transformers.normalize
时间: 2023-04-27 10:04:55 浏览: 172
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。
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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。
```python
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
对于使用 transformers 做 cifar10 图像分类的任务,可以使用 PyTorch 框架来实现。以下是一个简单的代码示例,包括训练模型、绘制 loss 和准确率曲线以及保存模型的过程:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from transformers import ViTModel, ViTConfig
# 定义模型
class ViTClassifier(nn.Module):
def __init__(self):
super(ViTClassifier, self).__init__()
config = ViTConfig(image_size=32, num_classes=10)
self.vit = ViTModel(config)
self.classifier = nn.Linear(config.hidden_size, config.num_classes)
def forward(self, x):
x = self.vit(x)
x = self.classifier(x.last_hidden_state[:, 0])
return x
# 数据预处理
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])
trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=2)
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.1, momentum=0.9, weight_decay=5e-4)
# 开始训练
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
net = ViTClassifier().to(device)
num_epochs = 50
loss_list = []
acc_list = []
for epoch in range(num_epochs):
running_loss = 0.0
total = 0
correct = 0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
inputs, labels = inputs.to(device), labels.to(device)
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
loss_list.append(running_loss / len(trainloader))
acc_list.append(correct / total)
print('[Epoch %d] Loss: %.3f | Acc: %.3f' % (epoch + 1, running_loss / len(trainloader), correct / total))
# 绘制 loss 和准确率曲线
import matplotlib.pyplot as plt
plt.plot(loss_list)
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.show()
plt.plot(acc_list)
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.show()
# 保存模型
torch.save(net.state_dict(), 'model.pth')
```
在训练过程中,我们记录了每个 epoch 的 loss 和准确率,并将它们存储在 `loss_list` 和 `acc_list` 中。然后使用 Matplotlib 库绘制出了相应的 loss 和准确率曲线。最后,我们使用 `torch.save()` 函数将模型保存到磁盘中,以便后续的推理和部署。