tensorflow_privacy/tensorflow_privacy/privacy/estimators/dnn.py

68 lines
2.2 KiB
Python

# Copyright 2020, The TensorFlow Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Estimator heads that allow integration with TF Privacy."""
import tensorflow as tf
from tensorflow_privacy.privacy.estimators import head_utils
from tensorflow_estimator.python.estimator import estimator
from tensorflow_estimator.python.estimator.canned import dnn
class DNNClassifier(estimator.Estimator):
"""DP version of `tf.estimator.DNNClassifier`."""
def __init__(
self,
hidden_units,
feature_columns,
model_dir=None,
n_classes=2,
weight_column=None,
label_vocabulary=None,
optimizer=None,
activation_fn=tf.nn.relu,
dropout=None,
config=None,
warm_start_from=None,
loss_reduction=tf.keras.losses.Reduction.NONE,
batch_norm=False,
):
"""See `tf.estimator.DNNClassifier`."""
head = head_utils.binary_or_multi_class_head(
n_classes,
weight_column=weight_column,
label_vocabulary=label_vocabulary,
loss_reduction=loss_reduction)
estimator._canned_estimator_api_gauge.get_cell('Classifier').set('DNN')
def _model_fn(features, labels, mode, config):
return dnn.dnn_model_fn_v2(
features=features,
labels=labels,
mode=mode,
head=head,
hidden_units=hidden_units,
feature_columns=tuple(feature_columns or []),
optimizer=optimizer,
activation_fn=activation_fn,
dropout=dropout,
config=config,
batch_norm=batch_norm)
super().__init__(
model_fn=_model_fn,
model_dir=model_dir,
config=config,
warm_start_from=warm_start_from)