用resnet18分析jaffe数据

可以使用预训练的ResNet-18模型对Jaffe数据进行分析，其中ResNet-18是一种深度卷积神经网络，特别适合进行图像分类等任务。在使用时，可以将Jaffe数据集中的图片输入到ResNet-18模型中，通过模型输出的分类结果进行分析和判断。可以通过训练新的模型来对该任务进行进一步的优化和改进。

相关问题

写一个用resnet18分析jaffe数据的代码

好的，下面是使用 PyTorch 写的基于 ResNet18 分析 JAFFE 数据集的代码：

import torch
from torch import nn
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, models
from PIL import Image
import numpy as np
import pandas as pd

class JAFFEDataset(Dataset):
    def __init__(self, csv_file, root_dir, transform=None):
        self.annotations = pd.read_csv(csv_file)
        self.root_dir = root_dir
        self.transform = transform

    def __len__(self):
        return len(self.annotations)

    def __getitem__(self, idx):
        img_path = self.root_dir + '/' + self.annotations.iloc[idx, 0]
        image = Image.open(img_path)

        emotion = self.annotations.iloc[idx, 1]
        labels_dict = {'NE':0, 'AN':1, 'DI':2, 'FE':3, 'HA':4, 'SA':5, 'SU':6}
        label = labels_dict[emotion]

        if self.transform:
            image = self.transform(image)

        return image, label

transform_train = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.RandomHorizontalFlip(),
    transforms.RandomRotation(10),
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

transform_test = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

train_set = JAFFEDataset('jaffe_train.csv', 'jaffe', transform_train)
test_set = JAFFEDataset('jaffe_test.csv', 'jaffe', transform_test)

train_loader = DataLoader(train_set, batch_size=32, shuffle=True)
test_loader = DataLoader(test_set, batch_size=32, shuffle=False)

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

model = models.resnet18(pretrained=True)
model.fc = nn.Linear(512, 7)
model.to(device)

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

num_epochs = 10
for epoch in range(num_epochs):
    for images, labels in train_loader:
        images = images.to(device)
        labels = labels.to(device)

        outputs = model(images)
        loss = criterion(outputs, labels)

        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')

with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        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()

    print('Accuracy of the network on the test images: %d %%' % (100 * correct / total))

以上是基于 ResNet18 分析 JAFFE 数据集的代码，该代码可用于训练并测试一个情绪识别模型。您可以将代码保存为 `.py` 文件并在命令行中运行。注意，您需要确保已经下载了 JAFFE 数据集，并相应修改代码中的文件名和路径。

写一个用resnet18分析jaffe数据集的代码，给出损失图，准确率图和混淆矩阵

好的，我会尽力回答你的问题，以下是代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import confusion_matrix

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# 数据预处理
transform = transforms.Compose([transforms.Resize((224,224)),
                                transforms.ToTensor(),
                                transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))])

train_dataset = torchvision.datasets.ImageFolder(root='jaffe/train', transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=5, shuffle=True, num_workers=2)

test_dataset = torchvision.datasets.ImageFolder(root='jaffe/test', transform=transform)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=5, shuffle=False, num_workers=2)

classes = train_dataset.classes

# 定义模型
class ResNet18(nn.Module):
    def __init__(self, num_classes=7):
        super(ResNet18, self).__init__()
        self.resnet18 = torchvision.models.resnet18(pretrained=True)
        self.linear = nn.Linear(1000, num_classes)

    def forward(self, x):
        x = self.resnet18(x)
        x = self.linear(x)
        return x

model = ResNet18()
model.to(device)

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.001, momentum=0.9)

# 训练模型
for epoch in range(10):
    running_loss = 0.0
    for i,data in enumerate(train_loader,0):
        inputs,labels = data
        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()
        if i % 10 == 9:
            print('[%d,%5d] loss: %.3f' % (epoch+1,i+1,running_loss/10))
            running_loss = 0.0

print('Finished Training')

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

print('Accuracy of the network on the test images: %d %%' % (100 * correct / total))

# 混淆矩阵
confusion_mat = confusion_matrix(true_labels, predictions)
print(confusion_mat)

# 准确率图
plt.plot([0,1,2,3,4,5,6], confusion_mat.diagonal()/confusion_mat.sum(1))
plt.xlabel('Classes')
plt.ylabel('Accuracy')
plt.show()

# 损失图
plt.plot(loss_list)
plt.xlabel('Iteration')
plt.ylabel('Loss')
plt.show()

请注意，上述示例仅作参考，需要根据具体情况进行修改。同时，混淆矩阵、准确率图和损失图的绘制需要额外的代码实现，不在此处展示。