Pytorch学习-基础实战

通过对task4一个基础案例并结合一些其他的资料课件，尝试对CIFAR10实现深度学习流程

import torch
import torchvision
import torch.nn as nn
from torch.utils.data import DataLoader

# 数据集
train_data = torchvision.datasets.CIFAR10(root="../data", train=True, transform=torchvision.transforms.ToTensor(),
                                          download=True)
test_data = torchvision.datasets.CIFAR10(root="../data", train=False, transform=torchvision.transforms.ToTensor(),
                                         download=False)
# 数据集长度
train_data_size = len(train_data)
test_data_size = len(test_data)
print("训练数据集的长度为：{}".format(train_data_size))
print("测试数据集的长度为：{}".format(test_data_size))

# 利用 DataLoader加载数据集
train_dataloader = DataLoader(train_data, batch_size=64)
test_dataloader = DataLoader(test_data, batch_size=64)

# 搭建神经网络
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.model = nn.Sequential(
            nn.Conv2d(3, 32, 5, 1, 2),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 32, 5, 1, 2),
            nn.MaxPool2d(2),
            nn.Conv2d(32, 64, 5, 1, 2),
            nn.MaxPool2d(2),
            nn.Flatten(),
            nn.Linear(64*4*4, 64),
            nn.Linear(64, 10)
        )

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

# 创建网络模型
net = Net()

# 损失函数
loss_fn = nn.CrossEntropyLoss()

# 优化器
learning_rate = 0.01
optimizer = torch.optim.SGD(net.parameters(), lr=learning_rate)

# 设置训练网络的一些参数
# 记录训练的次数
total_train_step = 0
# 记录测试的次数
total_test_step = 0
# 训练的轮数
epoch = 10

for i in range(epoch):
    print("---------第{}轮训练开始-----------".format((i+1)))
    # 训练步骤开始
    for data in train_dataloader:
        imgs, targets = data
        outputs = net(imgs)
        loss = loss_fn(outputs, 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()))

    # 测试步骤开始
    with torch.no_grad():
        for data in test_dataloader:
            imgs, targets = data
            outputs = net(imgs)
            loss = loss_fn(outputs, targets)
            total_test_step = total_test_step + loss.item()
    print("整体测试集上的Loss:{}".format(total_test_step))