acc = torch.sum(outputs == label)这句代码是什么意思

这句代码计算了一个张量 `outputs` 中等于标签 `label` 的元素数量，并将其赋值给变量 `acc`。其中，`==` 表示逐元素比较，返回一个布尔类型的张量，其中等于 `label` 的元素为 `True`，不等于 `label` 的元素为 `False`。在 PyTorch 中，布尔类型的张量可以被转化为整型类型，`True` 转化为 1，`False` 转化为 0。因此，`torch.sum(outputs == label)` 的结果即为 `outputs` 中等于 `label` 的元素数量。

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) 帮我用pytorch实现模型在模型训练中使用LDAM损失函数

好的，下面是使用LDAM损失函数训练模型的代码：

import torch.optim as optim
from torch.optim.lr_scheduler import CosineAnnealingLR
from torch.utils.data import DataLoader

# 定义LDAM损失函数
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((x.size(0), 1)) # size=(batch_size, 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)

# 定义模型
model = models.resnet18(pretrained=True)
num_ftrs = model.fc.in_features
model.fc = nn.Linear(num_ftrs, classes)
model.to(DEVICE)

# 定义优化器和学习率调整器
optimizer = optim.Adam(model.parameters(), lr=model_lr)
scheduler = CosineAnnealingLR(optimizer, T_max=EPOCHS, eta_min=1e-6)

# 定义LDAM损失函数
cls_num_list = [len(dataset_train[dataset_train.targets == t]) for t in range(classes)]
criterion = LDAMLoss(cls_num_list)

# 定义数据加载器
train_loader = DataLoader(dataset_train, batch_size=BATCH_SIZE, shuffle=True, num_workers=4, pin_memory=True)
test_loader = DataLoader(dataset_test, batch_size=BATCH_SIZE, shuffle=False, num_workers=4, pin_memory=True)

# 训练模型
best_acc = 0.0
for epoch in range(start_epoch, EPOCHS + 1):
    model.train()
    train_loss = 0.0
    train_corrects = 0
    for inputs, labels in train_loader:
        inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
        if use_dp:
            inputs, labels = dp(inputs, labels)
        if use_amp:
            with amp.autocast():
                inputs, labels = mixup_fn(inputs, labels)
                outputs = model(inputs)
                loss = criterion(outputs, labels)
            scaler.scale(loss).backward()
            scaler.unscale_(optimizer)
            torch.nn.utils.clip_grad_norm_(model.parameters(), CLIP_GRAD)
            scaler.step(optimizer)
            scaler.update()
        else:
            inputs, labels_a, labels_b, lam = mixup_fn(inputs, labels)
            outputs = model(inputs)
            loss = mixup_criterion(criterion, outputs, labels_a, labels_b, lam)
            loss.backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(), CLIP_GRAD)
            optimizer.step()
            optimizer.zero_grad()
        train_loss += loss.item() * inputs.size(0)
        _, preds = torch.max(outputs, 1)
        train_corrects += torch.sum(preds == labels.data)
    train_loss /= len(dataset_train)
    train_acc = train_corrects.double() / len(dataset_train)

    model.eval()
    test_loss = 0.0
    test_corrects = 0
    with torch.no_grad():
        for inputs, labels in test_loader:
            inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            test_loss += loss.item() * inputs.size(0)
            _, preds = torch.max(outputs, 1)
            test_corrects += torch.sum(preds == labels.data)
    test_loss /= len(dataset_test)
    test_acc = test_corrects.double() / len(dataset_test)

    # 更新最佳模型
    if test_acc > best_acc:
        if use_ema:
            ema_model.load_state_dict(model.state_dict())
        best_acc = test_acc

    # 更新学习率
    scheduler.step()

    # 打印训练结果
    print('Epoch [{}/{}], Train Loss: {:.4f}, Train Acc: {:.4f}, Test Loss: {:.4f}, Test Acc: {:.4f}'.format(
        epoch, EPOCHS, train_loss, train_acc, test_loss, test_acc))

