mse_loss(pred, y) mae = F.l1_loss(pred, y) mae = mae.cpu().detach() return pred, mae, mse else: return pred Example #10Source File: MeanTeacherv2.py From Tricks-of-Semi-supervisedDeepLeanring-Pytorch with MIT License 6 votes def __init__(self, model, ema_model, optimizer, ...
torch.nn.MSELoss(*size_average=None*, *reduce=None*, *reduction='mean'*) torch.nn.functional.mse_loss(*input*, *target*, *size_average=None*, *reduce=None*, *reduction='mean'*) Smooth L1 Loss Smooth L1 损失函数通过β结合了MSE 和 MAE 的优点,来自 Fast R-CNN 论文。 当真实值和预测...
CATCH_RETURN_Tensor( autoopts =torch::nn::functional::MSELossFuncOptions(); ApplyReduction(opts, reduction); res =ResultTensor(torch::nn::functional::mse_loss(*input, *target, opts)); ) } If this is indeed incorrect, I would be happy to submit a fix in a PR, but am still figuring...
目录PyTorch的⼗七个损失函数1.L1loss2.MSELoss3.CrossEntropyLoss 4. NLLLoss 5. PoissonNLLLoss 6.../回归精度变高呢?那么多种损失函数,应该如何选择呢?请来了解PyTorch中给出的十七种损失函数吧。1.L1lossclasstorch.nn.L1Loss(size_average=None 关于...
() mse = nn.MSELoss()(predictions, labels).item() mae = nn.L1Loss()(predictions, labels).item() - # Convert tensors to numpy arrays for NDCG computation predictions = predictions.detach().cpu().numpy() labels = labels.detach().cpu().numpy() - # Calculate NDCG ndcg = NDCG_k(...
torch.nn 与torch.nn.functional说起torch.nn,不得不说torch.nn.functional! 这两个库很类似,都涵盖了神经网络的各层操作,只是用法有点不同,比如在损失函数Loss中实现交叉熵! 但是两个库都可以实现神经网络的各层运算。其他包括卷积、池化、padding、激活(非线性层)、线性层、正则化层、其他损失函数Loss,两者都...
(100, 128) >>> input2 = torch.randn(100, 128) >>> output = pdist(input1, input2) Loss functions L1Loss class torch.nn.L1Loss(size_average=None, reduce=None, reduction='mean')[source] Creates a criterion that measures the mean absolute error (MAE) between each element in the input...
classtorch.nn.RNN(*args,**kwargs)[source] Applies a multi-layer Elman RNN with tanhtanhtanh or ReLUReLUReLU non-linearity to an input sequence. For each element in the input sequence, each layer computes the following function: ht=tanh(Wihxt+bih+Whhh(t−1)+bhh)h_t = \text{tanh}...
Both module and functional implementations are provided for all sparse point cloud operators. To construct a sparse convolutional network for point clouds, only a conversion from PyTorch's nn.Conv3d, nn.BatchNorm3d, and nn.ReLU to our spnn counterpart is required, as shown in Figure 3. Unlike...
() # Perform validation without gradient calculation val_loss_func = nn.CrossEntropyLoss(reduction="mean") val_loss_func_mae = nn.L1Loss(reduction="mean") val_losses = [] gt_one_episode = [] model_output_one_episode = [] # Loop through the validation dataset for idx, (obs, action)...