#!/usr/bin/env python
# vim: set fileencoding=utf-8 :
# @author: Amir Mohammadi <amir.mohammadi@idiap.ch>
import tensorflow as tf
[docs]def balanced_softmax_cross_entropy_loss_weights(labels, dtype="float32"):
"""Computes weights that normalizes your loss per class.
Labels must be a batch of one-hot encoded labels. The function takes labels and
computes the weights per batch. Weights will be smaller for classes that have more
samples in this batch. This is useful if you unbalanced classes in your dataset or
batch.
Parameters
----------
labels : ``tf.Tensor``
Labels of your current input. The shape must be [batch_size, n_classes]. If your
labels are not one-hot encoded, you can use ``tf.one_hot`` to convert them first
before giving them to this function.
dtype : ``tf.dtype``
The dtype that weights will have. It should be float. Best is to provide
logits.dtype as input.
Returns
-------
``tf.Tensor``
Computed weights that will cancel your dataset imbalance per batch.
Examples
--------
>>> import numpy
>>> import tensorflow as tf
>>> from bob.learn.tensorflow.loss import balanced_softmax_cross_entropy_loss_weights
>>> labels = numpy.array([[1, 0, 0],
... [1, 0, 0],
... [0, 0, 1],
... [0, 1, 0],
... [0, 0, 1],
... [1, 0, 0],
... [1, 0, 0],
... [0, 0, 1],
... [1, 0, 0],
... [1, 0, 0],
... [1, 0, 0],
... [1, 0, 0],
... [1, 0, 0],
... [1, 0, 0],
... [0, 1, 0],
... [1, 0, 0],
... [0, 1, 0],
... [1, 0, 0],
... [0, 0, 1],
... [0, 0, 1],
... [1, 0, 0],
... [0, 0, 1],
... [1, 0, 0],
... [1, 0, 0],
... [0, 1, 0],
... [1, 0, 0],
... [1, 0, 0],
... [1, 0, 0],
... [0, 1, 0],
... [1, 0, 0],
... [0, 0, 1],
... [1, 0, 0]], dtype="int32")
>>> session = tf.Session() # Eager execution is also possible check https://www.tensorflow.org/guide/eager
>>> session.run(tf.reduce_sum(labels, axis=0))
array([20, 5, 7], dtype=int32)
>>> session.run(balanced_softmax_cross_entropy_loss_weights(labels, dtype='float32'))
array([0.53333336, 0.53333336, 1.5238096 , 2.1333334 , 1.5238096 ,
0.53333336, 0.53333336, 1.5238096 , 0.53333336, 0.53333336,
0.53333336, 0.53333336, 0.53333336, 0.53333336, 2.1333334 ,
0.53333336, 2.1333334 , 0.53333336, 1.5238096 , 1.5238096 ,
0.53333336, 1.5238096 , 0.53333336, 0.53333336, 2.1333334 ,
0.53333336, 0.53333336, 0.53333336, 2.1333334 , 0.53333336,
1.5238096 , 0.53333336], dtype=float32)
You would use it like this:
>>> #weights = balanced_softmax_cross_entropy_loss_weights(labels, dtype=logits.dtype)
>>> #loss = tf.losses.softmax_cross_entropy(logits=logits, labels=labels, weights=weights)
"""
shape = tf.cast(tf.shape(labels), dtype=dtype)
batch_size, n_classes = shape[0], shape[1]
weights = tf.cast(tf.reduce_sum(labels, axis=0), dtype=dtype)
weights = batch_size / weights / n_classes
weights = tf.gather(weights, tf.argmax(labels, axis=1))
return weights
[docs]def balanced_sigmoid_cross_entropy_loss_weights(labels, dtype="float32"):
"""Computes weights that normalizes your loss per class.
Labels must be a batch of binary labels. The function takes labels and
computes the weights per batch. Weights will be smaller for the class that have more
samples in this batch. This is useful if you unbalanced classes in your dataset or
batch.
Parameters
----------
labels : ``tf.Tensor``
Labels of your current input. The shape must be [batch_size] and values must be
either 0 or 1.
dtype : ``tf.dtype``
The dtype that weights will have. It should be float. Best is to provide
logits.dtype as input.
Returns
-------
``tf.Tensor``
Computed weights that will cancel your dataset imbalance per batch.
Examples
--------
>>> import numpy
>>> import tensorflow as tf
>>> from bob.learn.tensorflow.loss import balanced_sigmoid_cross_entropy_loss_weights
>>> labels = numpy.array([1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0,
... 1, 1, 0, 1, 1, 1, 0, 1, 0, 1], dtype="int32")
>>> sum(labels), len(labels)
(20, 32)
>>> session = tf.Session() # Eager execution is also possible check https://www.tensorflow.org/guide/eager
>>> session.run(balanced_sigmoid_cross_entropy_loss_weights(labels, dtype='float32'))
array([0.8 , 0.8 , 1.3333334, 1.3333334, 1.3333334, 0.8 ,
0.8 , 1.3333334, 0.8 , 0.8 , 0.8 , 0.8 ,
0.8 , 0.8 , 1.3333334, 0.8 , 1.3333334, 0.8 ,
1.3333334, 1.3333334, 0.8 , 1.3333334, 0.8 , 0.8 ,
1.3333334, 0.8 , 0.8 , 0.8 , 1.3333334, 0.8 ,
1.3333334, 0.8 ], dtype=float32)
You would use it like this:
>>> #weights = balanced_sigmoid_cross_entropy_loss_weights(labels, dtype=logits.dtype)
>>> #loss = tf.losses.sigmoid_cross_entropy(logits=logits, labels=labels, weights=weights)
"""
labels = tf.cast(labels, dtype='int32')
batch_size = tf.cast(tf.shape(labels)[0], dtype=dtype)
weights = tf.cast(tf.reduce_sum(labels), dtype=dtype)
weights = tf.convert_to_tensor([batch_size - weights, weights])
weights = batch_size / weights / 2
weights = tf.gather(weights, labels)
return weights