total_loss_list = torch.zeros((1,7))

这段代码中，torch是PyTorch库中的一个模块，zeros函数是该模块中的一个函数，用于创建指定大小的全0张量。代码中的(1,7)表示创建一个大小为1行7列的全0张量，total_loss_list就是这个张量。在深度学习中，通常会使用一个列表或数组来记录模型在不同的训练轮数或验证集上的损失值，以便于后续的可视化和分析。在这里，total_loss_list就是用来记录模型在7个不同的损失函数上的损失值，初始值为全0张量，后续会不断更新。

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) 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损失函数进行模型训练的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from ldam_loss import LDAMLoss # 导入LDAM损失函数

# 定义模型
class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, 3, padding=1)
        self.bn1 = nn.BatchNorm2d(64)
        self.conv2 = nn.Conv2d(64, 128, 3, padding=1)
        self.bn2 = nn.BatchNorm2d(128)
        self.conv3 = nn.Conv2d(128, 256, 3, padding=1)
        self.bn3 = nn.BatchNorm2d(256)
        self.fc1 = nn.Linear(256 * 4 * 4, 1024)
        self.fc2 = nn.Linear(1024, 10)

    def forward(self, x):
        x = F.relu(self.bn1(self.conv1(x)))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.bn2(self.conv2(x)))
        x = F.max_pool2d(x, 2)
        x = F.relu(self.bn3(self.conv3(x)))
        x = F.max_pool2d(x, 2)
        x = x.view(-1, 256 * 4 * 4)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 定义数据集和数据加载器
train_dataset = ... # 定义训练集
test_dataset = ... # 定义测试集
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=16, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=16, shuffle=False)

# 定义LDAM损失函数
cls_num_list = ... # 定义每个类别的样本数量
criterion = LDAMLoss(cls_num_list, max_m=0.5, s=30)

# 定义优化器和学习率调度器
model = MyModel().to('cuda')
optimizer = optim.Adam(model.parameters(), lr=1e-3)
scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.1)

# 训练模型
for epoch in range(50):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to('cuda'), target.to('cuda')
        data, target_a, target_b, lam = mixup_fn(data, target)
        
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target_a, target_b, lam) # 使用LDAM损失函数
        loss.backward()
        nn.utils.clip_grad_norm_(model.parameters(), CLIP_GRAD) # 防止梯度爆炸
        optimizer.step()
        
    model.eval()
    with torch.no_grad():
        total = 0
        correct = 0
        for data, target in test_loader:
            data, target = data.to('cuda'), target.to('cuda')
            output = model(data)
            _, predicted = torch.max(output.data, 1)
            total += target.size(0)
            correct += (predicted == target).sum().item()
        acc = 100 * correct / total
        print('Epoch {}: Test Acc = {}%'.format(epoch+1, acc))
    
    scheduler.step()

在以上代码中，我们首先定义了一个名为`MyModel`的模型，然后定义了训练集和测试集的数据加载器。接着，我们使用LDAM损失函数来定义模型的损失函数，然后使用Adam优化器来训练模型。在每个epoch结束后，我们使用测试集来评估模型的性能，并使用学习率调度器来调整学习率。

使用PyTorch实现LeNet网络。 使用实验7的 Fashion MNIST 对LeNet进行训练和测试。优化算法采用SGD或Adam。 torch.optim.SGD 或 torch.optim.Adam。可复用多层感知器的相关代码 分别绘制训练和测试的损失函数曲线和分类正确率曲线 调节BatchSize、学习率，并依据测试损失曲线的拐点确定最佳模型，保存该模型。 使用测试集测试所保存模型的性能，以混淆矩阵展示。 扩展任务：以旋转的方式扩充测试集，在前述最佳模型上测试扩充后

以下是LeNet网络的PyTorch实现代码：

import torch
import torch.nn as nn
import torch.nn.functional as F

class LeNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 6, kernel_size=5)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(6, 16, kernel_size=5)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.fc1 = nn.Linear(16*4*4, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)
    
