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Merge pull request #611 from panyx0718/master 

Consolidate privacy/ and differential_privacy/.
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differential_privacy/README.md
View file @ 8505222e
<font size=4><b>Deep Learning with Differential Privacy</b></font>
Authors:
Martín Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, Li Zhang
Open Sourced By: Xin Pan (xpan@google.com, github: panyx0718)
<Introduction>
###Introduction for dp_sgd/README.md
Machine learning techniques based on neural networks are achieving remarkable
results in a wide variety of domains. Often, the training of models requires
......@@ -21,73 +18,14 @@ manageable cost in software complexity, training efficiency, and model quality.
paper: https://arxiv.org/abs/1607.00133
<b>Requirements:</b>
1. Tensorflow 0.10.0 (master branch)
Note: r0.11 might experience some problems
2. Bazel 0.3.1
3. Download MNIST data
TODO(xpan): Complete the link:
[train](http://download.tensorflow.org/models/)
[test](http://download.tensorflow.org/models/)
Alternatively, download the tfrecord format MNIST from:
https://github.com/panyx0718/models/tree/master/slim
<b>How to run:</b>
```shell
# Clone the codes under differential_privacy.
# Create an empty WORKSPACE file.
# Download the data to the data/ directory.
ls -R
.:
data differential_privacy WORKSPACE
./data:
mnist_test.tfrecord mnist_train.tfrecord
./differential_privacy:
dp_mnist dp_optimizer __init__.py per_example_gradients README.md
./differential_privacy/dp_mnist:
BUILD dp_mnist.py
./differential_privacy/dp_optimizer:
accountant.py BUILD dp_optimizer.py dp_pca.py sanitizer.py utils.py
./differential_privacy/per_example_gradients:
BUILD __init__.py per_example_gradients.py
# Build the codes.
bazel build -c opt differential_privacy/...
###Introduction for multiple_teachers/README.md
# Run the mnist differntial privacy training codes.
bazel-bin/differential_privacy/dp_mnist/dp_mnist \
--training_data_path=data/mnist_train.tfrecord \
--eval_data_path=data/mnist_test.tfrecord \
--save_path=/tmp/mnist_dir
This repository contains code to create a setup for learning privacy-preserving
student models by transferring knowledge from an ensemble of teachers trained
on disjoint subsets of the data for which privacy guarantees are to be provided.
...
step: 1
step: 2
...
step: 9
spent privacy: eps 0.1250 delta 0.72709
spent privacy: eps 0.2500 delta 0.24708
spent privacy: eps 0.5000 delta 0.0029139
spent privacy: eps 1.0000 delta 6.494e-10
spent privacy: eps 2.0000 delta 8.2242e-24
spent privacy: eps 4.0000 delta 1.319e-51
spent privacy: eps 8.0000 delta 3.3927e-107
train_accuracy: 0.53
eval_accuracy: 0.53
...
Knowledge acquired by teachers is transferred to the student in a differentially
private manner by noisily aggregating the teacher decisions before feeding them
to the student during training.
ls /tmp/mnist_dir/
checkpoint ckpt ckpt.meta results-0.json
```
paper: https://arxiv.org/abs/1610.05755
differential_privacy/dp_sgd/README.md 0 → 100644
View file @ 8505222e
<font size=4><b>Deep Learning with Differential Privacy</b></font>
Authors:
Martín Abadi, Andy Chu, Ian Goodfellow, H. Brendan McMahan, Ilya Mironov, Kunal Talwar, Li Zhang
Open Sourced By: Xin Pan (xpan@google.com, github: panyx0718)
<Introduction>
Machine learning techniques based on neural networks are achieving remarkable
results in a wide variety of domains. Often, the training of models requires
large, representative datasets, which may be crowdsourced and contain sensitive
information. The models should not expose private information in these datasets.
Addressing this goal, we develop new algorithmic techniques for learning and a
refined analysis of privacy costs within the framework of differential privacy.
Our implementation and experiments demonstrate that we can train deep neural
networks with non-convex objectives, under a modest privacy budget, and at a
manageable cost in software complexity, training efficiency, and model quality.
paper: https://arxiv.org/abs/1607.00133
<b>Requirements:</b>
1. Tensorflow 0.10.0 (master branch)
Note: r0.11 might experience some problems
2. Bazel 0.3.1
3. Download MNIST data
TODO(xpan): Complete the link:
[train](http://download.tensorflow.org/models/)
[test](http://download.tensorflow.org/models/)
Alternatively, download the tfrecord format MNIST from:
https://github.com/panyx0718/models/tree/master/slim
<b>How to run:</b>
```shell
# Clone the codes under differential_privacy.
# Create an empty WORKSPACE file.
# Download the data to the data/ directory.
# List the codes.
ls -R differential_privacy/
differential_privacy/:
dp_sgd __init__.py privacy_accountant README.md
differential_privacy/dp_sgd:
dp_mnist dp_optimizer per_example_gradients README.md
differential_privacy/dp_sgd/dp_mnist:
BUILD dp_mnist.py
differential_privacy/dp_sgd/dp_optimizer:
BUILD dp_optimizer.py dp_pca.py sanitizer.py utils.py
differential_privacy/dp_sgd/per_example_gradients:
BUILD per_example_gradients.py
differential_privacy/privacy_accountant:
python tf
differential_privacy/privacy_accountant/python:
BUILD gaussian_moments.py
differential_privacy/privacy_accountant/tf:
accountant.py accountant_test.py BUILD
# List the data.
ls -R data/
./data:
mnist_test.tfrecord mnist_train.tfrecord
# Build the codes.
bazel build -c opt differential_privacy/...
# Run the mnist differntial privacy training codes.
bazel-bin/differential_privacy/dp_sgd/dp_mnist/dp_mnist \
--training_data_path=data/mnist_train.tfrecord \
--eval_data_path=data/mnist_test.tfrecord \
--save_path=/tmp/mnist_dir
...
step: 1
step: 2
...
step: 9
spent privacy: eps 0.1250 delta 0.72709
spent privacy: eps 0.2500 delta 0.24708
spent privacy: eps 0.5000 delta 0.0029139
spent privacy: eps 1.0000 delta 6.494e-10
spent privacy: eps 2.0000 delta 8.2242e-24
spent privacy: eps 4.0000 delta 1.319e-51
spent privacy: eps 8.0000 delta 3.3927e-107
train_accuracy: 0.53
eval_accuracy: 0.53
...
ls /tmp/mnist_dir/
checkpoint ckpt ckpt.meta results-0.json
```
differential_privacy/dp_mnist/BUILD → differential_privacy/dp_sgd/dp_mnist/BUILD
View file @ 8505222e
......@@ -17,8 +17,8 @@ py_binary(
"dp_mnist.py",
],
deps = [
"//differential_privacy/dp_optimizer",
"//differential_privacy/dp_optimizer:dp_pca",
"//differential_privacy/dp_optimizer:utils",
"//differential_privacy/dp_sgd/dp_optimizer",
"//differential_privacy/dp_sgd/dp_optimizer:dp_pca",
"//differential_privacy/dp_sgd/dp_optimizer:utils",
],
)
differential_privacy/dp_mnist/dp_mnist.py → differential_privacy/dp_sgd/dp_mnist/dp_mnist.py
View file @ 8505222e
......@@ -24,11 +24,11 @@ import time
import numpy as np
import tensorflow as tf
from differential_privacy.dp_optimizer import accountant
from differential_privacy.dp_optimizer import dp_optimizer
from differential_privacy.dp_optimizer import dp_pca
from differential_privacy.dp_optimizer import sanitizer
from differential_privacy.dp_optimizer import utils
from differential_privacy.dp_sgd.dp_optimizer import dp_optimizer
from differential_privacy.dp_sgd.dp_optimizer import dp_pca
from differential_privacy.dp_sgd.dp_optimizer import sanitizer
from differential_privacy.dp_sgd.dp_optimizer import utils
from differential_privacy.privacy_accountant.tf import accountant
# parameters for the training
tf.flags.DEFINE_integer("batch_size", 600,
......
differential_privacy/dp_optimizer/BUILD → differential_privacy/dp_sgd/dp_optimizer/BUILD
View file @ 8505222e
......@@ -29,16 +29,6 @@ py_library(
],
)
py_library(
name = "accountant",
srcs = [
"accountant.py",
],
deps = [
":utils",
],
)
py_library(
name = "dp_optimizer",
srcs = [
......@@ -46,9 +36,9 @@ py_library(
"sanitizer.py",
],
deps = [
":accountant",
":utils",
"//differential_privacy/per_example_gradients",
"//differential_privacy/dp_sgd/per_example_gradients",
"//differential_privacy/privacy_accountant/tf:accountant",
],
)
differential_privacy/dp_optimizer/dp_optimizer.py → differential_privacy/dp_sgd/dp_optimizer/dp_optimizer.py
View file @ 8505222e
......@@ -19,8 +19,8 @@ from __future__ import division
import tensorflow as tf
from differential_privacy.dp_optimizer import utils
from differential_privacy.per_example_gradients import per_example_gradients
from differential_privacy.dp_sgd.dp_optimizer import utils
from differential_privacy.dp_sgd.per_example_gradients import per_example_gradients
class DPGradientDescentOptimizer(tf.train.GradientDescentOptimizer):
......
differential_privacy/dp_optimizer/dp_pca.py → differential_privacy/dp_sgd/dp_optimizer/dp_pca.py
View file @ 8505222e
......@@ -15,10 +15,9 @@
"""Differentially private optimizers.
