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Computes the cosine similarity between labels and predictions.
Inherits From: Loss
tf.keras.losses.CosineSimilarity(
axis=-1,
reduction=losses_utils.ReductionV2.AUTO,
name='cosine_similarity'
)
Note that it is a number between -1 and 1. When it is a negative number
between -1 and 0, 0 indicates orthogonality and values closer to -1
indicate greater similarity. The values closer to 1 indicate greater
dissimilarity. 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
|
The axis along which the cosine similarity is computed (the features axis). Defaults to -1. |
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 'cosine_similarity'. |
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.
|