TensorFlow 1 version | View source on GitHub |
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 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)
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'.
|
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. |