"""
import tensorflow as tf
from differential_privacy.dp_optimizer import sanitizer as san
from differential_privacy.dp_sgd.dp_optimizer import sanitizer as san
def ComputeDPPrincipalProjection(data, projection_dims,
......
differential_privacy/dp_optimizer/sanitizer.py → differential_privacy/dp_sgd/dp_optimizer/sanitizer.py
View file @ 8505222e
......@@ -26,7 +26,7 @@ import collections
import tensorflow as tf
from differential_privacy.dp_optimizer import utils
from differential_privacy.dp_sgd.dp_optimizer import utils
ClipOption = collections.namedtuple("ClipOption",
......
differential_privacy/per_example_gradients/per_example_gradients.py → differential_privacy/dp_sgd/per_example_gradients/per_example_gradients.py
View file @ 8505222e
......@@ -58,9 +58,10 @@ def _ListUnion(list_1, list_2):
def Interface(ys, xs):
"""
Returns a dict mapping each element of xs to any of its consumers that are
indirectly consumed by ys.
"""Maps xs to consumers.
Returns a dict mapping each element of xs to any of its consumers that are
indirectly consumed by ys.
Args:
ys: The outputs
......@@ -181,10 +182,10 @@ class MatMulPXG(object):
idx = list(self.op.inputs).index(x)
assert idx != -1
assert len(z_grads) == len(self.op.outputs)
assert idx == 1 # We expect weights to be arg 1
assert idx == 1 # We expect weights to be arg 1
# We don't expect anyone to per-example differentiate with repsect
# to anything other than the weights.
x, w = self.op.inputs
x, _ = self.op.inputs
z_grads, = z_grads
x_expanded = tf.expand_dims(x, 2)
z_grads_expanded = tf.expand_dims(z_grads, 1)
......@@ -305,7 +306,7 @@ class AddPXG(object):
assert idx == 1 # We expect biases to be arg 1
# We don't expect anyone to per-example differentiate with respect
# to anything other than the biases.
x, b = self.op.inputs
x, _ = self.op.inputs
z_grads, = z_grads
return z_grads
......@@ -336,10 +337,10 @@ def PerExampleGradients(ys, xs, grad_ys=None, name="gradients",
merged_interface = _ListUnion(merged_interface, interface[x])
# Differentiate with respect to the interface
interface_gradients = tf.gradients(ys, merged_interface, grad_ys=grad_ys,
name=name,
colocate_gradients_with_ops=
colocate_gradients_with_ops,
gate_gradients=gate_gradients)
name=name,
colocate_gradients_with_ops=
colocate_gradients_with_ops,
gate_gradients=gate_gradients)
grad_dict = OrderedDict(zip(merged_interface, interface_gradients))
# Build the per-example gradients with respect to the xs
if colocate_gradients_with_ops:
......
differential_privacy/multiple_teachers/BUILD 0 → 100644
View file @ 8505222e
package(default_visibility = [":internal"])
licenses(["notice"]) # Apache 2.0
exports_files(["LICENSE"])
package_group(
name = "internal",
packages = [
"//differential_privacy/...",
],
)
py_library(
name = "aggregation",
srcs = [
"aggregation.py",
],
deps = [
],
)
py_library(
name = "deep_cnn",
srcs = [
"deep_cnn.py",
],
deps = [
":utils",
],
)
py_library(
name = "input",
srcs = [
"input.py",
],
deps = [
],
)
py_library(
name = "metrics",
srcs = [
"metrics.py",
],
deps = [
],
)
py_library(
name = "utils",
srcs = [
"utils.py",
],
deps = [
],
)
py_binary(
name = "train_student",
srcs = [
"train_student.py",
],
deps = [
":aggregation",
":deep_cnn",
":input",
":metrics",
],
)
py_binary(
name = "train_teachers",
srcs = [
"train_teachers.py",
":deep_cnn",
":input",
":metrics",
],
deps = [
],
)
py_library(
name = "analysis",
srcs = [
"analysis.py",
],
deps = [
"//differential_privacy/multiple_teachers:input",
],
)
differential_privacy/multiple_teachers/README.md 0 → 100644
View file @ 8505222e
# Learning private models with multiple teachers
This repository contains code to create a setup for learning privacy-preserving
student models by transferring knowledge from an ensemble of teachers trained
on disjoint subsets of the data for which privacy guarantees are to be provided.
Knowledge acquired by teachers is transferred to the student in a differentially
private manner by noisily aggregating the teacher decisions before feeding them
to the student during training.
The paper describing the approach is [arXiv:1610.05755](https://arxiv.org/abs/1610.05755)
## Dependencies
This model uses `TensorFlow` to perform numerical computations associated with
machine learning models, as well as common Python libraries like: `numpy`,
`scipy`, and `six`. Instructions to install these can be found in their
respective documentations.
## How to run
This repository supports the MNIST and SVHN datasets. The following
instructions are given for MNIST but can easily be adapted by replacing the
flag `--dataset=mnist` by `--dataset=svhn`.
There are 2 steps: teacher training and student training. Data will be
automatically downloaded when you start the teacher training.
The following is a two-step process: first we train an ensemble of teacher
models and second we train a student using predictions made by this ensemble.
**Training the teachers:** first run the `train_teachers.py` file with at least
three flags specifying (1) the number of teachers, (2) the ID of the teacher
you are training among these teachers, and (3) the dataset on which to train.
For instance, to train teacher number 10 among an ensemble of 100 teachers for
MNIST, you use the following command:
```
python train_teachers.py --nb_teachers=100 --teacher_id=10 --dataset=mnist
```
Other flags like `train_dir` and `data_dir` should optionally be set to
respectively point to the directory where model checkpoints and temporary data
(like the dataset) should be saved. The flag `max_steps` (default at 3000)
controls the length of training. See `train_teachers.py` and `deep_cnn.py`
to find available flags and their descriptions.
**Training the student:** once the teachers are all trained, e.g., teachers
with IDs `0` to `99` are trained for `nb_teachers=100`, we are ready to train
the student. The student is trained by labeling some of the test data with
predictions from the teachers. The predictions are aggregated by counting the
votes assigned to each class among the ensemble of teachers, adding Laplacian
noise to these votes, and assigning the label with the maximum noisy vote count
to the sample. This is detailed in function `noisy_max` in the file
`aggregation.py`. To learn the student, use the following command:
```
python train_student.py --nb_teachers=100 --dataset=mnist --stdnt_share=5000
```
The flag `--stdnt_share=5000` indicates that the student should be able to
use the first `5000` samples of the dataset's test subset as unlabeled
training points (they will be labeled using the teacher predictions). The
remaining samples are used for evaluation of the student's accuracy, which
is displayed upon completion of training.
## Alternative deeper convolutional architecture
Note that a deeper convolutional model is available. Both the default and
deeper models graphs are defined in `deep_cnn.py`, respectively by
functions `inference` and `inference_deeper`. Use the flag `--deeper=true`
to switch to that model when launching `train_teachers.py` and
`train_student.py`.
## Privacy analysis
In the paper, we detail how data-dependent differential privacy bounds can be
computed to estimate the cost of training the student. In order to reproduce
the bounds given in the paper, we include the label predicted by our two
teacher ensembles: MNIST and SVHN. You can run the privacy analysis for each
dataset with the following commands:
```
python analysis.py --counts_file=mnist_250_teachers_labels.npy --indices_file=mnist_250_teachers_100_indices_used_by_student.npy
python analysis.py --counts_file=svhn_250_teachers_labels.npy --max_examples=1000 --delta=1e-6
```
To expedite experimentation with the privacy analysis of student training,
the `analysis.py` file is configured to download the labels produced by 250
teacher models, for MNIST and SVHN when running the two commands included
above. These 250 teacher models were trained using the following command lines,
where `XXX` takes values between `0` and `249`:
```
python train_teachers.py --nb_teachers=250 --teacher_id=XXX --dataset=mnist
python train_teachers.py --nb_teachers=250 --teacher_id=XXX --dataset=svhn
```
Note that these labels may also be used in lieu of function `ensemble_preds`
in `train_student.py`, to compare the performance of alternative student model
architectures and learning techniques. This facilitates future work, by
removing the need for training the MNIST and SVHN teacher ensembles when
proposing new student training approaches.
## Contact
To ask questions, please email `nicolas@papernot.fr` or open an issue on
the `tensorflow/models` issues tracker. Please assign issues to
[@npapernot](https://github.com/npapernot).
differential_privacy/multiple_teachers/aggregation.py 0 → 100644
View file @ 8505222e
# Copyright 2016 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.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
def labels_from_probs(probs):
"""
Helper function: computes argmax along last dimension of array to obtain
labels (max prob or max logit value)
:param probs: numpy array where probabilities or logits are on last dimension
:return: array with same shape as input besides last dimension with shape 1
now containing the labels
"""
# Compute last axis index
last_axis = len(np.shape(probs)) - 1
# Label is argmax over last dimension
labels = np.argmax(probs, axis=last_axis)
# Return as np.int32
return np.asarray(labels, dtype=np.int32)
def noisy_max(logits, lap_scale, return_clean_votes=False):
"""
This aggregation mechanism takes the softmax/logit output of several models
resulting from inference on identical inputs and computes the noisy-max of
the votes for candidate classes to select a label for each sample: it
adds Laplacian noise to label counts and returns the most frequent label.
