page_type: reference description: TextClassifier class for inference and exporting to tflite.

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tflite_model_maker.text_classifier.TextClassifier

<!-- 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/text_classifier.py#L57-L220"> </a> </td> </table>

TextClassifier class for inference and exporting to tflite.

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>tflite_model_maker.text_classifier.TextClassifier( model_spec, index_to_label, shuffle=True ) </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> </table>

Methods

<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/text_classifier.py#L177-L220">View source</a>

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>@classmethod</code> <code>create( train_data, model_spec=&#x27;average_word_vec&#x27;, validation_data=None, batch_size=None, epochs=3, steps_per_epoch=None, shuffle=False, do_train=True ) </code></pre>

Loads data and train the model for test classification.

<!-- 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> Training data. </td> </tr><tr> <td> `model_spec` </td> <td> Specification for the model. </td> </tr><tr> <td> `validation_data` </td> <td> Validation data. If None, skips validation process. </td> </tr><tr> <td> `batch_size` </td> <td> Batch size for training. </td> </tr><tr> <td> `epochs` </td> <td> Number of epochs for training. </td> </tr><tr> <td> `steps_per_epoch` </td> <td> Integer or None. Total number of steps (batches of samples) before declaring one epoch finished and starting the next epoch. If `steps_per_epoch` is None, the epoch will run until the input dataset is exhausted. </td> </tr><tr> <td> `shuffle` </td> <td> Whether the data should be shuffled. </td> </tr><tr> <td> `do_train` </td> <td> Whether to run training. </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 TextClassifier. </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/text_classifier.py#L84-L86">View source</a>

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>create_model( with_loss_and_metrics=True ) </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=&#x27;model.tflite&#x27;, label_filename=&#x27;labels.txt&#x27;, vocab_filename=&#x27;vocab.txt&#x27;, saved_model_filename=&#x27;saved_model&#x27;, tfjs_folder_name=&#x27;tfjs&#x27;, export_format=None, **kwargs ) </code></pre>

Converts the retrained model based on export_format.

<!-- Tabular view --> <table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2">Args</th></tr> <tr> <td> `export_dir` </td> <td> The directory to save exported files. </td> </tr><tr> <td> `tflite_filename` </td> <td> File name to save tflite model. The full export path is {export_dir}/{tflite_filename}. </td> </tr><tr> <td> `label_filename` </td> <td> File name to save labels. The full export path is {export_dir}/{label_filename}. </td> </tr><tr> <td> `vocab_filename` </td> <td> File name to save vocabulary. The full export path is {export_dir}/{vocab_filename}. </td> </tr><tr> <td> `saved_model_filename` </td> <td> Path to SavedModel or H5 file to save the model. The full export path is {export_dir}/{saved_model_filename}/{saved_model.pb|assets|variables}. </td> </tr><tr> <td> `tfjs_folder_name` </td> <td> Folder name to save tfjs model. The full export path is {export_dir}/{tfjs_folder_name}. </td> </tr><tr> <td> `export_format` </td> <td> List of export format that could be saved_model, tflite, label, vocab. </td> </tr><tr> <td> `**kwargs` </td> <td> Other parameters like `quantized_config` for TFLITE model. </td> </tr> </table> <h3 id="predict_top_k"><code>predict_top_k</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#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/text_classifier.py#L88-L120">View source</a>

<pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>train( train_data, validation_data=None, epochs=None, batch_size=None, steps_per_epoch=None ) </code></pre>

Feeds the training data for training.

<!-- 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.TFLITE: 'TFLITE'>, <ExportFormat.LABEL: 'LABEL'>, <ExportFormat.VOCAB: 'VOCAB'>, <ExportFormat.SAVED_MODEL: 'SAVED_MODEL'>, <ExportFormat.TFJS: 'TFJS'>)` </td> </tr><tr> <td> DEFAULT_EXPORT_FORMAT<a id="DEFAULT_EXPORT_FORMAT"></a> </td> <td> `(<ExportFormat.TFLITE: 'TFLITE'>, <ExportFormat.LABEL: 'LABEL'>, <ExportFormat.VOCAB: 'VOCAB'>)` </td> </tr> </table>