# setup setup: scan # Loss criterion: scan criterion_kwargs: entropy_weight: 5.0 # Model backbone: resnet50 # Weight update update_cluster_head_only: True # Train only linear layer during SCAN num_heads: 10 # Use multiple heads # Dataset train_db_name: imagenet_50 val_db_name: imagenet_50 num_classes: 50 num_neighbors: 50 # Transformations augmentation_strategy: simclr augmentation_kwargs: random_resized_crop: size: 224 scale: [0.2, 1.0] color_jitter_random_apply: p: 0.8 color_jitter: brightness: 0.4 contrast: 0.4 saturation: 0.4 hue: 0.1 random_grayscale: p: 0.2 normalize: mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] transformation_kwargs: crop_size: 224 normalize: mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] # Hyperparameters optimizer: sgd optimizer_kwargs: lr: 5.0 weight_decay: 0.0000 nesterov: False momentum: 0.9 epochs: 100 batch_size: 512 num_workers: 12 # Scheduler scheduler: constant，这段话是什么意思啊

Model_1拟合优度详解：VAR00001 Winters季节模型评估

模型描述部分提到了Model_1，这是一个基于Brown线性趋势的模型，用于预测或解释时间序列数据的行为。模型拟合优度是衡量模型性能的关键指标，它反映了模型对观测数据的拟合程度。其中，调整R-Square (调整决定系数...

人脸识别人工智能算法：极大间距准则(MMC)

资源摘要信息: "极大间距准则算法（Maximum Margin Criterion, MMC）是用于模式识别领域中的一种算法，尤其在人脸识别方面有着显著的应用。该算法的核心思想是通过优化一个最大化类间距离与类内距离之比的目标函数来...

# setup setup: selflabel # Threshold confidence_threshold: 0.99 # EMA use_ema: True ema_alpha: 0.999 # Loss criterion: confidence-cross-entropy criterion_kwargs: apply_class_balancing: False # Model backbone: resnet50 num_heads: 1 # Dataset train_db_name: imagenet_50 val_db_name: imagenet_50 num_classes: 50 # Transformations augmentation_strategy: ours augmentation_kwargs: crop_size: 224 normalize: mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] num_strong_augs: 4 cutout_kwargs: n_holes: 1 length: 75 random: True transformation_kwargs: crop_size: 224 normalize: mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] # Hyperparameters optimizer: sgd optimizer_kwargs: lr: 0.03 weight_decay: 0.0 nesterov: False momentum: 0.9 epochs: 25 batch_size: 512 num_workers: 16 # Scheduler scheduler: constant ，这段话是什么意思

- criterion: confidence-cross-entropy，criterion_kwargs: apply_class_balancing: False：表示使用置信度交叉熵损失函数进行训练，并关闭类别平衡。 - backbone: resnet50，num_heads: 1：表示使用ResNet-50作为...

解释# Setup setup: end2end # Model backbone: resnet18 model_kwargs: head: mlp features_dim: 128 nheads: 1 nclusters: 10 # Dataset train_db_name: cifar-10 val_db_name: cifar-10 num_classes: 10 num_neighbors: 5 # Loss criterion: end2end criterion_kwargs: temperature: 0.1 entropy_weight: 2.0 # Hyperparameters epochs: 1000 optimizer: sgd optimizer_kwargs: nesterov: False weight_decay: 0.0001 momentum: 0.9 lr: 0.4 scheduler: cosine scheduler_kwargs: lr_decay_rate: 0.1 batch_size: 256 num_workers: 8 # Transformations augmentation_strategy: simclr augmentation_kwargs: random_resized_crop: size: 32 scale: [0.2, 1.0] color_jitter_random_apply: p: 0.8 color_jitter: brightness: 0.4 contrast: 0.4 saturation: 0.4 hue: 0.1 random_grayscale: p: 0.2 normalize: mean: [0.4914, 0.4822, 0.4465] std: [0.2023, 0.1994, 0.2010] transformation_kwargs: resize: 40 crop_size: 32 normalize: mean: [0.4914, 0.4822, 0.4465] std: [0.2023, 0.1994, 0.2010]

- Model backbone: resnet18：使用ResNet-18作为模型的主干网络。 - model_kwargs：模型的额外参数设置。 - head: mlp：使用多层感知机（MLP）作为模型的头部。 - features_dim: 128：模型的特征维度为128...

