SYMBOL INDEX (349 symbols across 53 files)

FILE: 2017/assignments/chatbot/chatbot.py
  function _get_random_bucket (line 34) | def _get_random_bucket(train_buckets_scale):
  function _assert_lengths (line 40) | def _assert_lengths(encoder_size, decoder_size, encoder_inputs, decoder_...
  function run_step (line 53) | def run_step(sess, model, encoder_inputs, decoder_inputs, decoder_masks,...
  function _get_buckets (line 88) | def _get_buckets():
  function _get_skip_step (line 104) | def _get_skip_step(iteration):
  function _check_restore_parameters (line 110) | def _check_restore_parameters(sess, saver):
  function _eval_test_set (line 119) | def _eval_test_set(sess, model, test_buckets):
  function train (line 133) | def train():
  function _get_user_input (line 171) | def _get_user_input():
  function _find_right_bucket (line 177) | def _find_right_bucket(length):
  function _construct_response (line 182) | def _construct_response(output_logits, inv_dec_vocab):
  function chat (line 197) | def chat():
  function main (line 243) | def main():

FILE: 2017/assignments/chatbot/data.py
  function get_lines (line 28) | def get_lines():
  function get_convos (line 41) | def get_convos():
  function question_answers (line 56) | def question_answers(id2line, convos):
  function prepare_dataset (line 66) | def prepare_dataset(questions, answers):
  function make_dir (line 89) | def make_dir(path):
  function basic_tokenizer (line 96) | def basic_tokenizer(line, normalize_digits=True):
  function build_vocab (line 115) | def build_vocab(filename, normalize_digits=True):
  function load_vocab (line 145) | def load_vocab(vocab_path):
  function sentence2id (line 150) | def sentence2id(vocab, line):
  function token2id (line 153) | def token2id(data, mode):
  function prepare_raw_data (line 176) | def prepare_raw_data():
  function process_data (line 183) | def process_data():
  function load_data (line 192) | def load_data(enc_filename, dec_filename, max_training_size=None):
  function _pad_input (line 211) | def _pad_input(input_, size):
  function _reshape_batch (line 214) | def _reshape_batch(inputs, size, batch_size):
  function get_batch (line 224) | def get_batch(data_bucket, bucket_id, batch_size=1):

FILE: 2017/assignments/chatbot/model.py
  class ChatBotModel (line 26) | class ChatBotModel(object):
    method __init__ (line 27) | def __init__(self, forward_only, batch_size):
    method _create_placeholders (line 34) | def _create_placeholders(self):
    method _inference (line 47) | def _inference(self):
    method _create_loss (line 65) | def _create_loss(self):
    method _creat_optimizer (line 103) | def _creat_optimizer(self):
    method _create_summary (line 126) | def _create_summary(self):
    method build_graph (line 129) | def build_graph(self):

FILE: 2017/assignments/exercises/e01_sol.py
  function f1 (line 105) | def f1(): return tf.reduce_mean(tf.square(x - y))
  function f2 (line 106) | def f2(): return tf.reduce_sum(tf.abs(x - y))

FILE: 2017/assignments/style_transfer/style_transfer.py
  function _create_content_loss (line 56) | def _create_content_loss(p, f):
  function _gram_matrix (line 71) | def _gram_matrix(F, N, M):
  function _single_style_loss (line 78) | def _single_style_loss(a, g):
  function _create_style_loss (line 96) | def _create_style_loss(A, model):
  function _create_losses (line 107) | def _create_losses(model, input_image, content_image, style_image):
  function _create_summary (line 127) | def _create_summary(model):
  function train (line 140) | def train(model, generated_image, initial_image):
  function main (line 189) | def main():

FILE: 2017/assignments/style_transfer/utils.py
  function download (line 18) | def download(download_link, file_name, expected_bytes):
  function get_resized_image (line 32) | def get_resized_image(img_path, height, width, save=True):
  function generate_noise_image (line 46) | def generate_noise_image(content_image, height, width, noise_ratio=0.6):
  function save_image (line 51) | def save_image(path, image):
  function make_dir (line 57) | def make_dir(path):

