tf.keras.losses.CosineSimilarity

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`

View source

call(
    y_true, y_pred
)

`from_config`

View source

@classmethod
from_config(
    config
)

`get_config`

View source

get_config()

`call`

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__call__(
    y_true, y_pred, sample_weight=None
)

Call self as a function.

© 2022 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 4.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/keras/losses/CosineSimilarity