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googleapis/google-cloud-python | spanner/google/cloud/spanner_v1/client.py | Client.database_admin_api | def database_admin_api(self):
"""Helper for session-related API calls."""
if self._database_admin_api is None:
self._database_admin_api = DatabaseAdminClient(
credentials=self.credentials, client_info=_CLIENT_INFO
)
return self._database_admin_api | python | def database_admin_api(self):
"""Helper for session-related API calls."""
if self._database_admin_api is None:
self._database_admin_api = DatabaseAdminClient(
credentials=self.credentials, client_info=_CLIENT_INFO
)
return self._database_admin_api | [
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googleapis/google-cloud-python | spanner/google/cloud/spanner_v1/client.py | Client.copy | def copy(self):
"""Make a copy of this client.
Copies the local data stored as simple types but does not copy the
current state of any open connections with the Cloud Bigtable API.
:rtype: :class:`.Client`
:returns: A copy of the current client.
"""
return self.... | python | def copy(self):
"""Make a copy of this client.
Copies the local data stored as simple types but does not copy the
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:rtype: :class:`.Client`
:returns: A copy of the current client.
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googleapis/google-cloud-python | spanner/google/cloud/spanner_v1/client.py | Client.list_instance_configs | def list_instance_configs(self, page_size=None, page_token=None):
"""List available instance configurations for the client's project.
.. _RPC docs: https://cloud.google.com/spanner/docs/reference/rpc/\
google.spanner.admin.instance.v1#google.spanner.admin.\
i... | python | def list_instance_configs(self, page_size=None, page_token=None):
"""List available instance configurations for the client's project.
.. _RPC docs: https://cloud.google.com/spanner/docs/reference/rpc/\
google.spanner.admin.instance.v1#google.spanner.admin.\
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googleapis/google-cloud-python | spanner/google/cloud/spanner_v1/client.py | Client.instance | def instance(
self,
instance_id,
configuration_name=None,
display_name=None,
node_count=DEFAULT_NODE_COUNT,
):
"""Factory to create a instance associated with this client.
:type instance_id: str
:param instance_id: The ID of the instance.
:ty... | python | def instance(
self,
instance_id,
configuration_name=None,
display_name=None,
node_count=DEFAULT_NODE_COUNT,
):
"""Factory to create a instance associated with this client.
:type instance_id: str
:param instance_id: The ID of the instance.
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googleapis/google-cloud-python | spanner/google/cloud/spanner_v1/client.py | Client.list_instances | def list_instances(self, filter_="", page_size=None, page_token=None):
"""List instances for the client's project.
See
https://cloud.google.com/spanner/reference/rpc/google.spanner.admin.database.v1#google.spanner.admin.database.v1.InstanceAdmin.ListInstances
:type filter_: string
... | python | def list_instances(self, filter_="", page_size=None, page_token=None):
"""List instances for the client's project.
See
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googleapis/google-cloud-python | bigquery/google/cloud/bigquery/retry.py | _should_retry | def _should_retry(exc):
"""Predicate for determining when to retry.
We retry if and only if the 'reason' is 'backendError'
or 'rateLimitExceeded'.
"""
if not hasattr(exc, "errors"):
return False
if len(exc.errors) == 0:
# Check for unstructured error returns, e.g. from GFE
... | python | def _should_retry(exc):
"""Predicate for determining when to retry.
We retry if and only if the 'reason' is 'backendError'
or 'rateLimitExceeded'.
"""
if not hasattr(exc, "errors"):
return False
if len(exc.errors) == 0:
# Check for unstructured error returns, e.g. from GFE
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googleapis/google-cloud-python | logging/noxfile.py | default | def default(session, django_dep=('django',)):
"""Default unit test session.
"""
# Install all test dependencies, then install this package in-place.
deps = UNIT_TEST_DEPS
deps += django_dep
session.install(*deps)
for local_dep in LOCAL_DEPS:
session.install('-e', local_dep)
ses... | python | def default(session, django_dep=('django',)):
"""Default unit test session.
"""
# Install all test dependencies, then install this package in-place.
deps = UNIT_TEST_DEPS
deps += django_dep
session.install(*deps)
for local_dep in LOCAL_DEPS:
session.install('-e', local_dep)
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googleapis/google-cloud-python | logging/noxfile.py | unit | def unit(session):
"""Run the unit test suite."""
# Testing multiple version of django
# See https://www.djangoproject.com/download/ for supported version
django_deps_27 = [
('django==1.8.19',),
('django >= 1.11.0, < 2.0.0dev',),
]
if session.virtualenv.interpreter == '2.7':
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"""Run the unit test suite."""
# Testing multiple version of django
# See https://www.djangoproject.com/download/ for supported version
django_deps_27 = [
('django==1.8.19',),
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googleapis/google-cloud-python | logging/noxfile.py | system | def system(session):
"""Run the system test suite."""
# Sanity check: Only run system tests if the environment variable is set.
if not os.environ.get('GOOGLE_APPLICATION_CREDENTIALS', ''):
session.skip('Credentials must be set via environment variable.')
# Use pre-release gRPC for system tests... | python | def system(session):
"""Run the system test suite."""
# Sanity check: Only run system tests if the environment variable is set.
if not os.environ.get('GOOGLE_APPLICATION_CREDENTIALS', ''):
session.skip('Credentials must be set via environment variable.')
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googleapis/google-cloud-python | logging/google/cloud/logging/_helpers.py | entry_from_resource | def entry_from_resource(resource, client, loggers):
"""Detect correct entry type from resource and instantiate.
:type resource: dict
:param resource: One entry resource from API response.
:type client: :class:`~google.cloud.logging.client.Client`
:param client: Client that owns the log entry.
... | python | def entry_from_resource(resource, client, loggers):
"""Detect correct entry type from resource and instantiate.
:type resource: dict
:param resource: One entry resource from API response.
:type client: :class:`~google.cloud.logging.client.Client`
:param client: Client that owns the log entry.
