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!_TAG_FILE_FORMAT 2 /extended format; --format=1 will not append ;" to lines/
!_TAG_FILE_SORTED 1 /0=unsorted, 1=sorted, 2=foldcase/
!_TAG_OUTPUT_MODE u-ctags /u-ctags or e-ctags/
!_TAG_PROGRAM_AUTHOR Universal Ctags Team //
!_TAG_PROGRAM_NAME Universal Ctags /Derived from Exuberant Ctags/
!_TAG_PROGRAM_URL https://ctags.io/ /official site/
!_TAG_PROGRAM_VERSION 0.0.0 /1c8b98dd/
DATA_TYPE_COMPCARS main.py /^DATA_TYPE_COMPCARS = 'compcars'$/;" v
DATA_TYPE_CUB main.py /^DATA_TYPE_CUB = 'cub'$/;" v
DATA_TYPE_IMAGENET main.py /^DATA_TYPE_IMAGENET = 'imagenet'$/;" v
DEFAULT_CODE_DIR main.py /^DEFAULT_CODE_DIR = 'D:\\\\Jason_Folder\\\\Yonsei\\\\Research\\\\fine_grained\\\\code'$/;" v
DEFAULT_DATASET_DIR main.py /^DEFAULT_DATASET_DIR = 'D:\\\\Jason_Folder\\\\Yonsei\\\\Research\\\\dataset'$/;" v
LOG_DIR main.py /^LOG_DIR = os.path.join(DEFAULT_CODE_DIR, LOG_FOLDER)$/;" v
LOG_FOLDER main.py /^LOG_FOLDER = 'log'$/;" v
MyNetwork networks\\my_network.py /^class MyNetwork:$/;" c
NETWORKS_DIR main.py /^NETWORKS_DIR = os.path.join(DEFAULT_CODE_DIR, NETWORKS_FOLDER)$/;" v
NETWORKS_FOLDER main.py /^NETWORKS_FOLDER = 'networks'$/;" v
NetworkCommon networks\\network_common.py /^class NetworkCommon:$/;" c
ParseCub parsedata\\parse_cub.py /^class ParseCub:$/;" c
ST_VGG networks\\st_vgg.py /^class ST_VGG:$/;" c
Solver solver.py /^class Solver():$/;" c
VGG16 networks\\vgg16.py /^class VGG16:$/;" c
VGG19 networks\\vgg19.py /^class VGG19:$/;" c
WEIGHTS_DIR main.py /^WEIGHTS_DIR = os.path.join(NETWORKS_DIR, WEIGHTS_FOLDER)$/;" v
WEIGHTS_FOLDER main.py /^WEIGHTS_FOLDER = 'weights'$/;" v
WEIGHT_VGG16_IMAGENET main.py /^WEIGHT_VGG16_IMAGENET = 'vgg16.npy'$/;" v
WEIGHT_VGG19_IMAGENET main.py /^WEIGHT_VGG19_IMAGENET = 'vgg19.npy'$/;" v
W_conv1 networks\\stn\\cluttered_mnist.py /^W_conv1 = weight_variable([filter_size, filter_size, 1, n_filters_1])$/;" v
W_conv2 networks\\stn\\cluttered_mnist.py /^W_conv2 = weight_variable([filter_size, filter_size, n_filters_1, n_filters_2])$/;" v
W_fc1 networks\\stn\\cluttered_mnist.py /^W_fc1 = weight_variable([10 * 10 * n_filters_2, n_fc])$/;" v
W_fc1 networks\\stn\\example.py /^ W_fc1 = tf.Variable(tf.zeros([1200 * 1600 * 3, n_fc]), name='W_fc1')$/;" v
W_fc2 networks\\stn\\cluttered_mnist.py /^W_fc2 = weight_variable([n_fc, 10])$/;" v
W_fc_loc1 networks\\stn\\cluttered_mnist.py /^W_fc_loc1 = weight_variable([1600, 20])$/;" v
W_fc_loc2 networks\\stn\\cluttered_mnist.py /^W_fc_loc2 = weight_variable([20, 6])$/;" v
X_test networks\\stn\\cluttered_mnist.py /^X_test = mnist_cluttered['X_test']$/;" v
X_train networks\\stn\\cluttered_mnist.py /^X_train = mnist_cluttered['X_train']$/;" v
X_valid networks\\stn\\cluttered_mnist.py /^X_valid = mnist_cluttered['X_valid']$/;" v
Y_test networks\\stn\\cluttered_mnist.py /^Y_test = dense_to_one_hot(y_test, n_classes=10)$/;" v
Y_train networks\\stn\\cluttered_mnist.py /^Y_train = dense_to_one_hot(y_train, n_classes=10)$/;" v
Y_valid networks\\stn\\cluttered_mnist.py /^Y_valid = dense_to_one_hot(y_valid, n_classes=10)$/;" v
