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# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Keras estimator API."""
import tensorflow.compat.v2 as tf
from tensorflow.python.util.tf_export import keras_export
# Keras has undeclared dependency on tensorflow/estimator:estimator_py.
# As long as you depend //third_party/py/tensorflow:tensorflow target
# everything will work as normal.
_model_to_estimator_usage_gauge = tf.__internal__.monitoring.BoolGauge(
'/tensorflow/api/keras/model_to_estimator',
'Whether tf.keras.estimator.model_to_estimator() is called.', 'version')
# LINT.IfChange
@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/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)
```
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)
@keras_export('keras.estimator.model_to_estimator', v1=[])
def model_to_estimator_v2(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):
"""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)
```
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:
```python
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']`.
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('v2').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=True,
metric_names_map=metric_names_map,
export_outputs=export_outputs)
# LINT.ThenChange(//tensorflow_estimator/python/estimator/keras_lib.py)