tf.compat.v1.keras.estimator.model_to_estimator

Constructs an Estimator instance from given keras model.

tf.compat.v1.keras.estimator.model_to_estimator(
    keras_model=None, keras_model_path=None, custom_objects=None, model_dir=None,
    config=None, checkpoint_format='saver'
)

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)