diff --git a/tutorials/adult_tutorial.py b/tutorials/adult_tutorial.py new file mode 100644 index 0000000..b92b352 --- /dev/null +++ b/tutorials/adult_tutorial.py @@ -0,0 +1,178 @@ +# Copyright 2015 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 one-layer NN on Adult data with differentially private SGD 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 tensorflow as tf +import pandas as pd +from sklearn.model_selection import KFold + +# 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.optimizers import dp_optimizer + +from tensorflow_privacy.privacy.analysis.gdp_accountant import * + +#### 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', .15, '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', 1, 'Clipping norm') +flags.DEFINE_integer('epochs', 20, 'Number of epochs') +flags.DEFINE_integer('max_mu', 2, 'GDP upper limit') +flags.DEFINE_string('model_dir', None, 'Model directory') + +microbatches = 256 + +def nn_model_fn(features, labels, mode): + ''' Define CNN architecture using tf.keras.layers.''' + input_layer = tf.reshape(features['x'], [-1, 123]) + y = tf.keras.layers.Dense(16, activation='relu').apply(input_layer) + logits = tf.keras.layers.Dense(2).apply(y) + + # 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.DPGradientDescentGaussianOptimizer( + 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.GradientDescentOptimizer( + 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 = { + 'accuracy': + tf.compat.v1.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) + + return None + + +def load_adult(): + """Loads ADULT a2a as in LIBSVM and preprocesses to combine training and validation data.""" + # https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/binary.html + + X = pd.read_csv("adult.csv") + kf = KFold(n_splits=10) + for train_index, test_index in kf.split(X): + train, test = X.iloc[train_index, :], X.iloc[test_index, :] + train_data = train.iloc[:, range(X.shape[1]-1)].values.astype('float32') + test_data = test.iloc[:, range(X.shape[1]-1)].values.astype('float32') + + train_labels = (train.iloc[:, X.shape[1]-1] == 1).astype('int32').values + test_labels = (test.iloc[:, X.shape[1]-1] == 1).astype('int32').values + + return train_data, train_labels, test_data, test_labels + + +def main(unused_argv): + '''main''' + tf.compat.v1.logging.set_verbosity(0) + + # Load training and test data. + train_data, train_labels, test_data, test_labels = load_adult() + + # Instantiate the tf.Estimator. + adult_classifier = tf.compat.v1.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={'x': test_data}, + y=test_labels, + num_epochs=1, + shuffle=False) + + # Training loop. + steps_per_epoch = 29305 // 256 + test_accuracy_list = [] + for epoch in range(1, FLAGS.epochs + 1): + for step in range(steps_per_epoch): + whether = np.random.random_sample(29305) > (1-256/29305) + subsampling = [i for i in np.arange(29305) if whether[i]] + global microbatches + microbatches = len(subsampling) + + train_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn( + x={'x': train_data[subsampling]}, + y=train_labels[subsampling], + batch_size=len(subsampling), + num_epochs=1, + shuffle=True) + # Train the model for one step. + adult_classifier.train(input_fn=train_input_fn, steps=1) + + # Evaluate the model and print results + eval_results = adult_classifier.evaluate(input_fn=eval_input_fn) + test_accuracy = eval_results['accuracy'] + test_accuracy_list.append(test_accuracy) + print('Test accuracy 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, 29305, 256, 1e-5) + mu = compute_mu_Poisson(epoch, FLAGS.noise_multiplier, 29305, 256) + print('For delta=1e-5, the current epsilon is: %.2f' % eps) + print('For delta=1e-5, 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) diff --git a/tutorials/imdb_tutorial.py b/tutorials/imdb_tutorial.py index cdb94c3..2b37e7b 100644 --- a/tutorials/imdb_tutorial.py +++ b/tutorials/imdb_tutorial.py @@ -19,161 +19,163 @@ 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 tensorflow