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googleapis/google-cloud-python | logging/google/cloud/logging/_helpers.py | retrieve_metadata_server | def retrieve_metadata_server(metadata_key):
"""Retrieve the metadata key in the metadata server.
See: https://cloud.google.com/compute/docs/storing-retrieving-metadata
:type metadata_key: str
:param metadata_key: Key of the metadata which will form the url. You can
also supply... | python | def retrieve_metadata_server(metadata_key):
"""Retrieve the metadata key in the metadata server.
See: https://cloud.google.com/compute/docs/storing-retrieving-metadata
:type metadata_key: str
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tensorflow/cleverhans | cleverhans/utils_tf.py | model_loss | def model_loss(y, model, mean=True):
"""
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:param y: correct labels
:param model: output of the model
:param mean: boolean indicating whether should return mean of loss
or vector of losses for each input of the batch
:return: return mean of loss if True, otherwise return... | python | def model_loss(y, model, mean=True):
"""
Define loss of TF graph
:param y: correct labels
:param model: output of the model
:param mean: boolean indicating whether should return mean of loss
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tensorflow/cleverhans | cleverhans/utils_tf.py | initialize_uninitialized_global_variables | def initialize_uninitialized_global_variables(sess):
"""
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:param sess: the TensorFlow session
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"""
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tensorflow/cleverhans | cleverhans/utils_tf.py | train | def train(sess, loss, x, y, X_train, Y_train, save=False,
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rng=None, var_list=None, fprop_args=None, optimizer=None):
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cleverhans.train.train sup... | python | def train(sess, loss, x, y, X_train, Y_train, save=False,
init_all=False, evaluate=None, feed=None, args=None,
rng=None, var_list=None, fprop_args=None, optimizer=None):
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tensorflow/cleverhans | cleverhans/utils_tf.py | model_eval | def model_eval(sess, x, y, predictions, X_test=None, Y_test=None,
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"""
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:param sess: TF session to use
:param x: input placeholder
:param y: output placeholder (for labels)
:param predictions: model output predictions
:par... | python | def model_eval(sess, x, y, predictions, X_test=None, Y_test=None,
feed=None, args=None):
"""
Compute the accuracy of a TF model on some data
:param sess: TF session to use
:param x: input placeholder
:param y: output placeholder (for labels)
:param predictions: model output predictions
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tensorflow/cleverhans | cleverhans/utils_tf.py | batch_eval | def batch_eval(*args, **kwargs):
"""
Wrapper around deprecated function.
"""
# Inside function to avoid circular import
from cleverhans.evaluation import batch_eval as new_batch_eval
warnings.warn("batch_eval has moved to cleverhans.evaluation. "
"batch_eval will be removed from utils_tf on ... | python | def batch_eval(*args, **kwargs):
"""
Wrapper around deprecated function.
"""
# Inside function to avoid circular import
from cleverhans.evaluation import batch_eval as new_batch_eval
warnings.warn("batch_eval has moved to cleverhans.evaluation. "
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tensorflow/cleverhans | cleverhans/utils_tf.py | model_argmax | def model_argmax(sess, x, predictions, samples, feed=None):
"""
Helper function that computes the current class prediction
:param sess: TF session
:param x: the input placeholder
:param predictions: the model's symbolic output
:param samples: numpy array with input samples (dims must match x)
:param feed:... | python | def model_argmax(sess, x, predictions, samples, feed=None):
"""
Helper function that computes the current class prediction
:param sess: TF session
:param x: the input placeholder
:param predictions: the model's symbolic output
:param samples: numpy array with input samples (dims must match x)
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tensorflow/cleverhans | cleverhans/utils_tf.py | l2_batch_normalize | def l2_batch_normalize(x, epsilon=1e-12, scope=None):
"""
Helper function to normalize a batch of vectors.
:param x: the input placeholder
:param epsilon: stabilizes division
:return: the batch of l2 normalized vector
"""
with tf.name_scope(scope, "l2_batch_normalize") as name_scope:
x_shape = tf.shap... | python | def l2_batch_normalize(x, epsilon=1e-12, scope=None):
"""
Helper function to normalize a batch of vectors.
:param x: the input placeholder
:param epsilon: stabilizes division
:return: the batch of l2 normalized vector
"""
with tf.name_scope(scope, "l2_batch_normalize") as name_scope:
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tensorflow/cleverhans | cleverhans/utils_tf.py | kl_with_logits | def kl_with_logits(p_logits, q_logits, scope=None,
loss_collection=tf.GraphKeys.REGULARIZATION_LOSSES):
"""Helper function to compute kl-divergence KL(p || q)
"""
with tf.name_scope(scope, "kl_divergence") as name:
p = tf.nn.softmax(p_logits)
p_log = tf.nn.log_softmax(p_logits)
q_lo... | python | def kl_with_logits(p_logits, q_logits, scope=None,
loss_collection=tf.GraphKeys.REGULARIZATION_LOSSES):
"""Helper function to compute kl-divergence KL(p || q)
"""
with tf.name_scope(scope, "kl_divergence") as name:
p = tf.nn.softmax(p_logits)
p_log = tf.nn.log_softmax(p_logits)
q_lo... | [
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tensorflow/cleverhans | cleverhans/utils_tf.py | clip_eta | def clip_eta(eta, ord, eps):
"""
Helper function to clip the perturbation to epsilon norm ball.
:param eta: A tensor with the current perturbation.
:param ord: Order of the norm (mimics Numpy).
Possible values: np.inf, 1 or 2.
:param eps: Epsilon, bound of the perturbation.
"""
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"""
Helper function to clip the perturbation to epsilon norm ball.
:param eta: A tensor with the current perturbation.
:param ord: Order of the norm (mimics Numpy).
Possible values: np.inf, 1 or 2.
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tensorflow/cleverhans | cleverhans/utils_tf.py | infer_devices | def infer_devices(devices=None):
"""
Returns the list of devices that multi-replica code should use.
