tf.compat.v2.keras.estimator.model_to_estimator

Constructs an Estimator instance from given keras model.

tf.compat.v2.keras.estimator.model_to_estimator(
    keras_model=None, keras_model_path=None, custom_objects=None, model_dir=None,
    config=None, checkpoint_format='checkpoint'
)

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.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.

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.