SYMBOL INDEX (91 symbols across 8 files)

FILE: dogsandcats_keras/gpu_check.py
  function test (line 13) | def test():

FILE: dogsandcats_keras/utils.py
  function gray (line 57) | def gray(img):
  function to_plot (line 59) | def to_plot(img):
  function plot (line 61) | def plot(img):
  function floor (line 65) | def floor(x):
  function ceil (line 67) | def ceil(x):
  function plots (line 70) | def plots(ims, figsize=(12,6), rows=1, interp=False, titles=None):
  function do_clip (line 83) | def do_clip(arr, mx):
  function get_batches (line 88) | def get_batches(dirname, gen=image.ImageDataGenerator(), shuffle=True, b...
  function onehot (line 94) | def onehot(x):
  function wrap_config (line 98) | def wrap_config(layer):
  function copy_layer (line 102) | def copy_layer(layer): return layer_from_config(wrap_config(layer))
  function copy_layers (line 105) | def copy_layers(layers): return [copy_layer(layer) for layer in layers]
  function copy_weights (line 108) | def copy_weights(from_layers, to_layers):
  function copy_model (line 113) | def copy_model(m):
  function insert_layer (line 119) | def insert_layer(model, new_layer, index):
  function adjust_dropout (line 129) | def adjust_dropout(weights, prev_p, new_p):
  function get_data (line 134) | def get_data(path, target_size=(224,224)):
  function plot_confusion_matrix (line 139) | def plot_confusion_matrix(cm, classes, normalize=False, title='Confusion...
  function save_array (line 165) | def save_array(fname, arr):
  function load_array (line 170) | def load_array(fname):
  function mk_size (line 174) | def mk_size(img, r2c):
  function mk_square (line 189) | def mk_square(img):
  function vgg_ft (line 199) | def vgg_ft(out_dim):
  function vgg_ft_bn (line 205) | def vgg_ft_bn(out_dim):
  function get_classes (line 212) | def get_classes(path):
  function split_at (line 220) | def split_at(model, layer_type):
  class MixIterator (line 227) | class MixIterator(object):
    method __init__ (line 228) | def __init__(self, iters):
    method reset (line 236) | def reset(self):
    method __iter__ (line 239) | def __iter__(self):
    method next (line 242) | def next(self, *args, **kwargs):

FILE: dogsandcats_keras/vgg16.py
  function vgg_preprocess (line 22) | def vgg_preprocess(x):
  class Vgg16 (line 27) | class Vgg16():
    method __init__ (line 31) | def __init__(self):
    method get_classes (line 37) | def get_classes(self):
    method predict (line 44) | def predict(self, imgs, details=False):
    method ConvBlock (line 52) | def ConvBlock(self, layers, filters):
    method FCBlock (line 60) | def FCBlock(self):
    method create (line 66) | def create(self):
    method get_batches (line 85) | def get_batches(self, path, gen=image.ImageDataGenerator(), shuffle=Tr...
    method ft (line 90) | def ft(self, num):
    method finetune (line 97) | def finetune(self, batches):
    method compile (line 105) | def compile(self, lr=0.001):
    method fit_data (line 110) | def fit_data(self, trn, labels,  val, val_labels,  nb_epoch=1, batch_s...
    method fit (line 115) | def fit(self, batches, val_batches, nb_epoch=1):
    method test (line 120) | def test(self, path, batch_size=8):

FILE: dogsandcats_keras/vgg16bn.py
  function vgg_preprocess (line 22) | def vgg_preprocess(x):
  class Vgg16BN (line 27) | class Vgg16BN():
    method __init__ (line 31) | def __init__(self, size=(224,224), include_top=True):
    method get_classes (line 37) | def get_classes(self):
    method predict (line 44) | def predict(self, imgs, details=False):
    method ConvBlock (line 52) | def ConvBlock(self, layers, filters):
    method FCBlock (line 60) | def FCBlock(self):
    method create (line 67) | def create(self, size, include_top):
    method get_batches (line 94) | def get_batches(self, path, gen=image.ImageDataGenerator(), shuffle=Tr...
    method ft (line 99) | def ft(self, num):
    method finetune (line 106) | def finetune(self, batches):
    method compile (line 114) | def compile(self, lr=0.001):
    method fit_data (line 119) | def fit_data(self, trn, labels,  val, val_labels,  nb_epoch=1, batch_s...
    method fit (line 124) | def fit(self, batches, val_batches, nb_epoch=1):
    method test (line 129) | def test(self, path, batch_size=8):

FILE: insurance_scikit/metrics.py
  function confusion_matrix (line 6) | def confusion_matrix(rater_a, rater_b, min_rating=None, max_rating=None):
  function histogram (line 23) | def histogram(ratings, min_rating=None, max_rating=None):
  function quadratic_weighted_kappa (line 38) | def quadratic_weighted_kappa(rater_a, rater_b, min_rating=None, max_rati...
  function linear_weighted_kappa (line 88) | def linear_weighted_kappa(rater_a, rater_b, min_rating=None, max_rating=...
  function kappa (line 136) | def kappa(rater_a, rater_b, min_rating=None, max_rating=None):
  function mean_quadratic_weighted_kappa (line 187) | def mean_quadratic_weighted_kappa(kappas, weights=None):
  function weighted_mean_quadratic_weighted_kappa (line 218) | def weighted_mean_quadratic_weighted_kappa(solution, submission):

FILE: timeserie/utils_modified.py
  function get_batches (line 54) | def get_batches(dirname, gen=image.ImageDataGenerator(), shuffle=True, b...
  function onehot (line 60) | def onehot(x):
  function wrap_config (line 64) | def wrap_config(layer):
  function copy_layer (line 68) | def copy_layer(layer):
  function copy_layers (line 72) | def copy_layers(layers):
  function copy_weights (line 76) | def copy_weights(from_layers, to_layers):
  function copy_model (line 81) | def copy_model(m):
  function insert_layer (line 87) | def insert_layer(model, new_layer, index):
  function get_data (line 102) | def get_data(path, target_size=(224,224)):
  function save_array (line 133) | def save_array(fname, arr):
  function load_array (line 138) | def load_array(fname):
  function get_classes (line 142) | def get_classes(path):
  function split_at (line 150) | def split_at(model, layer_type):

FILE: vgg_segmentation_keras/fcn_keras2.py
  function convblock (line 9) | def convblock(cdim, nb, bits=3):
  function fcn32_blank (line 26) | def fcn32_blank(image_size=512):
  function fcn_32s_to_16s (line 89) | def fcn_32s_to_16s(fcn32model=None):
  function prediction (line 130) | def prediction(kmodel, crpimg, transform=False):

FILE: vgg_segmentation_keras/utils.py
  function convblock (line 10) | def convblock(cdim, nb, bits=3):
  function fcn32_blank (line 27) | def fcn32_blank(image_size=512):
  function fcn_32s_to_16s (line 94) | def fcn_32s_to_16s(fcn32model=None):
  function fcn_32s_to_8s (line 139) | def fcn_32s_to_8s(fcn32model=None):
  function prediction (line 210) | def prediction(kmodel, crpimg, transform=False):