:param logits: logits or probabilities for each sample
:param lap_scale: scale of the Laplacian noise to be added to counts
:param return_clean_votes: if set to True, also returns clean votes (without
Laplacian noise). This can be used to perform the
privacy analysis of this aggregation mechanism.
:return: pair of result and (if clean_votes is set to True) the clean counts
for each class per sample and the the original labels produced by
the teachers.
"""
# Compute labels from logits/probs and reshape array properly
labels = labels_from_probs(logits)
labels_shape = np.shape(labels)
labels = labels.reshape((labels_shape[0], labels_shape[1]))
# Initialize array to hold final labels
result = np.zeros(int(labels_shape[1]))
if return_clean_votes:
# Initialize array to hold clean votes for each sample
clean_votes = np.zeros((int(labels_shape[1]), 10))
# Parse each sample
for i in xrange(int(labels_shape[1])):
# Count number of votes assigned to each class
label_counts = np.bincount(labels[:, i], minlength=10)
if return_clean_votes:
# Store vote counts for export
clean_votes[i] = label_counts
# Cast in float32 to prepare before addition of Laplacian noise
label_counts = np.asarray(label_counts, dtype=np.float32)
# Sample independent Laplacian noise for each class
for item in xrange(10):
label_counts[item] += np.random.laplace(loc=0.0, scale=float(lap_scale))
# Result is the most frequent label
result[i] = np.argmax(label_counts)
# Cast labels to np.int32 for compatibility with deep_cnn.py feed dictionaries
result = np.asarray(result, dtype=np.int32)
if return_clean_votes:
# Returns several array, which are later saved:
# result: labels obtained from the noisy aggregation
# clean_votes: the number of teacher votes assigned to each sample and class
# labels: the labels assigned by teachers (before the noisy aggregation)
return result, clean_votes, labels
else:
# Only return labels resulting from noisy aggregation
return result
def aggregation_most_frequent(logits):
"""
This aggregation mechanism takes the softmax/logit output of several models
resulting from inference on identical inputs and computes the most frequent
label. It is deterministic (no noise injection like noisy_max() above.
:param logits: logits or probabilities for each sample
:return:
"""
# Compute labels from logits/probs and reshape array properly
labels = labels_from_probs(logits)
labels_shape = np.shape(labels)
labels = labels.reshape((labels_shape[0], labels_shape[1]))
# Initialize array to hold final labels
result = np.zeros(int(labels_shape[1]))
# Parse each sample
for i in xrange(int(labels_shape[1])):
# Count number of votes assigned to each class
label_counts = np.bincount(labels[:, i], minlength=10)
label_counts = np.asarray(label_counts, dtype=np.int32)
# Result is the most frequent label
result[i] = np.argmax(label_counts)
return np.asarray(result, dtype=np.int32)
differential_privacy/multiple_teachers/analysis.py 0 → 100644
View file @ 8505222e
# Copyright 2016 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.
# ==============================================================================
"""
This script computes bounds on the privacy cost of training the
student model from noisy aggregation of labels predicted by teachers.
It should be used only after training the student (and therefore the
teachers as well). We however include the label files required to
reproduce key results from our paper (https://arxiv.org/abs/1610.05755):
the epsilon bounds for MNIST and SVHN students.
The command that computes the epsilon bound associated
with the training of the MNIST student model (100 label queries
with a (1/20)*2=0.1 epsilon bound each) is:
python analysis.py
--counts_file=mnist_250_teachers_labels.npy
--indices_file=mnist_250_teachers_100_indices_used_by_student.npy
The command that computes the epsilon bound associated
with the training of the SVHN student model (1000 label queries
with a (1/20)*2=0.1 epsilon bound each) is:
python analysis.py
--counts_file=svhn_250_teachers_labels.npy
--max_examples=1000
--delta=1e-6
"""
import os
import math
import numpy as np
import tensorflow as tf
from differential_privacy.multiple_teachers.input import maybe_download
# These parameters can be changed to compute bounds for different failure rates
# or different model predictions.
tf.flags.DEFINE_integer("moments",8, "Number of moments")
tf.flags.DEFINE_float("noise_eps", 0.1, "Eps value for each call to noisymax.")
tf.flags.DEFINE_float("delta", 1e-5, "Target value of delta.")
tf.flags.DEFINE_float("beta", 0.09, "Value of beta for smooth sensitivity")
tf.flags.DEFINE_string("counts_file","","Numpy matrix with raw counts")
tf.flags.DEFINE_string("indices_file","",
"File containting a numpy matrix with indices used."
"Optional. Use the first max_examples indices if this is not provided.")
tf.flags.DEFINE_integer("max_examples",1000,
"Number of examples to use. We will use the first"
" max_examples many examples from the counts_file"
" or indices_file to do the privacy cost estimate")
tf.flags.DEFINE_float("too_small", 1e-10, "Small threshold to avoid log of 0")
tf.flags.DEFINE_bool("input_is_counts", False, "False if labels, True if counts")
FLAGS = tf.flags.FLAGS
def compute_q_noisy_max(counts, noise_eps):
"""returns ~ Pr[outcome != winner].
Args:
counts: a list of scores
noise_eps: privacy parameter for noisy_max
Returns:
q: the probability that outcome is different from true winner.
"""
# For noisy max, we only get an upper bound.
# Pr[ j beats i*] \leq (2+gap(j,i*))/ 4 exp(gap(j,i*)
# proof at http://mathoverflow.net/questions/66763/
# tight-bounds-on-probability-of-sum-of-laplace-random-variables
winner = np.argmax(counts)
counts_normalized = noise_eps * (counts - counts[winner])
counts_rest = np.array(
[counts_normalized[i] for i in xrange(len(counts)) if i != winner])
q = 0.0
for c in counts_rest:
gap = -c
q += (gap + 2.0) / (4.0 * math.exp(gap))
return min(q, 1.0 - (1.0/len(counts)))
def compute_q_noisy_max_approx(counts, noise_eps):
"""returns ~ Pr[outcome != winner].
Args:
counts: a list of scores
noise_eps: privacy parameter for noisy_max
Returns:
q: the probability that outcome is different from true winner.
"""
# For noisy max, we only get an upper bound.
# Pr[ j beats i*] \leq (2+gap(j,i*))/ 4 exp(gap(j,i*)
# proof at http://mathoverflow.net/questions/66763/
# tight-bounds-on-probability-of-sum-of-laplace-random-variables
# This code uses an approximation that is faster and easier
# to get local sensitivity bound on.
winner = np.argmax(counts)
counts_normalized = noise_eps * (counts - counts[winner])
counts_rest = np.array(
[counts_normalized[i] for i in xrange(len(counts)) if i != winner])
gap = -max(counts_rest)
q = (len(counts) - 1) * (gap + 2.0) / (4.0 * math.exp(gap))
return min(q, 1.0 - (1.0/len(counts)))
def logmgf_exact(q, priv_eps, l):
"""Computes the logmgf value given q and privacy eps.
The bound used is the min of three terms. The first term is from
https://arxiv.org/pdf/1605.02065.pdf.
The second term is based on the fact that when event has probability (1-q) for
q close to zero, q can only change by exp(eps), which corresponds to a
much smaller multiplicative change in (1-q)
The third term comes directly from the privacy guarantee.
Args:
q: pr of non-optimal outcome
priv_eps: eps parameter for DP
l: moment to compute.
Returns:
Upper bound on logmgf
"""
if q < 0.5:
t_one = (1-q) * math.pow((1-q) / (1 - math.exp(priv_eps) * q), l)
t_two = q * math.exp(priv_eps * l)
t = t_one + t_two
try:
log_t = math.log(t)
except ValueError:
print "Got ValueError in math.log for values :" + str((q, priv_eps, l, t))
log_t = priv_eps * l
else:
log_t = priv_eps * l
return min(0.5 * priv_eps * priv_eps * l * (l + 1), log_t, priv_eps * l)
def logmgf_from_counts(counts, noise_eps, l):
"""
ReportNoisyMax mechanism with noise_eps with 2*noise_eps-DP
in our setting where one count can go up by one and another
can go down by 1.
"""
q = compute_q_noisy_max(counts, noise_eps)
return logmgf_exact(q, 2.0 * noise_eps, l)
def sens_at_k(counts, noise_eps, l, k):
"""Return sensitivity at distane k.
