SYMBOL INDEX (114 symbols across 13 files)

FILE: augmentation.py
  function random_zoom (line 16) | def random_zoom(x, y, zoom_range, row_index=1, col_index=2, channel_inde...
  function random_rotation (line 37) | def random_rotation(x, y, rg, row_index=1, col_index=2, channel_index=0,
  function random_shear (line 51) | def random_shear(x, y, intensity, row_index=1, col_index=2, channel_inde...
  class CustomNumpyArrayIterator (line 65) | class CustomNumpyArrayIterator(Iterator):
    method __init__ (line 67) | def __init__(self, X, y, image_data_generator,
    method next (line 77) | def next(self):
  class CustomImageDataGenerator (line 93) | class CustomImageDataGenerator(object):
    method __init__ (line 94) | def __init__(self, zoom_range=(1,1), channel_shift_range=0, horizontal...
    method random_transform (line 111) | def random_transform(self, x, y, row_index=1, col_index=2, channel_ind...
    method flow (line 183) | def flow(self, X, Y, batch_size, shuffle=True, seed=None):
  function elastic_transform (line 189) | def elastic_transform(image, mask, alpha, sigma, alpha_affine=None, rand...
  function test (line 215) | def test():

FILE: average_ensembles.py
  function main (line 16) | def main():

FILE: current.py
  function zoom (line 17) | def zoom(x, zoom_range, row_index=1, col_index=2, channel_index=0,
  function run_test (line 39) | def run_test():
  function main (line 91) | def main():

FILE: current_ensemble.py
  function zoom (line 17) | def zoom(x, zoom_range, row_index=1, col_index=2, channel_index=0,
  function run_test (line 39) | def run_test():
  function main (line 115) | def main():

FILE: data.py
  function load_test_data (line 20) | def load_test_data():
  function load_test_ids (line 25) | def load_test_ids():
  function load_train_data (line 30) | def load_train_data():
  function load_patient_num (line 36) | def load_patient_num():
  function get_patient_nums (line 40) | def get_patient_nums(string):
  function create_train_data (line 45) | def create_train_data():
  function create_test_data (line 76) | def create_test_data():
  function main (line 103) | def main():

FILE: keras_plus.py
  class AdvancedLearnignRateScheduler (line 8) | class AdvancedLearnignRateScheduler(Callback):
    method __init__ (line 21) | def __init__(self, monitor='val_loss', patience=0,
    method on_epoch_end (line 51) | def on_epoch_end(self, epoch, logs={}):
  class LearningRateDecay (line 79) | class LearningRateDecay(Callback):
    method __init__ (line 87) | def __init__(self, decay, every_n=1, verbose=0):
    method on_epoch_end (line 93) | def on_epoch_end(self, epoch, logs={}):

FILE: metric.py
  function mean_length_error (line 7) | def mean_length_error(y_true, y_pred):
  function dice_coef (line 13) | def dice_coef(y_true, y_pred):
  function dice_coef_loss (line 19) | def dice_coef_loss(y_true, y_pred):
  function np_dice_coef (line 22) | def np_dice_coef(y_true, y_pred):
  function main (line 28) | def main():

FILE: submission.py
  function prep (line 9) | def prep(img):
  function run_length_enc (line 15) | def run_length_enc(label):
  function submission (line 30) | def submission():
  function main (line 70) | def main():

FILE: train.py
  function preprocess (line 18) | def preprocess(imgs, to_rows=None, to_cols=None):
  class Learner (line 28) | class Learner(object):
    method __init__ (line 39) | def __init__(self, model_func, validation_split):
    method _dir_init (line 45) | def _dir_init(self):
    method save_meanstd (line 53) | def save_meanstd(self):
    method load_meanstd (line 58) | def load_meanstd(cls):
    method save_valid_idx (line 64) | def save_valid_idx(cls, idx):
    method load_valid_idx (line 68) | def load_valid_idx(cls):
    method _init_mean_std (line 71) | def _init_mean_std(self, data):
    method get_object_existance (line 77) | def get_object_existance(self, mask_array):
    method standartize (line 80) | def standartize(self, array, to_float=False):
    method norm_mask (line 91) | def norm_mask(cls, mask_array):
    method shuffle_train (line 97) | def shuffle_train(cls, data, mask):
    method split_train_and_valid_by_patient (line 104) | def split_train_and_valid_by_patient(cls, data, mask, validation_split...
    method split_train_and_valid (line 124) | def split_train_and_valid(cls, data, mask, validation_split, shuffle=F...
    method test (line 134) | def test(self, model, batch_size=256):
    method __pretrain_model_load (line 148) | def __pretrain_model_load(self, model, pretrained_path):
    method augmentation (line 155) | def augmentation(self, X, Y):
    method fit (line 197) | def fit(self, x_train, y_train, x_valid, y_valid, pretrained_path):
    method train_and_predict (line 225) | def train_and_predict(self, pretrained_path=None, split_random=True):
  function main (line 250) | def main():

