Add keras_models example to tutorials.

tutorials/mnist_dpsgd_tutorial_keras_model.py

@@ -0,0 +1,140 @@
+# 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.
+"""Training a CNN on MNIST with Keras and the DP SGD optimizer."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from absl import app
+from absl import flags
+from absl import logging
+
+import numpy as np
+import tensorflow as tf
+
+from tensorflow_privacy.privacy.analysis.rdp_accountant import compute_rdp
+from tensorflow_privacy.privacy.analysis.rdp_accountant import get_privacy_spent
+from tensorflow_privacy.privacy.keras_models.dp_keras_model import DPSequential
+
+flags.DEFINE_boolean(
+    'dpsgd', True, 'If True, train with DP-SGD. If False, '
+    'train with vanilla SGD.')
+flags.DEFINE_float('learning_rate', 0.15, 'Learning rate for training')
+flags.DEFINE_float('noise_multiplier', 0.1,
+                   'Ratio of the standard deviation to the clipping norm')
+flags.DEFINE_float('l2_norm_clip', 1.0, 'Clipping norm')
+flags.DEFINE_integer('batch_size', 250, 'Batch size')
+flags.DEFINE_integer('epochs', 60, 'Number of epochs')
+flags.DEFINE_integer(
+    'microbatches', 250, 'Number of microbatches '
+    '(must evenly divide batch_size)')
+flags.DEFINE_string('model_dir', None, 'Model directory')
+
+FLAGS = flags.FLAGS
+
+
+def compute_epsilon(steps):
+  """Computes epsilon value for given hyperparameters."""
+  if FLAGS.noise_multiplier == 0.0:
+    return float('inf')
+  orders = [1 + x / 10. for x in range(1, 100)] + list(range(12, 64))
+  sampling_probability = FLAGS.batch_size / 60000
+  rdp = compute_rdp(q=sampling_probability,
+                    noise_multiplier=FLAGS.noise_multiplier,
+                    steps=steps,
+                    orders=orders)
+  # Delta is set to 1e-5 because MNIST has 60000 training points.
+  return get_privacy_spent(orders, rdp, target_delta=1e-5)[0]
+
+
+def load_mnist():
+  """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], 28, 28, 1))
+  test_data = test_data.reshape((test_data.shape[0], 28, 28, 1))
+
+  train_labels = np.array(train_labels, dtype=np.int32)
+  test_labels = np.array(test_labels, dtype=np.int32)
+
+  train_labels = tf.keras.utils.to_categorical(train_labels, num_classes=10)
+  test_labels = tf.keras.utils.to_categorical(test_labels, num_classes=10)
+
+  assert train_data.min() == 0.
+  assert train_data.max() == 1.
+  assert test_data.min() == 0.
+  assert test_data.max() == 1.
+
+  return train_data, train_labels, test_data, test_labels
+
+
+def main(unused_argv):
+  logging.set_verbosity(logging.INFO)
+  if FLAGS.dpsgd and FLAGS.batch_size % FLAGS.microbatches != 0:
+    raise ValueError('Number of microbatches should divide evenly batch_size')
+
+  # Load training and test data.
+  train_data, train_labels, test_data, test_labels = load_mnist()
+
+  # Define a sequential Keras model
+  layers = [
+      tf.keras.layers.Conv2D(16, 8,
+                             strides=2,
+                             padding='same',
+                             activation='relu',
+                             input_shape=(28, 28, 1)),
+      tf.keras.layers.MaxPool2D(2, 1),
+      tf.keras.layers.Conv2D(32, 4,
+                             strides=2,
+                             padding='valid',
+                             activation='relu'),
+      tf.keras.layers.MaxPool2D(2, 1),
+      tf.keras.layers.Flatten(),
+      tf.keras.layers.Dense(32, activation='relu'),
+      tf.keras.layers.Dense(10)
+  ]
+  if FLAGS.dpsgd:
+    model = DPSequential(l2_norm_clip=FLAGS.l2_norm_clip,
+                         noise_multiplier=FLAGS.noise_multiplier,
+                         layers=layers)
+  else:
+    model = tf.keras.Sequential(layers=layers)
+
+  optimizer = tf.keras.optimizers.SGD(learning_rate=FLAGS.learning_rate)
+  loss = tf.keras.losses.CategoricalCrossentropy(from_logits=True)
+
+  # Compile model with Keras
+  model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])
+
+  # Train model with Keras
+  model.fit(train_data, train_labels,
+            epochs=FLAGS.epochs,
+            validation_data=(test_data, test_labels),
+            batch_size=FLAGS.batch_size)
+
+  # Compute the privacy budget expended.
+  if FLAGS.dpsgd:
+    eps = compute_epsilon(FLAGS.epochs * 60000 // FLAGS.batch_size)
+    print('For delta=1e-5, the current epsilon is: %.2f' % eps)
+  else:
+    print('Trained with vanilla non-private SGD optimizer')
+
+if __name__ == '__main__':
+  app.run(main)