Args:
counts: an array of scores
noise_eps: noise parameter used
l: moment whose sensitivity is being computed
k: distance
Returns:
sensitivity: at distance k
"""
counts_sorted = sorted(counts, reverse=True)
if 0.5 * noise_eps * l > 1:
print "l too large to compute sensitivity"
return 0
# Now we can assume that at k, gap remains positive
# or we have reached the point where logmgf_exact is
# determined by the first term and ind of q.
if counts[0] < counts[1] + k:
return 0
counts_sorted[0] -= k
counts_sorted[1] += k
val = logmgf_from_counts(counts_sorted, noise_eps, l)
counts_sorted[0] -= 1
counts_sorted[1] += 1
val_changed = logmgf_from_counts(counts_sorted, noise_eps, l)
return val_changed - val
def smoothed_sens(counts, noise_eps, l, beta):
"""Compute beta-smooth sensitivity.
Args:
counts: array of scors
noise_eps: noise parameter
l: moment of interest
beta: smoothness parameter
Returns:
smooth_sensitivity: a beta smooth upper bound
"""
k = 0
smoothed_sensitivity = sens_at_k(counts, noise_eps, l, k)
while k < max(counts):
k += 1
sensitivity_at_k = sens_at_k(counts, noise_eps, l, k)
smoothed_sensitivity = max(
smoothed_sensitivity,
math.exp(-beta * k) * sensitivity_at_k)
if sensitivity_at_k == 0.0:
break
return smoothed_sensitivity
def main(unused_argv):
##################################################################
# If we are reproducing results from paper https://arxiv.org/abs/1610.05755,
# download the required binaries with label information.
##################################################################
# Binaries for MNIST results
paper_binaries_mnist = \
["https://github.com/npapernot/multiple-teachers-for-privacy/blob/master/mnist_250_teachers_labels.npy?raw=true",
"https://github.com/npapernot/multiple-teachers-for-privacy/blob/master/mnist_250_teachers_100_indices_used_by_student.npy?raw=true"]
if FLAGS.counts_file == "mnist_250_teachers_labels.npy" \
or FLAGS.indices_file == "mnist_250_teachers_100_indices_used_by_student.npy":
maybe_download(paper_binaries_mnist, os.getcwd())
# Binaries for SVHN results
paper_binaries_svhn = ["https://github.com/npapernot/multiple-teachers-for-privacy/blob/master/svhn_250_teachers_labels.npy?raw=true"]
if FLAGS.counts_file == "svhn_250_teachers_labels.npy":
maybe_download(paper_binaries_svhn, os.getcwd())
input_mat = np.load(FLAGS.counts_file)
if FLAGS.input_is_counts:
counts_mat = input_mat
else:
# In this case, the input is the raw predictions. Transform
num_teachers, n = input_mat.shape
counts_mat = np.zeros((n, 10)).astype(np.int32)
for i in range(n):
for j in range(num_teachers):
counts_mat[i, input_mat[j, i]] += 1
n = counts_mat.shape[0]
num_examples = min(n, FLAGS.max_examples)
if not FLAGS.indices_file:
indices = np.array(range(num_examples))
else:
index_list = np.load(FLAGS.indices_file)
indices = index_list[:num_examples]
l_list = 1.0 + np.array(xrange(FLAGS.moments))
beta = FLAGS.beta
total_log_mgf_nm = np.array([0.0 for _ in l_list])
total_ss_nm = np.array([0.0 for _ in l_list])
noise_eps = FLAGS.noise_eps
for i in indices:
total_log_mgf_nm += np.array(
[logmgf_from_counts(counts_mat[i], noise_eps, l)
for l in l_list])
total_ss_nm += np.array(
[smoothed_sens(counts_mat[i], noise_eps, l, beta)
for l in l_list])
delta = FLAGS.delta
# We want delta = exp(alpha - eps l).
# Solving gives eps = (alpha - ln (delta))/l
eps_list_nm = (total_log_mgf_nm - math.log(delta)) / l_list
print "Epsilons (Noisy Max): " + str(eps_list_nm)
print "Smoothed sensitivities (Noisy Max): " + str(total_ss_nm / l_list)
# If beta < eps / 2 ln (1/delta), then adding noise Lap(1) * 2 SS/eps
# is eps,delta DP
# Also if beta < eps / 2(gamma +1), then adding noise 2(gamma+1) SS eta / eps
# where eta has density proportional to 1 / (1+|z|^gamma) is eps-DP
# Both from Corolloary 2.4 in
# http://www.cse.psu.edu/~ads22/pubs/NRS07/NRS07-full-draft-v1.pdf
# Print the first one's scale
ss_eps = 2.0 * beta * math.log(1/delta)
ss_scale = 2.0 / ss_eps
print "To get an " + str(ss_eps) + "-DP estimate of epsilon, "
print "..add noise ~ " + str(ss_scale)
print "... times " + str(total_ss_nm / l_list)
print "Epsilon = " + str(min(eps_list_nm)) + "."
if min(eps_list_nm) == eps_list_nm[-1]:
print "Warning: May not have used enough values of l"
# Data indpendent bound, as mechanism is
# 2*noise_eps DP.
data_ind_log_mgf = np.array([0.0 for _ in l_list])
data_ind_log_mgf += num_examples * np.array(
[logmgf_exact(1.0, 2.0 * noise_eps, l) for l in l_list])
data_ind_eps_list = (data_ind_log_mgf - math.log(delta)) / l_list
print "Data independent bound = " + str(min(data_ind_eps_list)) + "."
return
if __name__ == "__main__":
tf.app.run()
differential_privacy/multiple_teachers/deep_cnn.py 0 → 100644
View file @ 8505222e
# Copyright 2016 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.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from datetime import datetime
import math
import numpy as np
import tensorflow as tf
import time
from differential_privacy.multiple_teachers import utils
FLAGS = tf.app.flags.FLAGS
# Basic model parameters.
tf.app.flags.DEFINE_integer('dropout_seed', 123, """seed for dropout.""")
tf.app.flags.DEFINE_integer('batch_size', 128, """Nb of images in a batch.""")
tf.app.flags.DEFINE_integer('epochs_per_decay', 350, """Nb epochs per decay""")
tf.app.flags.DEFINE_integer('learning_rate', 5, """100 * learning rate""")
tf.app.flags.DEFINE_boolean('log_device_placement', False, """see TF doc""")
# Constants describing the training process.
MOVING_AVERAGE_DECAY = 0.9999 # The decay to use for the moving average.
LEARNING_RATE_DECAY_FACTOR = 0.1 # Learning rate decay factor.
def _variable_on_cpu(name, shape, initializer):
"""Helper to create a Variable stored on CPU memory.
Args:
name: name of the variable
shape: list of ints
initializer: initializer for Variable
Returns:
Variable Tensor
"""
with tf.device('/cpu:0'):
var = tf.get_variable(name, shape, initializer=initializer)
return var
def _variable_with_weight_decay(name, shape, stddev, wd):
"""Helper to create an initialized Variable with weight decay.
Note that the Variable is initialized with a truncated normal distribution.
A weight decay is added only if one is specified.
Args:
name: name of the variable
shape: list of ints
stddev: standard deviation of a truncated Gaussian
wd: add L2Loss weight decay multiplied by this float. If None, weight
decay is not added for this Variable.
Returns:
Variable Tensor
"""
var = _variable_on_cpu(name, shape,
tf.truncated_normal_initializer(stddev=stddev))
if wd is not None:
weight_decay = tf.mul(tf.nn.l2_loss(var), wd, name='weight_loss')
tf.add_to_collection('losses', weight_decay)
return var
def inference(images, dropout=False):
"""Build the CNN model.
Args:
images: Images returned from distorted_inputs() or inputs().
dropout: Boolean controling whether to use dropout or not
Returns:
Logits
"""
if FLAGS.dataset == 'mnist':
first_conv_shape = [5, 5, 1, 64]
else:
first_conv_shape = [5, 5, 3, 64]
# conv1
with tf.variable_scope('conv1') as scope:
kernel = _variable_with_weight_decay('weights',
shape=first_conv_shape,
stddev=1e-4,
wd=0.0)
conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME')
biases = _variable_on_cpu('biases', [64], tf.constant_initializer(0.0))
bias = tf.nn.bias_add(conv, biases)
conv1 = tf.nn.relu(bias, name=scope.name)
if dropout:
conv1 = tf.nn.dropout(conv1, 0.3, seed=FLAGS.dropout_seed)
# pool1
pool1 = tf.nn.max_pool(conv1,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool1')
# norm1
norm1 = tf.nn.lrn(pool1,
4,
bias=1.0,
alpha=0.001 / 9.0,
beta=0.75,
name='norm1')
# conv2
with tf.variable_scope('conv2') as scope:
kernel = _variable_with_weight_decay('weights',
shape=[5, 5, 64, 128],
stddev=1e-4,
wd=0.0)
conv = tf.nn.conv2d(norm1, kernel, [1, 1, 1, 1], padding='SAME')
biases = _variable_on_cpu('biases', [128], tf.constant_initializer(0.1))
bias = tf.nn.bias_add(conv, biases)
conv2 = tf.nn.relu(bias, name=scope.name)
if dropout:
conv2 = tf.nn.dropout(conv2, 0.3, seed=FLAGS.dropout_seed)
# norm2
norm2 = tf.nn.lrn(conv2,
4,
bias=1.0,
alpha=0.001 / 9.0,
beta=0.75,
name='norm2')
# pool2
pool2 = tf.nn.max_pool(norm2,
ksize=[1, 3, 3, 1],
strides=[1, 2, 2, 1],
padding='SAME',
name='pool2')
# local3
with tf.variable_scope('local3') as scope:
# Move everything into depth so we can perform a single matrix multiply.
reshape = tf.reshape(pool2, [FLAGS.batch_size, -1])
dim = reshape.get_shape()[1].value
weights = _variable_with_weight_decay('weights',
shape=[dim, 384],
stddev=0.04,
wd=0.004)
biases = _variable_on_cpu('biases', [384], tf.constant_initializer(0.1))
local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)
if dropout:
local3 = tf.nn.dropout(local3, 0.5, seed=FLAGS.dropout_seed)
# local4
with tf.variable_scope('local4') as scope:
weights = _variable_with_weight_decay('weights',
shape=[384, 192],
stddev=0.04,
wd=0.004)
biases = _variable_on_cpu('biases', [192], tf.constant_initializer(0.1))
local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name=scope.name)
if dropout:
local4 = tf.nn.dropout(local4, 0.5, seed=FLAGS.dropout_seed)
# compute logits
with tf.variable_scope('softmax_linear') as scope:
weights = _variable_with_weight_decay('weights',
[192, FLAGS.nb_labels],
stddev=1/192.0,
wd=0.0)
biases = _variable_on_cpu('biases',
[FLAGS.nb_labels],
tf.constant_initializer(0.0))
logits = tf.add(tf.matmul(local4, weights), biases, name=scope.name)
return logits
def inference_deeper(images, dropout=False):
"""Build a deeper CNN model.
Args:
images: Images returned from distorted_inputs() or inputs().
dropout: Boolean controling whether to use dropout or not
Returns:
Logits
"""
if FLAGS.dataset == 'mnist':
first_conv_shape = [3, 3, 1, 96]
else:
first_conv_shape = [3, 3, 3, 96]
# conv1
with tf.variable_scope('conv1') as scope:
kernel = _variable_with_weight_decay('weights',
shape=first_conv_shape,
stddev=0.05,
wd=0.0)
conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME')
biases = _variable_on_cpu('biases', [96], tf.constant_initializer(0.0))
bias = tf.nn.bias_add(conv, biases)
conv1 = tf.nn.relu(bias, name=scope.name)
# conv2
with tf.variable_scope('conv2') as scope:
kernel = _variable_with_weight_decay('weights',
shape=[3, 3, 96, 96],
stddev=0.05,
wd=0.0)
conv = tf.nn.conv2d(conv1, kernel, [1, 1, 1, 1], padding='SAME')
biases = _variable_on_cpu('biases', [96], tf.constant_initializer(0.0))
bias = tf.nn.bias_add(conv, biases)
conv2 = tf.nn.relu(bias, name=scope.name)
# conv3
with tf.variable_scope('conv3') as scope:
kernel = _variable_with_weight_decay('weights',
shape=[3, 3, 96, 96],
stddev=0.05,
wd=0.0)
conv = tf.nn.conv2d(conv2, kernel, [1, 2, 2, 1], padding='SAME')
biases = _variable_on_cpu('biases', [96], tf.constant_initializer(0.0))
bias = tf.nn.bias_add(conv, biases)
conv3 = tf.nn.relu(bias, name=scope.name)
if dropout:
conv3 = tf.nn.dropout(conv3, 0.5, seed=FLAGS.dropout_seed)
# conv4
with tf.variable_scope('conv4') as scope:
kernel = _variable_with_weight_decay('weights',
shape=[3, 3, 96, 192],
stddev=0.05,
wd=0.0)
conv = tf.nn.conv2d(conv3, kernel, [1, 1, 1, 1], padding='SAME')
biases = _variable_on_cpu('biases', [192], tf.constant_initializer(0.0))
bias = tf.nn.bias_add(conv, biases)
conv4 = tf.nn.relu(bias, name=scope.name)
# conv5
with tf.variable_scope('conv5') as scope:
kernel = _variable_with_weight_decay('weights',
shape=[3, 3, 192, 192],
stddev=0.05,
wd=0.0)
conv = tf.nn.conv2d(conv4, kernel, [1, 1, 1, 1], padding='SAME')
biases = _variable_on_cpu('biases', [192], tf.constant_initializer(0.0))
bias = tf.nn.bias_add(conv, biases)
conv5 = tf.nn.relu(bias, name=scope.name)
# conv6
with tf.variable_scope('conv6') as scope:
kernel = _variable_with_weight_decay('weights',
shape=[3, 3, 192, 192],
stddev=0.05,
wd=0.0)
conv = tf.nn.conv2d(conv5, kernel, [1, 2, 2, 1], padding='SAME')
biases = _variable_on_cpu('biases', [192], tf.constant_initializer(0.0))
bias = tf.nn.bias_add(conv, biases)
conv6 = tf.nn.relu(bias, name=scope.name)
if dropout:
conv6 = tf.nn.dropout(conv6, 0.5, seed=FLAGS.dropout_seed)
# conv7
with tf.variable_scope('conv7') as scope:
kernel = _variable_with_weight_decay('weights',
shape=[5, 5, 192, 192],
stddev=1e-4,
wd=0.0)
conv = tf.nn.conv2d(conv6, kernel, [1, 1, 1, 1], padding='SAME')
biases = _variable_on_cpu('biases', [192], tf.constant_initializer(0.1))
bias = tf.nn.bias_add(conv, biases)
conv7 = tf.nn.relu(bias, name=scope.name)
# local1
with tf.variable_scope('local1') as scope:
# Move everything into depth so we can perform a single matrix multiply.
reshape = tf.reshape(conv7, [FLAGS.batch_size, -1])
dim = reshape.get_shape()[1].value
weights = _variable_with_weight_decay('weights',
shape=[dim, 192],
stddev=0.05,
wd=0)
biases = _variable_on_cpu('biases', [192], tf.constant_initializer(0.1))
local1 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name)
# local2
with tf.variable_scope('local2') as scope:
weights = _variable_with_weight_decay('weights',
shape=[192, 192],
stddev=0.05,
wd=0)
biases = _variable_on_cpu('biases', [192], tf.constant_initializer(0.1))
local2 = tf.nn.relu(tf.matmul(local1, weights) + biases, name=scope.name)
if dropout:
local2 = tf.nn.dropout(local2, 0.5, seed=FLAGS.dropout_seed)
# compute logits
with tf.variable_scope('softmax_linear') as scope:
weights = _variable_with_weight_decay('weights',
[192, FLAGS.nb_labels],
stddev=0.05,
wd=0.0)
biases = _variable_on_cpu('biases',
[FLAGS.nb_labels],
tf.constant_initializer(0.0))
logits = tf.add(tf.matmul(local2, weights), biases, name=scope.name)
return logits
def loss_fun(logits, labels):
"""Add L2Loss to all the trainable variables.
Add summary for "Loss" and "Loss/avg".
Args:
logits: Logits from inference().
labels: Labels from distorted_inputs or inputs(). 1-D tensor
of shape [batch_size]
distillation: if set to True, use probabilities and not class labels to
compute softmax loss
Returns:
Loss tensor of type float.
"""
# Calculate the cross entropy between labels and predictions
labels = tf.cast(labels, tf.int64)
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits, labels, name='cross_entropy_per_example')
# Calculate the average cross entropy loss across the batch.
cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
# Add to TF collection for losses
tf.add_to_collection('losses', cross_entropy_mean)
# The total loss is defined as the cross entropy loss plus all of the weight
# decay terms (L2 loss).
return tf.add_n(tf.get_collection('losses'), name='total_loss')
def moving_av(total_loss):
"""
Generates moving average for all losses
Args:
total_loss: Total loss from loss().
Returns:
loss_averages_op: op for generating moving averages of losses.
"""
# Compute the moving average of all individual losses and the total loss.
loss_averages = tf.train.ExponentialMovingAverage(0.9, name='avg')
losses = tf.get_collection('losses')
loss_averages_op = loss_averages.apply(losses + [total_loss])
return loss_averages_op
def train_op_fun(total_loss, global_step):
"""Train model.
Create an optimizer and apply to all trainable variables. Add moving
average for all trainable variables.
Args:
total_loss: Total loss from loss().
global_step: Integer Variable counting the number of training steps
processed.
Returns:
train_op: op for training.