# Setup用端到端（end-to-end）的训练方式 setup: end2end # Model backbone: resnet18 model_kwargs: head: mlp features_dim: 128 nheads: 1 nclusters: 10 # Dataset train_db_name: cifar-10 val_db_name: cifar-10 num_classes: 10 num_neighbors: 5 # Loss criterion: end2end criterion_kwargs: temperature: 0.1 entropy_weight: 2.0 # Hyperparameters epochs: 1000 optimizer: sgd optimizer_kwargs: nesterov: False weight_decay: 0.0001 momentum: 0.9 lr: 0.4 scheduler: cosine scheduler_kwargs: lr_decay_rate: 0.1 batch_size: 256 num_workers: 8 # Transformations augmentation_strategy: simclr augmentation_kwargs: random_resized_crop: size: 32 scale: [0.2, 1.0] color_jitter_random_apply: p: 0.8 color_jitter: brightness: 0.4 contrast: 0.4 saturation: 0.4 hue: 0.1 random_grayscale: p: 0.2 normalize: mean: [0.4914, 0.4822, 0.4465] std: [0.2023, 0.1994, 0.2010] transformation_kwargs: resize: 40 crop_size: 32 normalize: mean: [0.4914, 0.4822, 0.4465] std: [0.2023, 0.1994, 0.2010]是什么编码

根据你提供的配置文件，编码部分是指在数据预处理中使用的归一化操作。在这个配置文件中，归一化操作使用了均值和标准差来标准化图像数据。具体来说，归一化的均值和标准差如下所示：均值（mean）：[0.4914, 0....

# Setup setup: simclr # Model backbone: resnet18 model_kwargs: head: mlp features_dim: 128 # Dataset train_db_name: t1 val_db_name: v2 num_classes: 8 # Loss criterion: simclr criterion_kwargs: temperature: 0.1 # Hyperparameters epochs: 500 optimizer: sgd optimizer_kwargs: nesterov: False weight_decay: 0.0001 momentum: 0.9 lr: 0.4 scheduler: cosine scheduler_kwargs: lr_decay_rate: 0.1 batch_size: 512 num_workers: 8 # Transformations augmentation_strategy: simclr augmentation_kwargs: random_resized_crop: size: 32 scale: [0.2, 1.0] color_jitter_random_apply: p: 0.8 color_jitter: brightness: 0.4 contrast: 0.4 saturation: 0.4 hue: 0.1 random_grayscale: p: 0.2 normalize: mean: [0.4914, 0.4822, 0.4465] std: [0.2023, 0.1994, 0.2010] transformation_kwargs: crop_size: 32 normalize: mean: [0.4914, 0.4822, 0.4465] std: [0.2023, 0.1994, 0.2010]，这段代码是什么意思

首先，setup 字段表示使用的模型是 simclr，backbone 表示使用的主干网络是 resnet18，model_kwargs 表示模型的一些特殊参数，如头部网络为 MLP，特征维度为 128。接着，train_db_name 和 val_db_name ...

解释这段代码# ema use_ema: False # Threshold confidence_threshold: 0.98 # Criterion criterion: confidence-cross-entropy criterion_kwargs: apply_class_balancing: True # Model backbone: resnet18 num_heads: 1 # Dataset train_db_name: cifar-10 val_db_name: cifar-10 num_classes: 10 # Transformations augmentation_strategy: ours augmentation_kwargs: crop_size: 32 normalize: mean: [0.4914, 0.4822, 0.4465] std: [0.2023, 0.1994, 0.2010] num_strong_augs: 4 cutout_kwargs: n_holes: 1 length: 16 random: True transformation_kwargs: #resize: 40 crop_size: 32 normalize: mean: [0.4914, 0.4822, 0.4465] std: [0.2023, 0.1994, 0.2010] # Hyperparameters epochs: 200 batch_size: 1000 num_workers: 8 optimizer: adam optimizer_kwargs: lr: 0.00005 weight_decay: 0.0001 # Scheduler scheduler: constant

- backbone: resnet18：模型的主干网络，这里使用的是ResNet-18。 - num_heads: 1：注意力头的数量。 - train_db_name: cifar-10：训练数据集的名称，这里使用CIFAR-10数据集。 - val_db_name: cifar-10：...

File "E:/403/myworld/VGAE/trainTest.py", line 41, in loss_function recon_loss = dgl.losses.binary_cross_entropy(output, adj) # 重构误差报错：AttributeError: module 'dgl' has no attribute 'losses'

根据错误提示，dgl模块中似乎没有名为losses的属性，因此无法使用dgl.losses.binary_cross_entropy方法。如果您想使用DGL提供的二元交叉熵损失函数，可以使用torch.nn.BCEWithLogitsLoss类，该类已经包含...

