tf.keras.estimator.model_to_estimator

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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', metric_names_map=None
)

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)

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:

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'].

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.