pytorch迁移学习训练vgg16

好的，以下是关于使用PyTorch进行迁移学习和训练VGG16模型的一些基本步骤：

1. 导入必要的PyTorch库：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import torchvision
from torchvision import datasets, models, transforms

2. 加载数据集并进行数据增强：

data_transforms = {
    'train': transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    'val': transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
}

data_dir = 'path/to/dataset'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])
                  for x in ['train', 'val']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,
                                             shuffle=True, num_workers=4)
              for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes

3. 加载预训练的VGG16模型：

model_ft = models.vgg16(pretrained=True)

4. 修改VGG16的最后一层，使其适应数据集：

num_ftrs = model_ft.classifier[6].in_features
model_ft.classifier[6] = nn.Linear(num_ftrs, len(class_names))

5. 设置损失函数和优化器：

criterion = nn.CrossEntropyLoss()
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)

6. 设置学习率调整器：

exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)

7. 训练模型：

def train_model(model, criterion, optimizer, scheduler, num_epochs=25):
    for epoch in range(num_epochs):
        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
        print('-' * 10)

        # Each epoch has a training and validation phase
        for phase in ['train', 'val']:
            if phase == 'train':
                scheduler.step()
                model.train()  # Set model to training mode
            else:
                model.eval()   # Set model to evaluate mode

            running_loss = 0.0
            running_corrects = 0

            # Iterate over data.
            for inputs, labels in dataloaders[phase]:
                inputs = inputs.to(device)
                labels = labels.to(device)

                # zero the parameter gradients
                optimizer.zero_grad()

                # forward
                # track history if only in train
                with torch.set_grad_enabled(phase == 'train'):
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)
                    loss = criterion(outputs, labels)

                    # backward + optimize only if in training phase
                    if phase == 'train':
                        loss.backward()
                       好的，下面是关于使用 PyTorch 迁移学习 VGG16 模型的一些步骤和示例代码。

1. 导入 PyTorch 和 torchvision 库

import torch
import torch.nn as nn
import torchvision.models as models
import torchvision.transforms as transforms

2. 加载预训练的 VGG16 模型

vgg16 = models.vgg16(pretrained=True)

3. 冻结 VGG16 模型的参数，只训练最后的全连接层

for param in vgg16.parameters():
param.requires_grad = False

classifier = nn.Sequential(
nn.Linear(25088, 4096),
nn.ReLU(inplace=True),
nn.Dropout(p=0.5),
nn.Linear(4096, 4096),
nn.ReLU(inplace=True),
nn.Dropout(p=0.5),
nn.Linear(4096, 1000),
nn.ReLU(inplace=True),
nn.Dropout(p=0.5),
nn.Linear(1000, 2),
)
vgg16.classifier = classifier

4. 加载数据并进行预处理

data_transforms = {
'train': transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
'val': transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
]),
}

train_data = torchvision.datasets.ImageFolder('./data/train', transform=data_transforms['train'])
val_data = torchvision.datasets.ImageFolder('./data/val', transform=data_transforms['val'])

train_loader = torch.utils.data.DataLoader(train_data, batch_size=32, shuffle=True, num_workers=4)
val_loader = torch.utils.data.DataLoader(val_data, batch_size=32, shuffle=False, num_workers=4)

5. 定义损失函数和优化器

criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(vgg16.classifier.parameters(), lr=0.001)

6. 训练模型

num_epochs = 10

for epoch in range(num_epochs):
running_loss = 0.0
running_corrects = 0
for images, labels in train_loader:
images = images.cuda()
labels = labels.cuda()

optimizer.zero_grad()

outputs = vgg16(images)
_, preds = torch.max(outputs, 1)
loss = criterion(outputs, labels)

loss.backward()
optimizer.step()

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

epoch_loss = running_loss / len(train_data)
epoch_acc = running_corrects.double() / len(train_data)

print('Epoch [{}/{}], Loss: {:.4f}, Accuracy: {:.4f}'.format(epoch+1, num_epochs, epoch_loss, epoch_acc))

