|---| | [ View source on GitHub](https://github.com/keras-team/keras/tree/v2.15.0/keras/estimator/__init__.py#L191-L385) | Constructs an `Estimator` instance from given keras model. tf.keras.estimator.model_to_estimator( keras_model=None, keras_model_path=None, custom_objects=None, model_dir=None, config=None, checkpoint_format='checkpoint', metric_names_map=None, export_outputs=None ) If you use infrastructure or other tooling that relies on Estimators, you can still build a Keras model and use model_to_estimator to convert the Keras model to an Estimator for use with downstream systems. For usage example, please see: [Creating estimators from Keras Models](https://www.tensorflow.org/guide/estimators#creating_estimators_from_keras_models). #### Sample Weights: Estimators returned by `model_to_estimator` are configured so that they can handle sample weights (similar to `keras_model.fit(x, y, sample_weights)`). To pass sample weights when training or evaluating the Estimator, the first item returned by the input function should be a dictionary with keys `features` and `sample_weights`. Example below: keras_model = tf.keras.Model(...) keras_model.compile(...) estimator = tf.keras.estimator.model_to_estimator(keras_model) def input_fn(): return dataset_ops.Dataset.from_tensors( ({'features': features, 'sample_weights': sample_weights}, targets)) estimator.train(input_fn, steps=1) Example with customized export signature: inputs = {'a': tf.keras.Input(..., name='a'), 'b': tf.keras.Input(..., name='b')} outputs = {'c': tf.keras.layers.Dense(..., name='c')(inputs['a']), 'd': tf.keras.layers.Dense(..., name='d')(inputs['b'])} keras_model = tf.keras.Model(inputs, outputs) keras_model.compile(...) export_outputs = {'c': tf.estimator.export.RegressionOutput, 'd': tf.estimator.export.ClassificationOutput} estimator = tf.keras.estimator.model_to_estimator( keras_model, export_outputs=export_outputs) def input_fn(): return dataset_ops.Dataset.from_tensors( ({'features': features, 'sample_weights': sample_weights}, targets)) estimator.train(input_fn, steps=1) > [!NOTE] > **Note:** We do not support creating weighted metrics in Keras and converting them to weighted metrics in the Estimator API using `model_to_estimator`. You will have to create these metrics directly on the estimator spec using the `add_metrics` function. To customize the estimator `eval_metric_ops` names, you can pass in the `metric_names_map` dictionary mapping the keras model output metric names to the custom names as follows: input_a = tf.keras.layers.Input(shape=(16,), name='input_a') input_b = tf.keras.layers.Input(shape=(16,), name='input_b') dense = tf.keras.layers.Dense(8, name='dense_1') interm_a = dense(input_a) interm_b = dense(input_b) merged = tf.keras.layers.concatenate([interm_a, interm_b], name='merge') output_a = tf.keras.layers.Dense(3, activation='softmax', name='dense_2')( merged) output_b = tf.keras.layers.Dense(2, activation='softmax', name='dense_3')( merged) keras_model = tf.keras.models.Model( inputs=[input_a, input_b], outputs=[output_a, output_b]) keras_model.compile( loss='categorical_crossentropy', optimizer='rmsprop', metrics={ 'dense_2': 'categorical_accuracy', 'dense_3': 'categorical_accuracy' }) metric_names_map = { 'dense_2_categorical_accuracy': 'acc_1', 'dense_3_categorical_accuracy': 'acc_2', } keras_est = tf.keras.estimator.model_to_estimator( keras_model=keras_model, config=config, metric_names_map=metric_names_map)


| ## Args || |---|---| | `keras_model` | A compiled Keras model object. This argument is mutually exclusive with `keras_model_path`. Estimator's `model_fn` uses the structure of the model to clone the model. Defaults to `None`. | | `keras_model_path` | Path to a compiled Keras model saved on disk, in HDF5 format, which can be generated with the `save()` method of a Keras model. This argument is mutually exclusive with `keras_model`. Defaults to `None`. | | `custom_objects` | Dictionary for cloning customized objects. This is used with classes that is not part of this pip package. For example, if user maintains a `relu6` class that inherits from [`tf.keras.layers.Layer`](https://www.tensorflow.org/versions/r2.15/api_docs/python/tf/keras/layers/Layer), then pass `custom_objects={'relu6': relu6}`. Defaults to `None`. | | `model_dir` | Directory to save `Estimator` model parameters, graph, summary files for TensorBoard, etc. If unset a directory will be created with `tempfile.mkdtemp` | | `config` | `RunConfig` to config `Estimator`. Allows setting up things in `model_fn` based on configuration such as `num_ps_replicas`, or `model_dir`. If both `config.model_dir` and the `model_dir` argument (above) are specified the `model_dir` **argument** takes precedence. Defaults to `None`. | | `checkpoint_format` | Sets the format of the checkpoint saved by the estimator when training. May be `saver` or `checkpoint`, depending on whether to save checkpoints from [`tf.compat.v1.train.Saver`](https://www.tensorflow.org/versions/r2.15/api_docs/python/tf/compat/v1/train/Saver) or [`tf.train.Checkpoint`](https://www.tensorflow.org/versions/r2.15/api_docs/python/tf/train/Checkpoint). The default is `checkpoint`. Estimators use name-based `tf.train.Saver` checkpoints, while Keras models use object-based checkpoints from [`tf.train.Checkpoint`](https://www.tensorflow.org/versions/r2.15/api_docs/python/tf/train/Checkpoint). Currently, saving object-based checkpoints from `model_to_estimator` is only supported by Functional and Sequential models. Defaults to 'checkpoint'. | | `metric_names_map` | Optional dictionary mapping Keras model output metric names to custom names. This can be used to override the default Keras model output metrics names in a multi IO model use case and provide custom names for the `eval_metric_ops` in Estimator. The Keras model metric names can be obtained using `model.metrics_names` excluding any loss metrics such as total loss and output losses. For example, if your Keras model has two outputs `out_1` and `out_2`, with `mse` loss and `acc` metric, then `model.metrics_names` will be `['loss', 'out_1_loss', 'out_2_loss', 'out_1_acc', 'out_2_acc']`. The model metric names excluding the loss metrics will be `['out_1_acc', 'out_2_acc']`. | | `export_outputs` | Optional dictionary. This can be used to override the default Keras model output exports in a multi IO model use case and provide custom names for the `export_outputs` in [`tf.estimator.EstimatorSpec`](https://www.tensorflow.org/versions/r2.15/api_docs/python/tf/estimator/EstimatorSpec). Default is None, which is equivalent to {'serving_default': `tf.estimator.export.PredictOutput`}. If not None, the keys must match the keys of `model.output_names`. A dict `{name: output}` where:
- name: An arbitrary name for this output. - output: an `ExportOutput` class such as `ClassificationOutput`, `RegressionOutput`, or `PredictOutput`. Single-headed models only need to specify one entry in this dictionary. Multi-headed models should specify one entry for each head, one of which must be named using `tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY` If no entry is provided, a default `PredictOutput` mapping to `predictions` will be created. |


| ## Returns || |---|---| | An Estimator from given keras model. ||



| ## Raises || |---|---| | `ValueError` | If neither keras_model nor keras_model_path was given. | | `ValueError` | If both keras_model and keras_model_path was given. | | `ValueError` | If the keras_model_path is a GCS URI. | | `ValueError` | If keras_model has not been compiled. | | `ValueError` | If an invalid checkpoint_format was given. |