1.使用PyTorch实现LeNet网络 2.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 3.分别绘制训练和测试的损失函数曲线和分类正确率曲线 4.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 5.使用测试集测试所保存模型的性能，以混淆矩阵展示

好的，这是一个深度学习的任务，需要使用PyTorch实现LeNet网络并对Fashion MNIST数据集进行训练和测试，我来帮您一步一步完成这个任务。

首先，我们需要导入必要的包：torch、torchvision、numpy和matplotlib。

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import numpy as np
import matplotlib.pyplot as plt

接下来，我们定义LeNet网络的结构。

class LeNet(nn.Module):
    def __init__(self):
        super(LeNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, 5)
        self.pool1 = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.pool2 = nn.MaxPool2d(2, 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 = self.pool1(torch.relu(self.conv1(x)))
        x = self.pool2(torch.relu(self.conv2(x)))
        x = x.view(-1, 16 * 4 * 4)
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

这里我们定义了一个包含2个卷积层和3个全连接层的LeNet网络，其中第一个卷积层有6个5x5的卷积核，第二个卷积层有16个5x5的卷积核。每个卷积层后面都跟了一个2x2的最大池化层，然后是3个全连接层，分别有120、84和10个神经元。

接下来，我们加载Fashion MNIST数据集，并将其划分为训练集和验证集。

transform = torchvision.transforms.Compose([
    torchvision.transforms.ToTensor(),
    torchvision.transforms.Normalize((0.1307,), (0.3081,))
])

train_dataset = torchvision.datasets.FashionMNIST(root='./data', train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=64, shuffle=True)

val_dataset = torchvision.datasets.FashionMNIST(root='./data', train=False, download=True, transform=transform)
val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=64, shuffle=False)

这里我们使用了PyTorch内置的Fashion MNIST数据集，并使用了一个Compose对象将ToTensor和Normalize变换组合起来。我们将训练集和验证集分别放入DataLoader中，batch_size设置为64，shuffle设置为True和False，表示训练集需要打乱，而验证集不需要。

接下来，我们定义优化算法和损失函数。

net = LeNet()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.01)

这里我们使用了SGD优化算法和交叉熵损失函数，学习率设置为0.01。

接下来，我们开始训练模型。

train_losses = []
train_accs = []
val_losses = []
val_accs = []

for epoch in range(10):
    train_loss = 0.0
    train_acc = 0.0
    val_loss = 0.0
    val_acc = 0.0

    net.train()
    for i, (inputs, labels) in enumerate(train_loader):
        optimizer.zero_grad()

        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        train_loss += loss.item()
        _, predicted = torch.max(outputs.data, 1)
        train_acc += (predicted == labels).sum().item()

    net.eval()
    with torch.no_grad():
        for inputs, labels in val_loader:
            outputs = net(inputs)
            loss = criterion(outputs, labels)

            val_loss += loss.item()
            _, predicted = torch.max(outputs.data, 1)
            val_acc += (predicted == labels).sum().item()

    train_loss /= len(train_loader)
    train_acc /= len(train_dataset)
    val_loss /= len(val_loader)
    val_acc /= len(val_dataset)

    train_losses.append(train_loss)
    train_accs.append(train_acc)
    val_losses.append(val_loss)
    val_accs.append(val_acc)

    print('Epoch %d: train_loss=%.4f train_acc=%.4f val_loss=%.4f val_acc=%.4f' % (
        epoch+1, train_loss, train_acc, val_loss, val_acc))

这里我们训练了10个epoch，每个epoch分别对训练集进行一次迭代，同时在验证集上计算loss和accuracy。在每个epoch结束时，我们将训练集和验证集的loss和accuracy记录下来。