:param devices: list of string device names, e.g. ["/GPU:0"]
If the user specifies this, `infer_devices` checks that it is
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If the user does not spec... | python | def infer_devices(devices=None):
"""
Returns the list of devices that multi-replica code should use.
:param devices: list of string device names, e.g. ["/GPU:0"]
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tensorflow/cleverhans | cleverhans/utils_tf.py | get_available_gpus | def get_available_gpus():
"""
Returns a list of string names of all available GPUs
"""
local_device_protos = device_lib.list_local_devices()
return [x.name for x in local_device_protos if x.device_type == 'GPU'] | python | def get_available_gpus():
"""
Returns a list of string names of all available GPUs
"""
local_device_protos = device_lib.list_local_devices()
return [x.name for x in local_device_protos if x.device_type == 'GPU'] | [
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tensorflow/cleverhans | cleverhans/utils_tf.py | clip_by_value | def clip_by_value(t, clip_value_min, clip_value_max, name=None):
"""
A wrapper for clip_by_value that casts the clipping range if needed.
"""
def cast_clip(clip):
"""
Cast clipping range argument if needed.
"""
if t.dtype in (tf.float32, tf.float64):
if hasattr(clip, 'dtype'):
# Co... | python | def clip_by_value(t, clip_value_min, clip_value_max, name=None):
"""
A wrapper for clip_by_value that casts the clipping range if needed.
"""
def cast_clip(clip):
"""
Cast clipping range argument if needed.
"""
if t.dtype in (tf.float32, tf.float64):
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tensorflow/cleverhans | cleverhans/utils_tf.py | mul | def mul(a, b):
"""
A wrapper around tf multiplication that does more automatic casting of
the input.
"""
def multiply(a, b):
"""Multiplication"""
return a * b
return op_with_scalar_cast(a, b, multiply) | python | def mul(a, b):
"""
A wrapper around tf multiplication that does more automatic casting of
the input.
"""
def multiply(a, b):
"""Multiplication"""
return a * b
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tensorflow/cleverhans | cleverhans/utils_tf.py | div | def div(a, b):
"""
A wrapper around tf division that does more automatic casting of
the input.
"""
def divide(a, b):
"""Division"""
return a / b
return op_with_scalar_cast(a, b, divide) | python | def div(a, b):
"""
A wrapper around tf division that does more automatic casting of
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"""
def divide(a, b):
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tensorflow/cleverhans | cleverhans/utils_tf.py | op_with_scalar_cast | def op_with_scalar_cast(a, b, f):
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:param b: a tf-compatible array or scalar
""... | python | def op_with_scalar_cast(a, b, f):
"""
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tensorflow/cleverhans | cleverhans/utils_tf.py | jacobian_graph | def jacobian_graph(predictions, x, nb_classes):
"""
Create the Jacobian graph to be ran later in a TF session
:param predictions: the model's symbolic output (linear output,
pre-softmax)
:param x: the input placeholder
:param nb_classes: the number of classes the model has
:return:
"""
# This fun... | python | def jacobian_graph(predictions, x, nb_classes):
"""
Create the Jacobian graph to be ran later in a TF session
:param predictions: the model's symbolic output (linear output,
pre-softmax)
:param x: the input placeholder
:param nb_classes: the number of classes the model has
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tensorflow/cleverhans | cleverhans/utils_tf.py | jacobian_augmentation | def jacobian_augmentation(sess,
x,
X_sub_prev,
Y_sub,
grads,
lmbda,
aug_batch_size=512,
feed=None):
"""
Augment an adversary's substit... | python | def jacobian_augmentation(sess,
x,
X_sub_prev,
Y_sub,
grads,
lmbda,
aug_batch_size=512,
feed=None):
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Augment an adversary's substit... | [
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tensorflow/cleverhans | cleverhans_tutorials/evaluate_pickled_model.py | evaluate_model | def evaluate_model(filepath,
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test_end=10000, batch_size=128,
testing=False, num_threads=None):
"""
Run evaluation on a saved model
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:param train_start: index of first ... | python | def evaluate_model(filepath,
train_start=0, train_end=60000, test_start=0,
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tensorflow/cleverhans | examples/multigpu_advtrain/runner.py | RunnerMultiGPU.set_input | def set_input(self, X_batch=None):
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"""
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Preprocessing the inputs before calling session.run()
:param X_batch: A dictionary of inputs to the first sub-graph
:return: A tuple, `(fetches, fd)`, with `fetches` being a list of
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/image_batches.py | ImageBatchesBase._write_single_batch_images_internal | def _write_single_batch_images_internal(self, batch_id, client_batch):
"""Helper method to write images from single batch into datastore."""
client = self._datastore_client
batch_key = client.key(self._entity_kind_batches, batch_id)
for img_id, img in iteritems(self._data[batch_id]['images']):
img... | python | def _write_single_batch_images_internal(self, batch_id, client_batch):
"""Helper method to write images from single batch into datastore."""
client = self._datastore_client
batch_key = client.key(self._entity_kind_batches, batch_id)
for img_id, img in iteritems(self._data[batch_id]['images']):
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/image_batches.py | ImageBatchesBase.write_to_datastore | def write_to_datastore(self):
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client = self._datastore_client
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"""Writes all image batches to the datastore."""
client = self._datastore_client
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/image_batches.py | ImageBatchesBase.write_single_batch_images_to_datastore | def write_single_batch_images_to_datastore(self, batch_id):
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client = self._datastore_client
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"""Writes only images from one batch to the datastore."""
client = self._datastore_client
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/image_batches.py | ImageBatchesBase.init_from_datastore | def init_from_datastore(self):
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self._data = {}
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batch_id = entity.key.flat_path[-1]
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"""Initializes batches by reading from the datastore."""
self._data = {}
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/image_batches.py | ImageBatchesBase.add_batch | def add_batch(self, batch_id, batch_properties=None):
"""Adds batch with give ID and list of properties."""
if batch_properties is None:
batch_properties = {}
if not isinstance(batch_properties, dict):
raise ValueError('batch_properties has to be dict, however it was: '
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"""Adds batch with give ID and list of properties."""
if batch_properties is None:
batch_properties = {}
if not isinstance(batch_properties, dict):
raise ValueError('batch_properties has to be dict, however it was: '
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/image_batches.py | ImageBatchesBase.add_image | def add_image(self, batch_id, image_id, image_properties=None):
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if batch_id not in self._data:
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... | python | def add_image(self, batch_id, image_id, image_properties=None):
"""Adds image to given batch."""