"""
# Variables that affect learning rate.
nb_ex_per_train_epoch = int(60000 / FLAGS.nb_teachers)
num_batches_per_epoch = nb_ex_per_train_epoch / FLAGS.batch_size
decay_steps = int(num_batches_per_epoch * FLAGS.epochs_per_decay)
initial_learning_rate = float(FLAGS.learning_rate) / 100.0
# Decay the learning rate exponentially based on the number of steps.
lr = tf.train.exponential_decay(initial_learning_rate,
global_step,
decay_steps,
LEARNING_RATE_DECAY_FACTOR,
staircase=True)
tf.scalar_summary('learning_rate', lr)
# Generate moving averages of all losses and associated summaries.
loss_averages_op = moving_av(total_loss)
# Compute gradients.
with tf.control_dependencies([loss_averages_op]):
opt = tf.train.GradientDescentOptimizer(lr)
grads = opt.compute_gradients(total_loss)
# Apply gradients.
apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)
# Add histograms for trainable variables.
for var in tf.trainable_variables():
tf.histogram_summary(var.op.name, var)
# Track the moving averages of all trainable variables.
variable_averages = tf.train.ExponentialMovingAverage(
MOVING_AVERAGE_DECAY, global_step)
variables_averages_op = variable_averages.apply(tf.trainable_variables())
with tf.control_dependencies([apply_gradient_op, variables_averages_op]):
train_op = tf.no_op(name='train')
return train_op
def _input_placeholder():
"""
This helper function declares a TF placeholder for the graph input data
:return: TF placeholder for the graph input data
"""
if FLAGS.dataset == 'mnist':
image_size = 28
num_channels = 1
else:
image_size = 32
num_channels = 3
# Declare data placeholder
train_node_shape = (FLAGS.batch_size, image_size, image_size, num_channels)
return tf.placeholder(tf.float32, shape=train_node_shape)
def train(images, labels, ckpt_path, dropout=False):
"""
This function contains the loop that actually trains the model.
:param images: a numpy array with the input data
:param labels: a numpy array with the output labels
:param ckpt_path: a path (including name) where model checkpoints are saved
:param dropout: Boolean, whether to use dropout or not
:return: True if everything went well
"""
# Check training data
assert len(images) == len(labels)
assert images.dtype == np.float32
assert labels.dtype == np.int32
# Set default TF graph
with tf.Graph().as_default():
global_step = tf.Variable(0, trainable=False)
# Declare data placeholder
train_data_node = _input_placeholder()
# Create a placeholder to hold labels
train_labels_shape = (FLAGS.batch_size,)
train_labels_node = tf.placeholder(tf.int32, shape=train_labels_shape)
print("Done Initializing Training Placeholders")
# Build a Graph that computes the logits predictions from the placeholder
if FLAGS.deeper:
logits = inference_deeper(train_data_node, dropout=dropout)
else:
logits = inference(train_data_node, dropout=dropout)
# Calculate loss
loss = loss_fun(logits, train_labels_node)
# Build a Graph that trains the model with one batch of examples and
# updates the model parameters.
train_op = train_op_fun(loss, global_step)
# Create a saver.
saver = tf.train.Saver(tf.all_variables())
print("Graph constructed and saver created")
# Build an initialization operation to run below.
init = tf.initialize_all_variables()
# Create and init sessions
sess = tf.Session(config=tf.ConfigProto(log_device_placement=FLAGS.log_device_placement)) #NOLINT(long-line)
sess.run(init)
print("Session ready, beginning training loop")
# Initialize the number of batches
data_length = len(images)
nb_batches = math.ceil(data_length / FLAGS.batch_size)
for step in xrange(FLAGS.max_steps):
# for debug, save start time
start_time = time.time()
# Current batch number
batch_nb = step % nb_batches
# Current batch start and end indices
start, end = utils.batch_indices(batch_nb, data_length, FLAGS.batch_size)
# Prepare dictionnary to feed the session with
feed_dict = {train_data_node: images[start:end],
train_labels_node: labels[start:end]}
# Run training step
_, loss_value = sess.run([train_op, loss], feed_dict=feed_dict)
# Compute duration of training step
duration = time.time() - start_time
# Sanity check
assert not np.isnan(loss_value), 'Model diverged with loss = NaN'
# Echo loss once in a while
if step % 100 == 0:
num_examples_per_step = FLAGS.batch_size
examples_per_sec = num_examples_per_step / duration
sec_per_batch = float(duration)
format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '
'sec/batch)')
print (format_str % (datetime.now(), step, loss_value,
examples_per_sec, sec_per_batch))
# Save the model checkpoint periodically.
if step % 1000 == 0 or (step + 1) == FLAGS.max_steps:
saver.save(sess, ckpt_path, global_step=step)
return True
def softmax_preds(images, ckpt_path, return_logits=False):
"""
Compute softmax activations (probabilities) with the model saved in the path
specified as an argument
:param images: a np array of images
:param ckpt_path: a TF model checkpoint
:param logits: if set to True, return logits instead of probabilities
:return: probabilities (or logits if logits is set to True)
"""
# Compute nb samples and deduce nb of batches
data_length = len(images)
nb_batches = math.ceil(len(images) / FLAGS.batch_size)
# Declare data placeholder
train_data_node = _input_placeholder()
# Build a Graph that computes the logits predictions from the placeholder
if FLAGS.deeper:
logits = inference_deeper(train_data_node)
else:
logits = inference(train_data_node)
if return_logits:
# We are returning the logits directly (no need to apply softmax)
output = logits
else:
# Add softmax predictions to graph: will return probabilities
output = tf.nn.softmax(logits)
# Restore the moving average version of the learned variables for eval.
variable_averages = tf.train.ExponentialMovingAverage(MOVING_AVERAGE_DECAY)
variables_to_restore = variable_averages.variables_to_restore()
saver = tf.train.Saver(variables_to_restore)
# Will hold the result
preds = np.zeros((data_length, FLAGS.nb_labels), dtype=np.float32)
# Create TF session
with tf.Session() as sess:
# Restore TF session from checkpoint file
saver.restore(sess, ckpt_path)
# Parse data by batch
for batch_nb in xrange(0, int(nb_batches+1)):
# Compute batch start and end indices
start, end = utils.batch_indices(batch_nb, data_length, FLAGS.batch_size)
# Prepare feed dictionary
feed_dict = {train_data_node: images[start:end]}
# Run session ([0] because run returns a batch with len 1st dim == 1)
preds[start:end, :] = sess.run([output], feed_dict=feed_dict)[0]
# Reset graph to allow multiple calls
tf.reset_default_graph()
return preds
differential_privacy/multiple_teachers/input.py 0 → 100644
View file @ 8505222e
# Copyright 2016 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.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import cPickle
import gzip
import math
import numpy as np
import os
from scipy.io import loadmat as loadmat
from six.moves import urllib
import sys
import tarfile
import tensorflow as tf
FLAGS = tf.flags.FLAGS
def create_dir_if_needed(dest_directory):
"""
Create directory if doesn't exist
:param dest_directory:
:return: True if everything went well
"""
if not tf.gfile.IsDirectory(dest_directory):
tf.gfile.MakeDirs(dest_directory)
return True
def maybe_download(file_urls, directory):
"""
Download a set of files in temporary local folder
:param directory: the directory where to download
:return: a tuple of filepaths corresponding to the files given as input
"""
# Create directory if doesn't exist
assert create_dir_if_needed(directory)
# This list will include all URLS of the local copy of downloaded files
result = []
# For each file of the dataset
for file_url in file_urls:
# Extract filename
filename = file_url.split('/')[-1]
# If downloading from GitHub, remove suffix ?raw=True from local filename
if filename.endswith("?raw=true"):
filename = filename[:-9]
# Deduce local file url
#filepath = os.path.join(directory, filename)
filepath = directory + '/' + filename
# Add to result list
result.append(filepath)
# Test if file already exists
if not gfile.Exists(filepath):
def _progress(count, block_size, total_size):
sys.stdout.write('\r>> Downloading %s %.1f%%' % (filename,
float(count * block_size) / float(total_size) * 100.0))
sys.stdout.flush()
filepath, _ = urllib.request.urlretrieve(file_url, filepath, _progress)
print()
statinfo = os.stat(filepath)
print('Successfully downloaded', filename, statinfo.st_size, 'bytes.')
return result
def image_whitening(data):
"""
Subtracts mean of image and divides by adjusted standard variance (for
stability). Operations are per image but performed for the entire array.
:param image: 4D array (ID, Height, Weight, Channel)
:return: 4D array (ID, Height, Weight, Channel)
"""
assert len(np.shape(data)) == 4
# Compute number of pixels in image
nb_pixels = np.shape(data)[1] * np.shape(data)[2] * np.shape(data)[3]
# Subtract mean
mean = np.mean(data, axis=(1,2,3))
ones = np.ones(np.shape(data)[1:4], dtype=np.float32)
for i in xrange(len(data)):
data[i, :, :, :] -= mean[i] * ones
# Compute adjusted standard variance
adj_std_var = np.maximum(np.ones(len(data), dtype=np.float32) / math.sqrt(nb_pixels), np.std(data, axis=(1,2,3))) #NOLINT(long-line)
# Divide image
for i in xrange(len(data)):
data[i, :, :, :] = data[i, :, :, :] / adj_std_var[i]
print(np.shape(data))
return data
def extract_svhn(local_url):
"""
Extract a MATLAB matrix into two numpy arrays with data and labels
:param local_url:
:return:
"""
with gfile.Open(local_url, mode='r') as file_obj:
# Load MATLAB matrix using scipy IO
dict = loadmat(file_obj)
# Extract each dictionary (one for data, one for labels)
data, labels = dict["X"], dict["y"]
# Set np type
data = np.asarray(data, dtype=np.float32)
labels = np.asarray(labels, dtype=np.int32)
# Transpose data to match TF model input format
data = data.transpose(3, 0, 1, 2)
# Fix the SVHN labels which label 0s as 10s
labels[labels == 10] = 0
# Fix label dimensions
labels = labels.reshape(len(labels))
return data, labels
def unpickle_cifar_dic(file):
"""
Helper function: unpickles a dictionary (used for loading CIFAR)
:param file: filename of the pickle
:return: tuple of (images, labels)
"""
fo = open(file, 'rb')
dict = cPickle.load(fo)
fo.close()
return dict['data'], dict['labels']
def extract_cifar10(local_url, data_dir):
"""
Extracts the CIFAR-10 dataset and return numpy arrays with the different sets
:param local_url: where the tar.gz archive is located locally
:param data_dir: where to extract the archive's file
:return: a tuple (train data, train labels, test data, test labels)
"""