FILE: 2017/assignments/style_transfer/vgg_model.py
  function _weights (line 14) | def _weights(vgg_layers, layer, expected_layer_name):
  function _conv2d_relu (line 23) | def _conv2d_relu(vgg_layers, prev_layer, layer, layer_name):
  function _avgpool (line 51) | def _avgpool(prev_layer):
  function load_vgg (line 64) | def load_vgg(path, input_image):

FILE: 2017/assignments/style_transfer_starter/style_transfer.py
  function _create_content_loss (line 55) | def _create_content_loss(p, f):
  function _gram_matrix (line 70) | def _gram_matrix(F, N, M):
  function _single_style_loss (line 76) | def _single_style_loss(a, g):
  function _create_style_loss (line 90) | def _create_style_loss(A, model):
  function _create_losses (line 101) | def _create_losses(model, input_image, content_image, style_image):
  function _create_summary (line 121) | def _create_summary(model):
  function train (line 127) | def train(model, generated_image, initial_image):
  function main (line 171) | def main():

FILE: 2017/assignments/style_transfer_starter/utils.py
  function download (line 18) | def download(download_link, file_name, expected_bytes):
  function get_resized_image (line 32) | def get_resized_image(img_path, height, width, save=True):
  function generate_noise_image (line 46) | def generate_noise_image(content_image, height, width, noise_ratio=0.6):
  function save_image (line 51) | def save_image(path, image):
  function make_dir (line 57) | def make_dir(path):

FILE: 2017/assignments/style_transfer_starter/vgg_model.py
  function _weights (line 14) | def _weights(vgg_layers, layer, expected_layer_name):
  function _conv2d_relu (line 23) | def _conv2d_relu(vgg_layers, prev_layer, layer, layer_name):
  function _avgpool (line 46) | def _avgpool(prev_layer):
  function load_vgg (line 58) | def load_vgg(path, input_image):

FILE: 2017/examples/04_word2vec_no_frills.py
  function word2vec (line 29) | def word2vec(batch_gen):
  function main (line 79) | def main():

FILE: 2017/examples/04_word2vec_starter.py
  function word2vec (line 29) | def word2vec(batch_gen):
  function main (line 89) | def main():

FILE: 2017/examples/04_word2vec_visualize.py
  class SkipGramModel (line 31) | class SkipGramModel:
    method __init__ (line 33) | def __init__(self, vocab_size, embed_size, batch_size, num_sampled, le...
    method _create_placeholders (line 41) | def _create_placeholders(self):
    method _create_embedding (line 47) | def _create_embedding(self):
    method _create_loss (line 56) | def _create_loss(self):
    method _create_optimizer (line 77) | def _create_optimizer(self):
    method _create_summaries (line 83) | def _create_summaries(self):
    method build_graph (line 91) | def build_graph(self):
  function train_model (line 99) | def train_model(model, batch_gen, num_train_steps, weights_fld):
  function main (line 150) | def main():

FILE: 2017/examples/05_csv_reader.py
  function batch_generator (line 24) | def batch_generator(filenames):
  function generate_batches (line 69) | def generate_batches(data_batch, label_batch):
  function main (line 79) | def main():

FILE: 2017/examples/07_basic_filters.py
  function read_one_image (line 41) | def read_one_image(filename):
  function convolve (line 52) | def convolve(image, kernels, rgb=True, strides=[1, 3, 3, 1], padding='SA...
  function get_real_images (line 61) | def get_real_images(images):
  function show_images (line 70) | def show_images(images, rgb=True):
  function main (line 82) | def main():

