LogisticLoss

class LogisticLoss(weight=None, batch_axis=0, label_format='signed', **kwargs)

Calculates the logistic loss (for binary losses only):

(46)\[L = \sum_i \log(1 + \exp(- {pred}_i \cdot {label}_i))\]

where pred is the classifier prediction and label is the target tensor containing values -1 or 1 (0 or 1 if label_format is binary). label and pred can have arbitrary shape as long as they have the same number of elements.

Parameters:

weight : float or None

Global scalar weight for loss.

batch_axis : int, default 0

The axis that represents mini-batch.

label_format : str, default ‘signed’

Can be either ‘signed’ or ‘binary’. If the label_format is ‘signed’, all label values should be either -1 or 1. If the label_format is ‘binary’, all label values should be either 0 or 1.

Inputs:

• pred: prediction tensor with arbitrary shape.
• label: truth tensor with values -1/1 (label_format is ‘signed’) or 0/1 (label_format is ‘binary’). Must have the same size as pred.
• sample_weight: element-wise weighting tensor. Must be broadcastable to the same shape as pred. For example, if pred has shape (64, 10) and you want to weigh each sample in the batch separately, sample_weight should have shape (64, 1).

Outputs:

• loss: loss tensor with shape (batch_size,). Dimenions other than batch_axis are averaged out.