Computes the cosine similarity between labels and predictions.
tf.keras.losses.CosineSimilarity(
axis=-1, reduction=losses_utils.ReductionV2.AUTO, name='cosine_similarity'
)
Note that it is a negative quantity between -1 and 0, where 0 indicates
orthogonality and values closer to -1 indicate greater similarity. This makes
it usable as a loss function in a setting where you try to maximize the
proximity between predictions and targets. If either y_true
or y_pred
is a zero vector, cosine similarity will be 0 regardless of the proximity
between predictions and targets.
loss = -sum(l2_norm(y_true) * l2_norm(y_pred))
Standalone usage:
y_true = [[0., 1.], [1., 1.]]
y_pred = [[1., 0.], [1., 1.]]
# Using 'auto'/'sum_over_batch_size' reduction type.
cosine_loss = tf.keras.losses.CosineSimilarity(axis=1)
# l2_norm(y_true) = [[0., 1.], [1./1.414], 1./1.414]]]
# l2_norm(y_pred) = [[1., 0.], [1./1.414], 1./1.414]]]
# l2_norm(y_true) . l2_norm(y_pred) = [[0., 0.], [0.5, 0.5]]
# loss = mean(sum(l2_norm(y_true) . l2_norm(y_pred), axis=1))
# = -((0. + 0.) + (0.5 + 0.5)) / 2
cosine_loss(y_true, y_pred).numpy()
-0.5
# Calling with 'sample_weight'.
cosine_loss(y_true, y_pred, sample_weight=[0.8, 0.2]).numpy()
-0.0999
# Using 'sum' reduction type.
cosine_loss = tf.keras.losses.CosineSimilarity(axis=1,
reduction=tf.keras.losses.Reduction.SUM)
cosine_loss(y_true, y_pred).numpy()
-0.999
# Using 'none' reduction type.
cosine_loss = tf.keras.losses.CosineSimilarity(axis=1,
reduction=tf.keras.losses.Reduction.NONE)
cosine_loss(y_true, y_pred).numpy()
array([-0., -0.999], dtype=float32)
Usage with the compile()
API:
model.compile(optimizer='sgd', loss=tf.keras.losses.CosineSimilarity(axis=1))
Args |
axis
|
(Optional) Defaults to -1. The dimension along which the cosine
similarity is computed.
|
reduction
|
(Optional) 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 op.
|
Args |
fn
|
The loss function to wrap, with signature fn(y_true, y_pred,
**kwargs) .
|
reduction
|
(Optional) 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 loss.
|
**kwargs
|
The keyword arguments that are passed on to fn .
|
Methods
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.
|