FILE: 2017/examples/09_tfrecord_example.py
  function _int64_feature (line 21) | def _int64_feature(value):
  function _bytes_feature (line 24) | def _bytes_feature(value):
  function get_image_binary (line 27) | def get_image_binary(filename):
  function write_to_tfrecord (line 36) | def write_to_tfrecord(label, shape, binary_image, tfrecord_file):
  function write_tfrecord (line 50) | def write_tfrecord(label, image_file, tfrecord_file):
  function read_from_tfrecord (line 54) | def read_from_tfrecord(filenames):
  function read_tfrecord (line 75) | def read_tfrecord(tfrecord_file):
  function main (line 89) | def main():

FILE: 2017/examples/11_char_rnn_gist.py
  function vocab_encode (line 28) | def vocab_encode(text, vocab):
  function vocab_decode (line 31) | def vocab_decode(array, vocab):
  function read_data (line 34) | def read_data(filename, vocab, window=NUM_STEPS, overlap=NUM_STEPS//2):
  function read_batch (line 42) | def read_batch(stream, batch_size=BATCH_SIZE):
  function create_rnn (line 51) | def create_rnn(seq, hidden_size=HIDDEN_SIZE):
  function create_model (line 61) | def create_model(seq, temp, vocab, hidden=HIDDEN_SIZE):
  function training (line 73) | def training(vocab, seq, loss, optimizer, global_step, temp, sample, in_...
  function online_inference (line 94) | def online_inference(sess, vocab, seq, sample, temp, in_state, out_state...
  function main (line 109) | def main():

FILE: 2017/examples/autoencoder/autoencoder.py
  function encoder (line 5) | def encoder(input):
  function decoder (line 16) | def decoder(input):
  function autoencoder (line 29) | def autoencoder(input_shape):

FILE: 2017/examples/autoencoder/layer_utils.py
  function get_deconv2d_output_dims (line 3) | def get_deconv2d_output_dims(input_dims, filter_dims, stride_dims, paddi...

FILE: 2017/examples/autoencoder/layers.py
  function conv (line 5) | def conv(input, name, filter_dims, stride_dims, padding='SAME',
  function deconv (line 29) | def deconv(input, name, filter_dims, stride_dims, padding='SAME',
  function max_pool (line 59) | def max_pool(input, name, filter_dims, stride_dims, padding='SAME'):
  function fc (line 69) | def fc(input, name, out_dim, non_linear_fn=tf.nn.relu):

FILE: 2017/examples/autoencoder/train.py
  function calculate_loss (line 13) | def calculate_loss(original, reconstructed):
  function train (line 18) | def train(dataset):

FILE: 2017/examples/autoencoder/utils.py
  function load_dataset (line 14) | def load_dataset():
  function get_next_batch (line 17) | def get_next_batch(dataset, batch_size):
  function visualize (line 23) | def visualize(_original, _reconstructions, num_visualize):

FILE: 2017/examples/cgru/custom_getter.py
  function testGetterThatCreatesTwoVariablesAndSumsThem (line 2) | def testGetterThatCreatesTwoVariablesAndSumsThem(self):

FILE: 2017/examples/cgru/data_reader.py
  function examples_queue (line 1) | def examples_queue(data_sources, data_fields_to_features, training,
  function batch_examples (line 85) | def batch_examples(examples, batch_size, bucket_boundaries=None):

FILE: 2017/examples/cgru/my_layers.py
  function saturating_sigmoid (line 1) | def saturating_sigmoid(x):
  function embedding (line 8) | def embedding(x, vocab_size, dense_size, name=None, reuse=None):
  function conv_gru (line 16) | def conv_gru(x, kernel_size, filters, padding="same", dilation_rate=1,

FILE: 2017/examples/cgru/neural_gpu_v3.py
  function neural_gpu (line 1) | def neural_gpu(features, hparams, name=None):
  function mixed_curriculum (line 21) | def mixed_curriculum(inputs, hparams):
  function neural_gpu_curriculum (line 34) | def neural_gpu_curriculum(features, hparams, mode):
  function basic_params1 (line 48) | def basic_params1():
  function curriculum_params1 (line 69) | def curriculum_params1():

