Loss functions

Im lost too.

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mse_variance_loss

def mse_variance_loss(
    preds, target, representations, alpha:float=0.2
):

preds: [bs x num_patch x n_vars x patch_len] targets: [bs x num_patch x n_vars x patch_len] representations: [bs x nvars x d_model x num_patch]

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smoothl1_loss

def smoothl1_loss(
    preds, target
):

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huber_loss

def huber_loss(
    preds, target, delta:int=1
):

preds: [bs x num_patch x n_vars x patch_len] targets: [bs x num_patch x n_vars x patch_len]

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cosine_similarity_loss

def cosine_similarity_loss(
    preds, target
):

preds: [bs x num_patch x n_vars x patch_len] targets: [bs x num_patch x n_vars x patch_len]

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mape

def mape(
    preds, target
):

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mae_loss

def mae_loss(
    preds, target
):

preds: [bs x num_patch x n_vars x patch_len] targets: [bs x num_patch x n_vars x patch_len]

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mse_loss

def mse_loss(
    preds, target
):

preds: [bs x num_patch x n_vars x patch_len] targets: [bs x num_patch x n_vars x patch_len]

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CrossEntropyLoss

def CrossEntropyLoss(
    ignore_index:int=-100, reduction:str='mean', weight:NoneType=None, label_smoothing:int=0, soft_labels:bool=False
):

Cross entropy loss with ignore_index.

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FocalLoss

def FocalLoss(
    weight:NoneType=None, gamma:float=2.0, reduction:str='mean', ignore_index:int=-100
):

adapted from tsai, weighted multiclass focal loss https://github.com/timeseriesAI/tsai/blob/bdff96cc8c4c8ea55bc20d7cffd6a72e402f4cb2/tsai/losses.py#L116C1-L140C20