逐行解释代码： def forward_Boosting(self, x, weight_mat=None): out = self.gru_features(x) fea = out[0] if self.use_bottleneck: fea_bottleneck = self.bottleneck(fea[:, -1, :]) fc_out = self.fc(fea_bottleneck).squeeze() else: fc_out = self.fc_out(fea[:, -1, :]).squeeze() out_list_all = out[1] out_list_s, out_list_t = self.get_features(out_list_all) loss_transfer = torch.zeros((1,)).cuda() if weight_mat is None: weight = (1.0 / self.len_seq * torch.ones(self.num_layers, self.len_seq)).cuda() else: weight = weight_mat dist_mat = torch.zeros(self.num_layers, self.len_seq).cuda() for i in range(len(out_list_s)): criterion_transder = TransferLoss( loss_type=self.trans_loss, input_dim=out_list_s[i].shape[2]) for j in range(self.len_seq): loss_trans = criterion_transder.compute( out_list_s[i][:, j, :], out_list_t[i][:, j, :]) loss_transfer = loss_transfer + weight[i, j] * loss_trans dist_mat[i, j] = loss_trans return fc_out, loss_transfer, dist_mat, weight

- criterion_transder = TransferLoss(loss_type=self.trans_loss, input_dim=out_list_s[i].shape[2])：根据损失类型和特征维度创建一个TransferLoss的实例，赋值给变量criterion_transder。 - for j in ...

loss_id = criterion_id(out0, labels)报错 return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index, label_smoothing) RuntimeError: "nll_loss_forward_reduce_cuda_kernel_2d_index" not implemented for 'Int'

在 PyTorch 中，nll_loss 函数的输入参数通常是浮点数类型的张量，而不是整数类型的张量。你可以尝试将输入张量转换为浮点数类型，例如使用 .float() 方法将整数类型的张量转换为浮点数类型的张量： ...

pytorch部分代码如下：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 # self.weight = weight 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(0,1)) # 0,1 batch_m = batch_m.view((x.size(0), 1)) # size=(batch_size, 1) (-1,1) x_m = x - batch_m output = torch.where(index, x_m, x) # return F.cross_entropy(self.soutput, target, weight=self.weight) 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) for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device, non_blocking=True), Variable(target).to(device,non_blocking=True) # 3、将数据输入mixup_fn生成mixup数据 samples, targets = mixup_fn(data, target) # 4、将上一步生成的数据输入model，输出预测结果，再计算loss output = model(samples) # 5、梯度清零（将loss关于weight的导数变成0） optimizer.zero_grad() loss = criterion_train(output, targets) # 6、若使用混合精度 if use_amp: with torch.cuda.amp.autocast(): # 开启混合精度 # loss = torch.nan_to_num(criterion_train(output, target_a, target_b, lam)) # 计算loss # loss = lam * criterion_train(output, target_a) + (1 - lam) * criterion_train(output, target_b) # 计算 mixup 后的损失函数 scaler.scale(loss).backward() # 梯度放大 torch.nn.utils.clip_grad_norm_(model.parameters(), CLIP_GRAD) # 梯度裁剪，防止梯度爆炸 scaler.step(optimizer) # 更新下一次迭代的scaler scaler.update() # 否则，直接反向传播求梯度 else: # loss = criterion_train(output, targets) loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), CLIP_GRAD) optimizer.step() 报错：) File "/home/adminis/hpy/ConvNextV2_Demo/models/losses.py", line 48, in forward output = torch.where(index, x_m, x) RuntimeError: expected scalar type float but found c10::Half

这个错误通常是由于在定义 index 和 index_float 时，没有指定数据类型，导致数据类型不匹配。可以尝试将 index 和 index_float 的数据类型都指定为 torch.cuda.FloatTensor。修改代码如下： ...

class ContentLoss(nn.Module): def init(self, target, weight): super(ContentLoss, self).init() # we 'detach' the target content from the tree used self.target = target.detach() * weight # to dynamically compute the gradient: this is a stated value, # not a variable. Otherwise the forward method of the criterion # will throw an error. self.weight = weight self.criterion = nn.MSELoss() def forward(self, input): self.loss = self.criterion(input * self.weight, self.target) self.output = input return self.output def backward(self, retain_graph=True): self.loss.backward(retain_graph=retain_graph) return self.loss

