tf.keras.losses.CosineSimilarity | TensorFlow v2.16.1 (original) (raw)
tf.keras.losses.CosineSimilarity
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Computes the cosine similarity between y_true & y_pred.
Inherits From: Loss
tf.keras.losses.CosineSimilarity(
axis=-1,
reduction='sum_over_batch_size',
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. 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.
Formula:
loss = -sum(l2_norm(y_true) * l2_norm(y_pred))
| Args | |
|---|---|
| axis | The axis along which the cosine similarity is computed (the features axis). Defaults to -1. |
| reduction | Type of reduction to apply to the loss. In almost all cases this should be "sum_over_batch_size". Supported options are "sum", "sum_over_batch_size" or None. |
| name | Optional name for the loss instance. |
Methods
call
call(
y_true, y_pred
)
from_config
@classmethod
from_config( config )
get_config
get_config()
__call__
__call__(
y_true, y_pred, sample_weight=None
)
Call self as a function.
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Last updated 2024-06-07 UTC.