7. 在验证集上评估模型

vgg16.eval()

val_loss = 0.0
val_corrects = 0

for images, labels in val_loader:
images =好的，下面是关于使用 PyTorch 进行迁移学习和训练 VGG16 模型的步骤：

1.导入 PyTorch 和其他必要的库：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os

2.加载数据集并进行数据增强：

data_transforms = {
    'train': transforms.Compose([
        transforms.RandomResizedCrop(224),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
    'val': transforms.Compose([
        transforms.Resize(256),
        transforms.CenterCrop(224),
        transforms.ToTensor(),
        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ]),
}

data_dir = 'path/to/data'
image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),
                                          data_transforms[x])
                  for x in ['train', 'val']}
dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=4,
                                             shuffle=True, num_workers=4)
              for x in ['train', 'val']}
dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}
class_names = image_datasets['train'].classes
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

3.加载预训练的 VGG16 模型并替换最后一层：

model_ft = models.vgg16(pretrained=True)
num_ftrs = model_ft.classifier[6].in_features
model_ft.classifier[6] = nn.Linear(num_ftrs, 2)
model_ft = model_ft.to(device)
criterion = nn.CrossEntropyLoss()
optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.001, momentum=0.9)
exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=7, gamma=0.1)

4.训练模型：

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

    best_model_wts = copy.deepcopy(model.state_dict())
    best_acc = 0.0

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

        # Each epoch has a training and validation phase
        for phase in ['train', 'val']:
            if phase == 'train':
                scheduler.step()
                model.train()  # Set model to training mode
            else:
                model.eval()   # Set model to evaluate mode

            running_loss = 0.0
            running_corrects = 0

            # Iterate over data.
            for inputs, labels in dataloaders[phase]:
                inputs = inputs.to(device)
                labels = labels.to(device)

                # zero the parameter gradients
                optimizer.zero_grad()

                # forward
                # track history if only in train
                with torch.set_grad_enabled(phase == 'train'):
                    outputs = model(inputs)
                    _, preds = torch.max(outputs, 1)
                    loss = criterion好的，下面是关于使用PyTorch进行迁移学习和Fine-tuning VGG16模型的一些指导：

1. 下载预训练模型：
首先需要下载预训练模型，可以使用PyTorch中自带的torchvision库来下载：

import torchvision.models as models

vgg16 = models.vgg16(pretrained=True)

这将下载VGG16的预训练权重。

2. 用新数据集进行微调：
接下来，需要将新数据集加载到模型中进行微调，可以使用torchvision.datasets和torch.utils.data.DataLoader来完成数据的预处理和加载。

import torchvision.datasets as datasets
import torchvision.transforms as transforms
from torch.utils.data import DataLoader

# 数据集预处理
data_transforms = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
])

# 加载数据集
train_dataset = datasets.ImageFolder(root='./train/', transform=data_transforms)
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)

# 微调模型
for param in vgg16.parameters():
param.requires_grad = False

vgg16.classifier[6] = nn.Linear(4096, num_classes) # 修改分类层

optimizer = optim.SGD(vgg16.classifier[6].parameters(), lr=0.001, momentum=0.9)
criterion = nn.CrossEntropyLoss()

for epoch in range(num_epochs):
for inputs, labels in train_loader:
inputs, labels = inputs.to(device), labels.to(device)

optimizer.zero_grad()

outputs = vgg16(inputs)
loss = criterion(outputs, labels)

loss.backward()
optimizer.step()

在此代码中，我们首先使用transforms.Compose将数据集进行预处理，然后使用ImageFolder将数据集加载到内存中。接下来，我们使用for循环对模型进行微调。需要将所有参数的requires_grad设置为False，这是因为我们只想更新分类器层的参数。修改完分类器层之后，我们使用SGD作为优化器来更新参数。在每个epoch中，我们将数据集分批次加载到模型中，并计算输出和损失，然后使用反向传播来更新模型的权重。

3. 保存模型：
在训练完成后，可以使用torch.save来保存模型的权重。

torch.save(vgg16.state_dict(), 'vgg16_weights.pth')

以上是使用PyTorch迁移学习和Fine-tuning VGG16模型的基本指导，希望能对您有所帮助。