FILE: 2017/examples/deepdream/deepdream_exercise.py
  function get_layer (line 27) | def get_layer(layer):
  function maybe_download (line 40) | def maybe_download(data_dir):
  function normalize_image (line 59) | def normalize_image(image):
  function save_jpeg (line 74) | def save_jpeg(jpeg_file, image):
  function main (line 80) | def main(unused_argv):

FILE: 2017/examples/deepdream/deepdream_solution.py
  function get_layer (line 31) | def get_layer(layer):
  function maybe_download (line 44) | def maybe_download(data_dir):
  function normalize_image (line 63) | def normalize_image(image):
  function save_jpeg (line 78) | def save_jpeg(jpeg_file, image):
  function main (line 84) | def main(unused_argv):

FILE: 2017/examples/process_data.py
  function download (line 24) | def download(file_name, expected_bytes):
  function read_data (line 39) | def read_data(file_path):
  function build_vocab (line 48) | def build_vocab(words, vocab_size):
  function convert_words_to_index (line 64) | def convert_words_to_index(words, dictionary):
  function generate_sample (line 68) | def generate_sample(index_words, context_window_size):
  function get_batch (line 79) | def get_batch(iterator, batch_size):
  function process_data (line 88) | def process_data(vocab_size, batch_size, skip_window):
  function get_index_vocab (line 97) | def get_index_vocab(vocab_size):

FILE: 2017/examples/utils.py
  function huber_loss (line 5) | def huber_loss(labels, predictions, delta=1.0):
  function make_dir (line 11) | def make_dir(path):

FILE: assignments/01/q1_sol.py
  function f1 (line 111) | def f1(): return tf.reduce_mean(tf.square(x - y))
  function f2 (line 112) | def f2(): return tf.reduce_sum(tf.abs(x - y))

FILE: assignments/02_style_transfer/load_vgg.py
  class VGG (line 23) | class VGG(object):
    method __init__ (line 24) | def __init__(self, input_img):
    method _weights (line 30) | def _weights(self, layer_idx, expected_layer_name):
    method conv2d_relu (line 39) | def conv2d_relu(self, prev_layer, layer_idx, layer_name):
    method avgpool (line 61) | def avgpool(self, prev_layer, layer_name):
    method load (line 78) | def load(self):

FILE: assignments/02_style_transfer/load_vgg_sol.py
  class VGG (line 22) | class VGG(object):
    method __init__ (line 23) | def __init__(self, input_img):
    method _weights (line 29) | def _weights(self, layer_idx, expected_layer_name):
    method conv2d_relu (line 38) | def conv2d_relu(self, prev_layer, layer_idx, layer_name):
    method avgpool (line 71) | def avgpool(self, prev_layer, layer_name):
    method load (line 91) | def load(self):

FILE: assignments/02_style_transfer/style_transfer.py
  function setup (line 21) | def setup():
  class StyleTransfer (line 25) | class StyleTransfer(object):
    method __init__ (line 26) | def __init__(self, content_img, style_img, img_width, img_height):
    method create_input (line 52) | def create_input(self):
    method load_vgg (line 68) | def load_vgg(self):
    method _content_loss (line 83) | def _content_loss(self, P, F):
    method _gram_matrix (line 100) | def _gram_matrix(self, F, N, M):
    method _single_style_loss (line 109) | def _single_style_loss(self, a, g):
    method _style_loss (line 126) | def _style_loss(self, A):
    method losses (line 136) | def losses(self):
    method optimize (line 156) | def optimize(self):
    method create_summary (line 162) | def create_summary(self):
    method build (line 170) | def build(self):
    method train (line 177) | def train(self, n_iters):

