page_type: reference description: Options to build ScaNN.

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tflite_model_maker.searcher.ScaNNOptions

<!-- Insert buttons and diff --> <table class="tfo-notebook-buttons tfo-api nocontent" align="left"> <td> <a target="_blank" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/searcher.py#L131-L156"> </a> </td> </table>

Options to build ScaNN.

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>tflite_model_maker.searcher.ScaNNOptions( distance_measure: str, tree: Optional[<a href="../../tflite_model_maker/searcher/Tree"><code>tflite_model_maker.searcher.Tree</code></a>] = None, score_ah: Optional[<a href="../../tflite_model_maker/searcher/ScoreAH"><code>tflite_model_maker.searcher.ScoreAH</code></a>] = None, score_brute_force: Optional[<a href="../../tflite_model_maker/searcher/ScoreBruteForce"><code>tflite_model_maker.searcher.ScoreBruteForce</code></a>] = None ) </code></pre> <h3>Used in the notebooks</h3> <table class="vertical-rules"> <thead> <tr> <th>Used in the tutorials</th> </tr> </thead> <tbody> <tr> <td> <ul> <li><a href="https://www.tensorflow.org/lite/models/modify/model_maker/text_searcher">Text Searcher with TensorFlow Lite Model Maker</a></li> </ul> </td> </tr> </tbody> </table>

ScaNN (<a href="https://ai.googleblog.com/2020/07/announcing-scann-efficient-vector.html">https://ai.googleblog.com/2020/07/announcing-scann-efficient-vector.html</a>) is a highly efficient and scalable vector nearest neighbor retrieval library from Google Research. We use ScaNN to build the on-device search index, and do on-device retrieval with a simplified implementation.

<!-- Tabular view --> <table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2"><h2 class="add-link">Attributes</h2></th></tr> <tr> <td> `distance_measure`<a id="distance_measure"></a> </td> <td> How to compute the distance. Allowed values are 'dot_product' and 'squared_l2'. Please note that when distance is 'dot_product', we actually compute the negative dot product between query and database vectors, to preserve the notion that "smaller is closer". </td> </tr><tr> <td> `tree`<a id="tree"></a> </td> <td> Configure partitioning. If not set, no partitioning is performed. </td> </tr><tr> <td> `score_ah`<a id="score_ah"></a> </td> <td> Configure asymmetric hashing. Must defined this or `score_brute_force`. </td> </tr><tr> <td> `score_brute_force`<a id="score_brute_force"></a> </td> <td> Configure bruce force. Must defined this or `score_ah`. </td> </tr> </table>

Methods

<h3 id="__eq__"><code>__eq__</code></h3> <pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>__eq__( other ) </code></pre> <!-- Tabular view --> <table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2"><h2 class="add-link">Class Variables</h2></th></tr> <tr> <td> score_ah<a id="score_ah"></a> </td> <td> `None` </td> </tr><tr> <td> score_brute_force<a id="score_brute_force"></a> </td> <td> `None` </td> </tr><tr> <td> tree<a id="tree"></a> </td> <td> `None` </td> </tr> </table>