_BATCH_SIZE solver.py /^ _BATCH_SIZE = 32$/;" v class:Solver
_BOUNDING_BOXES_TXT parsedata\\parse_cub.py /^ _BOUNDING_BOXES_TXT = 'bounding_boxes.txt'$/;" v class:ParseCub
_CUB solver.py /^ _CUB = 'cub'$/;" v class:Solver
_CUB_FOLDER parsedata\\parse_cub.py /^ _CUB_FOLDER = 'CUB_200_2011'$/;" v class:ParseCub
_IMAGES_FOLDER parsedata\\parse_cub.py /^ _IMAGES_FOLDER = 'images'$/;" v class:ParseCub
_IMAGES_TXT parsedata\\parse_cub.py /^ _IMAGES_TXT = 'images.txt'$/;" v class:ParseCub
_IMAGE_CLASS_LABELS_TXT parsedata\\parse_cub.py /^ _IMAGE_CLASS_LABELS_TXT = 'image_class_labels.txt'$/;" v class:ParseCub
_MY_NETWORK solver.py /^ _MY_NETWORK = 'my_network'$/;" v class:Solver
_NUM_CLASSES parsedata\\parse_cub.py /^ _NUM_CLASSES = 200$/;" v class:ParseCub
_NUM_TEST_IMGS parsedata\\parse_cub.py /^ _NUM_TEST_IMGS = _NUM_TOTAL_IMGS - _NUM_TRAIN_IMGS - _NUM_VAL_IMGS$/;" v class:ParseCub
_NUM_TOTAL_IMGS parsedata\\parse_cub.py /^ _NUM_TOTAL_IMGS = 11788$/;" v class:ParseCub
_NUM_TRAIN_IMGS parsedata\\parse_cub.py /^ _NUM_TRAIN_IMGS = 8251$/;" v class:ParseCub
_NUM_VAL_IMGS parsedata\\parse_cub.py /^ _NUM_VAL_IMGS = 1768$/;" v class:ParseCub
_OUT_SIZE networks\\my_network.py /^ _OUT_SIZE = (7, 7)$/;" v class:MyNetwork
_PARTS_LOCS_TXT parsedata\\parse_cub.py /^ _PARTS_LOCS_TXT = 'parts\\\\part_locs.txt'$/;" v class:ParseCub
_RGB_MEAN networks\\my_network.py /^ _RGB_MEAN = [123.68, 116.779, 103.939]$/;" v class:MyNetwork
_ST_VGG solver.py /^ _ST_VGG = 'st_vgg'$/;" v class:Solver
_TEST_LIST parsedata\\parse_cub.py /^ _TEST_LIST = 'test_list.txt'$/;" v class:ParseCub
_TEST_LOG_FOLDER solver.py /^ _TEST_LOG_FOLDER = 'test'$/;" v class:Solver
_TRAIN_LIST parsedata\\parse_cub.py /^ _TRAIN_LIST = 'train_list.txt'$/;" v class:ParseCub
_TRAIN_LOG_FOLDER solver.py /^ _TRAIN_LOG_FOLDER = 'train'$/;" v class:Solver
_VAL_LIST parsedata\\parse_cub.py /^ _VAL_LIST = 'val_list.txt'$/;" v class:ParseCub
_VAL_LOG_FOLDER solver.py /^ _VAL_LOG_FOLDER = 'val'$/;" v class:Solver
_VGG16 solver.py /^ _VGG16 = 'vgg16'$/;" v class:Solver
_VGG19 solver.py /^ _VGG19 = 'vgg19'$/;" v class:Solver
_VGG_RGB_MEAN networks\\vgg16.py /^ _VGG_RGB_MEAN = [123.68, 116.779, 103.939]$/;" v class:VGG16
_VGG_RGB_MEAN networks\\vgg19.py /^ _VGG_RGB_MEAN = [123.68, 116.779, 103.939]$/;" v class:VGG19
__init__ networks\\my_network.py /^ def __init__(self, num_classes=None, npy_path=None,$/;" m class:MyNetwork
__init__ networks\\network_common.py /^ def __init__(self, init_layers, npy_path, trainable, initializer=None, stddev=0.001):$/;" m class:NetworkCommon
__init__ networks\\st_vgg.py /^ def __init__(self, num_classes=None, npy_path=None,$/;" m class:ST_VGG
__init__ networks\\vgg16.py /^ def __init__(self, num_classes=None, npy_path=None,$/;" m class:VGG16
__init__ networks\\vgg19.py /^ def __init__(self, num_classes=None, npy_path=None,$/;" m class:VGG19
__init__ parsedata\\parse_cub.py /^ def __init__(self, dataset_dir, resize, crop_shape, batch_size, isotropical=False, initial_load/;" m class:ParseCub
__init__ solver.py /^ def __init__($/;" m class:Solver
_file_list_2_bb parsedata\\parse_cub.py /^ def _file_list_2_bb(self):$/;" m class:ParseCub
