SYMBOL INDEX (107 symbols across 15 files)

FILE: cifar_blackbox.py
  function data_cifar10 (line 33) | def data_cifar10():
  function setup_tutorial (line 67) | def setup_tutorial():
  function prep_bbox (line 89) | def prep_bbox(sess, x, y, X_train, Y_train, X_test, Y_test,
  function train_sub (line 137) | def train_sub(sess, x, y, bbox_preds, X_sub, Y_sub, nb_classes,
  function cifar_blackbox (line 196) | def cifar_blackbox(train_start=0, train_end=60000, test_start=0,
  function main (line 299) | def main(argv=None):

FILE: l0_attack.py
  class CarliniL0 (line 22) | class CarliniL0:
    method __init__ (line 23) | def __init__(self, sess, model,
    method gradient_descent (line 67) | def gradient_descent(self, sess, model):
    method attack (line 173) | def attack(self, imgs, targets):
    method attack_single (line 186) | def attack_single(self, img, target):

FILE: l2_attack.py
  class CarliniL2 (line 22) | class CarliniL2:
    method __init__ (line 23) | def __init__(self, sess, model, batch_size=1, confidence = CONFIDENCE,
    method attack (line 140) | def attack(self, imgs, targets):
    method attack_batch (line 154) | def attack_batch(self, imgs, labs):

FILE: l2_attack_black.py
  function coordinate_ADAM (line 28) | def coordinate_ADAM(losses, indice, grad, hess, batch_size, mt_arr, vt_a...
  function coordinate_Newton (line 60) | def coordinate_Newton(losses, indice, grad, hess, batch_size, mt_arr, vt...
  function coordinate_Newton_ADAM (line 88) | def coordinate_Newton_ADAM(losses, indice, grad, hess, batch_size, mt_ar...
  class BlackBoxL2 (line 135) | class BlackBoxL2:
    method __init__ (line 136) | def __init__(self, sess, model, batch_size=1, confidence = CONFIDENCE,
    method max_pooling (line 350) | def max_pooling(self, image, size):
    method get_new_prob (line 359) | def get_new_prob(self, prev_modifier, gen_double = False):
    method resize_img (line 378) | def resize_img(self, small_x, small_y, reset_only = False):
    method fake_blackbox_optimizer (line 403) | def fake_blackbox_optimizer(self):
    method blackbox_optimizer (line 427) | def blackbox_optimizer(self, iteration):
    method attack (line 478) | def attack(self, imgs, targets):
    method attack_batch (line 494) | def attack_batch(self, img, lab):

FILE: li_attack.py
  class CarliniLi (line 23) | class CarliniLi:
    method __init__ (line 24) | def __init__(self, sess, model,
    method gradient_descent (line 68) | def gradient_descent(self, sess, model):
    method attack (line 150) | def attack(self, imgs, targets):
    method attack_single (line 162) | def attack_single(self, img, target):

FILE: mnist_blackbox.py
  function setup_tutorial (line 36) | def setup_tutorial():
  function prep_bbox (line 58) | def prep_bbox(sess, x, y, X_train, Y_train, X_test, Y_test,
  function substitute_model (line 103) | def substitute_model(img_rows=28, img_cols=28, nb_classes=10):
  function train_sub (line 136) | def train_sub(sess, x, y, bbox_preds, X_sub, Y_sub, nb_classes,
  function mnist_blackbox (line 204) | def mnist_blackbox(train_start=0, train_end=60000, test_start=0,
  function main (line 402) | def main(argv=None):

