第P2周：CIFAR10彩色图片识别

• 🍨本文为🔗365天深度学习训练营中的学习记录博客
• 🍖原作者：K同学啊

目录

一、 前期准备

1. 设置GPU

2. 导入数据

3. 数据可视化

二、构建简单的CNN网络

三、 训练模型

1. 设置超参数

2. 编写训练函数

3. 编写测试函数

4. 正式训练

四、 结果可视化

五、个人总结

一、 前期准备

1. 设置GPU

import torch import torch.nn as nn import matplotlib.pyplot as plt import torchvision device = torch.device("cuda" if torch.cuda.is_available() else "cpu") device

2. 导入数据

train_ds = torchvision.datasets.CIFAR10('data', train=True, transform=torchvision.transforms.ToTensor(), download=True) test_ds = torchvision.datasets.CIFAR10('data', train=False, transform=torchvision.transforms.ToTensor(), download=True) batch_size = 32 train_dl = torch.utils.data.DataLoader(train_ds, batch_size=batch_size, shuffle=True) test_dl = torch.utils.data.DataLoader(test_ds, batch_size=batch_size) imgs, labels = next(iter(train_dl)) imgs.shape

3. 数据可视化

import numpy as np plt.figure(figsize=(20, 5)) for i, imgs in enumerate(imgs[:20]): npimg = imgs.numpy().transpose((1, 2, 0)) plt.subplot(2, 10, i+1) plt.imshow(npimg, cmap=plt.cm.binary) plt.axis('off')

二、构建简单的CNN网络

import torch.nn.functional as F num_classes = 10 class Model(nn.Module): def __init__(self): super().__init__() self.conv1 = nn.Conv2d(3, 64, kernel_size=3) self.pool1 = nn.MaxPool2d(kernel_size=2) self.conv2 = nn.Conv2d(64, 64, kernel_size=3) self.pool2 = nn.MaxPool2d(kernel_size=2) self.conv3 = nn.Conv2d(64, 128, kernel_size=3) self.pool3 = nn.MaxPool2d(kernel_size=2) self.fc1 = nn.Linear(512, 256) self.fc2 = nn.Linear(256, num_classes) def forward(self, x): x = self.pool1(F.relu(self.conv1(x))) x = self.pool2(F.relu(self.conv2(x))) x = self.pool3(F.relu(self.conv3(x))) x = torch.flatten(x, start_dim=1) x = F.relu(self.fc1(x)) x = self.fc2(x) return x from torchinfo import summary # 将模型转移到GPU中（我们模型运行均在GPU中进行） model = Model().to(device) summary(model)

三、 训练模型

1. 设置超参数

loss_fn = nn.CrossEntropyLoss() # 创建损失函数 learn_rate = 1e-2 # 学习率 opt = torch.optim.SGD(model.parameters(),lr=learn_rate)

2. 编写训练函数

# 训练循环 def train(dataloader, model, loss_fn, optimizer): size = len(dataloader.dataset) # 训练集的大小，一共60000张图片 num_batches = len(dataloader) # 批次数目，1875（60000/32） train_loss, train_acc = 0, 0 # 初始化训练损失和正确率 for X, y in dataloader: # 获取图片及其标签 X, y = X.to(device), y.to(device) # 计算预测误差 pred = model(X) # 网络输出 loss = loss_fn(pred, y) # 计算网络输出和真实值之间的差距，targets为真实值，计算二者差值即为损失 # 反向传播 optimizer.zero_grad() # grad属性归零 loss.backward() # 反向传播 optimizer.step() # 每一步自动更新 # 记录acc与loss train_acc += (pred.argmax(1) == y).type(torch.float).sum().item() train_loss += loss.item() train_acc /= size train_loss /= num_batches return train_acc, train_loss

3. 编写测试函数

def test (dataloader, model, loss_fn): size = len(dataloader.dataset) # 测试集的大小，一共10000张图片 num_batches = len(dataloader) # 批次数目，313（10000/32=312.5，向上取整） test_loss, test_acc = 0, 0 # 当不进行训练时，停止梯度更新，节省计算内存消耗 with torch.no_grad(): for imgs, target in dataloader: imgs, target = imgs.to(device), target.to(device) # 计算loss target_pred = model(imgs) loss = loss_fn(target_pred, target) test_loss += loss.item() test_acc += (target_pred.argmax(1) == target).type(torch.float).sum().item() test_acc /= size test_loss /= num_batches return test_acc, test_loss

4. 正式训练

epochs = 10 train_loss = [] train_acc = [] test_loss = [] test_acc = [] for epoch in range(epochs): model.train() epoch_train_acc, epoch_train_loss = train(train_dl, model, loss_fn, opt) model.eval() epoch_test_acc, epoch_test_loss = test(test_dl, model, loss_fn) train_acc.append(epoch_train_acc) train_loss.append(epoch_train_loss) test_acc.append(epoch_test_acc) test_loss.append(epoch_test_loss) template = ('Epoch:{:2d}, Train_acc:{:.1f}%, Train_loss:{:.3f}, Test_acc:{:.1f}%，Test_loss:{:.3f}') print(template.format(epoch+1, epoch_train_acc*100, epoch_train_loss, epoch_test_acc*100, epoch_test_loss)) print('Done')

四、 结果可视化

import matplotlib.pyplot as plt #隐藏警告 import warnings warnings.filterwarnings("ignore") #忽略警告信息 plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签 plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号 plt.rcParams['figure.dpi'] = 100 #分辨率 from datetime import datetime current_time = datetime.now() # 获取当前时间 epochs_range = range(epochs) plt.figure(figsize=(12, 3)) plt.subplot(1, 2, 1) plt.plot(epochs_range, train_acc, label='Training Accuracy') plt.plot(epochs_range, test_acc, label='Test Accuracy') plt.legend(loc='lower right') plt.title('Training and Validation Accuracy') plt.xlabel(current_time) # 打卡请带上时间戳，否则代码截图无效 plt.subplot(1, 2, 2) plt.plot(epochs_range, train_loss, label='Training Loss') plt.plot(epochs_range, test_loss, label='Test Loss') plt.legend(loc='upper right') plt.title('Training and Validation Loss') plt.show()

五、个人总结

逐渐熟悉CNN模型构建过程，并逐步理解其原理。