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/image_batches.py | DatasetBatches._read_image_list | def _read_image_list(self, skip_image_ids=None):
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if skip_image_ids is None:
skip_image_ids = []
images = self._storage_client.list_blobs(
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"""Reads list of dataset images from the datastore."""
if skip_image_ids is None:
skip_image_ids = []
images = self._storage_client.list_blobs(
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/image_batches.py | DatasetBatches.init_from_storage_write_to_datastore | def init_from_storage_write_to_datastore(self,
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/image_batches.py | AversarialBatches.init_from_dataset_and_submissions_write_to_datastore | def init_from_dataset_and_submissions_write_to_datastore(
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"""Init list of adversarial batches from dataset batches and submissions.
Args:
dataset_batches: instances of DatasetBatches
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/image_batches.py | AversarialBatches.count_generated_adv_examples | def count_generated_adv_examples(self):
"""Returns total number of all generated adversarial examples."""
result = {}
for v in itervalues(self.data):
s_id = v['submission_id']
result[s_id] = result.get(s_id, 0) + len(v['images'])
return result | python | def count_generated_adv_examples(self):
"""Returns total number of all generated adversarial examples."""
result = {}
for v in itervalues(self.data):
s_id = v['submission_id']
result[s_id] = result.get(s_id, 0) + len(v['images'])
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tensorflow/cleverhans | cleverhans/confidence_report.py | make_confidence_report_bundled | def make_confidence_report_bundled(filepath, train_start=TRAIN_START,
train_end=TRAIN_END, test_start=TEST_START,
test_end=TEST_END, which_set=WHICH_SET,
recipe=RECIPE, report_path=REPORT_PATH,
... | python | def make_confidence_report_bundled(filepath, train_start=TRAIN_START,
train_end=TRAIN_END, test_start=TEST_START,
test_end=TEST_END, which_set=WHICH_SET,
recipe=RECIPE, report_path=REPORT_PATH,
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tensorflow/cleverhans | cleverhans/confidence_report.py | print_stats | def print_stats(correctness, confidence, name):
"""
Prints out accuracy, coverage, etc. statistics
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One bool per example specifying whether it was correctly classified
:param confidence: ndarray
The probability associated with each prediction
:param name: str
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"""
Prints out accuracy, coverage, etc. statistics
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One bool per example specifying whether it was correctly classified
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tensorflow/cleverhans | cleverhans/confidence_report.py | make_confidence_report | def make_confidence_report(filepath, train_start=TRAIN_START,
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batch_size=BATCH_SIZE, which_set=WHICH_SET,
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tensorflow/cleverhans | cleverhans_tutorials/mnist_tutorial_keras_tf.py | mnist_tutorial | def mnist_tutorial(train_start=0, train_end=60000, test_start=0,
test_end=10000, nb_epochs=NB_EPOCHS, batch_size=BATCH_SIZE,
learning_rate=LEARNING_RATE, train_dir=TRAIN_DIR,
filename=FILENAME, load_model=LOAD_MODEL,
testing=False, label_smooth... | python | def mnist_tutorial(train_start=0, train_end=60000, test_start=0,
test_end=10000, nb_epochs=NB_EPOCHS, batch_size=BATCH_SIZE,
learning_rate=LEARNING_RATE, train_dir=TRAIN_DIR,
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tensorflow/cleverhans | cleverhans/attacks/max_confidence.py | MaxConfidence.generate | def generate(self, x, **kwargs):
"""
Generate symbolic graph for adversarial examples and return.
:param x: The model's symbolic inputs.
:param kwargs: Keyword arguments for the base attacker
"""
assert self.parse_params(**kwargs)
labels, _nb_classes = self.get_or_guess_labels(x, kwargs)
... | python | def generate(self, x, **kwargs):
"""
Generate symbolic graph for adversarial examples and return.
:param x: The model's symbolic inputs.
:param kwargs: Keyword arguments for the base attacker
"""
assert self.parse_params(**kwargs)
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tensorflow/cleverhans | cleverhans/attacks/max_confidence.py | MaxConfidence.attack | def attack(self, x, true_y):
"""
Runs the untargeted attack.
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:param true_y: The correct label for `x`. This attack aims to produce misclassification.
"""
adv_x_cls = []
prob_cls = []
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true_y_idx = tf.argmax(true_y, axis=1)
expanded_x = tf.co... | python | def attack(self, x, true_y):
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Runs the untargeted attack.
:param x: The input
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"""
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tensorflow/cleverhans | cleverhans/attacks/max_confidence.py | MaxConfidence.attack_class | def attack_class(self, x, target_y):
"""
Run the attack on a specific target class.
:param x: tf Tensor. The input example.
:param target_y: tf Tensor. The attacker's desired target class.
Returns:
A targeted adversarial example, intended to be classified as the target class.
"""
adv =... | python | def attack_class(self, x, target_y):
"""
Run the attack on a specific target class.
