metric_cosine_similarity: Computes the cosine similarity between the labels and...

In keras: R Interface to 'Keras'

Computes the cosine similarity between the labels and predictions

Description

Computes the cosine similarity between the labels and predictions

Usage

metric_cosine_similarity(
  ...,
  axis = -1L,
  name = "cosine_similarity",
  dtype = NULL
)

Arguments

...	Passed on to the underlying metric. Used for forwards and backwards compatibility.
axis	(Optional) (1-based) Defaults to -1. The dimension along which the metric is computed.
name	(Optional) string name of the metric instance.
dtype	(Optional) data type of the metric result.

Details

cosine similarity = (a . b) / ||a|| ||b||

See: Cosine Similarity.

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

Value

A (subclassed) Metric instance that can be passed directly to compile(metrics = ), or used as a standalone object. See ?Metric for example usage.

Note

If you want to compute the cosine_similarity for each case in a mini-batch you can use loss_cosine_similarity().

See Also

Other metrics: custom_metric(), metric_accuracy(), metric_auc(), metric_binary_accuracy(), metric_binary_crossentropy(), metric_categorical_accuracy(), metric_categorical_crossentropy(), metric_categorical_hinge(), metric_false_negatives(), metric_false_positives(), metric_hinge(), metric_kullback_leibler_divergence(), metric_logcosh_error(), metric_mean_absolute_error(), metric_mean_absolute_percentage_error(), metric_mean_iou(), metric_mean_relative_error(), metric_mean_squared_error(), metric_mean_squared_logarithmic_error(), metric_mean_tensor(), metric_mean_wrapper(), metric_mean(), metric_poisson(), metric_precision_at_recall(), metric_precision(), metric_recall_at_precision(), metric_recall(), metric_root_mean_squared_error(), metric_sensitivity_at_specificity(), metric_sparse_categorical_accuracy(), metric_sparse_categorical_crossentropy(), metric_sparse_top_k_categorical_accuracy(), metric_specificity_at_sensitivity(), metric_squared_hinge(), metric_sum(), metric_top_k_categorical_accuracy(), metric_true_negatives(), metric_true_positives()

keras documentation built on Aug. 16, 2023, 1:07 a.m.