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tensorflow/cleverhans | cleverhans/experimental/certification/dual_formulation.py | DualFormulation.construct_lanczos_params | def construct_lanczos_params(self):
"""Computes matrices T and V using the Lanczos algorithm.
Args:
k: number of iterations and dimensionality of the tridiagonal matrix
Returns:
eig_vec: eigen vector corresponding to min eigenvalue
"""
# Using autograph to automatically handle
# the... | python | def construct_lanczos_params(self):
"""Computes matrices T and V using the Lanczos algorithm.
Args:
k: number of iterations and dimensionality of the tridiagonal matrix
Returns:
eig_vec: eigen vector corresponding to min eigenvalue
"""
# Using autograph to automatically handle
# the... | [
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tensorflow/cleverhans | cleverhans/experimental/certification/dual_formulation.py | DualFormulation.set_differentiable_objective | def set_differentiable_objective(self):
"""Function that constructs minimization objective from dual variables."""
# Checking if graphs are already created
if self.vector_g is not None:
return
# Computing the scalar term
bias_sum = 0
for i in range(0, self.nn_params.num_hidden_layers):
... | python | def set_differentiable_objective(self):
"""Function that constructs minimization objective from dual variables."""
# Checking if graphs are already created
if self.vector_g is not None:
return
# Computing the scalar term
bias_sum = 0
for i in range(0, self.nn_params.num_hidden_layers):
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tensorflow/cleverhans | cleverhans/experimental/certification/dual_formulation.py | DualFormulation.get_h_product | def get_h_product(self, vector, dtype=None):
"""Function that provides matrix product interface with PSD matrix.
Args:
vector: the vector to be multiplied with matrix H
Returns:
result_product: Matrix product of H and vector
"""
# Computing the product of matrix_h with beta (input vect... | python | def get_h_product(self, vector, dtype=None):
"""Function that provides matrix product interface with PSD matrix.
Args:
vector: the vector to be multiplied with matrix H
Returns:
result_product: Matrix product of H and vector
"""
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tensorflow/cleverhans | cleverhans/experimental/certification/dual_formulation.py | DualFormulation.get_psd_product | def get_psd_product(self, vector, dtype=None):
"""Function that provides matrix product interface with PSD matrix.
Args:
vector: the vector to be multiplied with matrix M
Returns:
result_product: Matrix product of M and vector
"""
# For convenience, think of x as [\alpha, \beta]
if... | python | def get_psd_product(self, vector, dtype=None):
"""Function that provides matrix product interface with PSD matrix.
Args:
vector: the vector to be multiplied with matrix M
Returns:
result_product: Matrix product of M and vector
"""
# For convenience, think of x as [\alpha, \beta]
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tensorflow/cleverhans | cleverhans/experimental/certification/dual_formulation.py | DualFormulation.get_full_psd_matrix | def get_full_psd_matrix(self):
"""Function that returns the tf graph corresponding to the entire matrix M.
Returns:
matrix_h: unrolled version of tf matrix corresponding to H
matrix_m: unrolled tf matrix corresponding to M
"""
if self.matrix_m is not None:
return self.matrix_h, self.m... | python | def get_full_psd_matrix(self):
"""Function that returns the tf graph corresponding to the entire matrix M.
Returns:
matrix_h: unrolled version of tf matrix corresponding to H
matrix_m: unrolled tf matrix corresponding to M
"""
if self.matrix_m is not None:
return self.matrix_h, self.m... | [
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tensorflow/cleverhans | cleverhans/experimental/certification/dual_formulation.py | DualFormulation.make_m_psd | def make_m_psd(self, original_nu, feed_dictionary):
"""Run binary search to find a value for nu that makes M PSD
Args:
original_nu: starting value of nu to do binary search on
feed_dictionary: dictionary of updated lambda variables to feed into M
Returns:
new_nu: new value of nu
"""
... | python | def make_m_psd(self, original_nu, feed_dictionary):
"""Run binary search to find a value for nu that makes M PSD
Args:
original_nu: starting value of nu to do binary search on
feed_dictionary: dictionary of updated lambda variables to feed into M
Returns:
new_nu: new value of nu
"""
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tensorflow/cleverhans | cleverhans/experimental/certification/dual_formulation.py | DualFormulation.get_lanczos_eig | def get_lanczos_eig(self, compute_m=True, feed_dict=None):
"""Computes the min eigen value and corresponding vector of matrix M or H
using the Lanczos algorithm.
Args:
compute_m: boolean to determine whether we should compute eig val/vec
for M or for H. True for M; False for H.
feed_dict... | python | def get_lanczos_eig(self, compute_m=True, feed_dict=None):
"""Computes the min eigen value and corresponding vector of matrix M or H
using the Lanczos algorithm.
Args:
compute_m: boolean to determine whether we should compute eig val/vec
for M or for H. True for M; False for H.
