所謂的多分類問題，實際上是使用了softmax函數，這一點和tensorflow是有區別的。tensorflow中的softmax和交叉熵函數是分開的，而pytorch是合并到一起的。
對mnist數據集使用多分類，代碼如下：

import torch from torchvision import transforms from torchvision import datasets from torch.utils.data import DataLoader import torch.nn.functional as F import torch.optim as optim# prepare datasetbatch_size = 64 transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) # 歸一化,均值和方差train_dataset = datasets.MNIST(root='E:\\tmp\\pytorch/', train=True, download=True, transform=transform) train_loader = DataLoader(train_dataset, shuffle=True, batch_size=batch_size) test_dataset = datasets.MNIST(root='E:\\tmp\\pytorch/', train=False, download=True, transform=transform) test_loader = DataLoader(test_dataset, shuffle=False, batch_size=batch_size)# design model using classclass Net(torch.nn.Module):def __init__(self):super(Net, self).__init__()self.l1 = torch.nn.Linear(784, 512)self.l2 = torch.nn.Linear(512, 256)self.l3 = torch.nn.Linear(256, 128)self.l4 = torch.nn.Linear(128, 64)self.l5 = torch.nn.Linear(64, 10)def forward(self, x):x = x.view(-1, 784) # -1其實就是自動獲取mini_batchx = F.relu(self.l1(x))x = F.relu(self.l2(x))x = F.relu(self.l3(x))x = F.relu(self.l4(x))return self.l5(x) # 最后一層不做激活，不進行非線性變換model = Net()# construct loss and optimizer criterion = torch.nn.CrossEntropyLoss() optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)# training cycle forward, backward, updatedef train(epoch):running_loss = 0.0for batch_idx, data in enumerate(train_loader, 0):# 獲得一個批次的數據和標簽inputs, target = dataoptimizer.zero_grad()# 獲得模型預測結果(64, 10)outputs = model(inputs)# 交叉熵代價函數outputs(64,10),target（64）loss = criterion(outputs, target)loss.backward()optimizer.step()running_loss += loss.item()if batch_idx % 300 == 299:print('[%d, %5d] loss: %.3f' % (epoch+1, batch_idx+1, running_loss/300))running_loss = 0.0def test():correct = 0total = 0with torch.no_grad():for data in test_loader:images, labels = dataoutputs = model(images)_, predicted = torch.max(outputs.data, dim=1) # dim = 1 列是第0個維度，行是第1個維度total += labels.size(0)correct += (predicted == labels).sum().item() # 張量之間的比較運算print('accuracy on test set: %d %% ' % (100*correct/total))if __name__ == '__main__':for epoch in range(10):train(epoch)test()

結果如下：

[1, 300] loss: 2.186 [1, 600] loss: 0.881 [1, 900] loss: 0.435 accuracy on test set: 89 % [2, 300] loss: 0.323 [2, 600] loss: 0.262 [2, 900] loss: 0.225 accuracy on test set: 94 % [3, 300] loss: 0.187 [3, 600] loss: 0.171 [3, 900] loss: 0.152 accuracy on test set: 95 % [4, 300] loss: 0.127 [4, 600] loss: 0.127 [4, 900] loss: 0.110 accuracy on test set: 96 % [5, 300] loss: 0.097 [5, 600] loss: 0.096 [5, 900] loss: 0.092 accuracy on test set: 96 % [6, 300] loss: 0.080 [6, 600] loss: 0.074 [6, 900] loss: 0.073 accuracy on test set: 97 % [7, 300] loss: 0.062 [7, 600] loss: 0.059 [7, 900] loss: 0.062 accuracy on test set: 97 % [8, 300] loss: 0.052 [8, 600] loss: 0.048 [8, 900] loss: 0.048 accuracy on test set: 97 % [9, 300] loss: 0.039 [9, 600] loss: 0.042 [9, 900] loss: 0.038 accuracy on test set: 96 % [10, 300] loss: 0.034 [10, 600] loss: 0.032 [10, 900] loss: 0.033 accuracy on test set: 97 %

努力加油a啊

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