# Copyright 2019, 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. """DP Logistic Regression on MNIST. DP Logistic Regression on MNIST with support for privacy-by-iteration analysis. Feldman, Vitaly, Ilya Mironov, Kunal Talwar, and Abhradeep Thakurta. "Privacy amplification by iteration." In 2018 IEEE 59th Annual Symposium on Foundations of Computer Science (FOCS), pp. 521-532. IEEE, 2018. https://arxiv.org/abs/1808.06651. """ from __future__ import absolute_import from __future__ import division from __future__ import print_function import math from absl import app from absl import flags from distutils.version import LooseVersion import numpy as np import tensorflow as tf from privacy.optimizers import dp_optimizer if LooseVersion(tf.__version__) < LooseVersion('2.0.0'): GradientDescentOptimizer = tf.train.GradientDescentOptimizer else: GradientDescentOptimizer = tf.optimizers.SGD # pylint: disable=invalid-name FLAGS = flags.FLAGS flags.DEFINE_boolean('dpsgd', True, 'If True, train with DP-SGD. If False, ' 'train with vanilla SGD.') flags.DEFINE_float('learning_rate', 0.001, 'Learning rate for training') flags.DEFINE_float('noise_multiplier', 0.02, 'Ratio of the standard deviation to the clipping norm') flags.DEFINE_integer('batch_size', 1, 'Batch size') flags.DEFINE_integer('epochs', 5, 'Number of epochs') flags.DEFINE_integer('microbatches', 1, 'Number of microbatches ' '(must evenly divide batch_size)') flags.DEFINE_float('regularizer', 0, 'L2 regularizer coefficient') flags.DEFINE_string('model_dir', None, 'Model directory') flags.DEFINE_float('data_l2_norm', 8, 'Bound on the L2 norm of normalized data.') def lr_model_fn(features, labels, mode, nclasses, dim): """Model function for logistic regression.""" input_layer = tf.reshape(features['x'], tuple([-1]) + dim) logits = tf.layers.dense(inputs=input_layer, units=nclasses, kernel_regularizer=tf.contrib.layers.l2_regularizer( scale=FLAGS.regularizer), bias_regularizer=tf.contrib.layers.l2_regularizer( scale=FLAGS.regularizer) ) # Calculate loss as a vector (to support microbatches in DP-SGD). vector_loss = tf.nn.sparse_softmax_cross_entropy_with_logits( labels=labels, logits=logits) + tf.losses.get_regularization_loss() # Define mean of loss across minibatch (for reporting through tf.Estimator). scalar_loss = tf.reduce_mean(vector_loss) # Configure the training op (for TRAIN mode). if mode == tf.estimator.ModeKeys.TRAIN: if FLAGS.dpsgd: # Use DP version of GradientDescentOptimizer. Other optimizers are # available in dp_optimizer. Most optimizers inheriting from # tf.train.Optimizer should be wrappable in differentially private # counterparts by calling dp_optimizer.optimizer_from_args(). # The loss function is L-Lipschitz with L = sqrt(2*(||x||^2 + 1)) where # ||x|| is the norm of the data. optimizer = dp_optimizer.DPGradientDescentGaussianOptimizer( l2_norm_clip=math.sqrt(2*(FLAGS.data_l2_norm**2 + 1)), noise_multiplier=FLAGS.noise_multiplier, num_microbatches=FLAGS.microbatches, learning_rate=FLAGS.learning_rate) opt_loss = vector_loss else: optimizer = GradientDescentOptimizer(learning_rate=FLAGS.learning_rate) opt_loss = scalar_loss global_step = tf.train.get_global_step() train_op = optimizer.minimize(loss=opt_loss, global_step=global_step) # In the following, we pass the mean of the loss (scalar_loss) rather than # the vector_loss because tf.estimator requires a scalar loss. This is only # used for evaluation and debugging by tf.estimator. The actual loss being # minimized is opt_loss defined above and passed to optimizer.minimize(). return