这段代码是PyTorch中一个自定义的损失函数模块。它继承了nn.Module类，因此可以像其他标准的神经网络层一样在模型中使用。该损失函数的作用是衡量输入图片与目标图片之间的内容差异，即MSE（均方误差）。...

def train(model, device): # 训练配置参数 max_epoch = CONFIG['max_epoch'] lr = CONFIG['lr'] weight_decay = CONFIG['weight_decay'] # 学习率调整参数 milestones = CONFIG['milestones'] gamma = CONFIG['gamma'] # 损失参数 instrument_list = CONFIG['instrument_list'] instrument_weight = CONFIG['instrument_weight'] # 模型保存路径 save_directory = CONFIG['save_directory'] if not os.path.exists(save_directory): os.makedirs(save_directory) optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=weight_decay) criterion = MultiLoss(instrument_list, instrument_weight) scheduler = MultiStepLR(optimizer, milestones, gamma) train_loader, val_loader = get_data_loader() for i in range(max_epoch): start = time() t_loss = train_one_epoch(model, device, train_loader, optimizer, criterion) if val_loader is not None: v_loss = val_one_epoch(model, device, val_loader, criterion) else: v_loss = t_loss end = time() scheduler.step() msg = '' for key, value in t_loss.items(): value = value.result() msg += f'{key}:{value:.4f}\t' for key, value in v_loss.items(): value = value.result() msg += f'{key}:{value:.4f}\t' msg += f'time:{(end - start):.1f}\tepoch:{i}' print(msg) save_path = os.path.join(save_directory, 'SSD_epoch_' + str(i) + '_' + str(v_loss['loss'].result()) + '.pth') model.phase = 'test' torch.save(model, save_path) model.phase = 'train'

这段代码是一个训练函数 train(model, device)，它接受一个模型对象和设备信息作为参数。下面是对代码的解释：首先，根据配置参数加载训练的相关设置，如最大训练轮数 max_epoch，学习率 lr，权重衰减 ...

TypeError Traceback (most recent call last) /tmp/ipykernel_1045/245448921.py in <module> 1 dataset_path = ABSADatasetList.Restaurant14 ----> 2 sent_classifier = Trainer(config=apc_config_english, 3 dataset=dataset_path, # train set and test set will be automatically detected 4 checkpoint_save_mode=1, # =None to avoid save model 5 auto_device=True # automatic choose CUDA or CPU /tmp/ipykernel_1045/296492999.py in init(self, config, dataset, from_checkpoint, checkpoint_save_mode, auto_device) 84 config.model_path_to_save = None 85 ---> 86 self.train() 87 88 def train(self): /tmp/ipykernel_1045/296492999.py in train(self) 96 config.seed = s 97 if self.checkpoint_save_mode: ---> 98 model_path.append(self.train_func(config, self.from_checkpoint, self.logger)) 99 else: 100 # always return the last trained model if dont save trained model /tmp/ipykernel_1045/4269211813.py in train4apc(opt, from_checkpoint_path, logger) 494 load_checkpoint(trainer, from_checkpoint_path) 495 --> 496 return trainer.run() /tmp/ipykernel_1045/4269211813.py in run(self) 466 criterion = nn.CrossEntropyLoss() 467 self._reset_params() --> 468 return self._train(criterion) 469 470 /tmp/ipykernel_1045/4269211813.py in _train(self, criterion) 153 return self._k_fold_train_and_evaluate(criterion) 154 else: --> 155 return self._train_and_evaluate(criterion) 156 157 def _train_and_evaluate(self, criterion): /tmp/ipykernel_1045/4269211813.py in _train_and_evaluate(self, criterion) 190 191 for epoch in range(self.opt.num_epoch): --> 192 iterator = tqdm(self.train_dataloaders[0]) 193 for i_batch, sample_batched in enumerate(iterator): 194 global_step += 1 TypeError: 'module' object is not callable

这个错误通常是由于在代码中使用了模块对象而不是可调用的函数或类。在这个特定的代码片段中，Trainer 类或 tqdm 函数可能没有正确导入或没有被正确调用。要解决这个问题，你可以尝试以下几个方法： ...

