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='saver'
)
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.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.
|
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.
|