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tensorflow/cleverhans | cleverhans/experimental/certification/dual_formulation.py | DualFormulation.compute_certificate | def compute_certificate(self, current_step, feed_dictionary):
""" Function to compute the certificate based either current value
or dual variables loaded from dual folder """
feed_dict = feed_dictionary.copy()
nu = feed_dict[self.nu]
second_term = self.make_m_psd(nu, feed_dict)
tf.logging.info('... | python | def compute_certificate(self, current_step, feed_dictionary):
""" Function to compute the certificate based either current value
or dual variables loaded from dual folder """
feed_dict = feed_dictionary.copy()
nu = feed_dict[self.nu]
second_term = self.make_m_psd(nu, feed_dict)
tf.logging.info('... | [
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tensorflow/cleverhans | cleverhans/attacks/spatial_transformation_method.py | SpatialTransformationMethod.generate | def generate(self, x, **kwargs):
"""
Generate symbolic graph for adversarial examples and return.
:param x: The model's symbolic inputs.
:param kwargs: See `parse_params`
"""
# Parse and save attack-specific parameters
assert self.parse_params(**kwargs)
from cleverhans.attacks_tf import... | python | def generate(self, x, **kwargs):
"""
Generate symbolic graph for adversarial examples and return.
:param x: The model's symbolic inputs.
:param kwargs: See `parse_params`
"""
# Parse and save attack-specific parameters
assert self.parse_params(**kwargs)
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tensorflow/cleverhans | cleverhans/attacks/spatial_transformation_method.py | SpatialTransformationMethod.parse_params | def parse_params(self,
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dx_max=0.1,
n_dxs=2,
dy_min=-0.1,
dy_max=0.1,
n_dys=2,
angle_min=-30,
angle_max=30,
... | python | def parse_params(self,
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dx_min=-0.1,
dx_max=0.1,
n_dxs=2,
dy_min=-0.1,
dy_max=0.1,
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tensorflow/cleverhans | cleverhans/utils_keras.py | conv_2d | def conv_2d(filters, kernel_shape, strides, padding, input_shape=None):
"""
Defines the right convolutional layer according to the
version of Keras that is installed.
:param filters: (required integer) the dimensionality of the output
space (i.e. the number output of filters in the
... | python | def conv_2d(filters, kernel_shape, strides, padding, input_shape=None):
"""
Defines the right convolutional layer according to the
version of Keras that is installed.
:param filters: (required integer) the dimensionality of the output
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tensorflow/cleverhans | cleverhans/utils_keras.py | cnn_model | def cnn_model(logits=False, input_ph=None, img_rows=28, img_cols=28,
channels=1, nb_filters=64, nb_classes=10):
"""
Defines a CNN model using Keras sequential model
:param logits: If set to False, returns a Keras model, otherwise will also
return logits tensor
:param input_ph: Th... | python | def cnn_model(logits=False, input_ph=None, img_rows=28, img_cols=28,
channels=1, nb_filters=64, nb_classes=10):
"""
Defines a CNN model using Keras sequential model
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tensorflow/cleverhans | cleverhans/utils_keras.py | KerasModelWrapper._get_softmax_name | def _get_softmax_name(self):
"""
Looks for the name of the softmax layer.
:return: Softmax layer name
"""
for layer in self.model.layers:
cfg = layer.get_config()
if 'activation' in cfg and cfg['activation'] == 'softmax':
return layer.name
raise Exception("No softmax layers ... | python | def _get_softmax_name(self):
"""
Looks for the name of the softmax layer.
:return: Softmax layer name
"""
for layer in self.model.layers:
cfg = layer.get_config()
if 'activation' in cfg and cfg['activation'] == 'softmax':
return layer.name
raise Exception("No softmax layers ... | [
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Usually these layers appears when model is stacked.
:return: List of abstracted layers
"""
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for layer in self.model.layers:
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abstract_layers.app... | python | def _get_abstract_layer_name(self):
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/validation_tool/validate_submission_lib.py | get_extract_command_template | def get_extract_command_template(filename):
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/validation_tool/validate_submission_lib.py | shell_call | def shell_call(command, **kwargs):
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command: command to run as a list of tokens
**kwargs: dirctionary with substitutions
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whether command was successful, i.e. returned 0 status code
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Args:
command: command to run as a list of tokens
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/validation_tool/validate_submission_lib.py | make_directory_writable | def make_directory_writable(dirname):
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/validation_tool/validate_submission_lib.py | SubmissionValidator._prepare_temp_dir | def _prepare_temp_dir(self):
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# NOTE: we do not create self._extracted_submission_dir
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/validation_tool/validate_submission_lib.py | SubmissionValidator._extract_submission | def _extract_submission(self, filename):
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/validation_tool/validate_submission_lib.py | SubmissionValidator._verify_docker_image_size | def _verify_docker_image_size(self, image_name):
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"""
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image_name: name of the Docker image.
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/validation_tool/validate_submission_lib.py | SubmissionValidator._prepare_sample_data | def _prepare_sample_data(self, submission_type):
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Args:
submission_type: type of the submission.
"""
# write images
images = np.random.randint(0, 256,
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for i in range(B... | python | def _prepare_sample_data(self, submission_type):
"""Prepares sample data for the submission.
Args:
submission_type: type of the submission.
