pytorch实现多层感知机（自动定义模型）对Fashion-MNIST数据集进行分类

导入模块：

import torch
from torch import nn
from torch.nn import init
import numpy as np

定义数据集：

class FlattenLayer(nn.Module): # 定义一个tensor形状转换的层def __init__(self):super(FlattenLayer, self).__init__()def forward(self, x): # x shape: (batch, *, *, ...)return x.view(x.shape[0], -1)mnist_train = torchvision.datasets.FashionMNIST(root='~/Datasets/FashionMNIST', train=True, download=True, transform=transforms.ToTensor())
mnist_test = torchvision.datasets.FashionMNIST(root='~/Datasets/FashionMNIST', train=False, download=True, transform=transforms.ToTensor())
batch_size = 256
if sys.platform.startswith('win'):num_workers = 0  # 0表示不用额外的进程来加速读取数据
else:num_workers = 4
train_iter = torch.utils.data.DataLoader(mnist_train, batch_size=batch_size, shuffle=True, num_workers=num_workers)
test_iter = torch.utils.data.DataLoader(mnist_test, batch_size=batch_size, shuffle=False, num_workers=num_workers)#loss函数
loss = torch.nn.CrossEntropyLoss()

定义模型：

num_inputs, num_outputs, num_hiddens = 784, 10, 256net = nn.Sequential(d2l.FlattenLayer(),nn.Linear(num_inputs, num_hiddens),nn.ReLU(),nn.Linear(num_hiddens, num_outputs), )
# 优化器
optimizer = torch.optim.SGD(net.parameters(), lr=0.5)for params in net.parameters():init.normal_(params, mean=0, std=0.01)

训练模型：

num_epochs = 5def train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size,params=None, lr=None, optimizer=None):for epoch in range(num_epochs):train_l_sum, train_acc_sum, n = 0.0, 0.0, 0for X, y in train_iter:y_hat = net(X)l = loss(y_hat, y).sum()# 梯度清零if optimizer is not None:optimizer.zero_grad() # 这里我们用到优化器，所以直接对优化器行梯度清零elif params is not None and params[0].grad is not None:for param in params:param.grad.data.zero_()l.backward()if optimizer is None:sgd(params, lr, batch_size)else:optimizer.step()  # 用到优化器这里train_l_sum += l.item()train_acc_sum += (y_hat.argmax(dim=1) == y).sum().item()n += y.shape[0] test_acc = evaluate_accuracy(test_iter, net)print('epoch %d, loss %.4f, train acc %.3f, test acc %.3f'% (epoch + 1, train_l_sum / n, train_acc_sum / n, test_acc))train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, None, None, optimizer)