transformers.normalize

transformers.normalize是指使用transformers库中的normalize函数对数据进行归一化处理。该函数可以将数据的值域缩放到[,1]或[-1,1]之间，以便更好地进行训练和预测。归一化可以提高模型的稳定性和准确性，减少过拟合的风险。

相关问题

Transformers做cifar-10图像分类任务pytorch版本的代码

下面是一个基于PyTorch实现的Transformers对cifar-10图像分类的代码，使用了BERT作为Transformers的模型。

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from transformers import BertModel

# 超参数设置
input_size = 32
hidden_size = 768
num_classes = 10
batch_size = 100
num_epochs = 10
learning_rate = 0.001

# CIFAR-10数据集下载和预处理
transform = transforms.Compose(
    [transforms.Resize((input_size, input_size)),
     transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                             download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size,
                                           shuffle=True, num_workers=2)

test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                            download=True, transform=transform)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size,
                                          shuffle=False, num_workers=2)

# BERT模型定义
class BERT(nn.Module):
    def __init__(self, hidden_size, num_classes):
        super(BERT, self).__init__()
        self.bert = BertModel.from_pretrained('bert-base-uncased')
        self.fc = nn.Linear(hidden_size, num_classes)

    def forward(self, x):
        # 前向传播 BERT
        out = self.bert(x)[0][:, 0, :]
        out = self.fc(out)
        return out

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = BERT(hidden_size, num_classes).to(device)

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

# 模型训练
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        # 将图像转换成BERT需要的形状
        images = images.reshape(-1, input_size * input_size * 3).to(device)
        labels = labels.to(device)

        # 前向传播
        outputs = model(images)
        loss = criterion(outputs, labels)

        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if (i+1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'
                  .format(epoch+1, num_epochs, i+1, len(train_loader), loss.item()))

# 模型测试
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        # 将图像转换成BERT需要的形状
        images = images.reshape(-1, input_size * input_size * 3).to(device)
        labels = labels.to(device)

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

    print('Test Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))

需要注意的是，这个代码中使用了一个将图像转换成BERT需要的形状的过程，即将每个图像的像素展平成一维向量。在这个例子中，我们使用了预训练的BERT模型来提取图像的特征，并将特征向量输入到一个全连接层中进行分类。

transformers做cifar10图像分类有loss曲线和准确率曲线并保存模型pytorch

当使用transformers库进行CIFAR-10图像分类时，你可以使用PyTorch中提供的CrossEntropyLoss作为损失函数，并选择Adam或SGD作为优化器。以下是一个简单的示例代码，用于训练和保存模型，并记录训练过程中的损失和准确率。

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader
from torchvision.datasets import CIFAR10
from torchvision.transforms import ToTensor, Normalize
from transformers import ViTModel

# 定义模型
model = ViTModel.from_pretrained('google/vit-base-patch16-224')
model.fc = nn.Linear(model.config.hidden_size, 10)

# 加载数据
train_data = CIFAR10(root='./data', train=True, transform=ToTensor(), download=True)
train_loader = DataLoader(train_data, batch_size=64, shuffle=True)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

# 训练模型
num_epochs = 10
for epoch in range(num_epochs):
    running_loss = 0.0
    correct = 0
    total = 0
    for i, (inputs, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = model(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # 统计损失和准确率
        running_loss += loss.item()
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    # 输出训练结果
    print('Epoch [%d/%d], Loss: %.4f, Accuracy: %.2f%%'
          % (epoch+1, num_epochs, running_loss / len(train_loader), 100 * correct / total))

# 保存模型
torch.save(model.state_dict(), 'cifar10_vit.pth')

在训练过程中，你可以使用tensorboard等工具记录损失和准确率，并可视化损失和准确率曲线。在代码中增加以下内容即可：

from torch.utils.tensorboard import SummaryWriter

# 定义tensorboard writer
writer = SummaryWriter(log_dir='./logs')

# 在每个epoch结束时记录损失和准确率
writer.add_scalar('Loss/train', running_loss / len(train_loader), epoch)
writer.add_scalar('Accuracy/train', 100 * correct / total, epoch)

# 关闭tensorboard writer
writer.close()

最后，你可以使用以下代码加载保存的模型，并进行测试：

# 加载模型
model.load_state_dict(torch.load('cifar10_vit.pth'))

# 加载测试数据
test_data = CIFAR10(root='./data', train=False, transform=ToTensor(), download=True)
test_loader = DataLoader(test_data, batch_size=64, shuffle=False)

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

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

希望这些代码对你有所帮助！