SYMBOL INDEX (145 symbols across 25 files)

FILE: cornell_preprocess.py
  function prepare_cornell_data (line 21) | def prepare_cornell_data():
  function loadLines (line 46) | def loadLines(fileName,
  function loadConversations (line 72) | def loadConversations(fileName, lines,
  function train_valid_test_split_by_conversation (line 106) | def train_valid_test_split_by_conversation(conversations, split_ratio=[0...
  function tokenize_conversation (line 133) | def tokenize_conversation(lines):
  function pad_sentences (line 138) | def pad_sentences(conversations, max_sentence_length=30, max_conversatio...
  function to_pickle (line 211) | def to_pickle(obj, path):
  function _tokenize_conversation (line 221) | def _tokenize_conversation(conv):

FILE: model/configs.py
  function str2bool (line 21) | def str2bool(v):
  class Config (line 31) | class Config(object):
    method __init__ (line 32) | def __init__(self, **kwargs):
    method __str__ (line 69) | def __str__(self):
  function get_config (line 76) | def get_config(parse=True, **optional_kwargs):

FILE: model/data_loader.py
  class DialogDataset (line 8) | class DialogDataset(Dataset):
    method __init__ (line 9) | def __init__(self, sentences, conversation_length, sentence_length, vo...
    method __getitem__ (line 26) | def __getitem__(self, index):
    method __len__ (line 38) | def __len__(self):
    method sent2id (line 41) | def sent2id(self, sentences):
  function get_loader (line 47) | def get_loader(sentences, conversation_length, sentence_length, vocab, b...

FILE: model/eval.py
  function load_pickle (line 10) | def load_pickle(path):

FILE: model/eval_embed.py
  function load_pickle (line 11) | def load_pickle(path):

FILE: model/layers/beam_search.py
  class Beam (line 5) | class Beam(object):
    method __init__ (line 6) | def __init__(self, batch_size, hidden_size, vocab_size, beam_size, max...
    method update (line 34) | def update(self, score, back_pointer, token_id):  # , h):
    method backtrack (line 43) | def backtrack(self):

FILE: model/layers/decoder.py
  class BaseRNNDecoder (line 12) | class BaseRNNDecoder(nn.Module):
    method __init__ (line 13) | def __init__(self):
    method use_lstm (line 18) | def use_lstm(self):
    method init_token (line 21) | def init_token(self, batch_size, SOS_ID=SOS_ID):
    method init_h (line 26) | def init_h(self, batch_size=None, zero=True, hidden=None):
    method batch_size (line 45) | def batch_size(self, inputs=None, h=None):
    method decode (line 62) | def decode(self, out):
    method forward (line 81) | def forward(self):
    method forward_step (line 85) | def forward_step(self):
    method embed (line 89) | def embed(self, x):
    method beam_decode (line 102) | def beam_decode(self,
  class DecoderRNN (line 212) | class DecoderRNN(BaseRNNDecoder):
    method __init__ (line 213) | def __init__(self, vocab_size, embedding_size,
    method forward_step (line 239) | def forward_step(self, x, h,
    method forward (line 273) | def forward(self, inputs, init_h=None, encoder_outputs=None, input_val...

FILE: model/layers/encoder.py
  class BaseRNNEncoder (line 10) | class BaseRNNEncoder(nn.Module):
    method __init__ (line 11) | def __init__(self):
    method use_lstm (line 16) | def use_lstm(self):
    method init_h (line 22) | def init_h(self, batch_size=None, hidden=None):
    method batch_size (line 39) | def batch_size(self, inputs=None, h=None):
    method forward (line 56) | def forward(self):
  class EncoderRNN (line 60) | class EncoderRNN(BaseRNNEncoder):
    method __init__ (line 61) | def __init__(self, vocab_size, embedding_size,
    method forward (line 91) | def forward(self, inputs, input_length, hidden=None):
  class ContextRNN (line 140) | class ContextRNN(BaseRNNEncoder):
    method __init__ (line 141) | def __init__(self, input_size, context_size, rnn=nn.GRU, num_layers=1,...
    method forward (line 167) | def forward(self, encoder_hidden, conversation_length, hidden=None):
    method step (line 210) | def step(self, encoder_hidden, hidden):

