深度学习\data\MNIST',
                             train=True,
                             download=True,
                             transform=transform)
train_loader=DataLoader(train_dataset,
                        shuffle=True,
                        batch_size=batch_size)


test_dataset=datasets.MNIST(root=r'D:\Python-Code\深度学习\data\MNIST',
                             train=False,
                             download=True,
                             transform=transform)
test_loader=DataLoader(test_dataset,
                       shuffle=False,
                       batch_size=batch_size)
#设计模型
class Net(torch.nn.Module):
    def __init__(self):
        super(Net,self).__init__()
        self.l1=torch.nn.Linear(784,512)
        self.l2=torch.nn.Linear(512,256)
        self.l3=torch.nn.Linear(256,128)
        self.l4=torch.nn.Linear(128,64)
        self.l5=torch.nn.Linear(64,10)
    def forward(self,x):
        x=x.view(-1,784)
        x = F.relu(self.l1(x))
        x = F.relu(self.l2(x))
        x = F.relu(self.l3(x))
        x = F.relu(self.l4(x))
        return self.l5(x)
model=Net()

# 损失函数和优化器
criterion=torch.nn.Cr