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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.kerascompile and fit, using AUTO or SUM_OVER_BATCH_SIZE
will raise an error. Please see this custom training [tutorial](
https://www.tensorflow.org/tutorials/distribute/custom_training) for
more details.
name
Optional name for the instance.
Methods
from_config
<b>python @classmethod</b>
from_config(
config
)
Instantiates a Loss from its config (output of get_config()).
Returns the config dictionary for a Loss instance.
__call__
__call__(
y_true, y_pred, sample_weight=None
)
Invokes the Loss instance.
Args
y_true
Ground truth values. shape = [batch_size, d0, .. dN], except
sparse loss functions such as sparse categorical crossentropy where
shape = [batch_size, d0, .. dN-1]
y_pred
The predicted values. shape = [batch_size, d0, .. dN]
sample_weight
Optional sample_weight acts as a coefficient for the
loss. If a scalar is provided, then the loss is simply scaled by the
given value. If sample_weight is a tensor of size [batch_size], then
the total loss for each sample of the batch is rescaled by the
corresponding element in the sample_weight vector. If the shape of
sample_weight is [batch_size, d0, .. dN-1] (or can be broadcasted to
this shape), then each loss element of y_pred is scaled
by the corresponding value of sample_weight. (Note ondN-1: all loss
functions reduce by 1 dimension, usually axis=-1.)
Returns
Weighted loss float Tensor. If reduction is NONE, this has
shape [batch_size, d0, .. dN-1]; otherwise, it is scalar. (Note dN-1
because all loss functions reduce by 1 dimension, usually axis=-1.)