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',
    dtype=None
)

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))

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", "mean", "mean_with_sample_weight" or None. "sum" sums the loss, "sum_over_batch_size" and "mean" sum the loss and divide by the sample size, and "mean_with_sample_weight" sums the loss and divides by the sum of the sample weights. "none" and None perform no aggregation. Defaults to "sum_over_batch_size".
name Optional name for the loss instance.
dtype The dtype of the loss's computations. Defaults to None, which means using keras.backend.floatx(). keras.backend.floatx() is a "float32" unless set to different value (via keras.backend.set_floatx()). If a keras.DTypePolicy is provided, then the compute_dtype will be utilized.

dtype

Methods

call

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call(
    y_true, y_pred
)

from_config

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@classmethod
from_config(
    config
)

get_config

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get_config()

__call__

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__call__(
    y_true, y_pred, sample_weight=None
)

Call self as a function.