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Computes the logarithm of the hyperbolic cosine of the prediction error.
Inherits From: Loss
tf.keras.losses.LogCosh(
reduction=losses_utils.ReductionV2.AUTO, name='log_cosh'
)
logcosh = log((exp(x) + exp(-x))/2)
,
where x is the error y_pred - y_true
.
Standalone usage:
y_true = [[0., 1.], [0., 0.]]
y_pred = [[1., 1.], [0., 0.]]
# Using 'auto'/'sum_over_batch_size' reduction type.
l = tf.keras.losses.LogCosh()
l(y_true, y_pred).numpy()
0.108
# Calling with 'sample_weight'.
l(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy()
0.087
# Using 'sum' reduction type.
l = tf.keras.losses.LogCosh(
reduction=tf.keras.losses.Reduction.SUM)
l(y_true, y_pred).numpy()
0.217
# Using 'none' reduction type.
l = tf.keras.losses.LogCosh(
reduction=tf.keras.losses.Reduction.NONE)
l(y_true, y_pred).numpy()
array([0.217, 0.], dtype=float32)
Usage with the compile()
API:
model.compile(optimizer='sgd', loss=tf.keras.losses.LogCosh())
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. Defaults to 'log_cosh'. |
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 keras.losses.Loss instance.
|
get_config
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.)
|
Raises | |
---|---|
ValueError
|
If the shape of sample_weight is invalid.
|