Logistic regression for mnist with new privacy analysis.

PiperOrigin-RevId: 252743967

tutorials/logistic_regression_mnist.py

@@ -0,0 +1,219 @@
+# 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)