tf.keras.metrics.CosineSimilarity

Computes the cosine similarity between the labels and predictions.

Inherits From: MeanMetricWrapper, Mean, Metric

tf.keras.metrics.CosineSimilarity(
    name='cosine_similarity', dtype=None, axis=-1
)

Formula:

loss = sum(l2_norm(y_true) * l2_norm(y_pred))

See: Cosine Similarity. This metric keeps the average cosine similarity between predictions and labels over a stream of data.

name (Optional) string name of the metric instance.
dtype (Optional) data type of the metric result.
axis (Optional) Defaults to -1. The dimension along which the cosine similarity is computed.

Example:

Example:

# 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]]
# result = mean(sum(l2_norm(y_true) . l2_norm(y_pred), axis=1))
#        = ((0. + 0.) +  (0.5 + 0.5)) / 2
m = keras.metrics.CosineSimilarity(axis=1)
m.update_state([[0., 1.], [1., 1.]], [[1., 0.], [1., 1.]])
m.result()
0.49999997
m.reset_state()
m.update_state([[0., 1.], [1., 1.]], [[1., 0.], [1., 1.]],
               sample_weight=[0.3, 0.7])
m.result()
0.6999999

Usage with compile() API:

model.compile(
    optimizer='sgd',
    loss='mse',
    metrics=[keras.metrics.CosineSimilarity(axis=1)])

dtype

variables

Methods

add_variable

View source

add_variable(
    shape, initializer, dtype=None, aggregation='sum', name=None
)

add_weight

View source

add_weight(
    shape=(), initializer=None, dtype=None, name=None
)

from_config

View source

@classmethod
from_config(
    config
)

get_config

View source

get_config()

Return the serializable config of the metric.

reset_state

View source

reset_state()

Reset all of the metric state variables.

This function is called between epochs/steps, when a metric is evaluated during training.

result

View source

result()

Compute the current metric value.

Returns
A scalar tensor, or a dictionary of scalar tensors.

stateless_reset_state

View source

stateless_reset_state()

stateless_result

View source

stateless_result(
    metric_variables
)

stateless_update_state

View source

stateless_update_state(
    metric_variables, *args, **kwargs
)

update_state

View source

update_state(
    y_true, y_pred, sample_weight=None
)

Accumulate statistics for the metric.

__call__

View source

__call__(
    *args, **kwargs
)

Call self as a function.