动手学深度学习（Pytorch版）代码实践 -深度学习基础-08多层感知机简洁版

08多层感知机简洁版

import torch
from torch import nn
from d2l import torch as d2l
import liliPytorch as lpnet = nn.Sequential(nn.Flatten(),nn.Linear(784,256),nn.ReLU(),nn.Linear(256,10)  
)#函数接受一个参数 m，通常是一个神经网络模块（例如，线性层，卷积层等）
def init_weights(m):
#这行代码检查传入的模块 m 是否是 nn.Linear 类型，即线性层（全连接层）if type(m) == nn.Linear:nn.init.normal_(m.weight,std=0.01)
#m.weight 是线性层的权重矩阵。
#std=0.01 指定了初始化权重的标准差为 0.01，表示权重将从均值为0，标准差为0.01的正态分布中随机采样。#model.apply(init_weights) 会遍历模型的所有模块，并对每个模块调用 init_weights 函数。
#如果模块是 nn.Linear 类型，则初始化它的权重。
net.apply(init_weights)batch_size, lr, num_epochs = 256, 0.1, 10
loss = nn.CrossEntropyLoss(reduction='none')
trainer = torch.optim.SGD(net.parameters(),lr=lr)
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)#训练
lp.train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer)#验证
lp.predict_ch3(net, test_iter)
d2l.plt.show() 

运行结果：

<Figure size 350x250 with 1 Axes>
epoch: 1,train_loss: 1.0443685918807983,train_acc: 0.64345,test_acc: 0.7608
<Figure size 350x250 with 1 Axes>
epoch: 2,train_loss: 0.5980708345413208,train_acc: 0.7904166666666667,test_acc: 0.7707
<Figure size 350x250 with 1 Axes>
epoch: 3,train_loss: 0.5194601311365763,train_acc: 0.8209166666666666,test_acc: 0.8143
<Figure size 350x250 with 1 Axes>
epoch: 4,train_loss: 0.4801325536727905,train_acc: 0.8319666666666666,test_acc: 0.827
<Figure size 350x250 with 1 Axes>
epoch: 5,train_loss: 0.4518238489786784,train_acc: 0.8414833333333334,test_acc: 0.8358