tf.compat.v1.saved_model.regression_signature

Convenience function to build a SavedModel suitable for serving. (deprecated)

tf.compat.v1.saved_model.simple_save(
    session, export_dir, inputs, outputs, legacy_init_op=None
)

In many common cases, saving models for serving will be as simple as:

simple_save(session,
            export_dir,
            inputs={"x": x, "y": y},
            outputs={"z": z})

Although in many cases it's not necessary to understand all of the many ways to configure a SavedModel, this method has a few practical implications:

It will be treated as a graph for inference / serving (i.e. uses the tag saved_model.SERVING)
The SavedModel will load in TensorFlow Serving and supports the Predict API. To use the Classify, Regress, or MultiInference APIs, please use either tf.Estimator or the lower level SavedModel APIs.
Some TensorFlow ops depend on information on disk or other information called "assets". These are generally handled automatically by adding the assets to the GraphKeys.ASSET_FILEPATHS collection. Only assets in that collection are exported; if you need more custom behavior, you'll need to use the SavedModelBuilder.

More information about SavedModel and signatures can be found here: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/saved_model/README.md

Args
`session`	The TensorFlow session from which to save the meta graph and variables.
`export_dir`	The path to which the SavedModel will be stored.
`inputs`	dict mapping string input names to tensors. These are added to the SignatureDef as the inputs.
`outputs`	dict mapping string output names to tensors. These are added to the SignatureDef as the outputs.
`legacy_init_op`	Legacy support for op or group of ops to execute after the restore op upon a load.

TensorFlow

tf

tf.audio

tf.autograph

tf.bitwise

tf.compat

tf.config

tf.data

tf.debugging

tf.distribute

tf.dtypes

tf.errors

tf.estimator

tf.experimental

tf.feature_column

tf.graph_util

tf.image

tf.initializers

tf.io

tf.keras

tf.linalg

tf.lite

tf.lookup

tf.losses

tf.math

tf.metrics

tf.nest

tf.nn

tf.optimizers

tf.quantization

tf.queue

tf.ragged

tf.random

tf.raw_ops

tf.saved_model

tf.sets

tf.signal

tf.sparse

tf.strings

tf.summary

tf.sysconfig

tf.test

tf.tpu

tf.train

tf.version

tf.xla

tf.compat / v1 / v1.saved_model.regression_signature_def

Args