FILE: assignments/02_style_transfer/style_transfer_sol.py
  function setup (line 11) | def setup():
  class StyleTransfer (line 15) | class StyleTransfer(object):
    method __init__ (line 16) | def __init__(self, content_img, style_img, img_width, img_height):
    method create_input (line 41) | def create_input(self):
    method load_vgg (line 57) | def load_vgg(self):
    method _content_loss (line 72) | def _content_loss(self, P, F):
    method _gram_matrix (line 90) | def _gram_matrix(self, F, N, M):
    method _single_style_loss (line 100) | def _single_style_loss(self, a, g):
    method _style_loss (line 121) | def _style_loss(self, A):
    method losses (line 134) | def losses(self):
    method optimize (line 154) | def optimize(self):
    method create_summary (line 161) | def create_summary(self):
    method build (line 173) | def build(self):
    method train (line 180) | def train(self, n_iters):

FILE: assignments/02_style_transfer/utils.py
  function download (line 20) | def download(download_link, file_name, expected_bytes):
  function get_resized_image (line 34) | def get_resized_image(img_path, width, height, save=True):
  function generate_noise_image (line 47) | def generate_noise_image(content_image, width, height, noise_ratio=0.6):
  function save_image (line 51) | def save_image(path, image):
  function safe_mkdir (line 56) | def safe_mkdir(path):

FILE: assignments/chatbot/chatbot.py
  function _get_random_bucket (line 31) | def _get_random_bucket(train_buckets_scale):
  function _assert_lengths (line 37) | def _assert_lengths(encoder_size, decoder_size, encoder_inputs, decoder_...
  function run_step (line 50) | def run_step(sess, model, encoder_inputs, decoder_inputs, decoder_masks,...
  function _get_buckets (line 85) | def _get_buckets():
  function _get_skip_step (line 101) | def _get_skip_step(iteration):
  function _check_restore_parameters (line 107) | def _check_restore_parameters(sess, saver):
  function _eval_test_set (line 116) | def _eval_test_set(sess, model, test_buckets):
  function train (line 130) | def train():
  function _get_user_input (line 168) | def _get_user_input():
  function _find_right_bucket (line 174) | def _find_right_bucket(length):
  function _construct_response (line 179) | def _construct_response(output_logits, inv_dec_vocab):
  function chat (line 194) | def chat():
  function main (line 240) | def main():

FILE: assignments/chatbot/data.py
  function get_lines (line 26) | def get_lines():
  function get_convos (line 46) | def get_convos():
  function question_answers (line 61) | def question_answers(id2line, convos):
  function prepare_dataset (line 71) | def prepare_dataset(questions, answers):
  function make_dir (line 94) | def make_dir(path):
  function basic_tokenizer (line 101) | def basic_tokenizer(line, normalize_digits=True):
  function build_vocab (line 120) | def build_vocab(filename, normalize_digits=True):
  function load_vocab (line 150) | def load_vocab(vocab_path):
  function sentence2id (line 155) | def sentence2id(vocab, line):
  function token2id (line 158) | def token2id(data, mode):
  function prepare_raw_data (line 181) | def prepare_raw_data():
  function process_data (line 188) | def process_data():
  function load_data (line 197) | def load_data(enc_filename, dec_filename, max_training_size=None):
  function _pad_input (line 216) | def _pad_input(input_, size):
  function _reshape_batch (line 219) | def _reshape_batch(inputs, size, batch_size):
  function get_batch (line 229) | def get_batch(data_bucket, bucket_id, batch_size=1):

FILE: assignments/chatbot/model.py
  class ChatBotModel (line 8) | class ChatBotModel:
    method __init__ (line 9) | def __init__(self, forward_only, batch_size):
    method _create_placeholders (line 16) | def _create_placeholders(self):
    method _inference (line 29) | def _inference(self):
    method _create_loss (line 51) | def _create_loss(self):
    method _creat_optimizer (line 91) | def _creat_optimizer(self):
    method _create_summary (line 114) | def _create_summary(self):
    method build_graph (line 117) | def build_graph(self):

