tf.keras.losses.MSLE

View source on GitHub

Types of loss reduction.

View aliases

Main aliases

tf.losses.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_obj(labels, predictions)) *
      (1. / global_batch_size)
```

Please see the custom training guide
for more details on this.

Methods

`all`

View source

@classmethod
all()

`validate`

View source

@classmethod
validate(
    key
)

Class Variables
AUTO	`'auto'`
NONE	`'none'`
SUM	`'sum'`
SUM_OVER_BATCH_SIZE	`'sum_over_batch_size'`

TensorFlow

tf.keras / losses / losses.MSLE

View aliases

Methods

`all`

`validate`

Class Variables

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.keras / losses / losses.MSLE

View aliases

Methods

all

validate

Class Variables

`all`

`validate`