写一个cnn模型代码用于图片情感分类 pytorch

### 回答1：
可以的，以下是一个使用 PyTorch 实现的 CNN 模型代码，用于图片情感分类：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets

# 定义 CNN 模型
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1)
        self.bn1 = nn.BatchNorm2d(16)
        self.relu1 = nn.ReLU()
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(32)
        self.relu2 = nn.ReLU()
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(32 * 8 * 8, 128)
        self.relu3 = nn.ReLU()
        self.fc2 = nn.Linear(128, 2)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)
        x = self.pool1(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu2(x)
        x = self.pool2(x)
        x = x.view(-1, 32 * 8 * 8)
        x = self.fc1(x)
        x = self.relu3(x)
        x = self.fc2(x)
        return x

# 加载数据集
transform = transforms.Compose([
    transforms.Resize((32, 32)),
    transforms.ToTensor(),
    transforms.Normalize(mean=[.5, .5, .5], std=[.5, .5, .5])
])
train_dataset = datasets.ImageFolder(root='./train', transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)
test_dataset = datasets.ImageFolder(root='./test', transform=transform)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=True)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=.001)

# 训练模型
model = CNN()
for epoch in range(10):
    running_loss = .
    for i, data in enumerate(train_loader, ):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
        if i % 100 == 99:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
            running_loss = .

# 测试模型
correct = 
total = 
with torch.no_grad():
    for data in test_loader:
        images, labels = data
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size()
        correct += (predicted == labels).sum().item()
print('Accuracy: %d %%' % (100 * correct / total))

这个模型可以用于对图片进行情感分类，你可以将训练集和测试集的图片放在 `./train` 和 `./test` 目录下，然后运行这个代码即可。

### 回答2：
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms

# 定义CNN模型
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=16, kernel_size=3, stride=1, padding=1)
self.relu1 = nn.ReLU()
self.pool1 = nn.MaxPool2d(kernel_size=2)
self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1)
self.relu2 = nn.ReLU()
self.pool2 = nn.MaxPool2d(kernel_size=2)
self.fc1 = nn.Linear(32 * 8 * 8, 128)
self.relu3 = nn.ReLU()
self.fc2 = nn.Linear(128, 2) # 假设只有2种情感，正面和负面

def forward(self, x):
x = self.conv1(x)
x = self.relu1(x)
x = self.pool1(x)
x = self.conv2(x)
x = self.relu2(x)
x = self.pool2(x)
x = x.view(x.size(0), -1) # 扁平化
x = self.fc1(x)
x = self.relu3(x)
x = self.fc2(x)
return x

# 数据预处理
transform = transforms.Compose([
transforms.Resize(32),
transforms.ToTensor()
])

# 加载训练集和测试集
train_dataset = datasets.ImageFolder(root='train_path', transform=transform)
test_dataset = datasets.ImageFolder(root='test_path', transform=transform)

# 创建数据加载器
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=True)

# 创建模型对象和损失函数
model = CNN()
criterion = nn.CrossEntropyLoss()

# 定义优化器
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
total_epochs = 10
for epoch in range(total_epochs):
for i, (images, labels) in enumerate(train_loader):
outputs = model(images)
loss = criterion(outputs, labels)
optimizer.zero_grad()
loss.backward()
optimizer.step()

if (i+1) % 10 == 0:
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, total_epochs, i+1, len(train_loader), loss.item()))

# 测试模型
model.eval()
with torch.no_grad():
correct = 0
total = 0
for images, labels in test_loader:
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()

print('Test Accuracy: {:.2f} %'.format(100 * correct / total))

### 回答3：
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import models
from torch.utils.data import DataLoader
from torchvision.datasets import ImageFolder
from torchvision.transforms import transforms

# 定义CNN模型
class CNNModel(nn.Module):
def __init__(self, num_classes):
super(CNNModel, self).__init__()
self.resnet = models.resnet18(pretrained=True)
self.resnet.fc = nn.Linear(512, num_classes)

def forward(self, x):
return self.resnet(x)

# 数据预处理
transform = transforms.Compose([
transforms.RandomResizedCrop(224),
transforms.RandomHorizontalFlip(),
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 = ImageFolder('./train', transform=transform)
test_dataset = ImageFolder('./test', transform=transform)

# 加载数据集至DataLoader
train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)

# 实例化模型和损失函数
model = CNNModel(num_classes=2) # 假设有2个情感类别
criterion = nn.CrossEntropyLoss()

# 迁移学习 - 冻结ResNet的所有参数
for param in model.resnet.parameters():
param.requires_grad = False

# 将分类层的参数设置为可训练
for param in model.resnet.fc.parameters():
param.requires_grad = True

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

# 优化器
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

# 训练模型
epochs = 10
for epoch in range(epochs):
running_loss = 0.0
for i, data in enumerate(train_loader):
inputs, labels = data
inputs = inputs.to(device)
labels = labels.to(device)

optimizer.zero_grad()

outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()

running_loss += loss.item()
if i % 10 == 9:
print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 10))
running_loss = 0.0

print('Training finished!')

# 在测试集上评估模型
correct = 0
total = 0
with torch.no_grad():
for data in test_loader:
images, labels = data
images = images.to(device)
labels = labels.to(device)

outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()

accuracy = 100 * correct / total
print('Accuracy on test set: {:.2f}%'.format(accuracy))