R/metrics.R

metric_cosine_similarity

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

Arguments Description
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](https://en.wikipedia.org/wiki/Cosine_similarity). This metric keeps the average cosine similarity betweenpredictionsandlabels` 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()