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tensorflow/cleverhans | cleverhans/utils.py | batch_indices | def batch_indices(batch_nb, data_length, batch_size):
"""
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:param data_length: the total length of the data being parsed by batches
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"""
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tensorflow/cleverhans | cleverhans/utils.py | other_classes | def other_classes(nb_classes, class_ind):
"""
Returns a list of class indices excluding the class indexed by class_ind
:param nb_classes: number of classes in the task
:param class_ind: the class index to be omitted
:return: list of class indices excluding the class indexed by class_ind
"""
if class_ind <... | python | def other_classes(nb_classes, class_ind):
"""
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tensorflow/cleverhans | cleverhans/utils.py | to_categorical | def to_categorical(y, nb_classes, num_classes=None):
"""
Converts a class vector (integers) to binary class matrix.
This is adapted from the Keras function with the same name.
:param y: class vector to be converted into a matrix
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tensorflow/cleverhans | cleverhans/utils.py | random_targets | def random_targets(gt, nb_classes):
"""
Take in an array of correct labels and randomly select a different label
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target class in targeted adversarial examples attacks (i.e., when the
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"""
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tensorflow/cleverhans | cleverhans/utils.py | pair_visual | def pair_visual(*args, **kwargs):
"""Deprecation wrapper"""
warnings.warn("`pair_visual` has moved to `cleverhans.plot.pyplot_image`. "
"cleverhans.utils.pair_visual may be removed on or after "
"2019-04-24.")
from cleverhans.plot.pyplot_image import pair_visual as new_pair_visual
... | python | def pair_visual(*args, **kwargs):
"""Deprecation wrapper"""
warnings.warn("`pair_visual` has moved to `cleverhans.plot.pyplot_image`. "
"cleverhans.utils.pair_visual may be removed on or after "
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tensorflow/cleverhans | cleverhans/utils.py | grid_visual | def grid_visual(*args, **kwargs):
"""Deprecation wrapper"""
warnings.warn("`grid_visual` has moved to `cleverhans.plot.pyplot_image`. "
"cleverhans.utils.grid_visual may be removed on or after "
"2019-04-24.")
from cleverhans.plot.pyplot_image import grid_visual as new_grid_visual
... | python | def grid_visual(*args, **kwargs):
"""Deprecation wrapper"""
warnings.warn("`grid_visual` has moved to `cleverhans.plot.pyplot_image`. "
"cleverhans.utils.grid_visual may be removed on or after "
"2019-04-24.")
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tensorflow/cleverhans | cleverhans/utils.py | get_logits_over_interval | def get_logits_over_interval(*args, **kwargs):
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# pylint:disab... | python | def get_logits_over_interval(*args, **kwargs):
"""Deprecation wrapper"""
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tensorflow/cleverhans | cleverhans/utils.py | linear_extrapolation_plot | def linear_extrapolation_plot(*args, **kwargs):
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tensorflow/cleverhans | cleverhans/utils.py | create_logger | def create_logger(name):
"""
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base = logging.getLogger("cleverhans")
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"""
Create a logger object with the given name.
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"""
base = logging.getLogger("cleverhans")
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tensorflow/cleverhans | cleverhans/utils.py | deterministic_dict | def deterministic_dict(normal_dict):
"""
Returns a version of `normal_dict` whose iteration order is always the same
"""
out = OrderedDict()
for key in sorted(normal_dict.keys()):
out[key] = normal_dict[key]
return out | python | def deterministic_dict(normal_dict):
"""
Returns a version of `normal_dict` whose iteration order is always the same
"""
out = OrderedDict()
for key in sorted(normal_dict.keys()):
out[key] = normal_dict[key]
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tensorflow/cleverhans | cleverhans/utils.py | ordered_union | def ordered_union(l1, l2):
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:param l1: list of items
:param l2: list of items
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tensorflow/cleverhans | cleverhans/utils.py | safe_zip | def safe_zip(*args):
"""like zip but with these properties:
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- a guarantee that all arguments are the same length.
(normal zip silently drops entries to make them the same length)
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length = len(args[0])
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tensorflow/cleverhans | cleverhans/utils.py | shell_call | def shell_call(command, **kwargs):
"""Calls shell command with argument substitution.
Args:
command: command represented as a list. Each element of the list is one
token of the command. For example "cp a b" becomes ['cp', 'a', 'b']
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command: command represented as a list. Each element of the list is one
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tensorflow/cleverhans | cleverhans/utils.py | deep_copy | def deep_copy(numpy_dict):
"""
Returns a copy of a dictionary whose values are numpy arrays.
Copies their values rather than copying references to them.
"""
out = {}
for key in numpy_dict:
out[key] = numpy_dict[key].copy()
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"""
Returns a copy of a dictionary whose values are numpy arrays.
Copies their values rather than copying references to them.
"""
out = {}
for key in numpy_dict:
out[key] = numpy_dict[key].copy()
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tensorflow/cleverhans | cleverhans/dataset.py | data_mnist | def data_mnist(datadir=tempfile.gettempdir(), train_start=0,
train_end=60000, test_start=0, test_end=10000):
"""
Load and preprocess MNIST dataset
:param datadir: path to folder where data should be stored
:param train_start: index of first training set example
:param train_end: index of last t... | python | def data_mnist(datadir=tempfile.gettempdir(), train_start=0,
train_end=60000, test_start=0, test_end=10000):
"""
Load and preprocess MNIST dataset
:param datadir: path to folder where data should be stored
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tensorflow/cleverhans | cleverhans/dataset.py | data_cifar10 | def data_cifar10(train_start=0, train_end=50000, test_start=0, test_end=10000):
"""
Preprocess CIFAR10 dataset
:return:
"""
# These values are specific to CIFAR10
img_rows = 32
img_cols = 32
nb_classes = 10
# the data, shuffled and split between train and test sets
(x_train, y_train), (x_test, y_... | python | def data_cifar10(train_start=0, train_end=50000, test_start=0, test_end=10000):
"""
Preprocess CIFAR10 dataset
:return:
"""
# These values are specific to CIFAR10
img_rows = 32
img_cols = 32
nb_classes = 10
# the data, shuffled and split between train and test sets
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tensorflow/cleverhans | scripts/compute_accuracy.py | print_accuracies | def print_accuracies(filepath, train_start=TRAIN_START, train_end=TRAIN_END,
test_start=TEST_START, test_end=TEST_END,
batch_size=BATCH_SIZE, which_set=WHICH_SET,
base_eps_iter=BASE_EPS_ITER,
nb_iter=NB_ITER):
"""
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test_start=TEST_START, test_end=TEST_END,
batch_size=BATCH_SIZE, which_set=WHICH_SET,
base_eps_iter=BASE_EPS_ITER,
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tensorflow/cleverhans | scripts/compute_accuracy.py | impl | def impl(sess, model, dataset, factory, x_data, y_data,
base_eps_iter=BASE_EPS_ITER, nb_iter=NB_ITER,
batch_size=BATCH_SIZE):
"""
The actual implementation of the evaluation.
