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', metric_names_map=None,
    export_outputs=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)
 Example with customized export signature: inputs = {'a': tf.keras.Input(..., name='a'),
          'b': tf.keras.Input(..., name='b')}
outputs = {'c': tf.keras.layers.Dense(..., name='c')(inputs['a']),
           'd': tf.keras.layers.Dense(..., name='d')(inputs['b'])}
keras_model = tf.keras.Model(inputs, outputs)
keras_model.compile(...)
export_outputs = {'c': tf.estimator.export.RegressionOutput,
                  'd': tf.estimator.export.ClassificationOutput}

estimator = tf.keras.estimator.model_to_estimator(
    keras_model, export_outputs=export_outputs)

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.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. Defaults to 'saver'.  
  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'].  
  export_outputs   Optional dictionary. This can be used to override the default Keras model output exports in a multi IO model use case and provide custom names for the export_outputs in tf.estimator.EstimatorSpec. Default is None, which is equivalent to {'serving_default': tf.estimator.export.PredictOutput}. If not None, the keys must match the keys of model.output_names. A dict {name: output} where:  name: An arbitrary name for this output. output: an ExportOutput class such as ClassificationOutput, RegressionOutput, or PredictOutput. Single-headed models only need to specify one entry in this dictionary. Multi-headed models should specify one entry for each head, one of which must be named using tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY If no entry is provided, a default PredictOutput mapping to predictions will be created. 

  
 


 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.