_file_list_2_part_locs parsedata\\parse_cub.py /^ def _file_list_2_part_locs(self):$/;" m class:ParseCub
_fill_bb_list parsedata\\parse_cub.py /^ def _fill_bb_list(id_list, bb_list):$/;" f member:ParseCub._file_list_2_bb file:
_fill_pl_list parsedata\\parse_cub.py /^ def _fill_pl_list(id_list, pl_list):$/;" f member:ParseCub._file_list_2_part_locs file:
_get_conv_var networks\\network_common.py /^ def _get_conv_var(self, filter_size, in_channels, out_channels, name):$/;" m class:NetworkCommon
_get_fc_var networks\\network_common.py /^ def _get_fc_var(self, in_size, out_size, init_weight, init_bias, name):$/;" m class:NetworkCommon
_get_var networks\\network_common.py /^ def _get_var(self, initial_value, name, idx, var_name):$/;" m class:NetworkCommon
_get_var_count solver.py /^ def _get_var_count(self):$/;" m class:Solver
_interpolate networks\\stn\\spatial_transformer.py /^ def _interpolate(im, x, y, out_size):$/;" f function:transformer file:
_load_list parsedata\\parse_cub.py /^ def _load_list(self):$/;" m class:ParseCub
_make_list parsedata\\parse_cub.py /^ def _make_list(self):$/;" m class:ParseCub
_meshgrid networks\\stn\\spatial_transformer.py /^ def _meshgrid(height, width):$/;" f function:transformer file:
_modify_crop_bb image_utils.py /^def _modify_crop_bb(crop_width, crop_height, ws, hs, bounding_box):$/;" f
_modify_crop_pl image_utils.py /^def _modify_crop_pl(crop_width, crop_height, ws, hs, part_locs):$/;" f
_repeat networks\\stn\\spatial_transformer.py /^ def _repeat(x, n_repeats):$/;" f function:transformer file:
_transform networks\\stn\\spatial_transformer.py /^ def _transform(theta, input_dim, out_size):$/;" f function:transformer file:
_update_head_2_pl_list parsedata\\parse_cub.py /^ def _update_head_2_pl_list(pl_list):$/;" f member:ParseCub._update_part_locs file:
_update_part_locs parsedata\\parse_cub.py /^ def _update_part_locs(self):$/;" m class:ParseCub
abs_pl_2_bb networks\\stn\\spatial_transformer.py /^def abs_pl_2_bb(bounding_boxes, abs_pl):$/;" f
accuracy networks\\stn\\cluttered_mnist.py /^accuracy = tf.reduce_mean(tf.cast(correct_prediction, 'float'))$/;" v
augment_image_batch image_utils.py /^def augment_image_batch($/;" f
avg_pool networks\\network_common.py /^ def avg_pool(self, inputs, name):$/;" m class:NetworkCommon
b_conv1 networks\\stn\\cluttered_mnist.py /^b_conv1 = bias_variable([n_filters_1])$/;" v
b_conv2 networks\\stn\\cluttered_mnist.py /^b_conv2 = bias_variable([n_filters_2])$/;" v
b_fc1 networks\\stn\\cluttered_mnist.py /^b_fc1 = bias_variable([n_fc])$/;" v
b_fc1 networks\\stn\\example.py /^ b_fc1 = tf.Variable(initial_value=initial, name='b_fc1')$/;" v
b_fc2 networks\\stn\\cluttered_mnist.py /^b_fc2 = bias_variable([10])$/;" v
b_fc_loc1 networks\\stn\\cluttered_mnist.py /^b_fc_loc1 = bias_variable([20])$/;" v
b_fc_loc2 networks\\stn\\cluttered_mnist.py /^b_fc_loc2 = tf.Variable(initial_value=initial, name='b_fc_loc2')$/;" v
