图像分类

李沐 《动手学深度学习》 第三章

from typing import Iterable

import torch
import torchvision

def load_data_fashion_mnist(batch_size, resize=None, root='./FashionMNIST'):
    """Download the fashion mnist dataset and then load into memory."""
    trans = []
    if resize:
        trans.append(torchvision.transforms.Resize(size=resize))
    trans.append(torchvision.transforms.ToTensor())

    transform = torchvision.transforms.Compose(trans)
    mnist_train = torchvision.datasets.FashionMNIST(root=root, train=True, download=True, transform=transform)
    mnist_test = torchvision.datasets.FashionMNIST(root=root, train=False, download=True, transform=transform)

    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)

    return train_iter, test_iter

读取数据

batch_size = 256
train_iter, test_iter = load_data_fashion_mnist(batch_size)

定义和初始化模型

num_inputs = 784
num_output = 10

class FlattenLayer(torch.nn.Module):
    """
    将 x 打平
    """

    def __init__(self):
        super().__init__()

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        将 x 打平, x.shape: (B, 28, 28) 打平后变成 x.shape: (B, 28*28)
        :param x:
        :return:
        """
        return x.view(x.shape[0], -1)


class Net(torch.nn.Module):
    """
    线性模型
    """

    def __init__(self):
        super().__init__()
        self.flatten = FlattenLayer()
        self.linear = torch.nn.Linear(in_features=num_inputs,
                                      out_features=num_output)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """
        进行运算
        :param x:
        :return:
        """
        x = self.flatten(x)
        y = self.linear(x)
        return y


net = Net()

print("net ...")

初始化参数

torch.nn.init.normal_(net.linear.weight, mean=0, std=0.01)
torch.nn.init.constant_(net.linear.bias, val=0)

也可以通过 name paramter 来初始化

for name, param in net.named_parameters():
    if name.endswith(".weight"):
        torch.nn.init.normal_(param, mean=0., std=0.01)
    elif name.endswith(".bias"):
        torch.nn.init.constant_(param, val=0)
    else:
        raise RuntimeError(f"{name} not be init")

定义损失函数

loss = torch.nn.CrossEntropyLoss(reduction="mean")

定义优化算法

optimizer = torch.optim.SGD(net.parameters(), lr=0.1)

训练模型

num_epochs = 5

def train():
    print("begin train...")

    for epoch in range(1, num_epochs + 1):
        total_loss = 0.
        total = 0
        true_sum = 0

        for x, y in train_iter:

            y_hat = net(x)

            # y_hat.shape: B*num_output, y.shape: B*1
            # ll 是标量, 默认mean
            ll = loss(y_hat, y)

            # 进行优化
            # 清空grad
            optimizer.zero_grad()

            ll.backward()

            optimizer.step()

            # ll是 mean, 所以需要乘以 y.shape[0]
            total += y.shape[0]
            total_loss += ll * y.shape[0]

            # 计算 acc
            true_sum += torch.sum(torch.argmax(y_hat, dim=-1) == y).item()

        epoch_loss = total_loss / total
        acc = true_sum / total
        print(f"epoch: {epoch}, total: {total}, loss: {epoch_loss}, acc: {acc}")

开始训练

train()