78 lines
2.9 KiB
Python
78 lines
2.9 KiB
Python
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# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""SignatureDef utility functions implementation."""
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from tensorflow.python.keras.saving.utils_v1 import unexported_constants
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from tensorflow.python.saved_model import signature_def_utils
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from tensorflow.python.saved_model import utils_impl as utils
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# LINT.IfChange
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def supervised_train_signature_def(
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inputs, loss, predictions=None, metrics=None):
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return _supervised_signature_def(
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unexported_constants.SUPERVISED_TRAIN_METHOD_NAME, inputs, loss=loss,
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predictions=predictions, metrics=metrics)
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def supervised_eval_signature_def(
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inputs, loss, predictions=None, metrics=None):
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return _supervised_signature_def(
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unexported_constants.SUPERVISED_EVAL_METHOD_NAME, inputs, loss=loss,
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predictions=predictions, metrics=metrics)
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def _supervised_signature_def(
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method_name, inputs, loss=None, predictions=None,
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metrics=None):
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"""Creates a signature for training and eval data.
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This function produces signatures that describe the inputs and outputs
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of a supervised process, such as training or evaluation, that
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results in loss, metrics, and the like. Note that this function only requires
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inputs to be not None.
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Args:
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method_name: Method name of the SignatureDef as a string.
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inputs: dict of string to `Tensor`.
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loss: dict of string to `Tensor` representing computed loss.
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predictions: dict of string to `Tensor` representing the output predictions.
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metrics: dict of string to `Tensor` representing metric ops.
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Returns:
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A train- or eval-flavored signature_def.
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Raises:
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ValueError: If inputs or outputs is `None`.
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"""
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if inputs is None or not inputs:
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raise ValueError('{} inputs cannot be None or empty.'.format(method_name))
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signature_inputs = {key: utils.build_tensor_info(tensor)
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for key, tensor in inputs.items()}
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signature_outputs = {}
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for output_set in (loss, predictions, metrics):
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if output_set is not None:
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sig_out = {key: utils.build_tensor_info(tensor)
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for key, tensor in output_set.items()}
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signature_outputs.update(sig_out)
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signature_def = signature_def_utils.build_signature_def(
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signature_inputs, signature_outputs, method_name)
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return signature_def
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# LINT.ThenChange(//tensorflow/python/keras/saving/utils_v1/signature_def_utils.py)
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