# These numpy dumps can be reloaded to avoid performing the pre-processing
# if they exist in the working directory.
# Changing the order of this list will ruin the indices below.
preprocessed_files = ['/cifar10_train.npy',
'/cifar10_train_labels.npy',
'/cifar10_test.npy',
'/cifar10_test_labels.npy']
all_preprocessed = True
for file in preprocessed_files:
if not tf.gfile.Exists(data_dir + file):
all_preprocessed = False
break
if all_preprocessed:
# Reload pre-processed training data from numpy dumps
with tf.gfile.Open(data_dir + preprocessed_files[0], mode='r') as file_obj:
train_data = np.load(file_obj)
with tf.gfile.Open(data_dir + preprocessed_files[1], mode='r') as file_obj:
train_labels = np.load(file_obj)
# Reload pre-processed testing data from numpy dumps
with tf.gfile.Open(data_dir + preprocessed_files[2], mode='r') as file_obj:
test_data = np.load(file_obj)
with tf.gfile.Open(data_dir + preprocessed_files[3], mode='r') as file_obj:
test_labels = np.load(file_obj)
else:
# Do everything from scratch
# Define lists of all files we should extract
train_files = ["data_batch_" + str(i) for i in xrange(1,6)]
test_file = ["test_batch"]
cifar10_files = train_files + test_file
# Check if all files have already been extracted
need_to_unpack = False
for file in cifar10_files:
if not tf.gfile.Exists(file):
need_to_unpack = True
break
# We have to unpack the archive
if need_to_unpack:
tarfile.open(local_url, 'r:gz').extractall(data_dir)
# Load training images and labels
images = []
labels = []
for file in train_files:
# Construct filename
filename = data_dir + "/cifar-10-batches-py/" + file
# Unpickle dictionary and extract images and labels
images_tmp, labels_tmp = unpickle_cifar_dic(filename)
# Append to lists
images.append(images_tmp)
labels.append(labels_tmp)
# Convert to numpy arrays and reshape in the expected format
train_data = np.asarray(images, dtype=np.float32).reshape((50000,3,32,32))
train_data = np.swapaxes(train_data, 1, 3)
train_labels = np.asarray(labels, dtype=np.int32).reshape(50000)
# Save so we don't have to do this again
np.save(data_dir + preprocessed_files[0], train_data)
np.save(data_dir + preprocessed_files[1], train_labels)
# Construct filename for test file
filename = data_dir + "/cifar-10-batches-py/" + test_file[0]
# Load test images and labels
test_data, test_images = unpickle_cifar_dic(filename)
# Convert to numpy arrays and reshape in the expected format
test_data = np.asarray(test_data,dtype=np.float32).reshape((10000,3,32,32))
test_data = np.swapaxes(test_data, 1, 3)
test_labels = np.asarray(test_images, dtype=np.int32).reshape(10000)
# Save so we don't have to do this again
np.save(data_dir + preprocessed_files[2], test_data)
np.save(data_dir + preprocessed_files[3], test_labels)
return train_data, train_labels, test_data, test_labels
def extract_mnist_data(filename, num_images, image_size, pixel_depth):
"""
Extract the images into a 4D tensor [image index, y, x, channels].
Values are rescaled from [0, 255] down to [-0.5, 0.5].
"""
# if not os.path.exists(file):
if not tf.gfile.Exists(filename+".npy"):
with gzip.open(filename) as bytestream:
bytestream.read(16)
buf = bytestream.read(image_size * image_size * num_images)
data = np.frombuffer(buf, dtype=np.uint8).astype(np.float32)
data = (data - (pixel_depth / 2.0)) / pixel_depth
data = data.reshape(num_images, image_size, image_size, 1)
np.save(filename, data)
return data
else:
with tf.gfile.Open(filename+".npy", mode='r') as file_obj:
return np.load(file_obj)
def extract_mnist_labels(filename, num_images):
"""
Extract the labels into a vector of int64 label IDs.
"""
# if not os.path.exists(file):
if not tf.gfile.Exists(filename+".npy"):
with gzip.open(filename) as bytestream:
bytestream.read(8)
buf = bytestream.read(1 * num_images)
labels = np.frombuffer(buf, dtype=np.uint8).astype(np.int32)
np.save(filename, labels)
return labels
else:
with tf.gfile.Open(filename+".npy", mode='r') as file_obj:
return np.load(file_obj)
def ld_svhn(extended=False, test_only=False):
"""
Load the original SVHN data
:param extended: include extended training data in the returned array
:param test_only: disables loading of both train and extra -> large speed up
:return: tuple of arrays which depend on the parameters
"""
# Define files to be downloaded
# WARNING: changing the order of this list will break indices (cf. below)
file_urls = ['http://ufldl.stanford.edu/housenumbers/train_32x32.mat',
'http://ufldl.stanford.edu/housenumbers/test_32x32.mat',
'http://ufldl.stanford.edu/housenumbers/extra_32x32.mat']
# Maybe download data and retrieve local storage urls
local_urls = maybe_download(file_urls, FLAGS.data_dir)
# Extra Train, Test, and Extended Train data
if not test_only:
# Load and applying whitening to train data
train_data, train_labels = extract_svhn(local_urls[0])
train_data = image_whitening(train_data)
# Load and applying whitening to extended train data
ext_data, ext_labels = extract_svhn(local_urls[2])
ext_data = image_whitening(ext_data)
# Load and applying whitening to test data
test_data, test_labels = extract_svhn(local_urls[1])
test_data = image_whitening(test_data)
if test_only:
return test_data, test_labels
else:
if extended:
# Stack train data with the extended training data
train_data = np.vstack((train_data, ext_data))
train_labels = np.hstack((train_labels, ext_labels))
return train_data, train_labels, test_data, test_labels
else:
# Return training and extended training data separately
return train_data,train_labels, test_data,test_labels, ext_data,ext_labels
def ld_cifar10(test_only=False):
"""
Load the original CIFAR10 data
:param extended: include extended training data in the returned array
:param test_only: disables loading of both train and extra -> large speed up
:return: tuple of arrays which depend on the parameters
"""
# Define files to be downloaded
file_urls = ['https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz']
# Maybe download data and retrieve local storage urls
local_urls = maybe_download(file_urls, FLAGS.data_dir)
# Extract archives and return different sets
dataset = extract_cifar10(local_urls[0], FLAGS.data_dir)
# Unpack tuple
train_data, train_labels, test_data, test_labels = dataset
# Apply whitening to input data
train_data = image_whitening(train_data)
test_data = image_whitening(test_data)
if test_only:
return test_data, test_labels
else:
return train_data, train_labels, test_data, test_labels
def ld_mnist(test_only=False):
"""
Load the MNIST dataset
:param extended: include extended training data in the returned array
:param test_only: disables loading of both train and extra -> large speed up
:return: tuple of arrays which depend on the parameters
"""
# Define files to be downloaded
# WARNING: changing the order of this list will break indices (cf. below)
file_urls = ['http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz',
'http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz',
'http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz',
'http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz',
]
# Maybe download data and retrieve local storage urls
local_urls = maybe_download(file_urls, FLAGS.data_dir)
# Extract it into np arrays.
train_data = extract_mnist_data(local_urls[0], 60000, 28, 1)
train_labels = extract_mnist_labels(local_urls[1], 60000)
test_data = extract_mnist_data(local_urls[2], 10000, 28, 1)
test_labels = extract_mnist_labels(local_urls[3], 10000)
if test_only:
return test_data, test_labels
else:
return train_data, train_labels, test_data, test_labels
def partition_dataset(data, labels, nb_teachers, teacher_id):
"""
Simple partitioning algorithm that returns the right portion of the data
needed by a given teacher out of a certain nb of teachers
:param data: input data to be partitioned
:param labels: output data to be partitioned
:param nb_teachers: number of teachers in the ensemble (affects size of each
partition)
:param teacher_id: id of partition to retrieve
:return:
"""
# Sanity check
assert len(data) == len(labels)
assert int(teacher_id) < int(nb_teachers)
# This will floor the possible number of batches
batch_len = int(len(data) / nb_teachers)
# Compute start, end indices of partition
start = teacher_id * batch_len
end = (teacher_id+1) * batch_len
# Slice partition off
partition_data = data[start:end]
partition_labels = labels[start:end]
return partition_data, partition_labels
differential_privacy/multiple_teachers/metrics.py 0 → 100644
View file @ 8505222e
# Copyright 2016 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.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
def accuracy(logits, labels):
"""
Return accuracy of the array of logits (or label predictions) wrt the labels
:param logits: this can either be logits, probabilities, or a single label
:param labels: the correct labels to match against
:return: the accuracy as a float
"""
assert len(logits) == len(labels)
if len(np.shape(logits)) > 1:
# Predicted labels are the argmax over axis 1
predicted_labels = np.argmax(logits, axis=1)
else:
# Input was already labels
assert len(np.shape(logits)) == 1
predicted_labels = logits
# Check against correct labels to compute correct guesses
correct = np.sum(predicted_labels == labels.reshape(len(labels)))
# Divide by number of labels to obtain accuracy
accuracy = float(correct) / len(labels)
