loss_fn = nn.CrossEntropyLoss() optimizer = torch.optim.Adam(model.parameters(), lr=1e-3) epochs = 30 since = time.time() for t in range(epochs): print(f"Epoch {t+1}\n-------------------------------") train(dataloaders['train'], model, loss_fn, optimizer) val(dataloaders['val'], model, loss_fn) time_elapsed = time.time() - since print(f'Training complete in {time_elapsed // 60:.0f}m {time_elapsed % 60:.0f}s') print("Done!")

这段代码使用了之前定义的`train()`和`val()`函数来进行模型的训练和验证，并打印出训练过程的损失和准确率。让我来解释一下它的功能：

- `loss_fn`：损失函数，这里使用了交叉熵损失函数`nn.CrossEntropyLoss()`。
- `optimizer`：优化器，这里使用了Adam优化器，通过`torch.optim.Adam()`来定义，学习率为1e-3。
- `epochs`：训练的轮数，这里设置为30。

以下是代码的主要步骤：

1. 通过`time.time()`记录训练开始的时间。
2. 对于每个epoch，在训练集上进行训练和验证集上进行验证：
- 打印当前epoch的信息。
- 调用`train()`函数进行模型训练，传入训练集的数据加载器、模型、损失函数和优化器。
- 调用`val()`函数进行模型验证，传入验证集的数据加载器、模型和损失函数。
3. 通过计算时间差来得到训练所花费的总时间，并打印出来。
4. 打印出训练完成的提示信息。

这段代码实现了模型的训练和验证过程，并在每个epoch结束后打印出相应的信息。你可以根据需要进行调整和修改。

相关问题

LDAM损失函数pytorch代码如下：class LDAMLoss(nn.Module): def init(self, cls_num_list, max_m=0.5, weight=None, s=30): super(LDAMLoss, self).init() m_list = 1.0 / np.sqrt(np.sqrt(cls_num_list)) m_list = m_list * (max_m / np.max(m_list)) m_list = torch.cuda.FloatTensor(m_list) self.m_list = m_list assert s > 0 self.s = s if weight is not None: weight = torch.FloatTensor(weight).cuda() self.weight = weight self.cls_num_list = cls_num_list def forward(self, x, target): index = torch.zeros_like(x, dtype=torch.uint8) index_float = index.type(torch.cuda.FloatTensor) batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(1,0)) # 0,1 batch_m = batch_m.view((16, 1)) # size=(batch_size, 1) (-1,1) x_m = x - batch_m output = torch.where(index, x_m, x) if self.weight is not None: output = output * self.weight[None, :] target = torch.flatten(target) # 将 target 转换成 1D Tensor logit = output * self.s return F.cross_entropy(logit, target, weight=self.weight) 模型部分参数如下：# 设置全局参数 model_lr = 1e-5 BATCH_SIZE = 16 EPOCHS = 50 DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu') use_amp = True use_dp = True classes = 7 resume = None CLIP_GRAD = 5.0 Best_ACC = 0 #记录最高得分 use_ema=True model_ema_decay=0.9998 start_epoch=1 seed=1 seed_everything(seed) # 数据增强 mixup mixup_fn = Mixup( mixup_alpha=0.8, cutmix_alpha=1.0, cutmix_minmax=None, prob=0.1, switch_prob=0.5, mode='batch', label_smoothing=0.1, num_classes=classes) # 读取数据集 dataset_train = datasets.ImageFolder('/home/adminis/hpy/ConvNextV2_Demo/RAF-DB/RAF/train', transform=transform) dataset_test = datasets.ImageFolder("/home/adminis/hpy/ConvNextV2_Demo/RAF-DB/RAF/valid", transform=transform_test)# 导入数据 train_loader = torch.utils.data.DataLoader(dataset_train, batch_size=BATCH_SIZE, shuffle=True,drop_last=True) test_loader = torch.utils.data.DataLoader(dataset_test, batch_size=BATCH_SIZE, shuffle=False) 帮我用pytorch实现模型在模型训练中使用LDAM损失函数

好的，以下是使用LDAM损失函数训练模型的Pytorch代码：

import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from torchvision import datasets, transforms
from ldam_loss import LDAMLoss

# 定义模型
class MyModel(nn.Module):
    def __init__(self, num_classes):
        super(MyModel, self).__init__()
        self.num_classes = num_classes
        self.features = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
        )
        self.classifier = nn.Sequential(
            nn.Linear(128 * 4 * 4, 256),
            nn.ReLU(inplace=True),
            nn.Linear(256, num_classes),
        )

    def forward(self, x):
        x = self.features(x)
        x = x.view(x.size(0), -1)
        x = self.classifier(x)
        return x