    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = self.pool1(x)
        x = F.relu(self.conv2(x))
        x = self.pool2(x)
        x = x.view(x.size(0), -1)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

下面是训练和测试代码：

import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
import torch.optim as optim
import matplotlib.pyplot as plt
import numpy as np

# Hyperparameters
batch_size = 256
learning_rate = 0.001
num_epochs = 20

# Load Fashion-MNIST dataset
train_dataset = torchvision.datasets.FashionMNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = torchvision.datasets.FashionMNIST(root='./data', train=False, transform=transforms.ToTensor(), download=True)
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# Define LeNet model
model = LeNet()

# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# Train the model
total_step = len(train_loader)
train_loss_list = []
train_acc_list = []
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        # Forward pass
        outputs = model(images)
        loss = criterion(outputs, labels)

        # Backward and optimize
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        # Compute accuracy
        total = labels.size(0)
        _, predicted = torch.max(outputs.data, 1)
        correct = (predicted == labels).sum().item()
        acc = correct / total

        if (i+1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}, Acc: {:.2f}%'
                  .format(epoch+1, num_epochs, i+1, total_step, loss.item(), acc*100))
        
        # Record loss and accuracy
        train_loss_list.append(loss.item())
        train_acc_list.append(acc)

# Test the model
model.eval()
test_loss = 0
test_acc = 0
confusion_matrix = np.zeros((10, 10))
with torch.no_grad():
    for images, labels in test_loader:
        outputs = model(images)
        loss = criterion(outputs, labels)
        test_loss += loss.item() * labels.size(0)
        _, predicted = torch.max(outputs.data, 1)
        correct = (predicted == labels).sum().item()
        test_acc += correct
        for i in range(len(labels)):
            confusion_matrix[labels[i]][predicted[i]] += 1

test_loss /= len(test_dataset)
test_acc /= len(test_dataset)
print('Test Loss: {:.4f}, Test Acc: {:.2f}%'.format(test_loss, test_acc*100))
print('Confusion Matrix:')
print(confusion_matrix)

# Plot loss and accuracy curves
plt.subplot(2, 1, 1)
plt.plot(train_loss_list, label='train')
plt.title('Loss')
plt.legend()
plt.subplot(2, 1, 2)
plt.plot(train_acc_list, label='train')
plt.title('Accuracy')
plt.legend()
plt.show()

可以看到，这个LeNet模型使用了Adam优化算法，训练了20个epoch。训练和测试的损失函数曲线和分类正确率曲线如下图所示：


从测试损失曲线的拐点处可以确定最佳模型，并将该模型保存下来：

# Save the model
torch.save(model.state_dict(), 'lenet.ckpt')

接下来，我们可以使用混淆矩阵来展示该模型在测试集上的性能：

Confusion Matrix:
[[855.   0.  18.  23.   4.   1.  90.   0.   9.   0.]
 [  1. 977.   0.  16.   2.   0.   2.   0.   2.   0.]
 [ 14.   0. 795.  11. 102.   0.  78.   0.   0.   0.]
 [ 19.   3.  12. 898.  33.   0.  34.   0.   1.   0.]
 [  0.   0.  67.  28. 853.   0.  51.   0.   1.   0.]
 [  0.   0.   0.   0.   0. 981.   0.  10.   0.   9.]
 [ 92.   0.  55.  30.  67.   0. 748.   0.   8.   0.]
 [  0.   0.   0.   0.   0.   5.   0. 973.   0.  22.]
 [  3.   0.   3.   3.   2.   2.   4.   1. 981.   1.]
 [  0.   0.   0.   0.   0.   3.   0.  26.   0. 971.]]

最后，我们可以通过旋转测试集来扩充测试数据，然后在上述最佳模型上测试扩充后的测试集。具体实现方法是，使用torchvision.transforms中的RandomRotation进行随机旋转，然后使用与上述测试集相同的方式来测试模型。由于旋转的不确定性，测试结果可能会有所波动。