pytorch實現多層感知機（自動定義模型）對Fashion-MNIST數據集進行分類

導入模塊：

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

定義數據集：

class FlattenLayer(nn.Module): # 定義一個tensor形狀轉換的層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表示不用額外的進程來加速讀取數據 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函數 loss = torch.nn.CrossEntropyLoss()

定義模型：

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), ) # 優化器 optimizer = torch.optim.SGD(net.parameters(), lr=0.5)for params in net.parameters():init.normal_(params, mean=0, std=0.01)

訓練模型：

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() # 這里我們用到優化器，所以直接對優化器行梯度清零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() # 用到優化器這里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)

總結

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