tf.keras.losses.cosine_similarity | TensorFlow v2.16.1 (original) (raw)
tf.keras.losses.cosine_similarity
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Computes the cosine similarity between labels and predictions.
tf.keras.losses.cosine_similarity(
y_true, y_pred, axis=-1
)
Formula:
loss = -sum(l2_norm(y_true) * l2_norm(y_pred))
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.
| Args | |
|---|---|
| y_true | Tensor of true targets. |
| y_pred | Tensor of predicted targets. |
| axis | Axis along which to determine similarity. Defaults to -1. |
| Returns |
|---|
| Cosine similarity tensor. |
Example:
y_true = [[0., 1.], [1., 1.], [1., 1.]]
y_pred = [[1., 0.], [1., 1.], [-1., -1.]]
loss = keras.losses.cosine_similarity(y_true, y_pred, axis=-1)
[-0., -0.99999994, 0.99999994]
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Last updated 2024-06-07 UTC.