CODEWITHSUNDEEP
machine-learningneural-networkclassificationkeras
Yakku
Yakku

Reputation: 451

Neural network for multi label classification with large number of classes outputs only zero

I am training a neural network for multilabel classification, with a large number of classes (1000). Which means more than one output can be active for every input. On an average, I have two classes active per output frame. On training with a cross entropy loss the neural network resorts to outputting only zeros, because it gets the least loss with this output since 99.8% of my labels are zeros. Any suggestions on how I can push the network to give more weight to the positive classes?

Upvotes: 13

Views: 4125

Answers (2)

James
James

Reputation: 1098

Many thanks to tobigue for the great solution.

The tensorflow and keras apis have changed since that answer. So the updated version of weighted_binary_crossentropy is below for Tensorflow 2.7.0.

import tensorflow as tf

POS_WEIGHT = 10

def weighted_binary_crossentropy(target, output):
    """
    Weighted binary crossentropy between an output tensor 
    and a target tensor. POS_WEIGHT is used as a multiplier 
    for the positive targets.

    Combination of the following functions:
    * keras.losses.binary_crossentropy
    * keras.backend.tensorflow_backend.binary_crossentropy
    * tf.nn.weighted_cross_entropy_with_logits
    """
    # transform back to logits
    _epsilon = tf.convert_to_tensor(tf.keras.backend.epsilon(), output.dtype.base_dtype)
    output = tf.clip_by_value(output, _epsilon, 1 - _epsilon)
    output = tf.math.log(output / (1 - output))
    loss = tf.nn.weighted_cross_entropy_with_logits(labels=target, logits=output, pos_weight=POS_WEIGHT)
    return tf.reduce_mean(loss, axis=-1)

Upvotes: 0

tobigue
tobigue

Reputation: 3607

Tensorflow has a loss function weighted_cross_entropy_with_logits, which can be used to give more weight to the 1's. So it should be applicable to a sparse multi-label classification setting like yours.

From the documentation:

This is like sigmoid_cross_entropy_with_logits() except that pos_weight, allows one to trade off recall and precision by up- or down-weighting the cost of a positive error relative to a negative error.

The argument pos_weight is used as a multiplier for the positive targets

If you use the tensorflow backend in Keras, you can use the loss function like this (Keras 2.1.1):

import tensorflow as tf
import keras.backend.tensorflow_backend as tfb

POS_WEIGHT = 10  # multiplier for positive targets, needs to be tuned

def weighted_binary_crossentropy(target, output):
    """
    Weighted binary crossentropy between an output tensor 
    and a target tensor. POS_WEIGHT is used as a multiplier 
    for the positive targets.

    Combination of the following functions:
    * keras.losses.binary_crossentropy
    * keras.backend.tensorflow_backend.binary_crossentropy
    * tf.nn.weighted_cross_entropy_with_logits
    """
    # transform back to logits
    _epsilon = tfb._to_tensor(tfb.epsilon(), output.dtype.base_dtype)
    output = tf.clip_by_value(output, _epsilon, 1 - _epsilon)
    output = tf.log(output / (1 - output))
    # compute weighted loss
    loss = tf.nn.weighted_cross_entropy_with_logits(targets=target,
                                                    logits=output,
                                                    pos_weight=POS_WEIGHT)
    return tf.reduce_mean(loss, axis=-1)

Then in your model:

model.compile(loss=weighted_binary_crossentropy, ...)

I have not found many resources yet which report well working values for the pos_weight in relation to the number of classes, average active classes, etc.

Upvotes: 9

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