# Return float value
return accuracy
differential_privacy/multiple_teachers/train_student.py 0 → 100644
View file @ 8505222e
# Copyright 2016 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.
# ==============================================================================
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
from differential_privacy.multiple_teachers import aggregation
from differential_privacy.multiple_teachers import deep_cnn
from differential_privacy.multiple_teachers import input
from differential_privacy.multiple_teachers import metrics
FLAGS = tf.flags.FLAGS
tf.flags.DEFINE_string('dataset', 'svhn', 'The name of the dataset to use')
tf.flags.DEFINE_integer('nb_labels', 10, 'Number of output classes')
tf.flags.DEFINE_string('data_dir','/tmp','Temporary storage')
tf.flags.DEFINE_string('train_dir','/tmp/train_dir','Where model chkpt are saved')
tf.flags.DEFINE_string('teachers_dir','/tmp/train_dir',
'Directory where teachers checkpoints are stored.')
tf.flags.DEFINE_integer('teachers_max_steps', 3000,
'Number of steps teachers were ran.')
tf.flags.DEFINE_integer('max_steps', 3000, 'Number of steps to run student.')
tf.flags.DEFINE_integer('nb_teachers', 10, 'Teachers in the ensemble.')
tf.flags.DEFINE_integer('stdnt_share', 1000,
'Student share (last index) of the test data')
tf.flags.DEFINE_integer('lap_scale', 10,
'Scale of the Laplacian noise added for privacy')
tf.flags.DEFINE_boolean('save_labels', False,
'Dump numpy arrays of labels and clean teacher votes')
tf.flags.DEFINE_boolean('deeper', False, 'Activate deeper CNN model')
def ensemble_preds(dataset, nb_teachers, stdnt_data):
"""
Given a dataset, a number of teachers, and some input data, this helper
function queries each teacher for predictions on the data and returns
all predictions in a single array. (That can then be aggregated into
one single prediction per input using aggregation.py (cf. function
prepare_student_data() below)
:param dataset: string corresponding to mnist, cifar10, or svhn
:param nb_teachers: number of teachers (in the ensemble) to learn from
:param stdnt_data: unlabeled student training data
:return: 3d array (teacher id, sample id, probability per class)
"""
# Compute shape of array that will hold probabilities produced by each
# teacher, for each training point, and each output class
result_shape = (nb_teachers, len(stdnt_data), FLAGS.nb_labels)
# Create array that will hold result
result = np.zeros(result_shape, dtype=np.float32)
# Get predictions from each teacher
for teacher_id in xrange(nb_teachers):
# Compute path of checkpoint file for teacher model with ID teacher_id
if FLAGS.deeper:
ckpt_path = FLAGS.teachers_dir + '/' + str(dataset) + '_' + str(nb_teachers) + '_teachers_' + str(teacher_id) + '_deep.ckpt-' + str(FLAGS.teachers_max_steps - 1) #NOLINT(long-line)
else:
ckpt_path = FLAGS.teachers_dir + '/' + str(dataset) + '_' + str(nb_teachers) + '_teachers_' + str(teacher_id) + '.ckpt-' + str(FLAGS.teachers_max_steps - 1) # NOLINT(long-line)
# Get predictions on our training data and store in result array
result[teacher_id] = deep_cnn.softmax_preds(stdnt_data, ckpt_path)
# This can take a while when there are a lot of teachers so output status
print("Computed Teacher " + str(teacher_id) + " softmax predictions")
return result
def prepare_student_data(dataset, nb_teachers, save=False):
"""
Takes a dataset name and the size of the teacher ensemble and prepares
training data for the student model, according to parameters indicated
in flags above.
:param dataset: string corresponding to mnist, cifar10, or svhn
:param nb_teachers: number of teachers (in the ensemble) to learn from
:param save: if set to True, will dump student training labels predicted by
the ensemble of teachers (with Laplacian noise) as npy files.
It also dumps the clean votes for each class (without noise) and
the labels assigned by teachers
:return: pairs of (data, labels) to be used for student training and testing
"""
assert input.create_dir_if_needed(FLAGS.train_dir)
# Load the dataset
if dataset == 'svhn':
test_data, test_labels = input.ld_svhn(test_only=True)
elif dataset == 'cifar10':
test_data, test_labels = input.ld_cifar10(test_only=True)
elif dataset == 'mnist':
test_data, test_labels = input.ld_mnist(test_only=True)
else:
print("Check value of dataset flag")
return False
# Make sure there is data leftover to be used as a test set
assert FLAGS.stdnt_share < len(test_data)
# Prepare [unlabeled] student training data (subset of test set)
stdnt_data = test_data[:FLAGS.stdnt_share]
# Compute teacher predictions for student training data
teachers_preds = ensemble_preds(dataset, nb_teachers, stdnt_data)
# Aggregate teacher predictions to get student training labels
if not save:
stdnt_labels = aggregation.noisy_max(teachers_preds, FLAGS.lap_scale)
else:
# Request clean votes and clean labels as well
stdnt_labels, clean_votes, labels_for_dump = aggregation.noisy_max(teachers_preds, FLAGS.lap_scale, return_clean_votes=True) #NOLINT(long-line)
# Prepare filepath for numpy dump of clean votes
filepath = FLAGS.data_dir + "/" + str(dataset) + '_' + str(nb_teachers) + '_student_clean_votes_lap_' + str(FLAGS.lap_scale) + '.npy' # NOLINT(long-line)
# Prepare filepath for numpy dump of clean labels
filepath_labels = FLAGS.data_dir + "/" + str(dataset) + '_' + str(nb_teachers) + '_teachers_labels_lap_' + str(FLAGS.lap_scale) + '.npy' # NOLINT(long-line)
# Dump clean_votes array
with tf.gfile.Open(filepath, mode='w') as file_obj:
np.save(file_obj, clean_votes)
# Dump labels_for_dump array
with tf.gfile.Open(filepath_labels, mode='w') as file_obj:
np.save(file_obj, labels_for_dump)
# Print accuracy of aggregated labels
ac_ag_labels = metrics.accuracy(stdnt_labels, test_labels[:FLAGS.stdnt_share])
print("Accuracy of the aggregated labels: " + str(ac_ag_labels))
# Store unused part of test set for use as a test set after student training
stdnt_test_data = test_data[FLAGS.stdnt_share:]
stdnt_test_labels = test_labels[FLAGS.stdnt_share:]
if save:
# Prepare filepath for numpy dump of labels produced by noisy aggregation
filepath = FLAGS.data_dir + "/" + str(dataset) + '_' + str(nb_teachers) + '_student_labels_lap_' + str(FLAGS.lap_scale) + '.npy' #NOLINT(long-line)
# Dump student noisy labels array
with tf.gfile.Open(filepath, mode='w') as file_obj:
np.save(file_obj, stdnt_labels)
return stdnt_data, stdnt_labels, stdnt_test_data, stdnt_test_labels
def train_student(dataset, nb_teachers):
"""
This function trains a student using predictions made by an ensemble of
teachers. The student and teacher models are trained using the same
neural network architecture.
:param dataset: string corresponding to mnist, cifar10, or svhn
:param nb_teachers: number of teachers (in the ensemble) to learn from
:return: True if student training went well
"""
assert input.create_dir_if_needed(FLAGS.train_dir)
# Call helper function to prepare student data using teacher predictions
stdnt_dataset = prepare_student_data(dataset, nb_teachers, save=True)
# Unpack the student dataset
stdnt_data, stdnt_labels, stdnt_test_data, stdnt_test_labels = stdnt_dataset
# Prepare checkpoint filename and path
if FLAGS.deeper:
ckpt_path = FLAGS.train_dir + '/' + str(dataset) + '_' + str(nb_teachers) + '_student_deeper.ckpt' #NOLINT(long-line)
else:
ckpt_path = FLAGS.train_dir + '/' + str(dataset) + '_' + str(nb_teachers) + '_student.ckpt' # NOLINT(long-line)
# Start student training
assert deep_cnn.train(stdnt_data, stdnt_labels, ckpt_path)
# Compute final checkpoint name for student (with max number of steps)
ckpt_path_final = ckpt_path + '-' + str(FLAGS.max_steps - 1)
# Compute student label predictions on remaining chunk of test set
student_preds = deep_cnn.softmax_preds(stdnt_test_data, ckpt_path_final)
# Compute teacher accuracy
precision = metrics.accuracy(student_preds, stdnt_test_labels)
print('Precision of student after training: ' + str(precision))
return True
def main(argv=None): # pylint: disable=unused-argument
# Run student training according to values specified in flags
assert train_student(FLAGS.dataset, FLAGS.nb_teachers)
if __name__ == '__main__':
tf.app.run()
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