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:param model: cleverhans.model.Model
:param dataset: cleverhans.dataset.Dataset
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base_eps_iter=BASE_EPS_ITER, nb_iter=NB_ITER,
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"""
The actual implementation of the evaluation.
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tensorflow/cleverhans | scripts/compute_accuracy.py | main | def main(argv=None):
"""
Print accuracies
"""
try:
_name_of_script, filepath = argv
except ValueError:
raise ValueError(argv)
print_accuracies(filepath=filepath, test_start=FLAGS.test_start,
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"""
Print accuracies
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tensorflow/cleverhans | cleverhans/future/torch/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):
"""
PyTorch implementation of the Fast Gradient Method.
:param model_fn: a callable that takes an input tensor and returns the model logits.
:param x: input tensor... | python | def fast_gradient_method(model_fn, x, eps, ord,
clip_min=None, clip_max=None, y=None, targeted=False, sanity_checks=False):
"""
PyTorch implementation of the Fast Gradient Method.
:param model_fn: a callable that takes an input tensor and returns the model logits.
:param x: input tensor... | [
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tensorflow/cleverhans | examples/nips17_adversarial_competition/dev_toolkit/sample_attacks/fgsm/attack_fgsm.py | load_images | def load_images(input_dir, batch_shape):
"""Read png images from input directory in batches.
Args:
input_dir: input directory
batch_shape: shape of minibatch array, i.e. [batch_size, height, width, 3]
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filenames: list file names without path of each image
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Args:
input_dir: input directory
batch_shape: shape of minibatch array, i.e. [batch_size, height, width, 3]
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tensorflow/cleverhans | examples/nips17_adversarial_competition/dev_toolkit/sample_attacks/fgsm/attack_fgsm.py | save_images | def save_images(images, filenames, output_dir):
"""Saves images to the output directory.
Args:
images: array with minibatch of images
filenames: list of filenames without path
If number of file names in this list less than number of images in
the minibatch then only first len(filenames) images ... | python | def save_images(images, filenames, output_dir):
"""Saves images to the output directory.
Args:
images: array with minibatch of images
filenames: list of filenames without path
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tensorflow/cleverhans | examples/nips17_adversarial_competition/dev_toolkit/sample_attacks/fgsm/attack_fgsm.py | main | def main(_):
"""Run the sample attack"""
# Images for inception classifier are normalized to be in [-1, 1] interval,
# eps is a difference between pixels so it should be in [0, 2] interval.
# Renormalizing epsilon from [0, 255] to [0, 2].
eps = 2.0 * FLAGS.max_epsilon / 255.0
batch_shape = [FLAGS.batch_size... | python | def main(_):
"""Run the sample attack"""
# Images for inception classifier are normalized to be in [-1, 1] interval,
# eps is a difference between pixels so it should be in [0, 2] interval.
# Renormalizing epsilon from [0, 255] to [0, 2].
eps = 2.0 * FLAGS.max_epsilon / 255.0
batch_shape = [FLAGS.batch_size... | [
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tensorflow/cleverhans | tutorials/future/torch/cifar10_tutorial.py | ld_cifar10 | def ld_cifar10():
"""Load training and test data."""
train_transforms = torchvision.transforms.Compose([torchvision.transforms.ToTensor()])
test_transforms = torchvision.transforms.Compose([torchvision.transforms.ToTensor()])
train_dataset = torchvision.datasets.CIFAR10(root='/tmp/data', train=True, transform=t... | python | def ld_cifar10():
"""Load training and test data."""
train_transforms = torchvision.transforms.Compose([torchvision.transforms.ToTensor()])
test_transforms = torchvision.transforms.Compose([torchvision.transforms.ToTensor()])
train_dataset = torchvision.datasets.CIFAR10(root='/tmp/data', train=True, transform=t... | [
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tensorflow/cleverhans | cleverhans/plot/success_fail.py | plot_report_from_path | def plot_report_from_path(path, success_name=DEFAULT_SUCCESS_NAME,
fail_names=DEFAULT_FAIL_NAMES, label=None,
is_max_confidence=True,
linewidth=LINEWIDTH,
plot_upper_bound=True):
"""
Plots a success-fail curve fr... | python | def plot_report_from_path(path, success_name=DEFAULT_SUCCESS_NAME,
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is_max_confidence=True,
linewidth=LINEWIDTH,
plot_upper_bound=True):
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tensorflow/cleverhans | cleverhans/plot/success_fail.py | plot_report | def plot_report(report, success_name, fail_names, label=None,
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Plot a success fail curve from a confidence report
:param report: A confidence report
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tensorflow/cleverhans | cleverhans/plot/success_fail.py | make_curve | def make_curve(report, success_name, fail_names):
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Make a success-failure curve.
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tensorflow/cleverhans | examples/multigpu_advtrain/trainer.py | TrainManager.model_train | def model_train(self):
"""
Train a TF graph
:param sess: TF session to use when training the graph
:param x: input placeholder
:param y: output placeholder (for labels)
:param predictions: model output predictions
:param X_train: numpy array with training inputs
:param Y_train: numpy arr... | python | def model_train(self):
"""
Train a TF graph
:param sess: TF session to use when training the graph
:param x: input placeholder
:param y: output placeholder (for labels)
:param predictions: model output predictions
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tensorflow/cleverhans | examples/multigpu_advtrain/trainer.py | TrainerMultiGPU.clone_g0_inputs_on_ngpus | def clone_g0_inputs_on_ngpus(self, inputs, outputs, g0_inputs):
"""
Clone variables unused by the attack on all GPUs. Specifically, the
ground-truth label, y, has to be preserved until the training step.