"""
# write images
images = np.random.randint(0, 256,
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/validation_tool/validate_submission_lib.py | SubmissionValidator._verify_output | def _verify_output(self, submission_type):
"""Verifies correctness of the submission output.
Args:
submission_type: type of the submission
Returns:
True if output looks valid
"""
result = True
if submission_type == 'defense':
try:
image_classification = load_defense_o... | python | def _verify_output(self, submission_type):
"""Verifies correctness of the submission output.
Args:
submission_type: type of the submission
Returns:
True if output looks valid
"""
result = True
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/validation_tool/validate_submission_lib.py | SubmissionValidator.validate_submission | def validate_submission(self, filename):
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Args:
filename: submission filename
Returns:
submission metadata or None if submission is invalid
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"""Validates submission.
Args:
filename: submission filename
Returns:
submission metadata or None if submission is invalid
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tensorflow/cleverhans | cleverhans/loss.py | Loss.save | def save(self, path):
"""Save loss in json format
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"""Save loss in json format
"""
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:param B: a matrix.
:returns: A tensor for the pairwise Euclidean between A and B.
"""
batchA = tf.shape(A)[0]
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"""Pairwise Euclidean distance between two matrices.
:param A: a matrix.
:param B: a matrix.
:returns: A tensor for the pairwise Euclidean between A and B.
"""
batchA = tf.shape(A)[0]
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:param B: a matrix.
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:param A: a matrix.
:param B: a matrix.
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tensorflow/cleverhans | cleverhans/loss.py | SNNLCrossEntropy.same_label_mask | def same_label_mask(y, y2):
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:param y: a list of labels
:param y2: a list of labels
:returns: A tensor for the masking matrix.
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return tf.cast(tf.squeeze(tf.equal(y, tf.expand_dims(y2, 1))), tf.float32) | python | def same_label_mask(y, y2):
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tensorflow/cleverhans | cleverhans/loss.py | SNNLCrossEntropy.SNNL | def SNNL(x, y, temp, cos_distance):
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tensorflow/cleverhans | cleverhans/plot/image.py | show | def show(ndarray, min_val=None, max_val=None):
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tensorflow/cleverhans | cleverhans/plot/image.py | save | def save(path, ndarray, min_val=None, max_val=None):
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pad = 3
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"""
Generates the adversarial sample for the given input.
:param x: The model's inputs.
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tensorflow/cleverhans | cleverhans/attacks_tfe.py | FastGradientMethod.fgm | def fgm(self, x, labels, targeted=False):
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tensorflow/cleverhans | cleverhans/devtools/mocks.py | random_feed_dict | def random_feed_dict(rng, placeholders):
"""
Returns random data to be used with `feed_dict`.
:param rng: A numpy.random.RandomState instance
:param placeholders: List of tensorflow placeholders
:return: A dict mapping placeholders to random numpy values
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output = {}
for placeholder in placeholders... | python | def random_feed_dict(rng, placeholders):
"""
Returns random data to be used with `feed_dict`.
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tensorflow/cleverhans | cleverhans/devtools/list_files.py | list_files | def list_files(suffix=""):
"""
Returns a list of all files in CleverHans with the given suffix.
Parameters
----------
suffix : str
Returns
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file_list : list
A list of all files in CleverHans whose filepath ends with `suffix`.
"""
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"""
Returns a list of all files in CleverHans with the given suffix.
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----------
suffix : str
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file_list : list
A list of all files in CleverHans whose filepath ends with `suffix`.
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/master.py | print_header | def print_header(text):
"""Prints header with given text and frame composed of '#' characters."""
print()
print('#'*(len(text)+4))
print('# ' + text + ' #')
print('#'*(len(text)+4))
print() | python | def print_header(text):
"""Prints header with given text and frame composed of '#' characters."""
print()
print('#'*(len(text)+4))
print('# ' + text + ' #')
print('#'*(len(text)+4))
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/master.py | save_dict_to_file | def save_dict_to_file(filename, dictionary):
"""Saves dictionary as CSV file."""
with open(filename, 'w') as f:
writer = csv.writer(f)
for k, v in iteritems(dictionary):
writer.writerow([str(k), str(v)]) | python | def save_dict_to_file(filename, dictionary):
"""Saves dictionary as CSV file."""
with open(filename, 'w') as f:
writer = csv.writer(f)
for k, v in iteritems(dictionary):
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/master.py | main | def main(args):
"""Main function which runs master."""
if args.blacklisted_submissions:
logging.warning('BLACKLISTED SUBMISSIONS: %s',
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if args.limited_dataset:
logging.info('Using limited dataset: 3 batches * 10 images')
max_dataset_num_images = 30
... | python | def main(args):
"""Main function which runs master."""
if args.blacklisted_submissions:
logging.warning('BLACKLISTED SUBMISSIONS: %s',
args.blacklisted_submissions)
if args.limited_dataset:
logging.info('Using limited dataset: 3 batches * 10 images')
max_dataset_num_images = 30
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/master.py | EvaluationMaster.ask_when_work_is_populated | def ask_when_work_is_populated(self, work):
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work: instance of WorkPiecesBase
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work: instance of WorkPiecesBase
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/master.py | EvaluationMaster.prepare_attacks | def prepare_attacks(self):
"""Prepares all data needed for evaluation of attacks."""