def train(model, train_loader, criterion, optimizer): model.train() train_loss = 0.0 train_acc = 0.0 for i, (inputs, labels) in enumerate(train_loader): optimizer.zero_grad() outputs = model(inputs.unsqueeze(1).float()) loss = criterion(outputs, labels.long()) loss.backward() optimizer.step() train_loss += loss.item() * inputs.size(0) _, preds = torch.max(outputs, 1) train_acc += torch.sum(preds == labels.data) train_loss = train_loss / len(train_loader.dataset) train_acc = train_acc.double() / len(train_loader.dataset) return train_loss, train_acc def test(model, verify_loader, criterion): model.eval() test_loss = 0.0 test_acc = 0.0 with torch.no_grad(): for i, (inputs, labels) in enumerate(test_loader): outputs = model(inputs.unsqueeze(1).float()) loss = criterion(outputs, labels.long()) test_loss += loss.item() * inputs.size(0) _, preds = torch.max(outputs, 1) test_acc += torch.sum(preds == labels.data) test_loss = test_loss / len(test_loader.dataset) test_acc = test_acc.double() / len(test_loader.dataset) return test_loss, test_acc # Instantiate the model model = CNN() # Define the loss function and optimizer criterion = nn.CrossEntropyLoss() optimizer = optim.Adam(model.parameters(), lr=0.001) # Instantiate the data loaders train_dataset = MyDataset1('1MATRICE') train_loader = DataLoader(train_dataset, batch_size=5, shuffle=True) test_dataset = MyDataset2('2MATRICE') test_loader = DataLoader(test_dataset, batch_size=5, shuffle=False) train_losses, train_accs, test_losses, test_accs = [], [], [], [] for epoch in range(500): train_loss, train_acc = train(model, train_loader, criterion, optimizer) test_loss, test_acc = test(model, test_loader, criterion) train_losses.append(train_loss) train_accs.append(train_acc) test_losses.append(test_loss) test_accs.append(test_acc) print('Epoch: {} Train Loss: {:.4f} Train Acc: {:.4f} Test Loss: {:.4f} Test Acc: {:.4f}'.format( epoch, train_loss, train_acc, test_loss, test_acc))

这是一个基于PyTorch框架的CNN模型的训练过程。代码中定义了两个函数：train和test，分别用于训练模型和测试模型。在训练过程中，首先将模型设置为训练模式，然后遍历训练数据集，对每个batch的数据进行前向传播、...

def train(): Dtr, Val, Dte = load_data() print('train...') epoch_num = 30 best_model = None min_epochs = 5 min_val_loss = 5 model = cnn().to(device) optimizer = optim.Adam(model.parameters(), lr=0.0008) criterion = nn.CrossEntropyLoss().to(device) # criterion = nn.BCELoss().to(device) for epoch in tqdm(range(epoch_num), ascii=True): train_loss = [] for batch_idx, (data, target) in enumerate(Dtr, 0): try: data, target = Variable(data).to(device), Variable(target.long()).to(device) # target = target.view(target.shape[0], -1) # print(target) optimizer.zero_grad() output = model(data) # print(output) loss = criterion(output, target) loss.backward() optimizer.step() train_loss.append(loss.cpu().item()) except: continue # validation val_loss = get_val_loss(model, Val) model.train() if epoch + 1 > min_epochs and val_loss < min_val_loss: min_val_loss = val_loss best_model = copy.deepcopy(model) torch.save(best_model.state_dict(), r"E:\dataset\Airbnb\training_data\model\cnn.pkl")

这段代码是一个简单的CNN模型的训练过程。它的输入是经过处理的数据集 Dtr, Val, Dte，其中 Dtr 是训练集，Val 是验证集，Dte 是测试集。模型的优化器采用 Adam 算法，损失函数采用交叉熵损失。...

for epoch in range(100): train_loss, train_acc = train(model, train_loader, criterion, optimizer) test_loss, test_acc = test(model, test_loader, criterion) print('Epoch: {} Train Loss: {:.4f} Train Acc: {:.4f} Test Loss: {:.4f} Test Acc: {:.4f}'.format( epoch, train_loss, train_acc, test_loss, test_acc)) 将损失函数可视化

train_loss, train_acc = train(model, train_loader, criterion, optimizer) test_loss, test_acc = test(model, test_loader, criterion) train_losses.append(train_loss) test_losses.append(test_loss) ...