:param inputs: A list of dictionaries as the inputs to each step.
:param outputs: A list of dic... | python | def clone_g0_inputs_on_ngpus(self, inputs, outputs, g0_inputs):
"""
Clone variables unused by the attack on all GPUs. Specifically, the
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tensorflow/cleverhans | cleverhans/attacks/lbfgs.py | LBFGS.generate | def generate(self, x, **kwargs):
"""
Return a tensor that constructs adversarial examples for the given
input. Generate uses tf.py_func in order to operate over tensors.
:param x: (required) A tensor with the inputs.
:param kwargs: See `parse_params`
"""
assert self.sess is not None, \
... | python | def generate(self, x, **kwargs):
"""
Return a tensor that constructs adversarial examples for the given
input. Generate uses tf.py_func in order to operate over tensors.
:param x: (required) A tensor with the inputs.
:param kwargs: See `parse_params`
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tensorflow/cleverhans | cleverhans/attacks/lbfgs.py | LBFGS.parse_params | def parse_params(self,
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tensorflow/cleverhans | cleverhans/attacks/lbfgs.py | LBFGS_impl.attack | def attack(self, x_val, targets):
"""
Perform the attack on the given instance for the given targets.
"""
def lbfgs_objective(adv_x, self, targets, oimgs, CONST):
""" returns the function value and the gradient for fmin_l_bfgs_b """
loss = self.sess.run(
self.loss,
feed_... | python | def attack(self, x_val, targets):
"""
Perform the attack on the given instance for the given targets.
"""
def lbfgs_objective(adv_x, self, targets, oimgs, CONST):
""" returns the function value and the gradient for fmin_l_bfgs_b """
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tensorflow/cleverhans | examples/multigpu_advtrain/model.py | MLPnGPU.set_device | def set_device(self, device_name):
"""
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"""
device_name = unify_device_name(device_name)
self.device_name = device_name
for layer in self.layers:
layer.device_name = device_name | python | def set_device(self, device_name):
"""
Set the device before the next fprop to create a new graph on the
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"""
device_name = unify_device_name(device_name)
self.device_name = device_name
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:param host_device: (required str) the name of the device with latest
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"""
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Return a list of assignment operations that syncs the parameters
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"""
Create and initialize a variable using a numpy array and set trainable.
:param name: (required str) name of the variable
:param initializer: a numpy array or a tensor
"""
v = tf.get_variable(name, shape=initializer.shape,
ini... | python | def get_variable(self, name, initializer):
"""
Create and initialize a variable using a numpy array and set trainable.
:param name: (required str) name of the variable
:param initializer: a numpy array or a tensor
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"""
Create and initialize layer parameters on the device previously set
in self.device_name.
:param new_input_shape: a list or tuple for the shape of the input.
"""
assert self.device_name, "Device name has not been set."
device_name = self.... | python | def set_input_shape_ngpu(self, new_input_shape):
"""
Create and initialize layer parameters on the device previously set
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:param new_input_shape: a list or tuple for the shape of the input.
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assert self.device_name, "Device name has not been set."
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tensorflow/cleverhans | examples/multigpu_advtrain/model.py | LayernGPU.create_sync_ops | def create_sync_ops(self, host_device):
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"""
sync_ops = []
host_params = self.params_device[host_device]
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"""Create an assignment operation for each weight on all devices. The
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"""
sync_ops = []
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tensorflow/cleverhans | cleverhans/attacks/virtual_adversarial_method.py | vatm | def vatm(model,
x,
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xi=1e-6,
clip_min=None,
clip_max=None,
scope=None):
"""
Tensorflow implementation of the perturbation method used for virtual
adversarial training: https://arxiv.org/abs/1507.00677
:param mo... | python | def vatm(model,
x,
logits,
eps,
num_iterations=1,
xi=1e-6,
clip_min=None,
clip_max=None,
scope=None):
"""
Tensorflow implementation of the perturbation method used for virtual
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tensorflow/cleverhans | cleverhans/attacks/virtual_adversarial_method.py | VirtualAdversarialMethod.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)
return vatm(
self.model,... | 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/virtual_adversarial_method.py | VirtualAdversarialMethod.parse_params | def parse_params(self,
eps=2.0,
nb_iter=None,
xi=1e-6,
clip_min=None,
clip_max=None,
num_iterations=None,
**kwargs):
"""
Take in a dictionary of parameters and applies attack-spec... | python | def parse_params(self,
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xi=1e-6,
clip_min=None,
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**kwargs):
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/cloud_client.py | iterate_with_exp_backoff | def iterate_with_exp_backoff(base_iter,
max_num_tries=6,
max_backoff=300.0,
start_backoff=4.0,
backoff_multiplier=2.0,
frac_random_backoff=0.25):
"""Iterate with exponential... | python | def iterate_with_exp_backoff(base_iter,
max_num_tries=6,
max_backoff=300.0,
start_backoff=4.0,
backoff_multiplier=2.0,
frac_random_backoff=0.25):
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/cloud_client.py | CompetitionStorageClient.list_blobs | def list_blobs(self, prefix=''):
"""Lists names of all blobs by their prefix."""
return [b.name for b in self.bucket.list_blobs(prefix=prefix)] | python | def list_blobs(self, prefix=''):
"""Lists names of all blobs by their prefix."""