print_header('PREPARING ATTACKS DATA')
# verify that attacks data not written yet
if not self.ask_when_work_is_populated(self.attack_work):
return
self.attack_work = eval_lib.AttackWorkPieces(
datastor... | python | def prepare_attacks(self):
"""Prepares all data needed for evaluation of attacks."""
print_header('PREPARING ATTACKS DATA')
# verify that attacks data not written yet
if not self.ask_when_work_is_populated(self.attack_work):
return
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/master.py | EvaluationMaster.prepare_defenses | def prepare_defenses(self):
"""Prepares all data needed for evaluation of defenses."""
print_header('PREPARING DEFENSE DATA')
# verify that defense data not written yet
if not self.ask_when_work_is_populated(self.defense_work):
return
self.defense_work = eval_lib.DefenseWorkPieces(
dat... | python | def prepare_defenses(self):
"""Prepares all data needed for evaluation of defenses."""
print_header('PREPARING DEFENSE DATA')
# verify that defense data not written yet
if not self.ask_when_work_is_populated(self.defense_work):
return
self.defense_work = eval_lib.DefenseWorkPieces(
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/master.py | EvaluationMaster._save_work_results | def _save_work_results(self, run_stats, scores, num_processed_images,
filename):
"""Saves statistics about each submission.
Saved statistics include score; number of completed and failed batches;
min, max, average and median time needed to run one batch.
Args:
run_stats:... | python | def _save_work_results(self, run_stats, scores, num_processed_images,
filename):
"""Saves statistics about each submission.
Saved statistics include score; number of completed and failed batches;
min, max, average and median time needed to run one batch.
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/master.py | EvaluationMaster._save_sorted_results | def _save_sorted_results(self, run_stats, scores, image_count, filename):
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run_stats: dictionary with runtime statistics for submissions,
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run_stats: dictionary with runtime statistics for submissions,
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/master.py | EvaluationMaster._read_dataset_metadata | def _read_dataset_metadata(self):
"""Reads dataset metadata.
Returns:
instance of DatasetMetadata
"""
blob = self.storage_client.get_blob(
'dataset/' + self.dataset_name + '_dataset.csv')
buf = BytesIO()
blob.download_to_file(buf)
buf.seek(0)
return eval_lib.DatasetMetadat... | python | def _read_dataset_metadata(self):
"""Reads dataset metadata.
Returns:
instance of DatasetMetadata
"""
blob = self.storage_client.get_blob(
'dataset/' + self.dataset_name + '_dataset.csv')
buf = BytesIO()
blob.download_to_file(buf)
buf.seek(0)
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/master.py | EvaluationMaster.compute_results | def compute_results(self):
"""Computes results (scores, stats, etc...) of competition evaluation.
Results are saved into output directory (self.results_dir).
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so it can resume computation if it was interrupted for some reason.
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/master.py | EvaluationMaster._show_status_for_work | def _show_status_for_work(self, work):
"""Shows status for given work pieces.
Args:
work: instance of either AttackWorkPieces or DefenseWorkPieces
"""
work_count = len(work.work)
work_completed = {}
work_completed_count = 0
for v in itervalues(work.work):
if v['is_completed']:
... | python | def _show_status_for_work(self, work):
"""Shows status for given work pieces.
Args:
work: instance of either AttackWorkPieces or DefenseWorkPieces
"""
work_count = len(work.work)
work_completed = {}
work_completed_count = 0
for v in itervalues(work.work):
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/master.py | EvaluationMaster._export_work_errors | def _export_work_errors(self, work, output_file):
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work: instance of either AttackWorkPieces or DefenseWorkPieces
output_file: name of the output file
"""
errors = set()
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Args:
work: instance of either AttackWorkPieces or DefenseWorkPieces
output_file: name of the output file
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/master.py | EvaluationMaster.show_status | def show_status(self):
"""Shows current status of competition evaluation.
Also this method saves error messages generated by attacks and defenses
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"""
print_header('Attack work statistics')
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"""Shows current status of competition evaluation.
Also this method saves error messages generated by attacks and defenses
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"""
print_header('Attack work statistics')
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/master.py | EvaluationMaster.cleanup_failed_attacks | def cleanup_failed_attacks(self):
"""Cleans up data of failed attacks."""
print_header('Cleaning up failed attacks')
attacks_to_replace = {}
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failed_submissions = set()
error_msg = set()
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"""Cleans up data of failed attacks."""
print_header('Cleaning up failed attacks')
attacks_to_replace = {}
self.attack_work.read_all_from_datastore()
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/master.py | EvaluationMaster.cleanup_attacks_with_zero_images | def cleanup_attacks_with_zero_images(self):
"""Cleans up data about attacks which generated zero images."""
print_header('Cleaning up attacks which generated 0 images.')
