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()