pytorch:lr, num_epochs = 5e-4, 10 batch_size = 12800是什么意思
时间: 2024-05-28 14:14:05 浏览: 17
这是一个使用 PyTorch 框架进行深度学习模型训练时的一些超参数设置。具体来说:
- lr (learning rate) 是学习率,表示每次参数更新的步长大小。这里设置为 5e-4,即 0.0005。
- num_epochs 是训练的轮数,这里设置为 10。
- batch_size 是每次训练时的批量大小,这里设置为 12800。
这些超参数的设置会影响模型的训练效果,需要根据具体的任务和数据集进行调整。
相关问题
pytorch代码实现在模型中使用LDAMLoss损失函数,数据集是RAF-DB,使用了mixup数据增强
好的,以下是使用LDAMLoss和mixup数据增强的RAF-DB数据集的PyTorch代码实现:
首先,我们需要导入必要的库:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torch.utils.data import DataLoader, Dataset
from torch.autograd import Variable
import numpy as np
import random
from PIL import Image
```
接下来,我们需要定义LDAM(Large Margin Cosine Loss)损失函数:
```python
class LDAMLoss(nn.Module):
def __init__(self, cls_num_list, max_m=0.5, weight=None, s=30):
super(LDAMLoss, self).__init__()
m_list = np.divide(1, np.sqrt(np.sqrt(cls_num_list)))
m_list = np.multiply(max_m, m_list)
m_list = torch.FloatTensor(m_list).cuda()
self.m_list = m_list
assert s > 0
self.s = s
if weight is not None:
self.weight = torch.FloatTensor(weight).cuda()
else:
self.weight = weight
def forward(self, x, target):
cosine = x
sine = torch.sqrt(1.0 - torch.pow(cosine, 2))
phi = cosine * self.m_list.unsqueeze(1) - sine * self.m_list.unsqueeze(1)
phi = phi.float()
target = target.long().view(-1, 1)
index = torch.zeros_like(phi)
index.scatter_(1, target, 1)
if self.weight is not None:
weight = self.weight.unsqueeze(0)
index = torch.matmul(index, weight.t())
index = index.clamp(min=1e-12, max=1 - 1e-12)
index = index.log()
loss = -index * torch.pow(torch.abs(phi), self.s)
loss = loss.sum(dim=1).mean()
else:
index = index.cuda()
loss = -torch.log(torch.abs(torch.gather(phi, 1, target)) + 1e-8)
loss = loss.squeeze(1)
loss = loss.mean()
return loss
```
接下来,我们需要定义mixup数据增强:
```python
def mixup_data(x, y, alpha=1.0):
if alpha > 0:
lam = np.random.beta(alpha, alpha)
else:
lam = 1
batch_size = x.size()[0]
index = torch.randperm(batch_size).cuda()
mixed_x = lam * x + (1 - lam) * x[index, :]
y_a, y_b = y, y[index]
return mixed_x, y_a, y_b, lam
```
然后,我们需要定义RAF-DB数据集的类:
```python
class RAFDataset(Dataset):
def __init__(self, data_path, transform=None):
self.data_path = data_path
self.transform = transform
self.data = []
self.labels = []
with open(self.data_path, 'r') as f:
for line in f:
line = line.strip()
img_path, label = line.split(' ')
self.data.append(img_path)
self.labels.append(int(label))
def __len__(self):
return len(self.data)
def __getitem__(self, index):
img_path = self.data[index]
label = self.labels[index]
img = Image.open(img_path).convert('RGB')
if self.transform is not None:
img = self.transform(img)
return img, label
```
接下来,我们需要定义模型:
```python
class MyModel(nn.Module):
def __init__(self):
super(MyModel, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1)
self.bn1 = nn.BatchNorm2d(64)
self.relu1 = nn.ReLU(inplace=True)
self.conv2 = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
self.bn2 = nn.BatchNorm2d(128)
self.relu2 = nn.ReLU(inplace=True)
self.conv3 = nn.Conv2d(128, 256, kernel_size=3, stride=1, padding=1)
self.bn3 = nn.BatchNorm2d(256)
self.relu3 = nn.ReLU(inplace=True)
self.conv4 = nn.Conv2d(256, 512, kernel_size=3, stride=1, padding=1)
self.bn4 = nn.BatchNorm2d(512)
self.relu4 = nn.ReLU(inplace=True)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.fc1 = nn.Linear(512 * 12 * 12, 1024)
self.drop1 = nn.Dropout(p=0.5)