最后，我们绘制训练和验证的损失函数曲线和分类正确率曲线。

fig, ax = plt.subplots(1, 2, figsize=(12, 6))

ax[0].plot(train_losses, label='train')
ax[0].plot(val_losses, label='val')
ax[0].set_xlabel('epoch')
ax[0].set_ylabel('loss')
ax[0].set_title('Training and validation loss')
ax[0].legend()

ax[1].plot(train_accs, label='train')
ax[1].plot(val_accs, label='val')
ax[1].set_xlabel('epoch')
ax[1].set_ylabel('accuracy')
ax[1].set_title('Training and validation accuracy')
ax[1].legend()

plt.show()

这里我们使用了matplotlib库来绘制图形，包括训练和验证的损失函数曲线和分类正确率曲线。

接下来，我们调节BatchSize和学习率，并依据测试损失曲线的拐点确定最佳模型，并保存该模型。

train_losses = []
train_accs = []
val_losses = []
val_accs = []

best_val_loss = float('inf')
best_model = None

batch_sizes = [16, 32, 64, 128, 256]
learning_rates = [0.001, 0.01, 0.1, 1]

for batch_size in batch_sizes:
    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
    val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=batch_size, shuffle=False)

    for learning_rate in learning_rates:
        net = LeNet()
        criterion = nn.CrossEntropyLoss()
        optimizer = optim.SGD(net.parameters(), lr=learning_rate)

        for epoch in range(10):
            train_loss = 0.0
            train_acc = 0.0
            val_loss = 0.0
            val_acc = 0.0

            net.train()
            for i, (inputs, labels) in enumerate(train_loader):
                optimizer.zero_grad()

                outputs = net(inputs)
                loss = criterion(outputs, labels)
                loss.backward()
                optimizer.step()

                train_loss += loss.item()
                _, predicted = torch.max(outputs.data, 1)
                train_acc += (predicted == labels).sum().item()

            net.eval()
            with torch.no_grad():
                for inputs, labels in val_loader:
                    outputs = net(inputs)
                    loss = criterion(outputs, labels)

                    val_loss += loss.item()
                    _, predicted = torch.max(outputs.data, 1)
                    val_acc += (predicted == labels).sum().item()

            train_loss /= len(train_loader)
            train_acc /= len(train_dataset)
            val_loss /= len(val_loader)
            val_acc /= len(val_dataset)

            train_losses.append(train_loss)
            train_accs.append(train_acc)
            val_losses.append(val_loss)
            val_accs.append(val_acc)

            if val_loss < best_val_loss:
                best_val_loss = val_loss
                best_model = net.state_dict()

            print('BatchSize=%d LearningRate=%.3f Epoch %d: train_loss=%.4f train_acc=%.4f val_loss=%.4f val_acc=%.4f' % (
                batch_size, learning_rate, epoch+1, train_loss, train_acc, val_loss, val_acc))

print('Best validation loss:', best_val_loss)

torch.save(best_model, 'best_model.pth')

这里我们使用了两个for循环，分别对BatchSize和学习率进行调节，并在每个epoch结束时记录train_loss、train_acc、val_loss和val_acc。在每次更新最佳模型时，我们将模型的参数保存下来。

最后，我们使用测试集测试所保存模型的性能，并以混淆矩阵展示。

test_dataset = torchvision.datasets.FashionMNIST(root='./data', train=False, download=True, transform=transform)
test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=64, shuffle=False)

net = LeNet()
net.load_state_dict(torch.load('best_model.pth'))

net.eval()
with torch.no_grad():
    all_predicted = []
    all_labels = []

    for inputs, labels in test_loader:
        outputs = net(inputs)
        _, predicted = torch.max(outputs.data, 1)

        all_predicted.extend(predicted.numpy())
        all_labels.extend(labels.numpy())

    all_predicted = np.array(all_predicted)
    all_labels = np.array(all_labels)

    confusion_matrix = np.zeros((10, 10))
    for i in range(len(all_predicted)):
        confusion_matrix[all_labels[i], all_predicted[i]] += 1

    print(confusion_matrix)

这里我们加载保存的最佳模型，并使用测试集进行测试。使用numpy库创建混淆矩阵，将模型预测结果与真实标签进行比对，并将结果输出。