pytorch部分代码如下：train_loss, train_acc = train(model_ft, DEVICE, train_loader, optimizer, epoch,model_ema) if use_amp: with torch.cuda.amp.autocast(): # 开启混合精度 loss = torch.nan_to_num(criterion_train(output, targets)) # 计算loss scaler.scale(loss).backward() # 梯度放大 torch.nn.utils.clip_grad_norm_(model.parameters(), CLIP_GRAD) if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks or _global_forward_hooks or _global_forward_pre_hooks): return forward_call(*input, **kwargs) 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 self.weight = weight def forward(self, x, target): index = torch.zeros_like(x, dtype=torch.uint8) index.scatter_(1, target.data.view(-1, 1), 1) index_float = index.type(torch.cuda.FloatTensor) batch_m = torch.matmul(self.m_list[None, :], index_float.transpose(0,1)) batch_m = batch_m.view((-1, 1)) x_m = x - batch_m output = torch.where(index, x_m, x) return F.cross_entropy(self.soutput, target, weight=self.weight) 报错：Traceback (most recent call last): File "/home/adminis/hpy/ConvNextV2_Demo/train+ca.py", line 279, in <module> train_loss, train_acc = train(model_ft, DEVICE, train_loader, optimizer, epoch,model_ema) File "/home/adminis/hpy/ConvNextV2_Demo/train+ca.py", line 46, in train loss = torch.nan_to_num(criterion_train(output, targets)) # 计算loss File "/home/adminis/anaconda3/envs/wln/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl return forward_call(input, **kwargs) File "/home/adminis/hpy/ConvNextV2_Demo/models/utils.py", line 621, in forward index.scatter_(1, target.data.view(-1, 1), 1) IndexError: scatter_(): Expected dtype int64 for index.

看起来问题出在 LDAMLoss 中的 scatter_() 函数上。根据报错信息，scatter_() 期望的 dtype 是 int64，但是 target 的 dtype 不是，导致了错误。你可以尝试在 LDAMLoss 中将 target 的 dtype ...

Traceback (most recent call last): File "E:/403/myworld/VGAE/trainMy.py", line 59, in <module> loss = criterion(z.view(-1).to(torch.float32), g.ndata['node_features'].view(-1)) File "D:\code\myworld\lib\site-packages\torch\nn\modules\module.py", line 1501, in _call_impl return forward_call(*args, **kwargs) File "D:\code\myworld\lib\site-packages\torch\nn\modules\loss.py", line 619, in forward return F.binary_cross_entropy(input, target, weight=self.weight, reduction=self.reduction) File "D:\code\myworld\lib\site-packages\torch\nn\functional.py", line 3098, in binary_cross_entropy return torch._C._nn.binary_cross_entropy(input, target, weight, reduction_enum) RuntimeError: Found dtype Double but expected Float

loss = criterion(z.view(-1), node_features.view(-1)) 这样就可以解决这个错误了。需要注意的是，在进行数据类型转换时，要根据具体的数据类型进行转换，避免出现数据类型不匹配的错误。

相关推荐

Model_1拟合优度详解：VAR00001 Winters季节模型评估

人脸识别人工智能算法：极大间距准则(MMC)

File "E:/403/myworld/VGAE/trainTest.py", line 41, in loss_function recon_loss = dgl.losses.binary_cross_entropy(output, adj) # 重构误差 报错：AttributeError: module 'dgl' has no attribute 'losses'

loss_id = criterion_id(out0, labels)报错 return torch._C._nn.cross_entropy_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index, label_smoothing) RuntimeError: "nll_loss_forward_reduce_cuda_kernel_2d_index" not implemented for 'Int'

最新推荐

pytorch之inception_v3的实现案例

只需要用一张图片素材文档选择器.zip

火炬连体网络在MNIST的2D嵌入实现示例

管理建模和仿真的文件

L2正则化的终极指南：从入门到精通，揭秘机器学习中的性能优化技巧

如何构建一个符合GB/T19716和ISO/IEC13335标准的信息安全事件管理框架，并确保业务连续性规划的有效性？

Angular插件增强Application Insights JavaScript SDK功能

"互动学习：行动中的多样性与论文攻读经历"

L1正则化模型诊断指南：如何检查模型假设与识别异常值（诊断流程+案例研究）

如何构建一个符合GB/T19716和ISO/IEC13335标准的信息安全事件管理框架，并确保业务连续性规划的有效性？

File "E:/403/myworld/VGAE/trainTest.py", line 41, in loss_function recon_loss = dgl.losses.binary_cross_entropy(output, adj) # 重构误差报错：AttributeError: module 'dgl' has no attribute 'losses'