self.relu5 = nn.ReLU(inplace=True)
self.fc2 = nn.Linear(1024, 7)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu1(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu2(x)
x = self.conv3(x)
x = self.bn3(x)
x = self.relu3(x)
x = self.conv4(x)
x = self.bn4(x)
x = self.relu4(x)
x = self.pool(x)
x = x.view(-1, 512 * 12 * 12)
x = self.fc1(x)
x = self.drop1(x)
x = self.relu5(x)
x = self.fc2(x)
return x
```
最后,我们需要定义训练和测试函数:
```python
def train(model, train_loader, optimizer, criterion, alpha):
model.train()
train_loss = 0
train_correct = 0
train_total = 0
for i, (inputs, targets) in enumerate(train_loader):
inputs, targets_a, targets_b, lam = mixup_data(inputs, targets, alpha=alpha)
inputs, targets_a, targets_b = map(Variable, (inputs, targets_a, targets_b))
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, targets_a) * lam + criterion(outputs, targets_b) * (1 - lam)
loss.backward()
optimizer.step()
train_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
train_total += targets.size(0)
train_correct += (lam * predicted.eq(targets_a.data).cpu().sum().float()
+ (1 - lam) * predicted.eq(targets_b.data).cpu().sum().float())
train_acc = train_correct / train_total
train_loss = train_loss / len(train_loader)
return train_acc, train_loss
def test(model, test_loader, criterion):
model.eval()
test_loss = 0
test_correct = 0
test_total = 0
with torch.no_grad():
for inputs, targets in test_loader:
inputs, targets = Variable(inputs), Variable(targets)
outputs = model(inputs)
loss = criterion(outputs, targets)
test_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
test_total += targets.size(0)
test_correct += predicted.eq(targets.data).cpu().sum().float()
test_acc = test_correct / test_total
test_loss = test_loss / len(test_loader)
return test_acc, test_loss
```
最后,我们需要定义主函数:
```python
if __name__ == '__main__':
# 设置随机种子,确保实验的可重复性
torch.manual_seed(233)
np.random.seed(233)
random.seed(233)
# 定义训练参数
batch_size = 64
num_epochs = 100
lr = 0.1
alpha = 1.0
cls_num_list = [2000, 2000, 2000, 2000, 2000, 2000, 2000]
train_data_path = 'train.txt'
test_data_path = 'test.txt'
# 定义数据增强和数据集
transform_train = transforms.Compose([
transforms.RandomCrop(44),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
transform_test = transforms.Compose([
transforms.CenterCrop(44),
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
train_dataset = RAFDataset(train_data_path, transform=transform_train)
test_dataset = RAFDataset(test_data_path, transform=transform_test)
# 定义数据加载器
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=4)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False, num_workers=4)
# 定义模型和优化器
model = MyModel().cuda()
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9, weight_decay=5e-4)
criterion = LDAMLoss(cls_num_list)
# 训练和测试
for epoch in range(num_epochs):
train_acc, train_loss = train(model, train_loader, optimizer, criterion, alpha)
test_acc, test_loss = test(model, test_loader, criterion)
print('Epoch [{}/{}], Train Loss: {:.4f}, Train Acc: {:.4f}, Test Loss: {:.4f}, Test Acc: {:.4f}'
.format(epoch + 1, num_epochs, train_loss, train_acc, test_loss, test_acc))
if (epoch + 1) % 10 == 0:
lr /= 10
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=0.9, weight_decay=5e-4)
```
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。
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