FILE: examples/04_linreg_eager.py
  function prediction (line 30) | def prediction(x):
  function squared_loss (line 34) | def squared_loss(y, y_predicted):
  function huber_loss (line 37) | def huber_loss(y, y_predicted, m=1.0):
  function train (line 45) | def train(loss_fn):

FILE: examples/04_linreg_eager_starter.py
  function prediction (line 35) | def prediction(x):
  function squared_loss (line 42) | def squared_loss(y, y_predicted):
  function huber_loss (line 48) | def huber_loss(y, y_predicted):
  function train (line 55) | def train(loss_fn):

FILE: examples/04_word2vec.py
  function word2vec (line 35) | def word2vec(dataset):
  function gen (line 89) | def gen():
  function main (line 93) | def main():

FILE: examples/04_word2vec_eager.py
  class Word2Vec (line 36) | class Word2Vec(object):
    method __init__ (line 37) | def __init__(self, vocab_size, embed_size, num_sampled=NUM_SAMPLED):
    method compute_loss (line 47) | def compute_loss(self, center_words, target_words):
  function gen (line 59) | def gen():
  function main (line 64) | def main():

FILE: examples/04_word2vec_eager_starter.py
  class Word2Vec (line 39) | class Word2Vec(object):
    method __init__ (line 40) | def __init__(self, vocab_size, embed_size, num_sampled=NUM_SAMPLED):
    method compute_loss (line 51) | def compute_loss(self, center_words, target_words):
  function gen (line 67) | def gen():
  function main (line 72) | def main():

FILE: examples/04_word2vec_visualize.py
  class SkipGramModel (line 35) | class SkipGramModel:
    method __init__ (line 37) | def __init__(self, dataset, vocab_size, embed_size, batch_size, num_sa...
    method _import_data (line 47) | def _import_data(self):
    method _create_embedding (line 54) | def _create_embedding(self):
    method _create_loss (line 64) | def _create_loss(self):
    method _create_optimizer (line 80) | def _create_optimizer(self):
    method _create_summaries (line 85) | def _create_summaries(self):
    method build_graph (line 93) | def build_graph(self):
    method train (line 101) | def train(self, num_train_steps):
    method visualize (line 132) | def visualize(self, visual_fld, num_visualize):
  function gen (line 168) | def gen():
  function main (line 172) | def main():

FILE: examples/05_variable_sharing.py
  function two_hidden_layers (line 15) | def two_hidden_layers(x):
  function two_hidden_layers_2 (line 26) | def two_hidden_layers_2(x):
  function fully_connected (line 53) | def fully_connected(x, output_dim, scope):
  function two_hidden_layers (line 59) | def two_hidden_layers(x):

FILE: examples/07_convnet_layers.py
  class ConvNet (line 16) | class ConvNet(object):
    method __init__ (line 17) | def __init__(self):
    method get_data (line 28) | def get_data(self):
    method inference (line 40) | def inference(self):
    method loss (line 72) | def loss(self):
    method optimize (line 83) | def optimize(self):
    method summary (line 91) | def summary(self):
    method eval (line 101) | def eval(self):
    method build (line 110) | def build(self):
    method train_one_epoch (line 121) | def train_one_epoch(self, sess, saver, init, writer, epoch, step):
    method eval_once (line 143) | def eval_once(self, sess, init, writer, epoch, step):
    method train (line 159) | def train(self, n_epochs):