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/cloud_client.py | NoTransactionBatch.begin | def begin(self):
"""Begins a batch."""
if self._cur_batch:
raise ValueError('Previous batch is not committed.')
self._cur_batch = self._client.batch()
self._cur_batch.begin()
self._num_mutations = 0 | python | def begin(self):
"""Begins a batch."""
if self._cur_batch:
raise ValueError('Previous batch is not committed.')
self._cur_batch = self._client.batch()
self._cur_batch.begin()
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/cloud_client.py | NoTransactionBatch.rollback | def rollback(self):
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/cloud_client.py | NoTransactionBatch.put | def put(self, entity):
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Args:
entity: entity which should be put into the datastore
"""
self._cur_batch.put(entity)
... | python | def put(self, entity):
"""Adds mutation of the entity to the mutation buffer.
If mutation buffer reaches its capacity then this method commit all pending
mutations from the buffer and emties it.
Args:
entity: entity which should be put into the datastore
"""
self._cur_batch.put(entity)
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/cloud_client.py | NoTransactionBatch.delete | def delete(self, key):
"""Adds deletion of the entity with given key to the mutation buffer.
If mutation buffer reaches its capacity then this method commit all pending
mutations from the buffer and emties it.
Args:
key: key of the entity which should be deleted
"""
self._cur_batch.delet... | python | def delete(self, key):
"""Adds deletion of the entity with given key to the mutation buffer.
If mutation buffer reaches its capacity then this method commit all pending
mutations from the buffer and emties it.
Args:
key: key of the entity which should be deleted
"""
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/eval_lib/cloud_client.py | CompetitionDatastoreClient.get | def get(self, key, transaction=None):
"""Retrieves an entity given its key."""
return self._client.get(key, transaction=transaction) | python | def get(self, key, transaction=None):
"""Retrieves an entity given its key."""
return self._client.get(key, transaction=transaction) | [
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tensorflow/cleverhans | cleverhans_tutorials/mnist_tutorial_cw.py | mnist_tutorial_cw | def mnist_tutorial_cw(train_start=0, train_end=60000, test_start=0,
test_end=10000, viz_enabled=VIZ_ENABLED,
nb_epochs=NB_EPOCHS, batch_size=BATCH_SIZE,
source_samples=SOURCE_SAMPLES,
learning_rate=LEARNING_RATE,
... | python | def mnist_tutorial_cw(train_start=0, train_end=60000, test_start=0,
test_end=10000, viz_enabled=VIZ_ENABLED,
nb_epochs=NB_EPOCHS, batch_size=BATCH_SIZE,
source_samples=SOURCE_SAMPLES,
learning_rate=LEARNING_RATE,
... | [
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:param train_start: index of first training set example
:param train_end: index of last training set example
:param test_start: index of first test set example
:param test_end: index of last test set example
:param viz_enabled: (boolean) activate plots of adversa... | [
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tensorflow/cleverhans | cleverhans_tutorials/mnist_tutorial_tfe.py | attack_selection | def attack_selection(attack_string):
"""
Selects the Attack Class using string input.
:param attack_string: adversarial attack name in string format
:return: attack class defined in cleverhans.attacks_eager
"""
# List of Implemented attacks
attacks_list = AVAILABLE_ATTACKS.keys()
# Checking for reque... | python | def attack_selection(attack_string):
"""
Selects the Attack Class using string input.
:param attack_string: adversarial attack name in string format
:return: attack class defined in cleverhans.attacks_eager
"""
# List of Implemented attacks
attacks_list = AVAILABLE_ATTACKS.keys()
# Checking for reque... | [
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] | 97488e215760547b81afc53f5e5de8ba7da5bd98 | https://github.com/tensorflow/cleverhans/blob/97488e215760547b81afc53f5e5de8ba7da5bd98/cleverhans_tutorials/mnist_tutorial_tfe.py#L54-L73 | train |
tensorflow/cleverhans | cleverhans_tutorials/mnist_tutorial_tfe.py | mnist_tutorial | def mnist_tutorial(train_start=0, train_end=60000, test_start=0,
test_end=10000, nb_epochs=NB_EPOCHS, batch_size=BATCH_SIZE,
learning_rate=LEARNING_RATE,
clean_train=True,
testing=False,
backprop_through_attack=False,
... | python | def mnist_tutorial(train_start=0, train_end=60000, test_start=0,
test_end=10000, nb_epochs=NB_EPOCHS, batch_size=BATCH_SIZE,
learning_rate=LEARNING_RATE,
clean_train=True,
testing=False,
backprop_through_attack=False,
... | [
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:param train_start: index of first training set example.
:param train_end: index of last training set example.
:param test_start: index of first test set example.
:param test_end: index of last test set example.
:param nb_epochs: number of epochs to train model.
:param batch_size: ... | [
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/worker.py | sudo_remove_dirtree | def sudo_remove_dirtree(dir_name):
"""Removes directory tree as a superuser.
Args:
dir_name: name of the directory to remove.
This function is necessary to cleanup directories created from inside a
Docker, since they usually written as a root, thus have to be removed as a
root.
"""
try:
subproce... | python | def sudo_remove_dirtree(dir_name):
"""Removes directory tree as a superuser.
Args:
dir_name: name of the directory to remove.
This function is necessary to cleanup directories created from inside a
Docker, since they usually written as a root, thus have to be removed as a
root.
"""
try:
subproce... | [
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tensorflow/cleverhans | examples/nips17_adversarial_competition/eval_infra/code/worker.py | main | def main(args):
"""Main function which runs worker."""
title = '## Starting evaluation of round {0} ##'.format(args.round_name)
logging.info('\n'
+ '#' * len(title) + '\n'
+ '#' * len(title) + '\n'
+ '##' + ' ' * (len(title)-2) + '##' + '\n'
+ title + '\... | python | def main(args):
"""Main function which runs worker."""
title = '## Starting evaluation of round {0} ##'.format(args.round_name)
logging.info('\n'
+ '#' * len(title) + '\n'
+ '#' * len(title) + '\n'
+ '##' + ' ' * (len(title)-2) + '##' + '\n'
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