softmax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(28*28,10))
## 权重初始化
def init_weights(m):
    if type(m) == nn.Linear:
        nn.init.normal_(m.weight,std=0.01)
        nn.init.zeros_(m.bias)

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=0.01)
##训练
d2l.train_ch3(net,train_iter,test_iter,loss,num_epochs,updater)
## 预测
d2l.predict_ch3(net,test_iter)