Loss base class.
tf.keras.losses.Loss(
reduction=losses_utils.ReductionV2.AUTO, name=None
)
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 tf.reduce_mean(tf.math.square(y_pred - y_true), axis=-1)
When using a Loss under a tf.distribute.Strategy
, except passing it
to Model.compile()
for use by Model.fit()
, please use reduction
types 'SUM' or 'NONE', and reduce losses explicitly. Using 'AUTO' or
'SUM_OVER_BATCH_SIZE' will raise an error when calling the Loss object
from a custom training loop or from user-defined code in Layer.call()
.
Please see this custom training
tutorial
for more details on this.
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 under a tf.distribute.Strategy , except via
Model.compile() and Model.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
call
View source
@abc.abstractmethod
call(
y_true, y_pred
)
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]
|
Returns |
Loss values with the shape [batch_size, d0, .. dN-1] .
|
from_config
View source
@classmethod
from_config(
config
)
Instantiates a Loss
from its config (output of get_config()
).
Args |
config
|
Output of get_config() .
|
get_config
View source
get_config()
Returns the config dictionary for a Loss
instance.
__call__
View source
__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.)
|
Raises |
ValueError
|
If the shape of sample_weight is invalid.
|