FILE: retrain.py
  function create_image_lists (line 124) | def create_image_lists(image_dir, testing_percentage, validation_percent...
  function get_image_path (line 208) | def get_image_path(image_lists, label_name, index, image_dir, category):
  function get_bottleneck_path (line 241) | def get_bottleneck_path(image_lists, label_name, index, bottleneck_dir,
  function create_model_graph (line 262) | def create_model_graph(model_info):
  function run_bottleneck_on_image (line 287) | def run_bottleneck_on_image(sess, image_data, image_data_tensor,
  function maybe_download_and_extract (line 313) | def maybe_download_and_extract(data_url):
  function ensure_dir_exists (line 343) | def ensure_dir_exists(dir_name):
  function create_bottleneck_file (line 356) | def create_bottleneck_file(bottleneck_path, image_lists, label_name, index,
  function get_or_create_bottleneck (line 379) | def get_or_create_bottleneck(sess, image_lists, label_name, index, image...
  function cache_bottlenecks (line 440) | def cache_bottlenecks(sess, image_lists, image_dir, bottleneck_dir,
  function get_random_cached_bottlenecks (line 484) | def get_random_cached_bottlenecks(sess, image_lists, how_many, category,
  function get_random_distorted_bottlenecks (line 554) | def get_random_distorted_bottlenecks(
  function should_distort_images (line 608) | def should_distort_images(flip_left_right, random_crop, random_scale,
  function add_input_distortions (line 626) | def add_input_distortions(flip_left_right, random_crop, random_scale,
  function variable_summaries (line 722) | def variable_summaries(var):
  function add_final_training_ops (line 735) | def add_final_training_ops(class_count, final_tensor_name, bottleneck_te...
  function add_evaluation_step (line 803) | def add_evaluation_step(result_tensor, ground_truth_tensor):
  function save_graph_to_file (line 825) | def save_graph_to_file(sess, graph, graph_file_name):
  function prepare_file_system (line 833) | def prepare_file_system():
  function create_model_info (line 843) | def create_model_info(architecture):
  function add_jpeg_decoding (line 939) | def add_jpeg_decoding(input_width, input_height, input_depth, input_mean,
  function main (line 967) | def main(_):

FILE: setup_cifar.py
  function load_batch (line 24) | def load_batch(fpath, label_key='labels'):
  function load_batch (line 47) | def load_batch(fpath):
  class CIFAR (line 62) | class CIFAR:
    method __init__ (line 63) | def __init__(self):
  class CIFARModel (line 90) | class CIFARModel:
    method __init__ (line 91) | def __init__(self, restore=None, session=None, use_log=False):
    method predict (line 124) | def predict(self, data):

FILE: setup_inception.py
  class NodeLookup (line 60) | class NodeLookup(object):
    method __init__ (line 63) | def __init__(self,
    method load (line 70) | def load(self, label_lookup_path):
    method id_to_string (line 95) | def id_to_string(self, node_id):
  function create_graph (line 101) | def create_graph():
  function run_inference_on_image (line 115) | def run_inference_on_image(image):
  class InceptionModelPrediction (line 163) | class InceptionModelPrediction:
    method __init__ (line 164) | def __init__(self, sess, use_log = False):
    method predict (line 176) | def predict(self, dat):
  class InceptionModel (line 197) | class InceptionModel:
    method __init__ (line 201) | def __init__(self, sess, use_log = False):
    method predict (line 210) | def predict(self, img):
  function maybe_download_and_extract (line 233) | def maybe_download_and_extract():
  function main (line 252) | def main(_):
  function readimg (line 275) | def readimg(ff):
  class ImageNet (line 286) | class ImageNet:
    method __init__ (line 287) | def __init__(self):

FILE: setup_mnist.py
  function extract_data (line 22) | def extract_data(filename, num_images):
  function extract_labels (line 31) | def extract_labels(filename, num_images):
  class MNIST (line 38) | class MNIST:
    method __init__ (line 39) | def __init__(self):
  class MNISTModel (line 63) | class MNISTModel:
    method __init__ (line 64) | def __init__(self, restore = None, session=None, use_log=False):
    method predict (line 98) | def predict(self, data):

FILE: substitute_blackbox.py
  function data_cifar10 (line 40) | def data_cifar10():
  function setup_tutorial (line 73) | def setup_tutorial():
  function prep_bbox (line 95) | def prep_bbox(sess, x, y, X_train, Y_train, X_test, Y_test,
  function substitute_model (line 143) | def substitute_model(img_rows=28, img_cols=28, nb_classes=10):
  function train_sub (line 176) | def train_sub(sess, x, y, bbox_preds, X_sub, Y_sub, nb_classes,
  function mnist_blackbox (line 247) | def mnist_blackbox(train_start=0, train_end=60000, test_start=0,
  function main (line 479) | def main(argv=None):

FILE: test_all.py
  function show (line 27) | def show(img, name = "output.png"):
  function generate_data (line 44) | def generate_data(data, samples, targeted=True, start=0, inception=False):
  function main (line 90) | def main(args):

FILE: test_attack.py
  function show (line 26) | def show(img, name = "output.png"):
  function generate_data (line 43) | def generate_data(data, samples, targeted=True, start=0, inception=False):

FILE: test_attack_black.py
  function show (line 25) | def show(img, name = "output.png"):
  function generate_data (line 43) | def generate_data(data, samples, targeted=True, start=0, inception=False):

FILE: train_models.py
  function train (line 21) | def train(data, file_name, params, num_epochs=50, batch_size=128, train_...
  function train_distillation (line 75) | def train_distillation(data, file_name, params, num_epochs=50, batch_siz...