tf.keras.estimator.model_to_estimator

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
)

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.

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)

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`, 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`. Defaults to `None`. If both `config.model_dir` and the `model_dir` argument (above) are specified the `model_dir` argument takes precedence.
`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` or `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`. 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']`.

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.

© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/keras/estimator/model_to_estimator

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Sample Weights: