模型训练过程

准备数据集

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train_data = torchvision.datasets.CIFAR10("./dataset1", train=True, download=True,
                                       transform=torchvision.transforms.ToTensor())
test_data = torchvision.datasets.CIFAR10("./dataset1", train=False, download=True,
                                       transform=torchvision.transforms.ToTensor())

数据集长度

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train_data_size = len(train_data)
test_data_size = len(test_data)
print("训练集长度：{}".format(train_data_size))
print("测试集长度：{}".format(test_data_size))

利用DataLoader加载数据集

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train_data_loader = DataLoader(train_data, batch_size=64)
test_data_loader = DataLoader(test_data, batch_size=64)

搭建神经网络

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class Test(nn.Module):
    def __init__(self):
        super().__init__()
        self.model1 = Sequential(
            Conv2d(3, 32, 5, 1, 2),
            MaxPool2d(2),
            Conv2d(32, 32, 5, padding=2),
            MaxPool2d(2),
            Conv2d(32, 64, 5, padding=2),
            MaxPool2d(2),
            Flatten(),
            Linear(1024, 64),
            Linear(64, 10)
        )

    def forward(self, x):
        x = self.model1(x)
        return x

创建网络模型

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test = Test()

创建损失函数

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loss_fn = nn.CrossEntropyLoss()

创建优化器

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# 创建优化器
# 0.01 = 1e-2
learning_rate = 1e-2
optimizer = torch.optim.SGD(test.parameters(), lr=learning_rate)

设置训练网络的一些参数

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# 设置训练网络的一些参数
# 训练次数
total_train_step = 0
# 测试次数
total_test_step = 0
# 训练轮数
epoch = 10

writer = SummaryWriter("./log_train")

训练与测试

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for i in range(epoch):
    print("----------第{}轮训练开始----------".format(i+1))

    # 测试步骤开始
    # 针对特定的层有作用
    # test.train()
    for data in train_data_loader:
        imgs, targets = data
        output = test(imgs)
        loss = loss_fn(output, targets)

        # 优化器优化模型
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        total_train_step = total_train_step + 1
        if total_train_step % 100 == 0:
            print("训练次数： {}，loss: {}".format(total_train_step, loss.item()))
            writer.add_scalar("train_loss", loss.item(), total_train_step)

    # 测试步骤
    # 针对特定的层有作用
    # test.eval()
    total_test_loss = 0
    # 整体正确的个数
    total_accuracy = 0
    with torch.no_grad():
        for data in test_data_loader:
            imgs, targets = data
            output = test(imgs)
            loss = loss_fn(output, targets)
            total_test_loss = total_test_loss + loss.item()
            accuracy = (output.argmax(1) == targets).sum()
            total_accuracy = total_accuracy + accuracy

    print("整体测试集的loss: {}".format(total_test_loss))
    print("整体测试集的正确率: {}".format(total_accuracy/test_data_size))
    writer.add_scalar("test_loss", total_test_loss, total_test_step)
    writer.add_scalar("test_accuracy", total_accuracy/test_data_size, total_test_step)
    total_test_step = total_test_step + 1

    torch.save(test, "test_{}.pth".format(i))
    print("模型已保存")

writer.close()