SYMBOL INDEX (58 symbols across 4 files)

FILE: net.py
  function sentence_block_embed (line 17) | def sentence_block_embed(embed, x):
  function seq_func (line 35) | def seq_func(func, x, reconstruct_shape=True):
  class LayerNormalizationSentence (line 55) | class LayerNormalizationSentence(L.LayerNormalization):
    method __init__ (line 64) | def __init__(self, *args, **kwargs):
    method __call__ (line 67) | def __call__(self, x):
  class ConvolutionSentence (line 72) | class ConvolutionSentence(L.Convolution2D):
    method __init__ (line 82) | def __init__(self, in_channels, out_channels,
    method __call__ (line 90) | def __call__(self, x):
  class MultiHeadAttention (line 108) | class MultiHeadAttention(chainer.Chain):
    method __init__ (line 117) | def __init__(self, n_units, h=8, dropout=0.1, self_attention=True):
    method __call__ (line 139) | def __call__(self, x, z=None, mask=None):
  class FeedForwardLayer (line 182) | class FeedForwardLayer(chainer.Chain):
    method __init__ (line 183) | def __init__(self, n_units):
    method __call__ (line 194) | def __call__(self, e):
  class EncoderLayer (line 201) | class EncoderLayer(chainer.Chain):
    method __init__ (line 202) | def __init__(self, n_units, h=8, dropout=0.1):
    method __call__ (line 211) | def __call__(self, e, xx_mask):
  class DecoderLayer (line 222) | class DecoderLayer(chainer.Chain):
    method __init__ (line 223) | def __init__(self, n_units, h=8, dropout=0.1):
    method __call__ (line 235) | def __call__(self, e, s, xy_mask, yy_mask):
  class Encoder (line 250) | class Encoder(chainer.Chain):
    method __init__ (line 251) | def __init__(self, n_layers, n_units, h=8, dropout=0.1):
    method __call__ (line 260) | def __call__(self, e, xx_mask):
  class Decoder (line 266) | class Decoder(chainer.Chain):
    method __init__ (line 267) | def __init__(self, n_layers, n_units, h=8, dropout=0.1):
    method __call__ (line 276) | def __call__(self, e, source, xy_mask, yy_mask):
  class Transformer (line 282) | class Transformer(chainer.Chain):
    method __init__ (line 284) | def __init__(self, n_layers, n_source_vocab, n_target_vocab, n_units,
    method initialize_position_encoding (line 308) | def initialize_position_encoding(self, length, n_units):
    method make_input_embedding (line 342) | def make_input_embedding(self, embed, block):
    method make_attention_mask (line 354) | def make_attention_mask(self, source_block, target_block):
    method make_history_mask (line 360) | def make_history_mask(self, block):
    method output (line 368) | def output(self, h):
    method output_and_loss (line 371) | def output_and_loss(self, h_block, t_block):
    method __call__ (line 413) | def __call__(self, x_block, y_in_block, y_out_block, get_prediction=Fa...
    method translate (line 440) | def translate(self, x_block, max_length=50, beam=5):
    method translate_beam (line 478) | def translate_beam(self, x_block, max_length=50, beam=5):
  function get_topk (line 531) | def get_topk(x, k=5, axis=1):

FILE: preprocess.py
  function split_sentence (line 15) | def split_sentence(s):
  function open_file (line 26) | def open_file(path):
  function count_lines (line 30) | def count_lines(path):
  function read_file (line 35) | def read_file(path):
  function count_words (line 44) | def count_words(path, max_vocab_size=40000):
  function make_dataset (line 54) | def make_dataset(path, vocab):
  function make_array (line 70) | def make_array(word_id, words):

FILE: subfuncs.py
  class VaswaniRule (line 6) | class VaswaniRule(extension.Extension):
    method __init__ (line 25) | def __init__(self, attr, d, warmup_steps=4000,
    method initialize (line 37) | def initialize(self, trainer):
    method __call__ (line 49) | def __call__(self, trainer):
    method serialize (line 57) | def serialize(self, serializer):
    method _get_optimizer (line 61) | def _get_optimizer(self, trainer):
    method _update_value (line 64) | def _update_value(self, optimizer, value):

FILE: train.py
  function seq2seq_pad_concat_convert (line 24) | def seq2seq_pad_concat_convert(xy_batch, device, eos_id=0, bos_id=2):
  function source_pad_concat_convert (line 71) | def source_pad_concat_convert(x_seqs, device, eos_id=0, bos_id=2):
  class CalculateBleu (line 85) | class CalculateBleu(chainer.training.Extension):
    method __init__ (line 90) | def __init__(
    method __call__ (line 99) | def __call__(self, trainer):
  function main (line 124) | def main():