def __init__(self,
model_fn,
model_dir=None,
config=None,
params=None,
warm_start_from=None):
self._config = estimator_lib.maybe_overwrite_model_dir_and_session_config(
config, model_dir)
self._train_distribution = self._config.train_distribute
# Model directory
self._model_dir = self._config.model_dir
self._session_config = self._config.session_config
logging.info('Using config: %s', str(vars(self._config)))
# None for local mode
self._device_fn = (self._config.device_fn
or estimator_lib._get_replica_device_setter(
self._config))
if model_fn is None:
raise ValueError('model_fn must be provided to Estimator.')
self._model_fn = model_fn
# We shouldn't deep copy params because of class instances(like model, solver instances) in its values
self._params = params or {}
self._feed_dict = None
self._warm_start_settings = estimator_lib._get_default_warm_start_settings(
warm_start_from)
self._train_with_eval = not self._params["args"].train_without_eval
self._predict_keys = None
self.dataset_handle_hook = None
def model_to_estimator(keras_model=None,
keras_model_path=None,
custom_objects=None,
model_dir=None,
config=None,
checkpoint_format=None,
use_v2_estimator=False):
# LINT.ThenChange(//tensorflow/python/keras/estimator/__init__.py)
"""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 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.
use_v2_estimator: Whether to convert the model to a V2 Estimator or V1
Estimator.
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.
"""
if not (keras_model or keras_model_path):
raise ValueError(
'Either `keras_model` or `keras_model_path` needs to be provided.')
if keras_model and keras_model_path:
raise ValueError(
'Please specity either `keras_model` or `keras_model_path`, '
'but not both.')
config = estimator_lib.maybe_overwrite_model_dir_and_session_config(
config, model_dir)
if not keras_model:
if keras_model_path.startswith(
'gs://') or 'storage.googleapis.com' in keras_model_path:
keras_model_path = _get_file_from_google_storage(
keras_model_path, config.model_dir)
tf.compat.v1.logging.info('Loading models from %s', keras_model_path)
keras_model = models.load_model(keras_model_path)
else:
tf.compat.v1.logging.info('Using the Keras model provided.')
keras_model = keras_model
if checkpoint_format is None or checkpoint_format == 'checkpoint':
if not (keras_model._is_graph_network
or isinstance(keras_model, models.Sequential)):
raise ValueError(
'Object-based checkpoints are currently not supported '
'with subclassed models.')
save_object_ckpt = True
elif checkpoint_format == 'saver':
save_object_ckpt = False
else:
raise ValueError(
'Checkpoint format must be one of "checkpoint" or "saver". Got {}'.
format(checkpoint_format))
if not hasattr(keras_model, 'optimizer') or not keras_model.optimizer:
raise ValueError('The given keras model has not been compiled yet. '
'Please compile the model with `model.compile()` '
'before calling `model_to_estimator()`.')
keras_model_fn = _create_keras_model_fn(keras_model, custom_objects,
save_object_ckpt)
if _any_weight_initialized(keras_model):
# Warn if config passed to estimator tries to update GPUOptions. If a
# session has already been created, the GPUOptions passed to the first
# session sticks.
if config.session_config.HasField('gpu_options'):
tf.compat.v1.logging.warn(
'The Keras backend session has already been set. '
'The _session_config passed to model_to_estimator will not be used.'
)
else:
# Pass the config into keras backend's default session.
sess = tf.compat.v1.Session(config=config.session_config)
K.set_session(sess)
warm_start_path = None
if keras_model._is_graph_network:
warm_start_path = _save_first_checkpoint(keras_model, custom_objects,
config, save_object_ckpt)
elif keras_model.built:
tf.compat.v1.logging.warn(
'You are creating an Estimator from a Keras model manually '
'subclassed from `Model`, that was already called on some '
'inputs (and thus already had weights). We are currently '
'unable to preserve the model\'s state (its weights) as '
'part of the estimator in this case. Be warned that the '
'estimator has been created using a freshly initialized '
'version of your model.\n'
'Note that this doesn\'t affect the state of the model '
'instance you passed as `keras_model` argument.')
if use_v2_estimator:
estimator_cls = estimator_lib.EstimatorV2
else:
estimator_cls = estimator_lib.Estimator
estimator = estimator_cls(keras_model_fn,
config=config,
warm_start_from=warm_start_path)
return estimator
def model_to_estimator(keras_model=None,
keras_model_path=None,
custom_objects=None,
model_dir=None,
config=None):
# LINT.ThenChange(//third_party/tensorflow/python/keras/estimator/__init__.py)
"""Constructs an `Estimator` instance from given keras model.
For usage example, please see:
[Creating estimators from Keras
Models](https://tensorflow.org/guide/estimators#model_to_estimator).
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`.
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.
