Types of loss reduction.
Contains the following values:
AUTO
: Indicates that the reduction option will be determined by the usage context. For almost all cases this defaults to SUM_OVER_BATCH_SIZE
. When used with tf.distribute.Strategy
, outside of built-in training loops such as tf.keras
compile
and fit
, we expect reduction value to be SUM
or NONE
. Using AUTO
in that case will raise an error.NONE
: Weighted losses with one dimension reduced (axis=-1, or axis specified by loss function). When this reduction type used with built-in Keras training loops like fit
/evaluate
, the unreduced vector loss is passed to the optimizer but the reported loss will be a scalar value.SUM
: Scalar sum of weighted losses.SUM_OVER_BATCH_SIZE
: Scalar SUM
divided by number of elements in losses. This reduction type is not supported when used with tf.distribute.Strategy
outside of built-in training loops like tf.keras
compile
/fit
.
You can implement 'SUM_OVER_BATCH_SIZE' using global batch size like:
with strategy.scope(): loss_obj = tf.keras.losses.CategoricalCrossentropy( reduction=tf.keras.losses.Reduction.NONE) .... loss = tf.reduce_sum(loss_object(labels, predictions)) * (1. / global_batch_size)
Please see https://www.tensorflow.org/alpha/tutorials/distribute/training_loops for more details on this.
all
@classmethod all()
validate
@classmethod validate( key )
AUTO = 'auto'
NONE = 'none'
SUM = 'sum'
SUM_OVER_BATCH_SIZE = 'sum_over_batch_size'
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Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/compat/v2/keras/losses/Reduction