B )²ô`É ã@s<dZddlmZddlmZddlmZGdd„deƒZdS)z3The TF2 version of the enum keras.losses.Reduction.é)Úabsolute_import)Údivision)Úprint_functionc@s8eZdZdZdZdZdZdZedd„ƒZ edd „ƒZ d S)ÚReductionV2aTypes 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 the [custom training guide](https://www.tensorflow.org/tutorials/distribute/custom_training) # pylint: disable=line-too-long for more details on this. ÚautoÚnoneÚsumZsum_over_batch_sizecCs|j|j|j|jfS)N)ÚAUTOÚNONEÚSUMÚSUM_OVER_BATCH_SIZE)Úcls©rúZ/home/dcms/DCMS/lib/python3.7/site-packages/tensorflow/python/ops/losses/loss_reduction.pyÚall=szReductionV2.allcCs|| ¡krtd|ƒ‚dS)NzInvalid Reduction Key %s.)rÚ ValueError)r ÚkeyrrrÚvalidateAszReductionV2.validateN)Ú__name__Ú __module__Ú__qualname__Ú__doc__r r rrÚclassmethodrrrrrrrs!rN)rÚ __future__rrrÚobjectrrrrrÚs