FILE: model/layers/feedforward.py
  class FeedForward (line 5) | class FeedForward(nn.Module):
    method __init__ (line 6) | def __init__(self, input_size, output_size, num_layers=1, hidden_size=...
    method forward (line 19) | def forward(self, input):

FILE: model/layers/loss.py
  function masked_cross_entropy (line 8) | def masked_cross_entropy(logits, target, length, per_example=False):

FILE: model/layers/rnncells.py
  class StackedLSTMCell (line 10) | class StackedLSTMCell(nn.Module):
    method __init__ (line 12) | def __init__(self, num_layers, input_size, rnn_size, dropout):
    method forward (line 22) | def forward(self, x, h_c):
  class StackedGRUCell (line 52) | class StackedGRUCell(nn.Module):
    method __init__ (line 54) | def __init__(self, num_layers, input_size, rnn_size, dropout):
    method forward (line 64) | def forward(self, x, h):

FILE: model/models.py
  class HRED (line 10) | class HRED(nn.Module):
    method __init__ (line 11) | def __init__(self, config):
    method forward (line 52) | def forward(self, input_sentences, input_sentence_length,
    method generate (line 122) | def generate(self, context, sentence_length, n_context):
  class VHRED (line 169) | class VHRED(nn.Module):
    method __init__ (line 170) | def __init__(self, config):
    method prior (line 239) | def prior(self, context_outputs):
    method posterior (line 246) | def posterior(self, context_outputs, encoder_hidden):
    method compute_bow_loss (line 252) | def compute_bow_loss(self, target_conversations):
    method forward (line 259) | def forward(self, sentences, sentence_length,
    method generate (line 350) | def generate(self, context, sentence_length, n_context):
  class VHCR (line 409) | class VHCR(nn.Module):
    method __init__ (line 410) | def __init__(self, config):
    method conv_prior (line 500) | def conv_prior(self):
    method conv_posterior (line 504) | def conv_posterior(self, context_inference_hidden):
    method sent_prior (line 510) | def sent_prior(self, context_outputs, z_conv):
    method sent_posterior (line 517) | def sent_posterior(self, context_outputs, encoder_hidden, z_conv):
    method forward (line 523) | def forward(self, sentences, sentence_length,
    method generate (line 677) | def generate(self, context, sentence_length, n_context):

FILE: model/solver.py
  class Solver (line 18) | class Solver(object):
    method __init__ (line 19) | def __init__(self, config, train_data_loader, eval_data_loader, vocab,...
    method build (line 29) | def build(self, cuda=True):
    method save_model (line 67) | def save_model(self, epoch):
    method load_model (line 73) | def load_model(self, checkpoint):
    method write_summary (line 80) | def write_summary(self, epoch_i):
    method train (line 124) | def train(self):
    method generate_sentence (line 206) | def generate_sentence(self, input_sentences, input_sentence_length,
    method evaluate (line 234) | def evaluate(self):
    method test (line 291) | def test(self):
    method embedding_metric (line 345) | def embedding_metric(self):
  class VariationalSolver (line 420) | class VariationalSolver(Solver):
    method __init__ (line 422) | def __init__(self, config, train_data_loader, eval_data_loader, vocab,...
    method train (line 432) | def train(self):
    method generate_sentence (line 543) | def generate_sentence(self, sentences, sentence_length,
    method evaluate (line 572) | def evaluate(self):
    method importance_sample (line 650) | def importance_sample(self):

FILE: model/train.py
  function load_pickle (line 9) | def load_pickle(path):

