Loss Function
Before reading this chapter, please read the MindSpore official website tutorial firstLoss Function.
The MindSpore official website tutorial on loss functions explains built-in, custom, and multi label loss functions, as well as guidance on their use in model training. Here is a list of differences in functionality and interface between MindSpore’s loss function and PyTorch’s loss function.
torch.nn |
torch.nn.functional |
mindspore.nn |
mindspore.ops |
Difference Description |
|---|---|---|---|---|
torch.nn.L1Loss |
torch.nn.functional.l1_loss |
mindspore.nn.L1Loss |
mindspore.ops.l1_loss |
consistent |
torch.nn.MSELoss |
torch.nn.functional.mse_loss |
mindspore.nn.MSELoss |
mindspore.ops.mse_loss |
consistent |
torch.nn.CrossEntropyLoss |
torch.nn.functional.cross_entropy |
mindspore.nn.CrossEntropyLoss |
mindspore.ops.cross_entropy |
|
torch.nn.CTCLoss |
torch.nn.functional.ctc_loss |
mindspore.nn.CTCLoss |
mindspore.ops.ctc_loss |
consistent |
torch.nn.NLLLoss |
torch.nn.functional.nll_loss |
mindspore.nn.NLLLoss |
mindspore.ops.nll_loss |
consistent |
torch.nn.PoissonNLLLoss |
torch.nn.functional.poisson_nll_loss |
mindspore.nn.PoissonNLLLoss |
- |
consistent |
torch.nn.GaussianNLLLoss |
torch.nn.functional.gaussian_nll_loss |
mindspore.nn.GaussianNLLLoss |
mindspore.ops.gaussian_nll_loss |
consistent |
torch.nn.KLDivLoss |
torch.nn.functional.kl_div |
mindspore.nn.KLDivLoss |
mindspore.ops.kl_div |
MindSpore does not support the |
torch.nn.BCELoss |
torch.nn.functional.binary_cross_entropy |
mindspore.nn.BCELoss |
mindspore.ops.binary_cross_entropy |
consistent |
torch.nn.BCEWithLogitsLoss |
torch.nn.functional.binary_cross_entropy_with_logits |
mindspore.nn.BCEWithLogitsLoss |
mindspore.ops.binary_cross_entropy_with_logits |
consistent |
torch.nn.MarginRankingLoss |
torch.nn.functional.margin_ranking_loss |
mindspore.nn.MarginRankingLoss |
mindspore.ops.margin_ranking_loss |
consistent |
torch.nn.HingeEmbeddingLoss |
torch.nn.functional.hinge_embedding_loss |
mindspore.nn.HingeEmbeddingLoss |
mindspore.ops.hinge_embedding_loss |
consistent |
torch.nn.MultiLabelMarginLoss |
torch.nn.functional.multilabel_margin_loss |
mindspore.nn.MultiLabelMarginLoss |
mindspore.ops.multilabel_margin_loss |
consistent |
torch.nn.HuberLoss |
torch.nn.functional.huber_loss |
mindspore.nn.HuberLoss |
mindspore.ops.huber_loss |
consistent |
torch.nn.SmoothL1Loss |
torch.nn.functional.smooth_l1_loss |
mindspore.nn.SmoothL1Loss |
mindspore.ops.smooth_l1_loss |
consistent |
torch.nn.SoftMarginLoss |
torch.nn.functional.soft_margin_loss |
mindspore.nn.SoftMarginLoss |
mindspore.ops.soft_margin_loss |
consistent |
torch.nn.MultiLabelSoftMarginLoss |
torch.nn.functional.multilabel_soft_margin_loss |
mindspore.nn.MultiLabelSoftMarginLoss |
mindspore.ops.multilabel_soft_margin_loss |
consistent |
torch.nn.CosineEmbeddingLoss |
torch.nn.functional.cosine_embedding_loss |
mindspore.nn.CosineEmbeddingLoss |
mindspore.ops.cosine_embedding_loss |
consistent |
torch.nn.MultiMarginLoss |
torch.nn.functional.multi_margin_loss |
mindspore.nn.MultiMarginLoss |
mindspore.ops.multi_margin_loss |
consistent |
torch.nn.TripletMarginLoss |
torch.nn.functional.triplet_margin_loss |
mindspore.nn.TripletMarginLoss |
mindspore.ops.triplet_margin_loss |
Functionality is consistent, but the number or order of parameters is not consistent |
torch.nn.TripletMarginWithDistanceLoss |
torch.nn.functional.triplet_margin_with_distance_loss |
mindspore.nn.TripletMarginWithDistanceLoss |
- |
consistent |