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Overview of the method is here: Google AI Blogpost
Also, explore the interactive visualization that demonstrates the practical properties of the Bi-Tempered logistic loss.
A replacement for standard logistic loss function: tf.losses.softmax_cross_entropy is available here
def bi_tempered_logistic_loss(activations,
labels,
t1,
t2,
label_smoothing=0.0,
num_iters=5):
"""Bi-Tempered Logistic Loss with custom gradient.
Args:
activations: A multi-dimensional tensor with last dimension `num_classes`.
labels: A tensor with shape and dtype as activations.
t1: Temperature 1 (< 1.0 for boundedness).
t2: Temperature 2 (> 1.0 for tail heaviness, < 1.0 for finite support).
label_smoothing: Label smoothing parameter between [0, 1).
num_iters: Number of iterations to run the method.
Returns:
A loss tensor.
"""Replacements are also available for the transfer functions:
Tempered version of tf.nn.sigmoid:
def tempered_sigmoid(activations, t, num_iters=5):
"""Tempered sigmoid function.
Args:
activations: Activations for the positive class for binary classification.
t: Temperature tensor > 0.0.
num_iters: Number of iterations to run the method.
Returns:
A probabilities tensor.
"""Tempered version of tf.nn.softmax:
def tempered_softmax(activations, t, num_iters=5):
"""Tempered softmax function.
Args:
activations: A multi-dimensional tensor with last dimension `num_classes`.
t: Temperature tensor > 0.0.
num_iters: Number of iterations to run the method.
Returns:
A probabilities tensor.
"""When referencing Bi-Tempered loss, cite this paper:
@article{bitemperedloss,
author = {Ehsan Amid and
Manfred K. Warmuth and
Rohan Anil and
Tomer Koren},
title = {Robust Bi-Tempered Logistic Loss Based on Bregman Divergences},
journal = {CoRR},
volume = {abs/1906.03361},
year = {2019},
url = {http://arxiv.org/abs/1906.03361},
}
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