# Copyright 2020 The TensorFlow Authors. All Rights Reserved. # # 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 deep NN on MovieLens with differentially private Adam optimizer.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function from absl import app from absl import flags import numpy as np import pandas as pd from scipy.stats import rankdata from sklearn.model_selection import train_test_split import tensorflow as tf from tensorflow_privacy.privacy.analysis.gdp_accountant import compute_eps_poisson from tensorflow_privacy.privacy.analysis.gdp_accountant import compute_mu_poisson from tensorflow_privacy.privacy.optimizers import dp_optimizer #### FLAGS 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', .01, 'Learning rate for training') flags.DEFINE_float('noise_multiplier', 0.55, 'Ratio of the standard deviation to the clipping norm') flags.DEFINE_float('l2_norm_clip', 5, 'Clipping norm') flags.DEFINE_integer('epochs', 25, 'Number of epochs') flags.DEFINE_integer('max_mu', 2, 'GDP upper limit') flags.DEFINE_string('model_dir', None, 'Model directory') sampling_batch = 10000 microbatches = 10000 num_examples = 800167 def nn_model_fn(features, labels, mode): """NN adapted from github.com/hexiangnan/neural_collaborative_filtering.""" n_latent_factors_user = 10 n_latent_factors_movie = 10 n_latent_factors_mf = 5 user_input = tf.reshape(features['user'], [-1, 1]) item_input = tf.reshape(features['movie'], [-1, 1]) # number of users: 6040; number of movies: 3706 mf_embedding_user = tf.keras.layers.Embedding( 6040, n_latent_factors_mf, input_length=1) mf_embedding_item = tf.keras.layers.Embedding( 3706, n_latent_factors_mf, input_length=1) mlp_embedding_user = tf.keras.layers.Embedding( 6040, n_latent_factors_user, input_length=1) mlp_embedding_item = tf.keras.layers.Embedding( 3706, n_latent_factors_movie, input_length=1) # GMF part # Flatten the embedding vector as latent features in GMF mf_user_latent = tf.keras.layers.Flatten()(mf_embedding_user(user_input)) mf_item_latent = tf.keras.layers.Flatten()(mf_embedding_item(item_input)) # Element-wise multiply mf_vector = tf.keras.layers.multiply([mf_user_latent, mf_item_latent]) # MLP part # Flatten the embedding vector as latent features in MLP mlp_user_latent = tf.keras.layers.Flatten()(mlp_embedding_user(user_input)) mlp_item_latent = tf.keras.layers.Flatten()(mlp_embedding_item(item_input)) # Concatenation of two latent features mlp_vector = tf.keras.layers.concatenate([mlp_user_latent, mlp_item_latent]) predict_vector = tf.keras.layers.concatenate([mf_vector, mlp_vector]) logits = tf.keras.layers.Dense(5)(predict_vector) # 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) # 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(). optimizer = dp_optimizer.DPAdamGaussianOptimizer( l2_norm_clip=FLAGS.l2_norm_clip, noise_multiplier=FLAGS.noise_multiplier, num_microbatches=microbatches, learning_rate=FLAGS.learning_rate) opt_loss = vector_loss else: optimizer = tf.compat.v1.train.AdamOptimizer( learning_rate=FLAGS.learning_rate) opt_loss = scalar_loss global_step = tf.compat.v1.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). if mode == tf.estimator.ModeKeys.EVAL: eval_metric_ops = { 'rmse': tf.compat.v1.metrics.root_mean_squared_error( labels=tf.cast(labels, tf.float32), predictions=tf.tensordot( a=tf.nn.softmax(logits, axis=1), b=tf.constant(np.array([0, 1, 2, 3, 4]), dtype=tf.float32), axes=1)) } return tf.estimator.EstimatorSpec( mode=mode, loss=scalar_loss, eval_metric_ops=eval_metric_ops) return None def load_movielens(): """Loads MovieLens 1M as from https://grouplens.org/datasets/movielens/1m.""" data = pd.read_csv( 'ratings.dat', sep='::', header=None, names=['userId', 'movieId', 'rating', 'timestamp']) n_users = len(set(data['userId'])) n_movies = len(set(data['movieId'])) print('number of movie: ', n_movies) print('number of user: ', n_users) # give unique dense movie index to movieId data['movieIndex'] = rankdata(data['movieId'], method='dense') # minus one to reduce the minimum value to 0, which is the start of col index print('number of ratings:', data.shape[0]) print('percentage of sparsity:', (1 - data.shape[0] / n_users / n_movies) * 100, '%') train, test = train_test_split(data, test_size=0.2, random_state=100) return train.values - 1, test.values - 1, np.mean(train['rating']) def main(unused_argv): tf.compat.v1.logging.set_verbosity(3) # Load training and test data. train_data, test_data, _ = load_movielens() # Instantiate the tf.Estimator. ml_classifier = tf.estimator.Estimator( model_fn=nn_model_fn, model_dir=FLAGS.model_dir) # Create tf.Estimator input functions for the training and test data. eval_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn( x={ 'user': test_data[:, 0], 'movie': test_data[:, 4] }, y=test_data[:, 2], num_epochs=1, shuffle=False) # Training loop. steps_per_epoch = num_examples // sampling_batch test_accuracy_list = [] for epoch in range(1, FLAGS.epochs + 1): for _ in range(steps_per_epoch): whether = np.random.random_sample(num_examples) > ( 1 - sampling_batch / num_examples) subsampling = [i for i in np.arange(num_examples) if whether[i]] global microbatches microbatches = len(subsampling) train_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn( x={ 'user': train_data[subsampling, 0], 'movie': train_data[subsampling, 4] }, y=train_data[subsampling, 2], batch_size=len(subsampling), num_epochs=1, shuffle=True) # Train the model for one step. ml_classifier.train(input_fn=train_input_fn, steps=1) # Evaluate the model and print results eval_results = ml_classifier.evaluate(input_fn=eval_input_fn) test_accuracy = eval_results['rmse'] test_accuracy_list.append(test_accuracy) print('Test RMSE after %d epochs is: %.3f' % (epoch, test_accuracy)) # Compute the privacy budget expended so far. if FLAGS.dpsgd: eps = compute_eps_poisson(epoch, FLAGS.noise_multiplier, num_examples, sampling_batch, 1e-6) mu = compute_mu_poisson(epoch, FLAGS.noise_multiplier, num_examples, sampling_batch) print('For delta=1e-6, the current epsilon is: %.2f' % eps) print('For delta=1e-6, the current mu is: %.2f' % mu) if mu > FLAGS.max_mu: break else: print('Trained with vanilla non-private SGD optimizer') if __name__ == '__main__': app.run(main)