# find out attack work to cleanup
self.adv_batches.init_from_datastore()
self.attack_work.read_all_from_datastore()
new_attack... | python | def cleanup_attacks_with_zero_images(self):
"""Cleans up data about attacks which generated zero images."""
print_header('Cleaning up attacks which generated 0 images.')
# find out attack work to cleanup
self.adv_batches.init_from_datastore()
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/master.py | EvaluationMaster._cleanup_keys_with_confirmation | def _cleanup_keys_with_confirmation(self, keys_to_delete):
"""Asks confirmation and then deletes entries with keys.
Args:
keys_to_delete: list of datastore keys for which entries should be deleted
"""
print('Round name: ', self.round_name)
print('Number of entities to be deleted: ', len(keys_... | python | def _cleanup_keys_with_confirmation(self, keys_to_delete):
"""Asks confirmation and then deletes entries with keys.
Args:
keys_to_delete: list of datastore keys for which entries should be deleted
"""
print('Round name: ', self.round_name)
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/master.py | EvaluationMaster.cleanup_defenses | def cleanup_defenses(self):
"""Cleans up all data about defense work in current round."""
print_header('CLEANING UP DEFENSES DATA')
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"""Cleans up all data about defense work in current round."""
print_header('CLEANING UP DEFENSES DATA')
work_ancestor_key = self.datastore_client.key('WorkType', 'AllDefenses')
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/master.py | EvaluationMaster.cleanup_datastore | def cleanup_datastore(self):
"""Cleans up datastore and deletes all information about current round."""
print_header('CLEANING UP ENTIRE DATASTORE')
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"""Cleans up datastore and deletes all information about current round."""
print_header('CLEANING UP ENTIRE DATASTORE')
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tensorflow/cleverhans | examples/nips17_adversarial_competition/dev_toolkit/sample_attacks/random_noise/attack_random_noise.py | main | def main(_):
"""Run the sample attack"""
eps = FLAGS.max_epsilon / 255.0
batch_shape = [FLAGS.batch_size, FLAGS.image_height, FLAGS.image_width, 3]
with tf.Graph().as_default():
x_input = tf.placeholder(tf.float32, shape=batch_shape)
noisy_images = x_input + eps * tf.sign(tf.random_normal(batch_shape))... | python | def main(_):
"""Run the sample attack"""
eps = FLAGS.max_epsilon / 255.0
batch_shape = [FLAGS.batch_size, FLAGS.image_height, FLAGS.image_width, 3]
with tf.Graph().as_default():
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tensorflow/cleverhans | cleverhans/attacks/saliency_map_method.py | jsma_symbolic | def jsma_symbolic(x, y_target, model, theta, gamma, clip_min, clip_max):
"""
TensorFlow implementation of the JSMA (see https://arxiv.org/abs/1511.07528
for details about the algorithm design choices).
:param x: the input placeholder
:param y_target: the target tensor
:param model: a cleverhans.model.Model... | python | def jsma_symbolic(x, y_target, model, theta, gamma, clip_min, clip_max):
"""
TensorFlow implementation of the JSMA (see https://arxiv.org/abs/1511.07528
for details about the algorithm design choices).
:param x: the input placeholder
:param y_target: the target tensor
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tensorflow/cleverhans | cleverhans/attacks/saliency_map_method.py | SaliencyMapMethod.generate | def generate(self, x, **kwargs):
"""
Generate symbolic graph for adversarial examples and return.
:param x: The model's symbolic inputs.
:param kwargs: See `parse_params`
"""
# Parse and save attack-specific parameters
assert self.parse_params(**kwargs)
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# C... | python | def generate(self, x, **kwargs):
"""
Generate symbolic graph for adversarial examples and return.
:param x: The model's symbolic inputs.
:param kwargs: See `parse_params`
"""
# Parse and save attack-specific parameters
assert self.parse_params(**kwargs)
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tensorflow/cleverhans | cleverhans/attacks/saliency_map_method.py | SaliencyMapMethod.parse_params | def parse_params(self,
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clip_max=1.,
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**kwargs):
"""
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tensorflow/cleverhans | examples/multigpu_advtrain/make_model.py | make_basic_ngpu | def make_basic_ngpu(nb_classes=10, input_shape=(None, 28, 28, 1), **kwargs):
"""
Create a multi-GPU model similar to the basic cnn in the tutorials.
"""
model = make_basic_cnn()
layers = model.layers
model = MLPnGPU(nb_classes, layers, input_shape)
return model | python | def make_basic_ngpu(nb_classes=10, input_shape=(None, 28, 28, 1), **kwargs):
"""
Create a multi-GPU model similar to the basic cnn in the tutorials.
"""
model = make_basic_cnn()
layers = model.layers
model = MLPnGPU(nb_classes, layers, input_shape)
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tensorflow/cleverhans | examples/multigpu_advtrain/make_model.py | make_madry_ngpu | def make_madry_ngpu(nb_classes=10, input_shape=(None, 28, 28, 1), **kwargs):
"""
Create a multi-GPU model similar to Madry et al. (arXiv:1706.06083).
"""
layers = [Conv2DnGPU(32, (5, 5), (1, 1), "SAME"),
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"""
Create a multi-GPU model similar to Madry et al. (arXiv:1706.06083).
