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'
)

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 to 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)

Args
`keras_model`	A compiled Keras model object. This argument is mutually exclusive with `keras_model_path`.
`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`.
`custom_objects`	Dictionary for custom objects.
`model_dir`	Directory to save `Estimator` model parameters, graph, summary files for TensorBoard, etc.
`config`	`RunConfig` to config `Estimator`.
`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.

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.

tf.keras.estimator.model_to_estimator

Args

Returns

Raises