本文共 4341 字，大约阅读时间需要 14 分钟。
注意几个点：
import torchfrom torchvision import transformsfrom torchvision import datasetsfrom torch.utils.data import DataLoaderimport torch.nn.functional as Fimport torch.optim as optim batch_size = 64# 这里是对输入的图片进行预处理操作—》# 图像变换操作：transforms.ToTensor()，将PIL Image或者 ndarray 转换为tensor，并且归一化（直接除于255）至[0-1]# 图像变换操作：transforms.Normalize，对数据按通道进行标准化，即先减均值，再除以标准差，注意是 hwctransform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) train_dataset = datasets.MNIST(root='./资料/data/mnist/', train=True, download=True, transform=transform)train_loader = DataLoader(train_dataset, shuffle=True, batch_size=batch_size)test_dataset = datasets.MNIST(root='./资料/data/mnist/', train=False, download=True, transform=transform)test_loader = DataLoader(test_dataset, shuffle=False, batch_size=batch_size)
D:\common_software\Anaconda\lib\site-packages\torchvision\datasets\mnist.py:498: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at ..\torch\csrc\utils\tensor_numpy.cpp:180.) return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)
class 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其实就是自动获取mini_batch x = 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) # 最后一层不做激活，不进行非线性变换 model = Net()
# construct loss and optimizercriterion = torch.nn.CrossEntropyLoss()optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)
# training cycle forward, backward, updatedef train(epoch): running_loss = 0.0 for batch_idx, data in enumerate(train_loader, 0): # 获得一个批次的数据和标签 inputs, target = data optimizer.zero_grad() # 获得模型预测结果(64, 10) outputs = model(inputs) # 交叉熵代价函数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 = 0.0 def test(): correct = 0 total = 0 with torch.no_grad(): for data in test_loader: images, labels = data outputs = model(images) _, predicted = torch.max(outputs.data, dim=1) # dim = 1 列是第0个维度，行是第1个维度 total += labels.size(0) correct += (predicted == labels).sum().item() # 张量之间的比较运算 print('accuracy on test set: %d %% ' % (100*correct/total)) if __name__ == '__main__': for epoch in range(10): train(epoch) test()
[1, 300] loss: 0.342[1, 600] loss: 0.300[1, 900] loss: 0.252accuracy on test set: 93 % [2, 300] loss: 0.212[2, 600] loss: 0.187[2, 900] loss: 0.165accuracy on test set: 95 % [3, 300] loss: 0.139[3, 600] loss: 0.134[3, 900] loss: 0.128accuracy on test set: 95 % [4, 300] loss: 0.106[4, 600] loss: 0.099[4, 900] loss: 0.098accuracy on test set: 96 % [5, 300] loss: 0.078[5, 600] loss: 0.083[5, 900] loss: 0.082accuracy on test set: 96 % [6, 300] loss: 0.062[6, 600] loss: 0.072[6, 900] loss: 0.063accuracy on test set: 97 % [7, 300] loss: 0.050[7, 600] loss: 0.056[7, 900] loss: 0.053accuracy on test set: 97 % [8, 300] loss: 0.041[8, 600] loss: 0.045[8, 900] loss: 0.043accuracy on test set: 97 % [9, 300] loss: 0.033[9, 600] loss: 0.035[9, 900] loss: 0.038accuracy on test set: 97 % [10, 300] loss: 0.024[10, 600] loss: 0.030[10, 900] loss: 0.030accuracy on test set: 97 %
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