"""
layers = [Conv2DnGPU(32, (5, 5), (1, 1), "SAME"),
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tensorflow/cleverhans | examples/multigpu_advtrain/resnet_tf.py | ResNetTF._build_model | def _build_model(self, x):
"""Build the core model within the graph."""
with tf.variable_scope('init'):
x = self._conv('init_conv', x, 3, x.shape[3], 16,
self._stride_arr(1))
strides = [1, 2, 2]
activate_before_residual = [True, False, False]
if self.hps.use_bottleneck:
... | python | def _build_model(self, x):
"""Build the core model within the graph."""
with tf.variable_scope('init'):
x = self._conv('init_conv', x, 3, x.shape[3], 16,
self._stride_arr(1))
strides = [1, 2, 2]
activate_before_residual = [True, False, False]
if self.hps.use_bottleneck:
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tensorflow/cleverhans | examples/multigpu_advtrain/resnet_tf.py | ResNetTF.build_cost | def build_cost(self, labels, logits):
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"""
op = logits.op
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"""
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"""
op = logits.op
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tensorflow/cleverhans | examples/multigpu_advtrain/resnet_tf.py | ResNetTF.build_train_op_from_cost | def build_train_op_from_cost(self, cost):
"""Build training specific ops for the graph."""
self.lrn_rate = tf.constant(self.hps.lrn_rate, tf.float32,
name='learning_rate')
self.momentum = tf.constant(self.hps.momentum, tf.float32,
name='momentu... | python | def build_train_op_from_cost(self, cost):
"""Build training specific ops for the graph."""
self.lrn_rate = tf.constant(self.hps.lrn_rate, tf.float32,
name='learning_rate')
self.momentum = tf.constant(self.hps.momentum, tf.float32,
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tensorflow/cleverhans | examples/multigpu_advtrain/resnet_tf.py | ResNetTF._layer_norm | def _layer_norm(self, name, x):
"""Layer normalization."""
if self.init_layers:
bn = LayerNorm()
bn.name = name
self.layers += [bn]
else:
bn = self.layers[self.layer_idx]
self.layer_idx += 1
bn.device_name = self.device_name
bn.set_training(self.training)
x = bn.fpr... | python | def _layer_norm(self, name, x):
"""Layer normalization."""
if self.init_layers:
bn = LayerNorm()
bn.name = name
self.layers += [bn]
else:
bn = self.layers[self.layer_idx]
self.layer_idx += 1
bn.device_name = self.device_name
bn.set_training(self.training)
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tensorflow/cleverhans | examples/multigpu_advtrain/resnet_tf.py | ResNetTF._residual | def _residual(self, x, in_filter, out_filter, stride,
activate_before_residual=False):
"""Residual unit with 2 sub layers."""
if activate_before_residual:
with tf.variable_scope('shared_activation'):
x = self._layer_norm('init_bn', x)
x = self._relu(x, self.hps.relu_leakine... | python | def _residual(self, x, in_filter, out_filter, stride,
activate_before_residual=False):
"""Residual unit with 2 sub layers."""
if activate_before_residual:
with tf.variable_scope('shared_activation'):
x = self._layer_norm('init_bn', x)
x = self._relu(x, self.hps.relu_leakine... | [
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tensorflow/cleverhans | examples/multigpu_advtrain/resnet_tf.py | ResNetTF._bottleneck_residual | def _bottleneck_residual(self, x, in_filter, out_filter, stride,
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"""Bottleneck residual unit with 3 sub layers."""
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with tf.variable_scope('common_bn_relu'):
x = self._layer_norm('init_bn', x)
x = self.... | python | def _bottleneck_residual(self, x, in_filter, out_filter, stride,
activate_before_residual=False):
"""Bottleneck residual unit with 3 sub layers."""
if activate_before_residual:
with tf.variable_scope('common_bn_relu'):
x = self._layer_norm('init_bn', x)
x = self.... | [
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tensorflow/cleverhans | examples/multigpu_advtrain/resnet_tf.py | ResNetTF._decay | def _decay(self):
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else:
for layer in s... | python | def _decay(self):
"""L2 weight decay loss."""
if self.decay_cost is not None:
return self.decay_cost
costs = []
if self.device_name is None:
for var in tf.trainable_variables():
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tensorflow/cleverhans | examples/multigpu_advtrain/resnet_tf.py | ResNetTF._conv | def _conv(self, name, x, filter_size, in_filters, out_filters, strides):
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self.layers += [conv]
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"""Convolution."""
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conv = Conv2DnGPU(out_filters,
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tensorflow/cleverhans | examples/multigpu_advtrain/resnet_tf.py | ResNetTF._fully_connected | def _fully_connected(self, x, out_dim):
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self.layer_idx += 1
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"""FullyConnected layer for final output."""
if self.init_layers:
fc = LinearnGPU(out_dim, w_name='DW')
fc.name = 'logits'
self.layers += [fc]
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/classification_results.py | read_classification_results | def read_classification_results(storage_client, file_path):
"""Reads classification results from the file in Cloud Storage.