# 设置超参数
model_lr = 1e-4
BATCH_SIZE = 16
EPOCHS = 50
DEVICE = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
use_amp = True
use_dp = True
classes = 7
resume = None
CLIP_GRAD = 5.0
Best_ACC = 0
use_ema = True
model_ema_decay = 0.9998
start_epoch = 1
seed = 1

# 设置随机种子
def seed_everything(seed):
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    np.random.seed(seed)
seed_everything(seed)

# 定义数据增强
transform = transforms.Compose([
    transforms.Resize(224),
    transforms.RandomHorizontalFlip(),
    transforms.RandomRotation(10),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225])
])
transform_test = transforms.Compose([
    transforms.Resize(224),
    transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406],
                         std=[0.229, 0.224, 0.225])
])

# 定义数据集
dataset_train = datasets.ImageFolder('/home/adminis/hpy/ConvNextV2_Demo/RAF-DB/RAF/train', transform=transform)
dataset_test = datasets.ImageFolder("/home/adminis/hpy/ConvNextV2_Demo/RAF-DB/RAF/valid", transform=transform_test)

# 定义数据加载器
train_loader = torch.utils.data.DataLoader(dataset_train, batch_size=BATCH_SIZE, shuffle=True, drop_last=True)
test_loader = torch.utils.data.DataLoader(dataset_test, batch_size=BATCH_SIZE, shuffle=False)

# 定义模型及优化器
model = MyModel(num_classes=classes).to(DEVICE)
optimizer = torch.optim.Adam(model.parameters(), lr=model_lr)

# 使用LDAM损失函数
cls_num_list = [dataset_train.targets.count(i) for i in range(classes)]
criterion = LDAMLoss(cls_num_list=cls_num_list, max_m=0.5, weight=None, s=30)

# 训练模型
for epoch in range(start_epoch, EPOCHS+1):
    model.train()
    for i, (data, target) in enumerate(train_loader):
        data, target = data.to(DEVICE), target.to(DEVICE)
        mixup_data, mixup_target = mixup_fn(data, target)  # 数据增强
        optimizer.zero_grad()
        output = model(mixup_data)
        loss = criterion(output, mixup_target)
        if use_dp:
            loss.backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(), CLIP_GRAD)
        else:
            with amp.scale_loss(loss, optimizer) as scaled_loss:
                scaled_loss.backward()
                torch.nn.utils.clip_grad_norm_(amp.master_params(optimizer), CLIP_GRAD)
        optimizer.step()
    if use_ema:
        ema_model = ModelEMA(model, decay=model_ema_decay)
        ema_model.update(model)
    else:
        ema_model = None
    test_acc = test(model, test_loader, DEVICE)
    if test_acc > Best_ACC:
        Best_ACC = test_acc
        save_checkpoint({
            'epoch': epoch,
            'state_dict': model.state_dict(),
            'optimizer': optimizer.state_dict(),
            'Best_ACC': Best_ACC,
        }, is_best=True)

pytorch如何使用adam优化器

PyTorch 中的 Adam（Adaptive Moment Estimation）优化器是一种常用的梯度下降优化算法，它结合了动量（momentum）和自适应学习率（RMSprop）的优点。以下是在 PyTorch 中使用 Adam 优化器的基本步骤：

1. 导入必要的库和模块：

import torch
import torch.optim as optim

2. 初始化模型和张量：

model = YourModel()  # 替换为你的模型实例
input = torch.randn(10, 20)  # 假设输入形状，具体根据你的任务
target = torch.randn(10)  # 假设输出形状

3. 创建 Adam 优化器：

optimizer = optim.Adam(model.parameters(), lr=0.001, betas=(0.9, 0.999), eps=1e-8)  # 设置学习率 lr、动量参数 beta1 和 beta2 及一个小数用于数值稳定性（eps）

在这里，`lr` 是学习率，`betas` 是动量的参数，`eps` 是一个很小的值，防止除以零。

4. 定义一个训练步骤（例如一个循环中的 `forward()` 和 `backward()` 调用）：

loss_fn = torch.nn.MSELoss()  # 或者其他合适的损失函数

for epoch in range(num_epochs):
    optimizer.zero_grad()  # 每轮开始前清空梯度

    output = model(input)  # 前向传播
    loss = loss_fn(output, target)  # 计算损失
    loss.backward()  # 反向传播，计算梯度

    optimizer.step()  # 使用优化器更新模型参数

5. 在训练过程中，你可以定期调整学习率或检查模型性能。