tf.estimator.EstimatorSpec(mode=mode, loss=scalar_loss, train_op=train_op) # Add evaluation metrics (for EVAL mode). elif mode == tf.estimator.ModeKeys.EVAL: eval_metric_ops = { 'accuracy': tf.metrics.accuracy( labels=labels, predictions=tf.argmax(input=logits, axis=1)) } return tf.estimator.EstimatorSpec(mode=mode, loss=scalar_loss, eval_metric_ops=eval_metric_ops) def normalize_data(data, data_l2_norm): """Normalizes data such that each samples has bounded L2 norm. Args: data: the dataset. Each row represents one samples. data_l2_norm: the target upper bound on the L2 norm. """ for i in range(data.shape[0]): norm = np.linalg.norm(data[i]) if norm > data_l2_norm: data[i] = data[i] / norm * data_l2_norm def load_mnist(data_l2_norm=float('inf')): """Loads MNIST and preprocesses to combine training and validation data.""" train, test = tf.keras.datasets.mnist.load_data() train_data, train_labels = train test_data, test_labels = test train_data = np.array(train_data, dtype=np.float32) / 255 test_data = np.array(test_data, dtype=np.float32) / 255 train_data = train_data.reshape(train_data.shape[0], -1) test_data = test_data.reshape(test_data.shape[0], -1) idx = np.random.permutation(len(train_data)) # shuffle data once train_data = train_data[idx] train_labels = train_labels[idx] normalize_data(train_data, data_l2_norm) normalize_data(test_data, data_l2_norm) train_labels = np.array(train_labels, dtype=np.int32) test_labels = np.array(test_labels, dtype=np.int32) return train_data, train_labels, test_data, test_labels def main(unused_argv): tf.logging.set_verbosity(tf.logging.INFO) if FLAGS.dpsgd and FLAGS.batch_size % FLAGS.microbatches != 0: raise ValueError('Number of microbatches should divide evenly batch_size') if FLAGS.data_l2_norm <= 0: raise ValueError('FLAGS.data_l2_norm needs to be positive.') if FLAGS.learning_rate > 8 / FLAGS.data_l2_norm**2: raise ValueError('The amplification by iteration analysis requires' 'learning_rate <= 2 / beta, where beta is the smoothness' 'of the loss function and is upper bounded by ||x||^2 / 4' 'with ||x|| being the largest L2 norm of the samples.') # Load training and test data. # Smoothness = ||x||^2 / 4 where ||x|| is the largest L2 norm of the samples. # To get bounded smoothness, we normalize the data such that each sample has a # bounded L2 norm. train_data, train_labels, test_data, test_labels = load_mnist( data_l2_norm=FLAGS.data_l2_norm) # Instantiate the tf.Estimator. # pylint: disable=g-long-lambda model_fn = lambda features, labels, mode: lr_model_fn(features, labels, mode, nclasses=10, dim=train_data.shape[1:] ) mnist_classifier = tf.estimator.Estimator( model_fn=model_fn, model_dir=FLAGS.model_dir) # Create tf.Estimator input functions for the training and test data. # To analyze the per-user privacy loss, we keep the same orders of samples in # each epoch by setting shuffle=False. train_input_fn = tf.estimator.inputs.numpy_input_fn( x={'x': train_data}, y=train_labels, batch_size=FLAGS.batch_size, num_epochs=FLAGS.epochs, shuffle=False) eval_input_fn = tf.estimator.inputs.numpy_input_fn( x={'x': test_data}, y=test_labels, num_epochs=1, shuffle=False) # Train the model steps_per_epoch = train_data.shape[0] // FLAGS.batch_size mnist_classifier.train(input_fn=train_input_fn, steps=steps_per_epoch * FLAGS.epochs) # Evaluate the model and print results eval_results = mnist_classifier.evaluate(input_fn=eval_input_fn) test_accuracy = eval_results['accuracy'] print('Test accuracy after %d epochs is: %.3f' % (FLAGS.epochs, test_accuracy)) if __name__ == '__main__': app.run(main)