batch networks\\stn\\example.py /^batch = np.append(batch, im, axis=0)$/;" v
batch networks\\stn\\example.py /^batch = np.append(im, im, axis=0)$/;" v
batch_transformer networks\\stn\\spatial_transformer.py /^def batch_transformer(U, thetas, out_size, name='BatchSpatialTransformer'):$/;" f
batch_xs networks\\stn\\cluttered_mnist.py /^ batch_xs = X_train[indices[iter_i]:indices[iter_i+1]]$/;" v
batch_ys networks\\stn\\cluttered_mnist.py /^ batch_ys = Y_train[indices[iter_i]:indices[iter_i+1]]$/;" v
bb_2_transcale networks\\stn\\spatial_transformer.py /^def bb_2_transcale(bounding_boxes):$/;" f
bb_pixels_2_relative image_utils.py /^def bb_pixels_2_relative(bounding_boxes, width, height):$/;" f
bias_variable networks\\stn\\tf_utils.py /^def bias_variable(shape):$/;" f
build networks\\my_network.py /^ def build(self, rgb, train_mode=None, **kwargs):$/;" m class:MyNetwork
build networks\\st_vgg.py /^ def build(self, rgb, train_mode=None, **kwargs):$/;" m class:ST_VGG
build networks\\vgg16.py /^ def build(self, rgb, train_mode=None):$/;" m class:VGG16
build networks\\vgg19.py /^ def build(self, rgb, train_mode=None):$/;" m class:VGG19
build_partial networks\\vgg16.py /^ def build_partial(self, rgb, build_until, return_intermediates=None, train_mode=None):$/;" m class:VGG16
build_partial networks\\vgg19.py /^ def build_partial(self, rgb, build_until, train_mode=None):$/;" m class:VGG19
centered_bgr_2_rgb image_utils.py /^def centered_bgr_2_rgb(centered_bgr, mean_rgb):$/;" f
central_crop image_utils.py /^def central_crop(input_img, crop_width, crop_height, bounding_box=None, part_locs=None):$/;" f
check_part_locs_boundary image_utils.py /^def check_part_locs_boundary(part_locs, crop_width, crop_height, part_key):$/;" f
clip_transcale networks\\stn\\spatial_transformer.py /^def clip_transcale(ts_matrix):$/;" f
conv2d networks\\stn\\tf_utils.py /^def conv2d(x, n_filters,$/;" f
conv_layer networks\\network_common.py /^ def conv_layer(self, inputs, filter_size, in_channels, out_channels, name, do_batch_norm=False)/;" m class:NetworkCommon
correct_prediction networks\\stn\\cluttered_mnist.py /^correct_prediction = tf.equal(tf.argmax(y_logits, 1), tf.argmax(y, 1))$/;" v
cross_entropy networks\\stn\\cluttered_mnist.py /^cross_entropy = tf.reduce_mean($/;" v
dense_to_one_hot networks\\stn\\tf_utils.py /^def dense_to_one_hot(labels, n_classes=2):$/;" f
dir_exists utils.py /^def dir_exists(dir_name):$/;" f
draw_bounding_boxes image_utils.py /^def draw_bounding_boxes(images, bounding_boxes_list):$/;" f
dropout_layer networks\\network_common.py /^ def dropout_layer(self, inputs, dropout_rate, train_mode, name):$/;" m class:NetworkCommon
entry_stop_gradients networks\\network_common.py /^ def entry_stop_gradients(self, target, mask):$/;" m class:NetworkCommon
extract_part_from_part_locs image_utils.py /^def extract_part_from_part_locs(part_locs, part_key):$/;" f
fc_layer networks\\network_common.py /^ def fc_layer(self, inputs, in_size, out_size, is_last, name, do_batch_norm=False, activation_fn/;" m class:NetworkCommon