This method reads file with classification results produced by running
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Args:
storage_client: instance of CompetitionStorageClient or Non... | python | def read_classification_results(storage_client, file_path):
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This method reads file with classification results produced by running
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/classification_results.py | analyze_one_classification_result | def analyze_one_classification_result(storage_client, file_path,
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dataset_meta):
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This method reads file with classification result and counts
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dataset_meta):
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filename: name of the file where to save matrix
remap_dim0: dictionary with mapping row indices to row names which should
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"""Saves matrix to the file.
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remap_dim0: dictionary with mapping row indices to row names which should
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/classification_results.py | ClassificationBatches.init_from_adversarial_batches_write_to_datastore | def init_from_adversarial_batches_write_to_datastore(self, submissions,
adv_batches):
"""Populates data from adversarial batches and writes to datastore.
Args:
submissions: instance of CompetitionSubmissions
adv_batches: instance of AversarialB... | python | def init_from_adversarial_batches_write_to_datastore(self, submissions,
adv_batches):
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/classification_results.py | ClassificationBatches.init_from_datastore | def init_from_datastore(self):
"""Initializes data by reading it from the datastore."""
self._data = {}
client = self._datastore_client
for entity in client.query_fetch(kind=KIND_CLASSIFICATION_BATCH):
class_batch_id = entity.key.flat_path[-1]
self.data[class_batch_id] = dict(entity) | python | def init_from_datastore(self):
"""Initializes data by reading it from the datastore."""
self._data = {}
client = self._datastore_client
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/classification_results.py | ClassificationBatches.read_batch_from_datastore | def read_batch_from_datastore(self, class_batch_id):
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/classification_results.py | ClassificationBatches.compute_classification_results | def compute_classification_results(self, adv_batches, dataset_batches,
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adv_batches: instance of AversarialBatches
dataset_batches: instance of DatasetBatches
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/submissions.py | participant_from_submission_path | def participant_from_submission_path(submission_path):
"""Parses type of participant based on submission filename.
Args:
submission_path: path to the submission in Google Cloud Storage
Returns:
dict with one element. Element key correspond to type of participant
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submission_path: path to the submission in Google Cloud Storage
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/submissions.py | CompetitionSubmissions._load_submissions_from_datastore_dir | def _load_submissions_from_datastore_dir(self, dir_suffix, id_pattern):
"""Loads list of submissions from the directory.
Args:
dir_suffix: suffix of the directory where submissions are stored,
one of the folowing constants: ATTACK_SUBDIR, TARGETED_ATTACK_SUBDIR
or DEFENSE_SUBDIR.
id... | python | def _load_submissions_from_datastore_dir(self, dir_suffix, id_pattern):
"""Loads list of submissions from the directory.
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dir_suffix: suffix of the directory where submissions are stored,
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/submissions.py | CompetitionSubmissions.init_from_storage_write_to_datastore | def init_from_storage_write_to_datastore(self):
"""Init list of sumibssions from Storage and saves them to Datastore.
Should be called only once (typically by master) during evaluation of
the competition.
"""
# Load submissions
self._attacks = self._load_submissions_from_datastore_dir(
... | python | def init_from_storage_write_to_datastore(self):
"""Init list of sumibssions from Storage and saves them to Datastore.
Should be called only once (typically by master) during evaluation of
the competition.
"""
# Load submissions
self._attacks = self._load_submissions_from_datastore_dir(
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/submissions.py | CompetitionSubmissions._write_to_datastore | def _write_to_datastore(self):
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# Populate datastore
roots_and_submissions = zip([ATTACKS_ENTITY_KEY,
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... | python | def _write_to_datastore(self):
"""Writes all submissions to datastore."""
# Populate datastore
roots_and_submissions = zip([ATTACKS_ENTITY_KEY,
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/submissions.py | CompetitionSubmissions.init_from_datastore | def init_from_datastore(self):
"""Init list of submission from Datastore.
Should be called by each worker during initialization.
"""
self._attacks = {}
self._targeted_attacks = {}
self._defenses = {}
for entity in self._datastore_client.query_fetch(kind=KIND_SUBMISSION):
submission_id... | python | def init_from_datastore(self):
"""Init list of submission from Datastore.
Should be called by each worker during initialization.
"""
self._attacks = {}
self._targeted_attacks = {}
self._defenses = {}
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/submissions.py | CompetitionSubmissions.get_all_attack_ids | def get_all_attack_ids(self):
"""Returns IDs of all attacks (targeted and non-targeted)."""
return list(self.attacks.keys()) + list(self.targeted_attacks.keys()) | python | def get_all_attack_ids(self):
"""Returns IDs of all attacks (targeted and non-targeted)."""
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/submissions.py | CompetitionSubmissions.find_by_id | def find_by_id(self, submission_id):
"""Finds submission by ID.
Args:
submission_id: ID of the submission
Returns:
SubmissionDescriptor with information about submission or None if
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"""
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"""Finds submission by ID.
Args:
submission_id: ID of the submission
Returns:
SubmissionDescriptor with information about submission or None if
submission is not found.
