forked from 626_privacy/tensorflow_privacy
71 lines
2.4 KiB
Python
71 lines
2.4 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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"""Common tools for DP-SGD MNIST tutorials."""
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import tensorflow as tf
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import tensorflow_datasets as tfds
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def get_cnn_model(features):
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"""Given input features, returns the logits from a simple CNN model."""
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input_layer = tf.reshape(features, [-1, 28, 28, 1])
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y = tf.keras.layers.Conv2D(
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16, 8, strides=2, padding='same', activation='relu')(
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input_layer)
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y = tf.keras.layers.MaxPool2D(2, 1)(y)
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y = tf.keras.layers.Conv2D(
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32, 4, strides=2, padding='valid', activation='relu')(
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y)
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y = tf.keras.layers.MaxPool2D(2, 1)(y)
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y = tf.keras.layers.Flatten()(y)
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y = tf.keras.layers.Dense(32, activation='relu')(y)
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logits = tf.keras.layers.Dense(10)(y)
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return logits
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def make_input_fn(split, input_batch_size=256, repetitions=-1, tpu=False):
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"""Make input function on given MNIST split."""
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def input_fn(params=None):
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"""A simple input function."""
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batch_size = params.get('batch_size', input_batch_size)
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def parser(example):
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image, label = example['image'], example['label']
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image = tf.cast(image, tf.float32)
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image /= 255.0
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label = tf.cast(label, tf.int32)
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return image, label
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dataset = tfds.load(name='mnist', split=split)
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dataset = dataset.map(parser).shuffle(60000).repeat(repetitions).batch(
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batch_size)
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# If this input function is not meant for TPUs, we can stop here.
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# Otherwise, we need to explicitly set its shape. Note that for unknown
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# reasons, returning the latter format causes performance regression
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# on non-TPUs.
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if not tpu:
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return dataset
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# Give inputs statically known shapes; needed for TPUs.
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images, labels = tf.data.make_one_shot_iterator(dataset).get_next()
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# return images, labels
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images.set_shape([batch_size, 28, 28, 1])
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labels.set_shape([
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batch_size,
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])
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return images, labels
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return input_fn
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