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Original link: https://blog.csdn.net/shanglianlm/article/details/85019768
01
criterion = LossCriterion() # Constructor has its own parameters
loss = criterion(x, y) # Call standard also has parameters02
2-1 L1 Norm Loss L1Loss
Calculates the absolute difference between output and target.
torch.nn.L1Loss(reduction='mean')Parameters:
reduction – three values, none: no reduction; mean: returns the average of losses; sum: returns the sum of losses. Default: mean.
2-2 Mean Squared Error Loss MSELoss
Calculates the mean squared difference between output and target.
torch.nn.MSELoss(reduction='mean')Parameters:
reduction – three values, none: no reduction; mean: returns the average of losses; sum: returns the sum of losses. Default: mean.
2-3 Cross Entropy Loss CrossEntropyLoss
Effective for training classification problems with C categories. The optional parameter weight must be a 1D Tensor, weights will be assigned to each category. Very effective for unbalanced training sets.
In multi-class tasks, softmax activation function + cross-entropy loss function is often used, because cross-entropy describes the difference between two probability distributions, while the neural network outputs a vector, not in the form of a probability distribution. So the softmax activation function is needed to “normalize” a vector into the form of a probability distribution, and then use the cross-entropy loss function to calculate loss.
torch.nn.CrossEntropyLoss(weight=None, ignore_index=-100, reduction='mean')Parameters:
weight (Tensor, optional) – Custom weights for each category. Must be a Tensor of length C
ignore_index (int, optional) – Sets a target value that will be ignored, thus not affecting the input gradient.
reduction – three values, none: no reduction; mean: returns the average of losses; sum: returns the sum of losses. Default: mean.
2-4 KL Divergence Loss KLDivLoss
Calculates the KL divergence between input and target. KL divergence can be used to measure the distance between different continuous distributions, and is very effective when performing direct regression on the space of continuous output distributions (discrete sampling).
torch.nn.KLDivLoss(reduction='mean')Parameters:
reduction – three values, none: no reduction; mean: returns the average of losses; sum: returns the sum of losses. Default: mean.
2-5 Binary Cross Entropy Loss BCELoss
Cross-entropy calculation function for binary classification tasks. Used to measure reconstruction errors, e.g., in autoencoders. Note that the target value t[i] ranges between 0 and 1.
torch.nn.BCELoss(weight=None, reduction='mean')Parameters:
weight (Tensor, optional) – Custom weights for each batch element’s loss. Must be a Tensor of length “nbatch”
pos_weight (Tensor, optional) – Custom weights for each positive sample’s loss. Must be a Tensor of length “classes”
2-6 BCEWithLogitsLoss
The BCEWithLogitsLoss function integrates the Sigmoid layer into the BCELoss class. This version is numerically more stable than using a simple Sigmoid layer and BCELoss, because it combines these two operations into one layer, allowing the use of log-sum-exp techniques for numerical stability.
torch.nn.BCEWithLogitsLoss(weight=None, reduction='mean', pos_weight=None)Parameters:
weight (Tensor, optional) – Custom weights for each batch element’s loss. Must be a Tensor of length “nbatch”
pos_weight (Tensor, optional) – Custom weights for each positive sample’s loss. Must be a Tensor of length “classes”
2-7 Margin Ranking Loss
torch.nn.MarginRankingLoss(margin=0.0, reduction='mean')For each instance in the mini-batch, the loss function is as follows:
Parameters:
margin: default value 0
2-8 Hinge Embedding Loss
torch.nn.HingeEmbeddingLoss(margin=1.0, reduction='mean')For each instance in the mini-batch, the loss function is as follows:
Parameters:
margin: default value 1
2-9 Multi-label Classification Loss MultiLabelMarginLoss
torch.nn.MultiLabelMarginLoss(reduction='mean')For each sample in the mini-batch, the loss is calculated as follows:
2-10 Smooth L1 Loss SmoothL1Loss
Also known as Huber loss function.
torch.nn.SmoothL1Loss(reduction='mean')
Where
2-11 Logistic Loss for Binary Classification SoftMarginLoss
torch.nn.SoftMarginLoss(reduction='mean')
2-12 Multi-label One-vs-All Loss MultiLabelSoftMarginLoss
torch.nn.MultiLabelSoftMarginLoss(weight=None, reduction='mean')
2-13 Cosine Loss CosineEmbeddingLoss
torch.nn.CosineEmbeddingLoss(margin=0.0, reduction='mean')
Parameters:
margin: default value 0
2-14 Multi-class Hinge Loss MultiMarginLoss
torch.nn.MultiMarginLoss(p=1, margin=1.0, weight=None, reduction='mean')
Parameters:
p=1 or 2 Default: 1
margin: default value 1
2-15 Triplet Loss TripletMarginLoss
torch.nn.TripletMarginLoss(margin=1.0, p=2.0, eps=1e-06, swap=False, reduction='mean')
Where:
2-16 CTC Loss CTCLoss
CTC (Connectionist Temporal Classification) loss can automatically align data that is not aligned, mainly used for training serialized data without prior alignment, such as speech recognition, OCR recognition, etc.
torch.nn.CTCLoss(blank=0, reduction='mean')Parameters:
reduction – three values, none: no reduction; mean: returns the average of losses; sum: returns the sum of losses. Default: mean.
2-17 Negative Log Likelihood Loss NLLLoss
Negative log likelihood loss. Used for training classification problems with C categories.
torch.nn.NLLLoss(weight=None, ignore_index=-100, reduction='mean')Parameters:
weight (Tensor, optional) – Custom weights for each category. Must be a Tensor of length C
ignore_index (int, optional) – Sets a target value that will be ignored, thus not affecting the input gradient.
2-18 NLLLoss2d
Negative log likelihood loss for image input. It calculates the negative log likelihood loss for each pixel.
torch.nn.NLLLoss2d(weight=None, ignore_index=-100, reduction='mean')Parameters:
weight (Tensor, optional) – Custom weights for each category. Must be a Tensor of length C
reduction – three values, none: no reduction; mean: returns the average of losses; sum: returns the sum of losses. Default: mean.
2-19 Poisson NLL Loss PoissonNLLLoss
Negative log likelihood loss for a target value following a Poisson distribution
torch.nn.PoissonNLLLoss(log_input=True, full=False, eps=1e-08, reduction='mean')Parameters:
log_input (bool, optional) – If set to True, loss will be calculated according to the formula exp(input) – target * input. If set to False, loss will be calculated according to input – target * log(input+eps).
full (bool, optional) – Whether to compute the full loss, i.e., including the Stirling approximation term target * log(target) – target + 0.5 * log(2 * pi * target).
eps (float, optional) – Default value: 1e-8
References
http://www.voidcn.com/article/p-rtzqgqkz-bpg.html
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