5493a3baf0
accessing it via tf.estimator and depend on the tensorflow estimator target. PiperOrigin-RevId: 438419860
134 lines
5.3 KiB
Python
134 lines
5.3 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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"""Train a CNN on MNIST with differentially private SGD optimizer."""
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import time
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from absl import app
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from absl import flags
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from absl import logging
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import tensorflow as tf
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from tensorflow import estimator as tf_estimator
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from tensorflow_privacy.privacy.analysis import compute_dp_sgd_privacy_lib
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from tensorflow_privacy.privacy.optimizers import dp_optimizer
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import mnist_dpsgd_tutorial_common as common
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flags.DEFINE_boolean(
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'dpsgd', True, 'If True, train with DP-SGD. If False, '
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'train with vanilla SGD.')
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flags.DEFINE_float('learning_rate', .15, 'Learning rate for training')
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flags.DEFINE_float('noise_multiplier', 1.1,
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'Ratio of the standard deviation to the clipping norm')
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flags.DEFINE_float('l2_norm_clip', 1.0, 'Clipping norm')
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flags.DEFINE_integer('batch_size', 256, 'Batch size')
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flags.DEFINE_integer('epochs', 30, 'Number of epochs')
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flags.DEFINE_integer(
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'microbatches', 256, 'Number of microbatches '
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'(must evenly divide batch_size)')
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flags.DEFINE_string('model_dir', None, 'Model directory')
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FLAGS = flags.FLAGS
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def cnn_model_fn(features, labels, mode, params): # pylint: disable=unused-argument
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"""Model function for a CNN."""
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# Define CNN architecture.
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logits = common.get_cnn_model(features)
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# Calculate loss as a vector (to support microbatches in DP-SGD).
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vector_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(
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labels=labels, logits=logits)
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# Define mean of loss across minibatch (for reporting through tf.Estimator).
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scalar_loss = tf.reduce_mean(input_tensor=vector_loss)
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# Configure the training op (for TRAIN mode).
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if mode == tf_estimator.ModeKeys.TRAIN:
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if FLAGS.dpsgd:
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# Use DP version of GradientDescentOptimizer. Other optimizers are
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# available in dp_optimizer. Most optimizers inheriting from
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# tf.compat.v1.train.Optimizer should be wrappable in differentially
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# private counterparts by calling dp_optimizer.optimizer_from_args().
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optimizer = dp_optimizer.DPGradientDescentGaussianOptimizer(
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l2_norm_clip=FLAGS.l2_norm_clip,
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noise_multiplier=FLAGS.noise_multiplier,
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num_microbatches=FLAGS.microbatches,
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learning_rate=FLAGS.learning_rate)
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opt_loss = vector_loss
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else:
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optimizer = tf.compat.v1.train.GradientDescentOptimizer(
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learning_rate=FLAGS.learning_rate)
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opt_loss = scalar_loss
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global_step = tf.compat.v1.train.get_global_step()
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train_op = optimizer.minimize(loss=opt_loss, global_step=global_step)
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# In the following, we pass the mean of the loss (scalar_loss) rather than
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# the vector_loss because tf.estimator requires a scalar loss. This is only
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# used for evaluation and debugging by tf.estimator. The actual loss being
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# minimized is opt_loss defined above and passed to optimizer.minimize().
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return tf_estimator.EstimatorSpec(
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mode=mode, loss=scalar_loss, train_op=train_op)
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# Add evaluation metrics (for EVAL mode).
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elif mode == tf_estimator.ModeKeys.EVAL:
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eval_metric_ops = {
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'accuracy':
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tf.metrics.accuracy(
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labels=labels, predictions=tf.argmax(input=logits, axis=1))
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}
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return tf_estimator.EstimatorSpec(
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mode=mode, loss=scalar_loss, eval_metric_ops=eval_metric_ops)
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def main(unused_argv):
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logging.set_verbosity(logging.INFO)
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if FLAGS.dpsgd and FLAGS.batch_size % FLAGS.microbatches != 0:
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raise ValueError('Number of microbatches should divide evenly batch_size')
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# Instantiate the tf.Estimator.
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mnist_classifier = tf_estimator.Estimator(
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model_fn=cnn_model_fn, model_dir=FLAGS.model_dir)
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# Training loop.
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steps_per_epoch = 60000 // FLAGS.batch_size
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for epoch in range(1, FLAGS.epochs + 1):
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start_time = time.time()
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# Train the model for one epoch.
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mnist_classifier.train(
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input_fn=common.make_input_fn('train', FLAGS.batch_size),
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steps=steps_per_epoch)
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end_time = time.time()
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logging.info('Epoch %d time in seconds: %.2f', epoch, end_time - start_time)
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# Evaluate the model and print results
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eval_results = mnist_classifier.evaluate(
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input_fn=common.make_input_fn('test', FLAGS.batch_size, 1))
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test_accuracy = eval_results['accuracy']
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print('Test accuracy after %d epochs is: %.3f' % (epoch, test_accuracy))
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# Compute the privacy budget expended.
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if FLAGS.dpsgd:
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if FLAGS.noise_multiplier > 0.0:
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eps, _ = compute_dp_sgd_privacy_lib.compute_dp_sgd_privacy(
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60000, FLAGS.batch_size, FLAGS.noise_multiplier, epoch, 1e-5)
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print('For delta=1e-5, the current epsilon is: %.2f' % eps)
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else:
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print('Trained with DP-SGD but with zero noise.')
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else:
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print('Trained with vanilla non-private SGD optimizer')
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if __name__ == '__main__':
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app.run(main)
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