test_out = weight2.*(weight*labels(neighbor_index,:)/(sum(weight)));

这段代码是使用权重weight2对邻居样本的标签进行加权平均的操作，并将结果保存在test_out变量中。

首先，它计算权重weight与邻居样本的标签labels(neighbor_index,:)的乘积。接着，将乘积的和除以权重的总和(sum(weight))，得到一个加权平均值。最后，将权重weight2与加权平均值相乘，得到最终的结果test_out。

这个操作的目的是对邻居样本的标签进行加权平均，其中权重weight2用于进一步调整不同类别之间的样本分布，以应对类别不平衡问题。

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))

加载数据集 train_data = pd.read_csv('mnist_dataset/mnist_train.csv') test_data = pd.read_csv('mnist_dataset/mnist_test.csv') # 提取特征和标签 train_features, train_labels = train_data.iloc[:, 1:], train_data.iloc[:, 0] test_features, test_labels = test_data.iloc[:, 1:], test_data.iloc[:, 0]# 对标签进行二进制编码 lb = LabelBinarizer() train_labels = lb.fit_transform(train_labels) test_labels = lb.fit_transform(test_labels) # 将特征值缩放到0-1之间并且reshape为(28,28,1) train_features = train_features.values.reshape(-1, 28, 28, 1) / 255. test_features = test_features.values.reshape(-1, 28, 28, 1) / 255.在以上代码基础上使用tensorflow构建深度神经网络处理mnist数据集，给出代码，请注意是深度神经网络，不是卷积神经网络

好的，以下是使用 TensorFlow 构建深度神经网络处理 MNIST 数据集的代码：

import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten

# 加载数据集
train_data = pd.read_csv('mnist_dataset/mnist_train.csv')
test_data = pd.read_csv('mnist_dataset/mnist_test.csv')

# 提取特征和标签
train_features, train_labels = train_data.iloc[:, 1:], train_data.iloc[:, 0]
test_features, test_labels = test_data.iloc[:, 1:], test_data.iloc[:, 0]

# 对标签进行二进制编码
lb = LabelBinarizer()
train_labels = lb.fit_transform(train_labels)
test_labels = lb.fit_transform(test_labels)

# 将特征值缩放到0-1之间并且reshape为(28,28,1)
train_features = train_features.values.reshape(-1, 28, 28, 1) / 255.
test_features = test_features.values.reshape(-1, 28, 28, 1) / 255.

# 构建模型
model = tf.keras.Sequential([
    Flatten(input_shape=(28, 28, 1)),
    Dense(256, activation='relu'),
    Dense(128, activation='relu'),
    Dense(10, activation='softmax')
])

# 编译模型
model.compile(optimizer='adam',
              loss='categorical_crossentropy',
              metrics=['accuracy'])

# 训练模型
model.fit(train_features, train_labels, epochs=10, validation_data=(test_features, test_labels))

# 评估模型
test_loss, test_acc = model.evaluate(test_features, test_labels, verbose=2)
print('Test accuracy:', test_acc)

这个模型有两个隐藏层，分别是 256 和 128 个神经元，激活函数都使用 ReLU，输出层有 10 个神经元，激活函数使用 softmax。在编译模型时使用了 Adam 优化器和交叉熵损失函数。最后训练模型并评估模型的性能。