Python深度学习pytorch神经网络多层感知机简洁实现

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

net = nn.Sequential(nn.Flatten(),
					nn.Linear(784, 256),
					nn.ReLU(),
					nn.Linear(256, 10))
def init_weights(m):
	if type(m) == nn.Linear:
		nn.init.normal_(m.weight, std=0.01)
net.apply(init_weights)

batch_size, lr, num_epochs = 256, 0.1, 10 # 批量大小为256，学习率为0.1，类型为10
loss = nn.CrossEntropyLoss() # 使用交叉熵损失函数
trainer = torch.optim.SGD(net.parameters(), lr=lr) # 开始训练

train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, trainer)