file_exists utils.py /^def file_exists(file_name):$/;" f
filter_size networks\\stn\\cluttered_mnist.py /^filter_size = 3$/;" v
get_image_mean image_utils.py /^def get_image_mean(list_file, resize=None, isotropical=False):$/;" f
get_next_test_batch parsedata\\parse_cub.py /^ def get_next_test_batch(self):$/;" m class:ParseCub
get_next_train_batch parsedata\\parse_cub.py /^ def get_next_train_batch(self, augment=True):$/;" m class:ParseCub
get_next_val_batch parsedata\\parse_cub.py /^ def get_next_val_batch(self):$/;" m class:ParseCub
get_num_classes parsedata\\parse_cub.py /^ def get_num_classes(self):$/;" m class:ParseCub
grads networks\\stn\\cluttered_mnist.py /^grads = opt.compute_gradients(cross_entropy, [b_fc_loc2])$/;" v
h_conv1 networks\\stn\\cluttered_mnist.py /^h_conv1 = tf.nn.relu($/;" v
h_conv2 networks\\stn\\cluttered_mnist.py /^h_conv2 = tf.nn.relu($/;" v
h_conv2_flat networks\\stn\\cluttered_mnist.py /^h_conv2_flat = tf.reshape(h_conv2, [-1, 10 * 10 * n_filters_2])$/;" v
h_fc1 networks\\stn\\cluttered_mnist.py /^h_fc1 = tf.nn.relu(tf.matmul(h_conv2_flat, W_fc1) + b_fc1)$/;" v
h_fc1 networks\\stn\\example.py /^ h_fc1 = tf.matmul(tf.zeros([num_batch, 1200 * 1600 * 3]), W_fc1) + b_fc1$/;" v
h_fc1_drop networks\\stn\\cluttered_mnist.py /^h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)$/;" v
h_fc_loc1 networks\\stn\\cluttered_mnist.py /^h_fc_loc1 = tf.nn.tanh(tf.matmul(x, W_fc_loc1) + b_fc_loc1)$/;" v
h_fc_loc1_drop networks\\stn\\cluttered_mnist.py /^h_fc_loc1_drop = tf.nn.dropout(h_fc_loc1, keep_prob)$/;" v
h_fc_loc2 networks\\stn\\cluttered_mnist.py /^h_fc_loc2 = tf.nn.tanh(tf.matmul(h_fc_loc1_drop, W_fc_loc2) + b_fc_loc2)$/;" v
h_trans networks\\stn\\cluttered_mnist.py /^h_trans = transformer(x_tensor, h_fc_loc2, out_size)$/;" v
h_trans networks\\stn\\example.py /^ h_trans = transformer(x, h_fc1, out_size)$/;" v
iaa image_utils.py /^from imgaug import augmenters as iaa$/;" x
im networks\\stn\\example.py /^im = im \/ 255.$/;" v
im networks\\stn\\example.py /^im = im.astype('float32')$/;" v
im networks\\stn\\example.py /^im = im.reshape(1, 1200, 1600, 3)$/;" v
im networks\\stn\\example.py /^im = ndimage.imread('cat.jpg')$/;" v
indices networks\\stn\\cluttered_mnist.py /^indices = indices.astype('int')$/;" v
indices networks\\stn\\cluttered_mnist.py /^indices = np.linspace(0, 10000 - 1, iter_per_epoch)$/;" v
initial networks\\stn\\cluttered_mnist.py /^initial = initial.astype('float32')$/;" v
initial networks\\stn\\cluttered_mnist.py /^initial = initial.flatten()$/;" v
initial networks\\stn\\cluttered_mnist.py /^initial = np.array([[1., 0, 0], [0, 1., 0]])$/;" v
initial networks\\stn\\example.py /^ initial = initial.astype('float32')$/;" v
initial networks\\stn\\example.py /^ initial = initial.flatten()$/;" v
initial networks\\stn\\example.py /^ initial = np.array([[0.5, 0, 0], [0, 0.5, 0]])$/;" v
isotropical_resize image_utils.py /^def isotropical_resize(input_img, base, upscale, bounding_box=None, part_locs=None):$/;" f
iter_per_epoch networks\\stn\\cluttered_mnist.py /^iter_per_epoch = 100$/;" v