"""
return self._attacks.get(
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] | 97488e215760547b81afc53f5e5de8ba7da5bd98 | https://github.com/tensorflow/cleverhans/blob/97488e215760547b81afc53f5e5de8ba7da5bd98/examples/nips17_adversarial_competition/eval_infra/code/eval_lib/submissions.py#L198-L212 | train |
tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/submissions.py | CompetitionSubmissions.get_external_id | def get_external_id(self, submission_id):
"""Returns human readable submission external ID.
Args:
submission_id: internal submission ID.
Returns:
human readable ID.
"""
submission = self.find_by_id(submission_id)
if not submission:
return None
if 'team_id' in submission.p... | python | def get_external_id(self, submission_id):
"""Returns human readable submission external ID.
Args:
submission_id: internal submission ID.
Returns:
human readable ID.
"""
submission = self.find_by_id(submission_id)
if not submission:
return None
if 'team_id' in submission.p... | [
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submission_id: internal submission ID.
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tensorflow/cleverhans | examples/nips17_adversarial_competition/dev_toolkit/validation_tool/submission_validator_lib.py | SubmissionValidator._prepare_temp_dir | def _prepare_temp_dir(self):
"""Cleans up and prepare temporary directory."""
shell_call(['rm', '-rf', os.path.join(self._temp_dir, '*')])
# NOTE: we do not create self._extracted_submission_dir
# this is intentional because self._tmp_extracted_dir or it's subdir
# will be renames into self._extract... | python | def _prepare_temp_dir(self):
"""Cleans up and prepare temporary directory."""
shell_call(['rm', '-rf', os.path.join(self._temp_dir, '*')])
# NOTE: we do not create self._extracted_submission_dir
# this is intentional because self._tmp_extracted_dir or it's subdir
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] | 97488e215760547b81afc53f5e5de8ba7da5bd98 | https://github.com/tensorflow/cleverhans/blob/97488e215760547b81afc53f5e5de8ba7da5bd98/examples/nips17_adversarial_competition/dev_toolkit/validation_tool/submission_validator_lib.py#L133-L143 | train |
tensorflow/cleverhans | examples/nips17_adversarial_competition/dev_toolkit/validation_tool/submission_validator_lib.py | SubmissionValidator._load_and_verify_metadata | def _load_and_verify_metadata(self, submission_type):
"""Loads and verifies metadata.
Args:
submission_type: type of the submission
Returns:
dictionaty with metadata or None if metadata not found or invalid
"""
metadata_filename = os.path.join(self._extracted_submission_dir,
... | python | def _load_and_verify_metadata(self, submission_type):
"""Loads and verifies metadata.
Args:
submission_type: type of the submission
Returns:
dictionaty with metadata or None if metadata not found or invalid
"""
metadata_filename = os.path.join(self._extracted_submission_dir,
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submission_type: type of the submission
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] | 97488e215760547b81afc53f5e5de8ba7da5bd98 | https://github.com/tensorflow/cleverhans/blob/97488e215760547b81afc53f5e5de8ba7da5bd98/examples/nips17_adversarial_competition/dev_toolkit/validation_tool/submission_validator_lib.py#L204-L245 | train |
tensorflow/cleverhans | examples/nips17_adversarial_competition/dev_toolkit/validation_tool/submission_validator_lib.py | SubmissionValidator._run_submission | def _run_submission(self, metadata):
"""Runs submission inside Docker container.
Args:
metadata: dictionary with submission metadata
Returns:
True if status code of Docker command was success (i.e. zero),
False otherwise.
"""
if self._use_gpu:
docker_binary = 'nvidia-docker... | python | def _run_submission(self, metadata):
"""Runs submission inside Docker container.
Args:
metadata: dictionary with submission metadata
Returns:
True if status code of Docker command was success (i.e. zero),
False otherwise.
"""
if self._use_gpu:
docker_binary = 'nvidia-docker... | [
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metadata: dictionary with submission metadata
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] | 97488e215760547b81afc53f5e5de8ba7da5bd98 | https://github.com/tensorflow/cleverhans/blob/97488e215760547b81afc53f5e5de8ba7da5bd98/examples/nips17_adversarial_competition/dev_toolkit/validation_tool/submission_validator_lib.py#L290-L333 | train |
tensorflow/cleverhans | cleverhans/future/tf2/attacks/fast_gradient_method.py | fast_gradient_method | def fast_gradient_method(model_fn, x, eps, ord, clip_min=None, clip_max=None, y=None,
targeted=False, sanity_checks=False):
"""
Tensorflow 2.0 implementation of the Fast Gradient Method.
:param model_fn: a callable that takes an input tensor and returns the model logits.
:param x: input... | python | def fast_gradient_method(model_fn, x, eps, ord, clip_min=None, clip_max=None, y=None,
targeted=False, sanity_checks=False):
"""
Tensorflow 2.0 implementation of the Fast Gradient Method.
:param model_fn: a callable that takes an input tensor and returns the model logits.
:param x: input... | [
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:param x: input tensor.
:param eps: epsilon (input variation parameter); see https://arxiv.org/abs/1412.6572.
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