From 45e9280a8ccd69a7b22d3e299fe37d5b79d8fd36 Mon Sep 17 00:00:00 2001 From: woodyx218 Date: Thu, 2 Jan 2020 16:35:37 +0800 Subject: [PATCH] Initial commit --- tutorials/adult_tutorial.py | 179 ++++++++++++++++++++++++++ tutorials/imdb_tutorial.py | 179 ++++++++++++++++++++++++++ tutorials/movielens_tutorial.py | 216 ++++++++++++++++++++++++++++++++ 3 files changed, 574 insertions(+) create mode 100644 tutorials/adult_tutorial.py create mode 100644 tutorials/imdb_tutorial.py create mode 100644 tutorials/movielens_tutorial.py diff --git a/tutorials/adult_tutorial.py b/tutorials/adult_tutorial.py new file mode 100644 index 0000000..a406306 --- /dev/null +++ b/tutorials/adult_tutorial.py @@ -0,0 +1,179 @@ +# 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 + +import numpy as np +import tensorflow as tf +from scipy.stats import norm + +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 privacy_accountants 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', .15, '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', 1, 'Clipping norm') +tf.flags.DEFINE_integer('epochs', 20, 'Number of epochs') +tf.flags.DEFINE_integer('max_mu', 2, 'GDP upper limit') +tf.flags.DEFINE_string('model_dir', None, 'Model directory') + +FLAGS = tf.flags.FLAGS + +microbatches=256 +np.random.seed(0) +tf.set_random_seed(0) + +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.train.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 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""" + import pandas as pd + from sklearn.model_selection import KFold + + 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): + tf.logging.set_verbosity(3) + + # Load training and test data. + train_data, train_labels, test_data, test_labels = load_adult() + + # Instantiate the tf.Estimator. + adult_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) + + # Training loop. + steps_per_epoch = 29305 // 256 + 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(29305)>(1-256/29305) + subsampling=[i for i in np.arange(29305) 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=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_epsP(epoch,FLAGS.noise_multiplier,29305,256,1e-5) + mu= compute_muP(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__': + tf.app.run() diff --git a/tutorials/imdb_tutorial.py b/tutorials/imdb_tutorial.py new file mode 100644 index 0000000..48ec599 --- /dev/null +++ b/tutorials/imdb_tutorial.py @@ -0,0 +1,179 @@ +# 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 deep NN on IMDB reviews with differentially private Adam optimizer.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np +import tensorflow as tf +from scipy.stats import norm + +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 privacy_accountants 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 = tf.flags.FLAGS + +microbatches=512 +np.random.seed(0) +tf.set_random_seed(0) + +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) + + # 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) + + # 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 load_imdb(): + (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 + + +def main(unused_argv): + tf.logging.set_verbosity(3) + + # 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) + + # 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) + + # 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) + + 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) + + # 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() diff --git a/tutorials/movielens_tutorial.py b/tutorials/movielens_tutorial.py new file mode 100644 index 0000000..035e119 --- /dev/null +++ b/tutorials/movielens_tutorial.py @@ -0,0 +1,216 @@ +# 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 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 tensorflow as tf +from scipy.stats import norm + +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 privacy_accountants 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 = tf.flags.FLAGS + +microbatches=10000 +np.random.seed(0) +tf.set_random_seed(0) + +n_users=6040 +n_movies=3706 + +def nn_model_fn(features, labels, mode): +# Adapted from https://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) + + # 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.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) + + # 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) + + +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) + + # give unique dense movie index to movieId + from scipy.stats import rankdata + 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,'%') + + 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) + + # 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) + + # 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) + + # 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) + + 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()