keep_prob networks\\stn\\cluttered_mnist.py /^keep_prob = tf.placeholder(tf.float32)$/;" v
linear networks\\stn\\tf_utils.py /^def linear(x, n_units, scope=None, stddev=0.02,$/;" f
log image_utils.py /^import logging as log$/;" I
log main.py /^import logging as log$/;" I
log networks\\my_network.py /^import logging as log$/;" I
log networks\\network_common.py /^import logging as log$/;" I
log networks\\st_vgg.py /^import logging as log$/;" I
log networks\\vgg16.py /^import logging as log$/;" I
log networks\\vgg19.py /^import logging as log$/;" I
log parsedata\\parse_cub.py /^import logging as log$/;" I
log solver.py /^import logging as log$/;" I
log utils.py /^import logging as log$/;" I
loss networks\\stn\\cluttered_mnist.py /^ loss = sess.run(cross_entropy,$/;" v
max_pool networks\\network_common.py /^ def max_pool(self, inputs, name, stride=2):$/;" m class:NetworkCommon
mnist_cluttered networks\\stn\\cluttered_mnist.py /^mnist_cluttered = np.load('.\/data\/mnist_sequence1_sample_5distortions5x5.npz')$/;" v
my_network_func main.py /^def my_network_func(mode='train'):$/;" f
n_epochs networks\\stn\\cluttered_mnist.py /^n_epochs = 500$/;" v
n_fc networks\\stn\\cluttered_mnist.py /^n_fc = 1024$/;" v
n_fc networks\\stn\\example.py /^ n_fc = 6$/;" v
n_filters_1 networks\\stn\\cluttered_mnist.py /^n_filters_1 = 16$/;" v
n_filters_2 networks\\stn\\cluttered_mnist.py /^n_filters_2 = 16$/;" v
np image_utils.py /^import numpy as np$/;" I
np main.py /^import numpy as np$/;" I
np networks\\my_network.py /^import numpy as np$/;" I
np networks\\network_common.py /^import numpy as np$/;" I
np networks\\st_vgg.py /^import numpy as np$/;" I
np networks\\stn\\cluttered_mnist.py /^import numpy as np$/;" I
np networks\\stn\\example.py /^import numpy as np$/;" I
np networks\\stn\\tf_utils.py /^import numpy as np$/;" I
np networks\\vgg16.py /^import numpy as np$/;" I
np networks\\vgg19.py /^import numpy as np$/;" I
np parsedata\\parse_cub.py /^import numpy as np$/;" I
np solver.py /^import numpy as np$/;" I
np utils.py /^import numpy as np$/;" I
num_batch networks\\stn\\example.py /^num_batch = 3$/;" v
opt networks\\stn\\cluttered_mnist.py /^opt = tf.train.AdamOptimizer()$/;" v
optimizer networks\\stn\\cluttered_mnist.py /^optimizer = opt.minimize(cross_entropy)$/;" v
out_size networks\\stn\\cluttered_mnist.py /^out_size = (40, 40)$/;" v
out_size networks\\stn\\example.py /^out_size = (600, 800)$/;" v
patches parsedata\\parse_cub.py /^import matplotlib.patches as patches$/;" I
pl_pixels_2_relative image_utils.py /^def pl_pixels_2_relative(part_locs, width, height):$/;" f
plt main.py /^import matplotlib.pyplot as plt$/;" I
plt networks\\stn\\example.py /^import matplotlib.pyplot as plt$/;" I
plt parsedata\\parse_cub.py /^import matplotlib.pyplot as plt$/;" I
plt solver.py /^import matplotlib.pyplot as plt$/;" I
predictor solver.py /^ def predictor(self, image):$/;" m class:Solver
print_prob utils.py /^def print_prob(prob, file_path):$/;" f
random_crop image_utils.py /^def random_crop(input_img, crop_width, crop_height, bounding_box=None, part_locs=None):$/;" f
rel_trans_2_abs_pl networks\\stn\\spatial_transformer.py /^def rel_trans_2_abs_pl(bounding_boxes, t_matrix):$/;" f
resize_image image_utils.py /^def resize_image(input_img, shape, bounding_box=None, part_locs=None):$/;" f
rgb_2_centered_bgr image_utils.py /^def rgb_2_centered_bgr(rgb, mean_rgb):$/;" f
save_npy solver.py /^ def save_npy(self, save_path, scope=None):$/;" m class:Solver
sess networks\\stn\\cluttered_mnist.py /^sess = tf.Session()$/;" v
sess networks\\stn\\example.py /^sess = tf.Session()$/;" v
set_logging main.py /^def set_logging():$/;" f
st_vgg_func main.py /^def st_vgg_func(mode='train'):$/;" f
tester solver.py /^ def tester(self):$/;" m class:Solver
tf image_utils.py /^import tensorflow as tf$/;" I
tf main.py /^import tensorflow as tf$/;" I
tf networks\\my_network.py /^import tensorflow as tf$/;" I
tf networks\\network_common.py /^import tensorflow as tf$/;" I
tf networks\\st_vgg.py /^import tensorflow as tf$/;" I
tf networks\\stn\\cluttered_mnist.py /^import tensorflow as tf$/;" I
tf networks\\stn\\example.py /^import tensorflow as tf$/;" I
tf networks\\stn\\spatial_transformer.py /^import tensorflow as tf$/;" I
tf networks\\stn\\tf_utils.py /^import tensorflow as tf$/;" I
tf networks\\vgg16.py /^import tensorflow as tf$/;" I
tf networks\\vgg19.py /^import tensorflow as tf$/;" I
tf parsedata\\parse_cub.py /^import tensorflow as tf$/;" I
tf solver.py /^import tensorflow as tf$/;" I
tf utils.py /^import tensorflow as tf$/;" I
train_size networks\\stn\\cluttered_mnist.py /^train_size = 10000$/;" v
trainer solver.py /^ def trainer($/;" m class:Solver
trans_2_affine networks\\stn\\spatial_transformer.py /^def trans_2_affine(elems):$/;" f
transcale_2_affine networks\\stn\\spatial_transformer.py /^def transcale_2_affine(ts_matrix):$/;" f
transcale_2_bb networks\\stn\\spatial_transformer.py /^def transcale_2_bb(ts_matrix):$/;" f
transformer networks\\stn\\spatial_transformer.py /^def transformer(U, theta, out_size, name='SpatialTransformer', **kwargs):$/;" f
vgg_cub_ft_func main.py /^def vgg_cub_ft_func(mode='train'):$/;" f
weight_variable networks\\stn\\tf_utils.py /^def weight_variable(shape):$/;" f
x networks\\stn\\cluttered_mnist.py /^x = tf.placeholder(tf.float32, [None, 1600])$/;" v
x networks\\stn\\example.py /^x = tf.cast(batch, 'float32')$/;" v
x networks\\stn\\example.py /^x = tf.placeholder(tf.float32, [None, 1200, 1600, 3])$/;" v
x_tensor networks\\stn\\cluttered_mnist.py /^x_tensor = tf.reshape(x, [-1, 40, 40, 1])$/;" v
y networks\\stn\\cluttered_mnist.py /^y = tf.placeholder(tf.float32, [None, 10])$/;" v
y networks\\stn\\example.py /^y = sess.run(h_trans, feed_dict={x: batch})$/;" v
y_logits networks\\stn\\cluttered_mnist.py /^y_logits = tf.matmul(h_fc1_drop, W_fc2) + b_fc2$/;" v
y_test networks\\stn\\cluttered_mnist.py /^y_test = mnist_cluttered['y_test']$/;" v
y_train networks\\stn\\cluttered_mnist.py /^y_train = mnist_cluttered['y_train']$/;" v
y_valid networks\\stn\\cluttered_mnist.py /^y_valid = mnist_cluttered['y_valid']$/;" v