as tf -from scipy.stats import norm +from keras.preprocessing import sequence -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.analysis.rdp_accountant import compute_rdp +#from tensorflow_privacy.privacy.analysis.rdp_accountant import get_privacy_spent from tensorflow_privacy.privacy.optimizers import dp_optimizer -from GDprivacy_accountants import * +from tensorflow_privacy.privacy.analysis.gdp_accountant import * + -from keras.preprocessing import sequence #### FLAGS -tf.flags.DEFINE_boolean('dpsgd', True, 'If True, train with DP-SGD. If False, ' - 'train with vanilla SGD.') -tf.flags.DEFINE_float('learning_rate', 0.02, 'Learning rate for training') -tf.flags.DEFINE_float('noise_multiplier', 0.56, - 'Ratio of the standard deviation to the clipping norm') -tf.flags.DEFINE_float('l2_norm_clip', 1, 'Clipping norm') -tf.flags.DEFINE_integer('epochs', 25, 'Number of epochs') -tf.flags.DEFINE_integer('max_mu', 2, 'GDP upper limit') -tf.flags.DEFINE_string('model_dir', None, 'Model directory') +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', 0.02, 'Learning rate for training') +flags.DEFINE_float('noise_multiplier', 0.56, + 'Ratio of the standard deviation to the clipping norm') +flags.DEFINE_float('l2_norm_clip', 1, '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') -FLAGS = tf.flags.FLAGS -microbatches=512 -np.random.seed(0) -tf.set_random_seed(0) +microbatches = 512 max_features = 10000 # cut texts after this number of words (among top max_features most common words) maxlen = 256 -def rnn_model_fn(features, labels, mode): - # Define CNN architecture using tf.keras.layers. - input_layer = tf.reshape(features['x'], [-1,maxlen]) - y = tf.keras.layers.Embedding(max_features,16).apply(input_layer) - y=tf.keras.layers.GlobalAveragePooling1D().apply(y) - y= tf.keras.layers.Dense(16, activation='relu').apply(y) - logits= tf.keras.layers.Dense(2).apply(y) - - # 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) +def nn_model_fn(features, labels, mode): + '''Define NN architecture using tf.keras.layers.''' + input_layer = tf.reshape(features['x'], [-1, maxlen]) + y = tf.keras.layers.Embedding(max_features, 16).apply(input_layer) + y = tf.keras.layers.GlobalAveragePooling1D().apply(y) + y = tf.keras.layers.Dense(16, activation='relu').apply(y) + logits = tf.keras.layers.Dense(2).apply(y) - # Configure the training op (for TRAIN mode). - if mode == tf.estimator.ModeKeys.TRAIN: + # 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) - 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.train.AdamOptimizer( - 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) + # 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 - # 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) + 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 = { + 'accuracy': + tf.compat.v1.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) + return None def load_imdb(): - (train_data,train_labels), (test_data,test_labels) = tf.keras.datasets.imdb.load_data(num_words=max_features) + '''Load IMDB movie reviews data''' + (train_data, train_labels), (test_data, test_labels) = \ + tf.keras.datasets.imdb.load_data(num_words=max_features) - train_data = sequence.pad_sequences(train_data, maxlen=maxlen).astype('float32') - test_data = sequence.pad_sequences(test_data, maxlen=maxlen).astype('float32') - return train_data,train_labels,test_data,test_labels + train_data = sequence.pad_sequences(train_data, maxlen=maxlen).astype('float32') + test_data = sequence.pad_sequences(test_data, maxlen=maxlen).astype('float32') + return train_data, train_labels, test_data, test_labels def main(unused_argv): - tf.logging.set_verbosity(3) + '''main''' + tf.compat.v1.logging.set_verbosity(3) - # Load training and test data. - train_data,train_labels,test_data,test_labels = load_imdb() + # Load training and test data. + train_data, train_labels, test_data, test_labels = load_imdb() - # Instantiate the tf.Estimator. - imdb_classifier = tf.estimator.Estimator(model_fn=rnn_model_fn, - model_dir=FLAGS.model_dir) + # Instantiate the tf.Estimator. + imdb_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.estimator.inputs.numpy_input_fn( - x={'x': test_data}, - y=test_labels, - num_epochs=1, - shuffle=False) + # Create tf.Estimator input functions for the training and test data. + eval_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn( + x={'x': test_data}, + y=test_labels, + num_epochs=1, + shuffle=False) - # Training loop. - steps_per_epoch = 25000 // 512 - test_accuracy_list = [] + # Training loop. + steps_per_epoch = 25000 // 512 + test_accuracy_list = [] - for epoch in range(1, FLAGS.epochs + 1): - np.random.seed(epoch) - for step in range(steps_per_epoch): - tf.set_random_seed(0) - whether=np.random.random_sample(25000)>(1-512/25000) - subsampling=[i for i in np.arange(25000) if whether[i]] - global microbatches - microbatches=len(subsampling) + for epoch in range(1, FLAGS.epochs + 1): + for step in range(steps_per_epoch): + whether = np.random.random_sample(25000) > (1-512/25000) + subsampling = [i for i in np.arange(25000) if whether[i]] + global microbatches + microbatches = len(subsampling) - train_input_fn = tf.estimator.inputs.numpy_input_fn( - x={'x': train_data[subsampling]}, - y=train_labels[subsampling], - batch_size=len(subsampling), - num_epochs=1, - shuffle=False) - # Train the model for one step. - imdb_classifier.train(input_fn=train_input_fn, steps=1) + train_input_fn = tf.compat.v1.estimator.inputs.numpy_input_fn( + x={'x': train_data[subsampling]}, + y=train_labels[subsampling], + batch_size=len(subsampling), + num_epochs=1, + shuffle=False) + # Train the model for one step. + imdb_classifier.train(input_fn=train_input_fn, steps=1) + + # Evaluate the model and print results + eval_results = imdb_classifier.evaluate(input_fn=eval_input_fn) + test_accuracy = eval_results['accuracy'] + test_accuracy_list.append(test_accuracy) + print('Test accuracy 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, 25000, 512, 1e-5) + mu = compute_mu_Poisson(epoch, FLAGS.noise_multiplier, 25000, 512) + print('For delta=1e-5, the current epsilon is: %.2f' % eps) + print('For delta=1e-5, the current mu is: %.2f' % mu) + + if mu > FLAGS.max_mu: + break + else: + print('Trained with vanilla non-private SGD optimizer') - # Evaluate the model and print results - eval_results = imdb_classifier.evaluate(input_fn=eval_input_fn) - test_accuracy = eval_results['accuracy'] - test_accuracy_list.append(test_accuracy) - print('Test accuracy after %d epochs is: %.3f' % (epoch, test_accuracy)) - - # Compute the privacy budget expended so far. - if FLAGS.dpsgd: - eps = compute_epsP(epoch,FLAGS.noise_multiplier,25000,512,1e-5) - mu= compute_muP(epoch,FLAGS.noise_multiplier,25000,512) - print('For delta=1e-5, the current epsilon is: %.2f' % eps) - print('For delta=1e-5, the current mu is: %.2f' % mu) - - if mu>FLAGS.max_mu: - break - else: - print('Trained with vanilla non-private SGD optimizer') - if __name__ == '__main__': - tf.app.run() + app.run(main) diff --git a/tutorials/movielens_tutorial.py b/tutorials/movielens_tutorial.py index a5c31c2..31ae7d1 100644 --- a/tutorials/movielens_tutorial.py +++ b/tutorials/movielens_tutorial.py @@ -24,48 +24,46 @@ from absl import flags import numpy as np import tensorflow as tf -from scipy.stats import norm +import pandas as pd +from scipy.stats import rankdata +from sklearn.model_selection import train_test_split -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.analysis.rdp_accountant import compute_rdp +#from tensorflow_privacy.privacy.analysis.rdp_accountant import get_privacy_spent from tensorflow_privacy.privacy.optimizers import dp_optimizer -from GDprivacy_accountants import * +from tensorflow_privacy.privacy.analysis.gdp_accountant import * #### FLAGS -tf.flags.DEFINE_boolean('dpsgd', True, 'If True, train with DP-SGD. If False, ' - 'train with vanilla SGD.') -tf.flags.DEFINE_float('learning_rate', .01, 'Learning rate for training') -tf.flags.DEFINE_float('noise_multiplier', 