FILE: model/utils/bow.py
  function to_bow (line 10) | def to_bow(sentence, vocab_size):
  function bag_of_words_loss (line 29) | def bag_of_words_loss(bow_logits, target_bow, weight=None):

FILE: model/utils/convert.py
  function to_var (line 5) | def to_var(x, on_cpu=False, gpu_id=None, async=False):
  function to_tensor (line 13) | def to_tensor(x):
  function reverse_order (line 19) | def reverse_order(tensor, dim=0):
  function reverse_order_valid (line 35) | def reverse_order_valid(tensor, length_list, dim=0):

FILE: model/utils/embedding_metric.py
  function cosine_similarity (line 4) | def cosine_similarity(s, g):
  function embedding_metric (line 11) | def embedding_metric(samples, ground_truth, word2vec, method='average'):

FILE: model/utils/mask.py
  function sequence_mask (line 5) | def sequence_mask(sequence_length, max_len=None):

FILE: model/utils/pad.py
  function pad (line 6) | def pad(tensor, length):
  function pad_and_pack (line 22) | def pad_and_pack(tensor_list):

FILE: model/utils/probability.py
  function normal_logpdf (line 6) | def normal_logpdf(x, mean, var):
  function normal_kl_div (line 20) | def normal_kl_div(mu1, var1,

FILE: model/utils/tensorboard.py
  class TensorboardWriter (line 3) | class TensorboardWriter(SummaryWriter):
    method __init__ (line 4) | def __init__(self, logdir):
    method update_parameters (line 14) | def update_parameters(self, module, step_i):
    method update_loss (line 21) | def update_loss(self, loss, step_i, name='loss'):
    method update_histogram (line 24) | def update_histogram(self, values, step_i, name='hist'):

FILE: model/utils/time_track.py
  function base_time_desc_decorator (line 5) | def base_time_desc_decorator(method, desc='test_description'):
  function time_desc_decorator (line 31) | def time_desc_decorator(desc): return partial(base_time_desc_decorator, ...
  function time_test (line 35) | def time_test(arg, kwarg='this is kwarg'):
  function no_arg_method (line 43) | def no_arg_method():

FILE: model/utils/tokenizer.py
  function clean_str (line 4) | def clean_str(string):
  class Tokenizer (line 24) | class Tokenizer():
    method __init__ (line 25) | def __init__(self, tokenizer='whitespace', clean_string=True):
    method __call__ (line 53) | def __call__(self, string):

FILE: model/utils/vocab.py
  class Vocab (line 17) | class Vocab(object):
    method __init__ (line 18) | def __init__(self, tokenizer=None, max_size=None, min_freq=1):
    method update (line 25) | def update(self, max_size=None, min_freq=1):
    method __len__ (line 73) | def __len__(self):
    method load (line 76) | def load(self, word2id_path=None, id2word_path=None):
    method add_word (line 90) | def add_word(self, word):
    method add_sentence (line 94) | def add_sentence(self, sentence, tokenized=False):
    method add_dataframe (line 100) | def add_dataframe(self, conversation_df, tokenized=True):
    method pickle (line 105) | def pickle(self, word2id_path, id2word_path):
    method to_list (line 112) | def to_list(self, list_like):
    method id2sent (line 122) | def id2sent(self, id_list):
    method sent2id (line 134) | def sent2id(self, sentence, var=False):
    method decode (line 141) | def decode(self, id_list):

FILE: ubuntu_preprocess.py
  function prepare_ubuntu_data (line 30) | def prepare_ubuntu_data():
  function get_dialog_path_list (line 72) | def get_dialog_path_list(dataset='train'):
  function read_and_tokenize (line 89) | def read_and_tokenize(dialog_path, min_turn=3):
  function pad_sentences (line 137) | def pad_sentences(conversations, max_sentence_length=30, max_conversatio...
  function to_pickle (line 200) | def to_pickle(obj, path):
  function _tokenize_conversation (line 213) | def _tokenize_conversation(dialog_path):