"""
if not (keras_model or keras_model_path):
raise ValueError(
'Either `keras_model` or `keras_model_path` needs to be provided.')
if keras_model and keras_model_path:
raise ValueError(
'Please specity either `keras_model` or `keras_model_path`, '
'but not both.')
config = estimator_lib.maybe_overwrite_model_dir_and_session_config(
config, model_dir)
if not keras_model:
if keras_model_path.startswith(
'gs://') or 'storage.googleapis.com' in keras_model_path:
keras_model_path = _get_file_from_google_storage(
keras_model_path, config.model_dir)
logging.info('Loading models from %s', keras_model_path)
keras_model = models.load_model(keras_model_path)
else:
logging.info('Using the Keras model provided.')
keras_model = keras_model
if not hasattr(keras_model, 'optimizer') or not keras_model.optimizer:
raise ValueError('The given keras model has not been compiled yet. '
'Please compile the model with `model.compile()` '
'before calling `model_to_estimator()`.')
keras_model_fn = _create_keras_model_fn(keras_model, custom_objects)
if _any_weight_initialized(keras_model):
# Warn if config passed to estimator tries to update GPUOptions. If a
# session has already been created, the GPUOptions passed to the first
# session sticks.
if config.session_config.HasField('gpu_options'):
logging.warning(
'The Keras backend session has already been set. '
'The _session_config passed to model_to_estimator will not be used.'
)
else:
# Pass the config into keras backend's default session.
sess = session.Session(config=config.session_config)
K.set_session(sess)
warm_start_path = None
if keras_model._is_graph_network:
warm_start_path = _save_first_checkpoint(keras_model, custom_objects,
config)
elif keras_model.built:
logging.warning(
'You are creating an Estimator from a Keras model manually '
'subclassed from `Model`, that was already called on some '
'inputs (and thus already had weights). We are currently '
'unable to preserve the model\'s state (its weights) as '
'part of the estimator in this case. Be warned that the '
'estimator has been created using a freshly initialized '
'version of your model.\n'
'Note that this doesn\'t affect the state of the model '
'instance you passed as `keras_model` argument.')
estimator = estimator_lib.Estimator(keras_model_fn,
config=config,
warm_start_from=warm_start_path)
return estimator
def model_to_estimator(keras_model=None,
keras_model_path=None,
custom_objects=None,
model_dir=None,
config=None,
checkpoint_format=None,
use_v2_estimator=False,
metric_names_map=None,
export_outputs=None):
# LINT.ThenChange(//tensorflow/python/keras/estimator/__init__.py)
"""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/estimator#create_an_estimator_from_a_keras_model).
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)
```
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.
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.
use_v2_estimator: Whether to convert the model to a V2 Estimator or V1
Estimator. Defaults to `False`.
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`}.
A dict `{name: output}` where:
* name: An arbitrary name for this output. This becomes the signature
name in the SavedModel.
* output: an `ExportOutput` object 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.
"""
if not (keras_model or keras_model_path):
raise ValueError(
'Either `keras_model` or `keras_model_path` needs to be provided.')
if keras_model and keras_model_path:
raise ValueError(
'Please specity either `keras_model` or `keras_model_path`, '
'but not both.')
if keras_model:
_assert_valid_model(keras_model, custom_objects)
config = estimator_lib.maybe_overwrite_model_dir_and_session_config(
config, model_dir)
if not keras_model:
if keras_model_path.startswith(
'gs://') or 'storage.googleapis.com' in keras_model_path:
keras_model_path = _get_file_from_google_storage(
keras_model_path, config.model_dir)
tf.compat.v1.logging.info('Loading models from %s', keras_model_path)
keras_model = tf.keras.models.load_model(keras_model_path)
else:
tf.compat.v1.logging.info('Using the Keras model provided.')
keras_model = keras_model
if checkpoint_format is None or checkpoint_format == 'checkpoint':
if not (keras_model._is_graph_network
or isinstance(keras_model, tf.keras.models.Sequential)):
raise ValueError(
'Object-based checkpoints are currently not supported '
'with subclassed models.')
save_object_ckpt = True
elif checkpoint_format == 'saver':
save_object_ckpt = False
else:
raise ValueError(
'Checkpoint format must be one of "checkpoint" or "saver". Got {}'.
format(checkpoint_format))
if not hasattr(keras_model, 'optimizer') or not keras_model.optimizer:
raise ValueError('The given keras model has not been compiled yet. '
'Please compile the model with `model.compile()` '
'before calling `model_to_estimator()`.')
keras_model_fn = _create_keras_model_fn(keras_model, custom_objects,
save_object_ckpt, metric_names_map,
export_outputs)
if _any_weight_initialized(keras_model):
# Warn if config passed to estimator tries to update GPUOptions. If a
# session has already been created, the GPUOptions passed to the first
# session sticks.
if config.session_config.HasField('gpu_options'):
tf.compat.v1.logging.warn(
'The Keras backend session has already been set. '
'The _session_config passed to model_to_estimator will not be used.'
)
else:
# Pass the config into keras backend's default session.
sess = tf.compat.v1.Session(config=config.session_config)
tf.compat.v1.keras.backend.set_session(sess)
warm_start_path = None
if keras_model._is_graph_network and config.is_chief:
warm_start_path = _save_first_checkpoint(keras_model, custom_objects,
config, save_object_ckpt)
elif keras_model.built:
tf.compat.v1.logging.warn(
'You are creating an Estimator from a Keras model manually '
'subclassed from `Model`, that was already called on some '
'inputs (and thus already had weights). We are currently '
'unable to preserve the model\'s state (its weights) as '
'part of the estimator in this case. Be warned that the '
'estimator has been created using a freshly initialized '
'version of your model.\n'
'Note that this doesn\'t affect the state of the model '
'instance you passed as `keras_model` argument.')
if use_v2_estimator:
estimator_cls = estimator_lib.EstimatorV2
else:
estimator_cls = estimator_lib.Estimator
estimator = estimator_cls(keras_model_fn,
config=config,
warm_start_from=warm_start_path)
return estimator
