tensorflow/lite/g3doc/api_docs/python/tflite_model_maker/audio_classifier/AudioClassifier.md
page_type: reference description: Audio classifier for training/inference and exporing.
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Audio classifier for training/inference and exporing.
<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>tflite_model_maker.audio_classifier.AudioClassifier( model_spec, index_to_label, shuffle, train_whole_model ) </code></pre> <!-- Placeholder for "Used in" --> <!-- Tabular view --> <table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2"><h2 class="add-link">Args</h2></th></tr> <tr> <td> `model_spec`<a id="model_spec"></a> </td> <td> Specification for the model. </td> </tr><tr> <td> `index_to_label`<a id="index_to_label"></a> </td> <td> A list that map from index to label class name. </td> </tr><tr> <td> `shuffle`<a id="shuffle"></a> </td> <td> Whether the data should be shuffled. </td> </tr><tr> <td> `train_whole_model`<a id="train_whole_model"></a> </td> <td> If true, the Hub module is trained together with the classification layer on top. Otherwise, only train the top classification layer. </td> </tr> </table><a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/audio_classifier.py#L86-L101">View source</a>
<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>confusion_matrix( data, batch_size=32 ) </code></pre> <h3 id="create"><code>create</code></h3><a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/audio_classifier.py#L103-L140">View source</a>
<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>@classmethod</code> <code>create( train_data, model_spec, validation_data=None, batch_size=32, epochs=5, model_dir=None, do_train=True, train_whole_model=False ) </code></pre>Loads data and retrains the model.
<!-- Tabular view --> <table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2">Args</th></tr> <tr> <td> `train_data` </td> <td> A instance of audio_dataloader.DataLoader class. </td> </tr><tr> <td> `model_spec` </td> <td> Specification for the model. </td> </tr><tr> <td> `validation_data` </td> <td> Validation DataLoader. If None, skips validation process. </td> </tr><tr> <td> `batch_size` </td> <td> Number of samples per training step. If `use_hub_library` is False, it represents the base learning rate when train batch size is 256 and it's linear to the batch size. </td> </tr><tr> <td> `epochs` </td> <td> Number of epochs for training. </td> </tr><tr> <td> `model_dir` </td> <td> The location of the model checkpoint files. </td> </tr><tr> <td> `do_train` </td> <td> Whether to run training. </td> </tr><tr> <td> `train_whole_model` </td> <td> Boolean. By default, only the classification head is trained. When True, the base model is also trained. </td> </tr> </table> <!-- Tabular view --> <table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2">Returns</th></tr> <tr class="alt"> <td colspan="2"> An instance based on AudioClassifier. </td> </tr> </table> <h3 id="create_model"><code>create_model</code></h3><a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/audio_classifier.py#L59-L62">View source</a>
<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>create_model( num_classes, train_whole_model ) </code></pre> <h3 id="create_serving_model"><code>create_serving_model</code></h3><a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/custom_model.py#L170-L176">View source</a>
<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>create_serving_model() </code></pre>Returns the underlining Keras model for serving.
<h3 id="evaluate"><code>evaluate</code></h3><a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/classification_model.py#L53-L65">View source</a>
<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>evaluate( data, batch_size=32 ) </code></pre>Evaluates the model.
<!-- Tabular view --> <table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2">Args</th></tr> <tr> <td> `data` </td> <td> Data to be evaluated. </td> </tr><tr> <td> `batch_size` </td> <td> Number of samples per evaluation step. </td> </tr> </table> <!-- Tabular view --> <table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2">Returns</th></tr> <tr class="alt"> <td colspan="2"> The loss value and accuracy. </td> </tr> </table> <h3 id="evaluate_tflite"><code>evaluate_tflite</code></h3><a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/classification_model.py#L105-L143">View source</a>
<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>evaluate_tflite( tflite_filepath, data, postprocess_fn=None ) </code></pre>Evaluates the tflite model.
<!-- Tabular view --> <table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2">Args</th></tr> <tr> <td> `tflite_filepath` </td> <td> File path to the TFLite model. </td> </tr><tr> <td> `data` </td> <td> Data to be evaluated. </td> </tr><tr> <td> `postprocess_fn` </td> <td> Postprocessing function that will be applied to the output of `lite_runner.run` before calculating the probabilities. </td> </tr> </table> <!-- Tabular view --> <table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2">Returns</th></tr> <tr class="alt"> <td colspan="2"> The evaluation result of TFLite model - accuracy. </td> </tr> </table> <h3 id="export"><code>export</code></h3><a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/custom_model.py#L95-L168">View source</a>
<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>export( export_dir, tflite_filename='model.tflite', label_filename='labels.txt', vocab_filename='vocab.txt', saved_model_filename='saved_model', tfjs_folder_name='tfjs', export_format=None, **kwargs ) </code></pre>Converts the retrained model based on export_format.
<a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/classification_model.py#L67-L95">View source</a>
<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>predict_top_k( data, k=1, batch_size=32 ) </code></pre>Predicts the top-k predictions.
<!-- Tabular view --> <table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2">Args</th></tr> <tr> <td> `data` </td> <td> Data to be evaluated. Either an instance of DataLoader or just raw data entries such TF tensor or numpy array. </td> </tr><tr> <td> `k` </td> <td> Number of top results to be predicted. </td> </tr><tr> <td> `batch_size` </td> <td> Number of samples per evaluation step. </td> </tr> </table> <!-- Tabular view --> <table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2">Returns</th></tr> <tr class="alt"> <td colspan="2"> top k results. Each one is (label, probability). </td> </tr> </table> <h3 id="summary"><code>summary</code></h3><a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/custom_model.py#L65-L66">View source</a>
<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>summary() </code></pre> <h3 id="train"><code>train</code></h3><a target="_blank" class="external" href="https://github.com/tensorflow/examples/blob/master/tensorflow_examples/lite/model_maker/core/task/audio_classifier.py#L36-L57">View source</a>
<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>train( train_data, validation_data, epochs, batch_size ) </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> ALLOWED_EXPORT_FORMAT<a id="ALLOWED_EXPORT_FORMAT"></a> </td> <td> `(<ExportFormat.LABEL: 'LABEL'>, <ExportFormat.TFLITE: 'TFLITE'>, <ExportFormat.SAVED_MODEL: 'SAVED_MODEL'>)` </td> </tr><tr> <td> DEFAULT_EXPORT_FORMAT<a id="DEFAULT_EXPORT_FORMAT"></a> </td> <td> `<ExportFormat.TFLITE: 'TFLITE'>` </td> </tr> </table>