使用pytorch设计一个基于CNN鸟类五分类问题的代码并且使用VGG16网络

好的，下面是使用PyTorch设计基于VGG16网络的鸟类五分类代码：

首先，导入需要的库：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.autograd import Variable
from torchvision import datasets, models, transforms
import time
import os

然后，定义一些超参数：

# 数据路径
data_dir = "path/to/data"
# 训练数据的文件夹名称
train_dir = "train"
# 验证数据的文件夹名称
val_dir = "val"
# 测试数据的文件夹名称
test_dir = "test"
# batch size
batch_size = 32
# 训练的epoch数
num_epochs = 10
# 是否使用GPU
use_gpu = torch.cuda.is_available()
# 是否进行fine-tuning
feature_extract = False

接着，定义数据预处理方式：

# 数据预处理
data_transforms = {
    train_dir: transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    val_dir: transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    test_dir: transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])
}

接着，加载数据集：

# 加载数据集
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])
                  for x in [train_dir, val_dir, test_dir]}
# 定义数据加载器
dataloaders_dict = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=True, num_workers=4)
                    for x in [train_dir, val_dir, test_dir]}

然后，定义模型：

# 定义VGG16网络
model_ft = models.vgg16(pretrained=True)
# 替换最后一层全连接层
num_ftrs = model_ft.classifier[6].in_features
model_ft.classifier[6] = nn.Linear(num_ftrs, 5)
# 判断是否使用GPU
if use_gpu:
    model_ft = model_ft.cuda()

接着，定义优化器和学习率衰减：

# 定义优化器
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
# 定义学习率衰减
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)

然后，定义训练函数：

def train_model(model, dataloaders, criterion, optimizer, scheduler, num_epochs=25):
    since = time.time()

    best_model_wts = model.state_dict()
    best_acc = 0.0

    for epoch in range(num_epochs):
        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
        print('-' * 10)

        # 每个epoch都有训练和验证阶段
        for phase in [train_dir, val_dir]:
            if phase == train_dir:
                scheduler.step()
                model.train(True)  # 设置model为训练模式
            else:
                model.train(False)  # 设置model为评估模式

            running_loss = 0.0
            running_corrects = 0

            # 遍历数据
            for data in dataloaders[phase]:
                inputs, labels = data
                if use_gpu:
                    inputs = Variable(inputs.cuda())
                    labels = Variable(labels.cuda())
                else:
                    inputs, labels = Variable(inputs), Variable(labels)

                # 梯度清零
                optimizer.zero_grad()

                # 前向传播
                outputs = model(inputs)
                _, preds = torch.max(outputs.data, 1)
                loss = criterion(outputs, labels)

                # 反向传播和优化
                if phase == train_dir:
                    loss.backward()
                    optimizer.step()

                # 统计
                running_loss += loss.data.item() * inputs.size(0)
                running_corrects += torch.sum(preds == labels.data)

            epoch_loss = running_loss / len(dataloaders[phase].dataset)
            epoch_acc = running_corrects.double() / len(dataloaders[phase].dataset)

            print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))

            # 保存最好的模型权重
            if phase == val_dir and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = model.state_dict()

        print()

    time_elapsed = time.time() - since
    print('Training complete in {:.0f}m {:.0f}s'.format(
        time_elapsed // 60, time_elapsed % 60))
    print('Best val Acc: {:.4f}'.format(best_acc))

    # 加载最好的模型权重
    model.load_state_dict(best_model_wts)
    return model

接着，定义损失函数：

# 定义损失函数
criterion = nn.CrossEntropyLoss()

最后，进行训练和测试：

# 训练模型
model_ft = train_model(model_ft, dataloaders_dict, criterion, optimizer_ft, exp_lr_scheduler, num_epochs=num_epochs)

# 测试模型
model_ft.eval()
test_loss = 0
correct = 0
for data, target in dataloaders_dict[test_dir]:
    if use_gpu:
        data, target = data.cuda(), target.cuda()
    output = model_ft(data)
    test_loss += criterion(output, target).data.item()  # 将所有batch的损失相加
    pred = output.data.max(1, keepdim=True)[1]  # 找到概率最大的下标
    correct += pred.eq(target.data.view_as(pred)).cpu().sum()

test_loss /= len(dataloaders_dict[test_dir].dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
    test_loss, correct, len(dataloaders_dict[test_dir].dataset),
    100. * correct / len(dataloaders_dict[test_dir].dataset)))

以上就是使用PyTorch设计基于VGG16网络的鸟类五分类代码的全部内容。