tfa.seq2seq.SequenceLoss

Weighted cross-entropy loss for a sequence of logits.

tfa.seq2seq.SequenceLoss(
    average_across_timesteps: bool = False,
    average_across_batch: bool = False,
    sum_over_timesteps: bool = True,
    sum_over_batch: bool = True,
    softmax_loss_function: Optional[Callable] = None,
    name: Optional[str] = None
)

Args
`reduction`	Type of `tf.keras.losses.Reduction` to apply to loss. Default value is `AUTO`. `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`, using `AUTO` or `SUM_OVER_BATCH_SIZE` will raise an error. Please see this custom training tutorial for more details.
`name`	Optional name for the instance.

Args

reduction Type of tf.keras.losses.Reduction to apply to loss. Default value is AUTO. 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, using AUTO or SUM_OVER_BATCH_SIZE will raise an error. Please see this custom training tutorial for more details.

name Optional name for the instance.

Methods

`from_config`

@classmethod
from_config(
    config
)

Instantiates a Loss from its config (output of get_config()).

Args
`config`	Output of `get_config()`.

Returns
A `Loss` instance.

`get_config`

get_config()

Returns the config dictionary for a Loss instance.

`call`

View source

__call__(
    y_true, y_pred, sample_weight=None
)

Override the parent call to have a customized reduce behavior.