pytorch构建模型训练数据集

pytorch构建模型训练数据集

1.AlexNet:

1.1.导入必要的库：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
from torchvision import datasets, transforms, models
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt

1.2.数据预处理和增强：

transform = transforms.Compose([
    transforms.Resize((227, 227)),  # AlexNet需要227x227像素的输入
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])  # AlexNet的标准归一化参数
])

1.3.加载数据集：

data_path = 'D:/工坊/Pytorch的框架/flower_photos'
dataset = datasets.ImageFolder(data_path, transform=transform)

1.4.划分测试集和训练集：

train_size = int(0.8 * len(dataset))
test_size = len(dataset) - train_size

train_dataset, test_dataset = torch.utils.data.random_split(dataset, [train_size, test_size])

1.5.创建数据加载器：

train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)

1.6.加载AlexNet模型：

model = models.alexnet(pretrained=True)

1.7.修改模型以适应您的数据集类别数

num_classes = len(dataset.classes)
model.classifier[6] = nn.Linear(model.classifier[6].in_features, num_classes)

1.8.定义损失函数和优化器

criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters())

1.9.将模型移动到GPU（如果可用）

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)

1.10.初始化列表来存储每个epoch的损失和准确率

train_losses = []
train_accuracies = []

1.11.训练模型

num_epochs = 50
for epoch in range(num_epochs):
    model.train()
    running_loss = 0.0
    correct = 0
    total = 0
    for inputs, labels in train_loader:
        inputs, labels = inputs.to(device), labels.to(device)
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()
    epoch_loss = running_loss / len(train_loader)
    epoch_accuracy = 100 * correct / total
    train_losses.append(epoch_loss)
    train_accuracies.append(epoch_accuracy)
print(f'Epoch {
     epoch + 1}/{
     num_epochs}, Loss: {
     epoch_loss}, Accuracy: {
     epoch_accuracy}%')

运行结果：

1.12.绘制损失图表和准确率图标：

#创建图表
plt.figure(figsize=(10, 5))
 #绘制损失
plt.subplot(1, 2, 1)
plt.plot(range(1, len(train_losses) + 1), train_losses, 'bo-', label='Training Loss')
plt.title('Training Loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
#绘制准确率
plt.subplot(1, 2, 2)
plt.plot(range(1,