tensorflow_similarity/models/contrastive_model.py [976:1003]:
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        if verbose:
            print("|-Computing embeddings")
        predictions = self.predict(x)

        if verbose:
            print("|-Computing retrieval metrics")

        results = self._index.evaluate_retrieval(
            predictions=predictions,
            target_labels=y,
            retrieval_metrics=retrieval_metrics,
            verbose=verbose,
        )

        if verbose:
            table = zip(results.keys(), results.values())
            headers = ["metric", "Value"]
            print("\n [Summary]\n")
            print(tabulate(table, headers=headers))

        return results

    def evaluate_classification(
        self,
        x: Tensor,
        y: IntTensor,
        k: int = 1,
        extra_metrics: MutableSequence[Union[str, ClassificationMetric]] = [
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tensorflow_similarity/models/similarity_model.py [609:636]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        if verbose:
            print("|-Computing embeddings")
        predictions = self.predict(x)

        if verbose:
            print("|-Computing retrieval metrics")

        results = self._index.evaluate_retrieval(
            predictions=predictions,
            target_labels=y,
            retrieval_metrics=retrieval_metrics,
            verbose=verbose,
        )

        if verbose:
            table = zip(results.keys(), results.values())
            headers = ["metric", "Value"]
            print("\n [Summary]\n")
            print(tabulate(table, headers=headers))

        return results

    def evaluate_classification(
        self,
        x: Tensor,
        y: IntTensor,
        k: int = 1,
        extra_metrics: MutableSequence[Union[str, ClassificationMetric]] = [
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