FILE: train_generator.py
  function preprocess (line 20) | def preprocess(imgs, to_rows=None, to_cols=None):
  class Learner (line 29) | class Learner(object):
    method __init__ (line 40) | def __init__(self, model_func, validation_split):
    method _dir_init (line 46) | def _dir_init(self):
    method save_meanstd (line 54) | def save_meanstd(self):
    method load_meanstd (line 59) | def load_meanstd(cls):
    method save_valid_idx (line 65) | def save_valid_idx(cls, idx):
    method load_valid_idx (line 69) | def load_valid_idx(cls):
    method _init_mean_std (line 72) | def _init_mean_std(self, data):
    method get_object_existance (line 78) | def get_object_existance(self, mask_array):
    method standartize (line 81) | def standartize(self, array, to_float=False):
    method norm_mask (line 92) | def norm_mask(cls, mask_array):
    method shuffle_train (line 98) | def shuffle_train(cls, data, mask):
    method split_train_and_valid_by_patient (line 105) | def split_train_and_valid_by_patient(cls, data, mask, validation_split...
    method split_train_and_valid (line 125) | def split_train_and_valid(cls, data, mask, validation_split, shuffle=F...
    method test (line 135) | def test(self, model, batch_size=256):
    method __pretrain_model_load (line 149) | def __pretrain_model_load(self, model, pretrained_path):
    method augmentation (line 156) | def augmentation(self, X, Y):
    method fit (line 204) | def fit(self, x_train, y_train, x_valid, y_valid, pretrained_path):
    method train_and_predict (line 242) | def train_and_predict(self, pretrained_path=None, split_random=True):
  function main (line 266) | def main():

FILE: train_kfold.py
  function preprocess (line 20) | def preprocess(imgs, to_rows=None, to_cols=None):
  class Learner (line 29) | class Learner(object):
    method __init__ (line 40) | def __init__(self, model_func, validation_split):
    method _dir_init (line 46) | def _dir_init(self):
    method save_meanstd (line 54) | def save_meanstd(self):
    method load_meanstd (line 59) | def load_meanstd(cls):
    method save_valid_idx (line 65) | def save_valid_idx(cls, idx):
    method load_valid_idx (line 69) | def load_valid_idx(cls):
    method _init_mean_std (line 72) | def _init_mean_std(self, data):
    method get_object_existance (line 78) | def get_object_existance(self, mask_array):
    method standartize (line 81) | def standartize(self, array, to_float=False):
    method norm_mask (line 92) | def norm_mask(cls, mask_array):
    method shuffle_train (line 98) | def shuffle_train(cls, data, mask):
    method __pretrain_model_load (line 104) | def __pretrain_model_load(self, model, pretrained_path):
    method augmentation (line 111) | def augmentation(self, X, Y):
    method fit (line 155) | def fit(self, x_train, y_train, nfolds=8):
    method train_and_predict (line 186) | def train_and_predict(self, pretrained_path=None):
  function main (line 199) | def main():

FILE: u_model.py
  function _shortcut (line 10) | def _shortcut(_input, residual):
  function inception_block (line 25) | def inception_block(inputs, depth, batch_mode=0, splitted=False, activat...
  function rblock (line 61) | def rblock(inputs, num, depth, scale=0.1):
  function NConvolution2D (line 69) | def NConvolution2D(nb_filter, nb_row, nb_col, border_mode='same', subsam...
  function BNA (line 78) | def BNA(_input):
  function reduction_a (line 82) | def reduction_a(inputs, k=64, l=64, m=96, n=96):
  function reduction_b (line 97) | def reduction_b(inputs):
  function get_unet_inception_2head (line 118) | def get_unet_inception_2head(optimizer):
  function main (line 190) | def main():

FILE: utils.py
  function load_pickle (line 3) | def load_pickle(file_path):
  function save_pickle (line 9) | def save_pickle(file_path, data):
  function count_enum (line 13) | def count_enum(words):