FILE: examples/07_convnet_mnist.py
  function conv_relu (line 16) | def conv_relu(inputs, filters, k_size, stride, padding, scope_name):
  function maxpool (line 31) | def maxpool(inputs, ksize, stride, padding='VALID', scope_name='pool'):
  function fully_connected (line 40) | def fully_connected(inputs, out_dim, scope_name='fc'):
  class ConvNet (line 53) | class ConvNet(object):
    method __init__ (line 54) | def __init__(self):
    method get_data (line 65) | def get_data(self):
    method inference (line 77) | def inference(self):
    method loss (line 98) | def loss(self):
    method optimize (line 109) | def optimize(self):
    method summary (line 117) | def summary(self):
    method eval (line 127) | def eval(self):
    method build (line 136) | def build(self):
    method train_one_epoch (line 147) | def train_one_epoch(self, sess, saver, init, writer, epoch, step):
    method eval_once (line 169) | def eval_once(self, sess, init, writer, epoch, step):
    method train (line 185) | def train(self, n_epochs):

FILE: examples/07_convnet_mnist_starter.py
  function conv_relu (line 16) | def conv_relu(inputs, filters, k_size, stride, padding, scope_name):
  function maxpool (line 25) | def maxpool(inputs, ksize, stride, padding='VALID', scope_name='pool'):
  function fully_connected (line 32) | def fully_connected(inputs, out_dim, scope_name='fc'):
  class ConvNet (line 41) | class ConvNet(object):
    method __init__ (line 42) | def __init__(self):
    method get_data (line 52) | def get_data(self):
    method inference (line 64) | def inference(self):
    method loss (line 73) | def loss(self):
    method optimize (line 86) | def optimize(self):
    method summary (line 97) | def summary(self):
    method eval (line 107) | def eval(self):
    method build (line 116) | def build(self):
    method train_one_epoch (line 127) | def train_one_epoch(self, sess, saver, init, writer, epoch, step):
    method eval_once (line 148) | def eval_once(self, sess, init, writer, epoch, step):
    method train (line 163) | def train(self, n_epochs):

FILE: examples/07_run_kernels.py
  function read_one_image (line 29) | def read_one_image(filename):
  function convolve (line 38) | def convolve(image, kernels, rgb=True, strides=[1, 3, 3, 1], padding='SA...
  function show_images (line 50) | def show_images(images, rgb=True):
  function main (line 62) | def main():

FILE: examples/11_char_rnn.py
  function vocab_encode (line 20) | def vocab_encode(text, vocab):
  function vocab_decode (line 23) | def vocab_decode(array, vocab):
  function read_data (line 26) | def read_data(filename, vocab, window, overlap):
  function read_batch (line 38) | def read_batch(stream, batch_size):
  class CharRNN (line 47) | class CharRNN(object):
    method __init__ (line 48) | def __init__(self, model):
    method create_rnn (line 68) | def create_rnn(self, seq):
    method create_model (line 80) | def create_model(self):
    method train (line 92) | def train(self):
    method online_infer (line 124) | def online_infer(self, sess):
  function main (line 140) | def main():

FILE: examples/utils.py
  function huber_loss (line 13) | def huber_loss(labels, predictions, delta=14.0):
  function safe_mkdir (line 19) | def safe_mkdir(path):
  function read_birth_life_data (line 26) | def read_birth_life_data(filename):
  function download_one_file (line 41) | def download_one_file(download_url,
  function download_mnist (line 68) | def download_mnist(path):
  function parse_data (line 86) | def parse_data(path, dataset, flatten):
  function read_mnist (line 107) | def read_mnist(path, flatten=True, num_train=55000):
  function get_mnist_dataset (line 121) | def get_mnist_dataset(batch_size):
  function show (line 137) | def show(image):

FILE: examples/word2vec_utils.py
  function read_data (line 14) | def read_data(file_path):
  function build_vocab (line 22) | def build_vocab(words, vocab_size, visual_fld):
  function convert_words_to_index (line 43) | def convert_words_to_index(words, dictionary):
  function generate_sample (line 47) | def generate_sample(index_words, context_window_size):
  function most_common_words (line 58) | def most_common_words(visual_fld, num_visualize):
  function batch_gen (line 69) | def batch_gen(download_url, expected_byte, vocab_size, batch_size,