SYMBOL INDEX (78 symbols across 8 files)

FILE: beam_search.py
  class Beam (line 7) | class Beam(object):
    method __init__ (line 8) | def __init__(self, start_id, end_id, unk_id, hidden_state, context):
    method get_current_state (line 23) | def get_current_state(self):
    method advance (line 31) | def advance(self, prob_dist, hidden_state, context, sum_temporal_srcs,...
    method get_best (line 63) | def get_best(self):
    method get_all (line 72) | def get_all(self):
  function beam_search (line 79) | def beam_search(enc_hid, enc_out, enc_padding_mask, ct_e, extra_zeros, e...

FILE: data_util/batcher.py
  class Example (line 18) | class Example(object):
    method __init__ (line 20) | def __init__(self, article, abstract_sentences, vocab):
    method get_dec_inp_targ_seqs (line 58) | def get_dec_inp_targ_seqs(self, sequence, max_len, start_id, stop_id):
    method pad_decoder_inp_targ (line 70) | def pad_decoder_inp_targ(self, max_len, pad_id):
    method pad_encoder_input (line 77) | def pad_encoder_input(self, max_len, pad_id):
  class Batch (line 84) | class Batch(object):
    method __init__ (line 85) | def __init__(self, example_list, vocab, batch_size):
    method init_encoder_seq (line 93) | def init_encoder_seq(self, example_list):
    method init_decoder_seq (line 124) | def init_decoder_seq(self, example_list):
    method store_orig_strings (line 143) | def store_orig_strings(self, example_list):
  class Batcher (line 149) | class Batcher(object):
    method __init__ (line 152) | def __init__(self, data_path, vocab, mode, batch_size, single_pass):
    method next_batch (line 191) | def next_batch(self):
    method fill_example_queue (line 202) | def fill_example_queue(self):
    method fill_batch_queue (line 222) | def fill_batch_queue(self):
    method watch_threads (line 245) | def watch_threads(self):
    method text_generator (line 268) | def text_generator(self, example_generator):

FILE: data_util/data.py
  class Vocab (line 21) | class Vocab(object):
    method __init__ (line 23) | def __init__(self, vocab_file, max_size):
    method word2id (line 55) | def word2id(self, word):
    method id2word (line 60) | def id2word(self, word_id):
    method size (line 65) | def size(self):
    method write_metadata (line 68) | def write_metadata(self, fpath):
  function example_generator (line 77) | def example_generator(data_path, single_pass):
  function article2ids (line 98) | def article2ids(article_words, vocab):
  function abstract2ids (line 114) | def abstract2ids(abstract_words, vocab, article_oovs):
  function outputids2words (line 130) | def outputids2words(id_list, vocab, article_oovs):
  function abstract2sents (line 146) | def abstract2sents(abstract):
  function show_art_oovs (line 159) | def show_art_oovs(article, vocab):
  function show_abs_oovs (line 167) | def show_abs_oovs(abstract, vocab, article_oovs):

FILE: eval.py
  function get_cuda (line 18) | def get_cuda(tensor):
  class Evaluate (line 23) | class Evaluate(object):
    method __init__ (line 24) | def __init__(self, data_path, opt, batch_size = config.batch_size):
    method setup_valid (line 31) | def setup_valid(self):
    method print_original_predicted (line 38) | def print_original_predicted(self, decoded_sents, ref_sents, article_s...
    method evaluate_batch (line 47) | def evaluate_batch(self, print_sents = False):

FILE: make_data_files.py
  function make_folder (line 21) | def make_folder(folder_path):
  function delete_folder (line 25) | def delete_folder(folder_path):
  function shuffle_text_data (line 29) | def shuffle_text_data(unshuffled_art, unshuffled_abs, shuffled_art, shuf...
  function write_to_bin (line 48) | def write_to_bin(article_path, abstract_path, out_file, vocab_counter = ...
  function creating_finished_data (line 82) | def creating_finished_data():
  function chunk_file (line 91) | def chunk_file(set_name, chunks_dir, bin_file):

FILE: model.py
  function init_lstm_wt (line 8) | def init_lstm_wt(lstm):
  function init_linear_wt (line 21) | def init_linear_wt(linear):
  function init_wt_normal (line 26) | def init_wt_normal(wt):
  class Encoder (line 30) | class Encoder(nn.Module):
    method __init__ (line 31) | def __init__(self):
    method forward (line 42) | def forward(self, x, seq_lens):
  class encoder_attention (line 55) | class encoder_attention(nn.Module):
    method __init__ (line 57) | def __init__(self):
    method forward (line 64) | def forward(self, st_hat, h, enc_padding_mask, sum_temporal_srcs):
  class decoder_attention (line 104) | class decoder_attention(nn.Module):
    method __init__ (line 105) | def __init__(self):
    method forward (line 112) | def forward(self, s_t, prev_s):
  class Decoder (line 138) | class Decoder(nn.Module):
    method __init__ (line 139) | def __init__(self):
    method forward (line 155) | def forward(self, x_t, s_t, enc_out, enc_padding_mask, ct_e, extra_zer...
  class Model (line 185) | class Model(nn.Module):
    method __init__ (line 186) | def __init__(self):

FILE: train.py
  class Train (line 25) | class Train(object):
    method __init__ (line 26) | def __init__(self, opt):
    method save_model (line 37) | def save_model(self, iter):
    method setup_train (line 45) | def setup_train(self):
    method train_batch_MLE (line 61) | def train_batch_MLE(self, enc_out, enc_hidden, enc_padding_mask, ct_e,...
    method train_batch_RL (line 97) | def train_batch_RL(self, enc_out, enc_hidden, enc_padding_mask, ct_e, ...
    method reward_function (line 164) | def reward_function(self, decoded_sents, original_sents):
    method train_one_batch (line 194) | def train_one_batch(self, batch, iter):
    method trainIters (line 232) | def trainIters(self):

FILE: train_util.py
  function get_cuda (line 5) | def get_cuda(tensor):
  function get_enc_data (line 10) | def get_enc_data(batch):
  function get_dec_data (line 37) | def get_dec_data(batch):