多层感知机的简洁实现

import torch
from torch import nn
from d2l import torch as d2l

## 参数
batch_size = 256
num_epochs=10

##模型
net=nn.Sequential(nn.Flatten(),
                  nn.Linear(784, 256),
                  nn.ReLU(),
                  nn.Linear(256, 10))

##权重初始化
def init_weights(m):
    if isinstance(m, nn.Linear):
        nn.init.xavier_uniform_(m.weight)

net.apply(init_weights)

##读取数据集
train_iter,test_iter=d2l.load_data_fashion_mnist(batch_size)

## 损失函数
loss=nn.CrossEntropyLoss()

##优化器
updater=torch.optim.SGD(net.parameters(),lr=0.1)

## 训练
d2l.train_ch3(net,train_iter,test_iter,loss,num_epochs,updater)

##预测
d2l.predict_ch3(net,test_iter)