forked from 626_privacy/tensorflow_privacy
68 lines
2.2 KiB
Python
68 lines
2.2 KiB
Python
# Copyright 2020, The TensorFlow Authors.
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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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"""Estimator heads that allow integration with TF Privacy."""
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import tensorflow as tf
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from tensorflow_privacy.privacy.estimators import head_utils
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from tensorflow_estimator.python.estimator import estimator
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from tensorflow_estimator.python.estimator.canned import dnn
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class DNNClassifier(estimator.Estimator):
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"""DP version of `tf.estimator.DNNClassifier`."""
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def __init__(
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self,
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hidden_units,
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feature_columns,
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model_dir=None,
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n_classes=2,
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weight_column=None,
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label_vocabulary=None,
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optimizer=None,
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activation_fn=tf.nn.relu,
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dropout=None,
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config=None,
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warm_start_from=None,
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loss_reduction=tf.keras.losses.Reduction.NONE,
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batch_norm=False,
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):
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"""See `tf.estimator.DNNClassifier`."""
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head = head_utils.binary_or_multi_class_head(
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n_classes,
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weight_column=weight_column,
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label_vocabulary=label_vocabulary,
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loss_reduction=loss_reduction)
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estimator._canned_estimator_api_gauge.get_cell('Classifier').set('DNN')
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def _model_fn(features, labels, mode, config):
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return dnn.dnn_model_fn_v2(
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features=features,
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labels=labels,
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mode=mode,
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head=head,
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hidden_units=hidden_units,
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feature_columns=tuple(feature_columns or []),
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optimizer=optimizer,
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activation_fn=activation_fn,
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dropout=dropout,
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config=config,
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batch_norm=batch_norm)
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super().__init__(
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model_fn=_model_fn,
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model_dir=model_dir,
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config=config,
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warm_start_from=warm_start_from)
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