tf.keras.Loss

Loss base class.

tf.keras.Loss(
    name=None, reduction='sum_over_batch_size', dtype=None
)

This is the class to subclass in order to create new custom losses.

reduction Type of reduction to apply to the loss. In almost all cases this should be "sum_over_batch_size". Supported options are "sum", "sum_over_batch_size", "mean", "mean_with_sample_weight" or None. "sum" sums the loss, "sum_over_batch_size" and "mean" sum the loss and divide by the sample size, and "mean_with_sample_weight" sums the loss and divides by the sum of the sample weights. "none" and None perform no aggregation. Defaults to "sum_over_batch_size".
name Optional name for the loss instance.
dtype The dtype of the loss's computations. Defaults to None, which means using keras.backend.floatx(). keras.backend.floatx() is a "float32" unless set to different value (via keras.backend.set_floatx()). If a keras.DTypePolicy is provided, then the compute_dtype will be utilized.

To be implemented by subclasses:

  • call(): Contains the logic for loss calculation using y_true, y_pred.

Example subclass implementation:

class MeanSquaredError(Loss):
    def call(self, y_true, y_pred):
        return ops.mean(ops.square(y_pred - y_true), axis=-1)

dtype

Methods

call

View source

call(
    y_true, y_pred
)

from_config

View source

@classmethod
from_config(
    config
)

get_config

View source

get_config()

__call__

View source

__call__(
    y_true, y_pred, sample_weight=None
)

Call self as a function.