0.55, - 'Ratio of the standard deviation to the clipping norm') -tf.flags.DEFINE_float('l2_norm_clip', 5, 'Clipping norm') -tf.flags.DEFINE_integer('epochs', 25, 'Number of epochs') -tf.flags.DEFINE_integer('max_mu', 2, 'GDP upper limit') -tf.flags.DEFINE_string('model_dir', None, 'Model directory') +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') -FLAGS = tf.flags.FLAGS -microbatches=10000 -np.random.seed(0) -tf.set_random_seed(0) - -n_users=6040 -n_movies=3706 +microbatches = 10000 def nn_model_fn(features, labels, mode): -# Adapted from https://github.com/hexiangnan/neural_collaborative_filtering + '''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]) - mf_embedding_user = tf.keras.layers.Embedding(n_users,n_latent_factors_mf,input_length=1) - mf_embedding_item = tf.keras.layers.Embedding(n_movies,n_latent_factors_mf,input_length=1) - mlp_embedding_user = tf.keras.layers.Embedding(n_users,n_latent_factors_user,input_length=1) - mlp_embedding_item = tf.keras.layers.Embedding(n_movies,n_latent_factors_movie,input_length=1) - + 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)) @@ -79,138 +77,137 @@ def nn_model_fn(features, labels, mode): 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) + + 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.train.AdamOptimizer( - 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) + 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). - elif mode == tf.estimator.ModeKeys.EVAL: - eval_metric_ops = { - 'rmse': - tf.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) + 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_adult(): - import pandas as pd - import numpy as np - - 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) + """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 - from scipy.stats import rankdata - data['movieIndex']=rankdata(data['movieId'], method='dense') + 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,'%') + 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) - from sklearn.model_selection import train_test_split - 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.logging.set_verbosity(3) + '''main''' + tf.compat.v1.logging.set_verbosity(3) - # Load training and test data. - train_data, test_data, mean = load_adult() + # Load training and test data. + train_data, test_data, mean = load_adult() - # Instantiate the tf.Estimator. - adult_classifier = tf.estimator.Estimator(model_fn=nn_model_fn, - model_dir=FLAGS.model_dir) + # 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.estimator.inputs.numpy_input_fn( - x={'user': test_data[:,0], 'movie': test_data[:,4]}, - y=test_data[:,2], - num_epochs=1, - shuffle=False) + # 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 = 800167 // 10000 - test_accuracy_list = [] - for epoch in range(1, FLAGS.epochs + 1): - np.random.seed(epoch) - for step in range(steps_per_epoch): - tf.set_random_seed(0) - whether=np.random.random_sample(800167)>(1-10000/800167) - subsampling=[i for i in np.arange(800167) if whether[i]] - global microbatches - microbatches=len(subsampling) + # Training loop. + steps_per_epoch = 800167 // 10000 + test_accuracy_list = [] + for epoch in range(1, FLAGS.epochs + 1): + for step in range(steps_per_epoch): + whether = np.random.random_sample(800167) > (1-10000/800167) + subsampling = [i for i in np.arange(800167) 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, 800167, 10000, 1e-6) + mu = compute_mu_Poisson(epoch, FLAGS.noise_multiplier, 800167, 10000) + 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') - train_input_fn = tf.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. - adult_classifier.train(input_fn=train_input_fn, steps=1) - # Evaluate the model and print results - eval_results = adult_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_epsP(epoch,FLAGS.noise_multiplier,800167,10000,1e-6) - mu= compute_muP(epoch,FLAGS.noise_multiplier,800167,10000) - 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__': - tf.app.run() + app.run(main)