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Exports requested train/eval/predict graphs as separate SavedModels. (deprecated)

See tf.contrib.estimator.export_all_saved_models for the currently exposed version of this function.

For each mode passed in via the input_receiver_fn_map, this method builds a new graph by calling the input_receiver_fn to obtain feature and label Tensors. Next, this method calls the Estimator's model_fn in the passed mode to generate the model graph based on those features and labels, and restores the given checkpoint (or, lacking that, the most recent checkpoint) into the graph. Only one of the modes is used for saving variables to the SavedModel (order of preference: TRAIN, EVAL, then PREDICT), such that up to three MetaGraphDefs are saved with a single set of variables in a single SavedModel directory.

For prediction, the exported MetaGraphDef will provide one SignatureDef for each element of the export_outputs dict returned from the model_fn, named using the same keys. One of these keys is always signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY, indicating which signature will be served when a serving request does not specify one. For each signature, the outputs are provided by the corresponding ExportOutputs, and the inputs are always the input receivers provided by the serving_input_receiver_fn.

For training and evaluation, the train_op is stored in an extra collection, and loss, metrics, and predictions are included in a SignatureDef for the mode in question.

Extra assets may be written into the SavedModel via the assets_extra argument. This should be a dict, where each key gives a destination path (including the filename) relative to the assets.extra directory. The corresponding value gives the full path of the source file to be copied. For example, the simple case of copying a single file without renaming it is specified as {'my_asset_file.txt': '/path/to/my_asset_file.txt'}.

Sample usage:

classifier = tf.estimator.LinearClassifier(
    feature_columns=[age, language])

feature_spec = {
    'age': tf.placeholder(dtype=tf.int64),
    'language': array_ops.placeholder(dtype=tf.string)
label_spec = tf.placeholder(dtype=dtypes.int64)

train_rcvr_fn = tf.contrib.estimator.build_raw_supervised_input_receiver_fn(
    feature_spec, label_spec)

serve_rcvr_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(

rcvr_fn_map = {
    model_fn_lib.ModeKeys.TRAIN: train_rcvr_fn,
    model_fn_lib.ModeKeys.PREDICT: serve_rcvr_fn,

export_dir = tf.contrib.estimator.export_all_saved_models(

# export_dir is a directory with SavedModels, which can be used for serving,
# analysis with TFMA, or directly loaded in.
with ops.Graph().as_default() as graph:
  with session.Session(graph=graph) as sess:
    loader.load(sess, [tag_constants.TRAINING], export_dir)
    weights = graph.get_tensor_by_name('linear/linear_model/age/weights')

estimator an instance of tf.estimator.Estimator
export_dir_base A string containing a directory in which to create timestamped subdirectories containing exported SavedModels.
input_receiver_fn_map dict of tf.estimator.ModeKeys to input_receiver_fn mappings, where the input_receiver_fn is a function that takes no argument and returns the appropriate subclass of InputReceiver.
assets_extra A dict specifying how to populate the assets.extra directory within the exported SavedModel, or None if no extra assets are needed.
as_text whether to write the SavedModel proto in text format.
checkpoint_path The checkpoint path to export. If None (the default), the most recent checkpoint found within the model directory is chosen.

The string path to the exported directory.

ValueError if any input_receiver_fn is None, no export_outputs are provided, or no checkpoint can be found.