Module keras.api.keras.estimator
Public API for tf.keras.estimator namespace.
Expand source code
# This file is MACHINE GENERATED! Do not edit.
# Generated by: tensorflow/python/tools/api/generator/create_python_api.py script.
"""Public API for tf.keras.estimator namespace.
"""
from __future__ import print_function as _print_function
import sys as _sys
from keras.estimator import model_to_estimator
del _print_function
from tensorflow.python.util import module_wrapper as _module_wrapper
if not isinstance(_sys.modules[__name__], _module_wrapper.TFModuleWrapper):
_sys.modules[__name__] = _module_wrapper.TFModuleWrapper(
_sys.modules[__name__], "keras.estimator", public_apis=None, deprecation=True,
has_lite=False)
Functions
def model_to_estimator(keras_model=None, keras_model_path=None, custom_objects=None, model_dir=None, config=None, checkpoint_format='saver', metric_names_map=None, export_outputs=None)
-
Constructs an
Estimator
instance from given keras model.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 tokeras_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
andsample_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)
Args
keras_model
- A compiled Keras model object. This argument is mutually
exclusive with
keras_model_path
. Estimator'smodel_fn
uses the structure of the model to clone the model. Defaults toNone
. 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 withkeras_model
. Defaults toNone
. 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 fromtf.keras.layers.Layer
, then passcustom_objects={'relu6': relu6}
. Defaults toNone
. model_dir
- Directory to save
Estimator
model parameters, graph, summary files for TensorBoard, etc. If unset a directory will be created withtempfile.mkdtemp
config
RunConfig
to configEstimator
. Allows setting up things inmodel_fn
based on configuration such asnum_ps_replicas
, ormodel_dir
. Defaults toNone
. If bothconfig.model_dir
and themodel_dir
argument (above) are specified themodel_dir
argument takes precedence.checkpoint_format
- Sets the format of the checkpoint saved by the estimator
when training. May be
saver
orcheckpoint
, depending on whether to save checkpoints fromtf.train.Saver
ortf.train.Checkpoint
. This argument currently defaults tosaver
. When 2.0 is released, the default will becheckpoint
. Estimators use name-basedtf.train.Saver
checkpoints, while Keras models use object-based checkpoints fromtf.train.Checkpoint
. Currently, saving object-based checkpoints frommodel_to_estimator()
is only supported by Functional and Sequential models. Defaults to 'saver'. 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 usingmodel.metrics_names
excluding any loss metrics such as total loss and output losses. For example, if your Keras model has two outputsout_1
andout_2
, withmse
loss andacc
metric, thenmodel.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
intf.estimator.EstimatorSpec
. Default is None, which is equivalent to {'serving_default':tf.estimator.export.PredictOutput
}. If not None, the keys must match the keys ofmodel.output_names
. A dict{name: output}
where: * name: An arbitrary name for this output. * output: anExportOutput
class such asClassificationOutput
,RegressionOutput
, orPredictOutput
. 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 usingtf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY
If no entry is provided, a defaultPredictOutput
mapping topredictions
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.
Expand source code
@keras_export(v1=['keras.estimator.model_to_estimator']) def model_to_estimator( keras_model=None, keras_model_path=None, custom_objects=None, model_dir=None, config=None, checkpoint_format='saver', metric_names_map=None, export_outputs=None): """Constructs an `Estimator` instance from given keras model. 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: ```python 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: ```python 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) ``` 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.train.Saver` or `tf.train.Checkpoint`. This argument currently defaults to `saver`. When 2.0 is released, the default will be `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 'saver'. 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`. 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. """ try: from tensorflow_estimator.python.estimator import keras_lib # pylint: disable=g-import-not-at-top except ImportError: raise NotImplementedError( 'tf.keras.estimator.model_to_estimator function not available in your ' 'installation.') _model_to_estimator_usage_gauge.get_cell('v1').set(True) return keras_lib.model_to_estimator( # pylint:disable=unexpected-keyword-arg keras_model=keras_model, keras_model_path=keras_model_path, custom_objects=custom_objects, model_dir=model_dir, config=config, checkpoint_format=checkpoint_format, use_v2_estimator=False, metric_names_map=metric_names_map, export_outputs=export_outputs)