SYMBOL INDEX (154 symbols across 18 files)

FILE: model_seq/crf.py
  class CRF (line 14) | class CRF(nn.Module):
    method __init__ (line 27) | def __init__(self,
    method rand_init (line 36) | def rand_init(self):
    method forward (line 43) | def forward(self, feats):
  class CRFLoss (line 63) | class CRFLoss(nn.Module):
    method __init__ (line 76) | def __init__(self,
    method forward (line 85) | def forward(self, scores, target, mask):
  class CRFDecode (line 129) | class CRFDecode():
    method __init__ (line 139) | def __init__(self, y_map: dict):
    method decode (line 146) | def decode(self, scores, mask):
    method to_spans (line 189) | def to_spans(self, sequence):

FILE: model_seq/dataset.py
  class SeqDataset (line 19) | class SeqDataset(object):
    method __init__ (line 46) | def __init__(self,
    method shuffle (line 72) | def shuffle(self):
    method get_tqdm (line 78) | def get_tqdm(self, device):
    method construct_index (line 90) | def construct_index(self, dataset):
    method reader (line 109) | def reader(self, device):
    method batchify (line 131) | def batchify(self, batch, device):

FILE: model_seq/elmo.py
  class EBUnit (line 18) | class EBUnit(nn.Module):
    method __init__ (line 31) | def __init__(self, ori_unit, droprate, fix_rate):
    method forward (line 40) | def forward(self, x):
  class ERNN (line 61) | class ERNN(nn.Module):
    method __init__ (line 74) | def __init__(self, ori_drnn, droprate, fix_rate):
    method regularizer (line 93) | def regularizer(self):
    method forward (line 104) | def forward(self, x):
  class ElmoLM (line 125) | class ElmoLM(nn.Module):
    method __init__ (line 141) | def __init__(self, ori_lm, backward, droprate, fix_rate):
    method init_hidden (line 155) | def init_hidden(self):
    method regularizer (line 161) | def regularizer(self):
    method prox (line 172) | def prox(self, lambda0):
    method forward (line 178) | def forward(self, w_in, ind=None):

FILE: model_seq/evaluator.py
  class eval_batch (line 14) | class eval_batch:
    method __init__ (line 23) | def __init__(self, decoder):
    method reset (line 26) | def reset(self):
    method calc_f1_batch (line 36) | def calc_f1_batch(self, decoded_data, target_data):
    method calc_acc_batch (line 60) | def calc_acc_batch(self, decoded_data, target_data):
    method f1_score (line 82) | def f1_score(self):
    method acc_score (line 96) | def acc_score(self):
    method eval_instance (line 105) | def eval_instance(self, best_path, gold):
  class eval_wc (line 130) | class eval_wc(eval_batch):
    method __init__ (line 141) | def __init__(self, decoder, score_type):
    method calc_score (line 151) | def calc_score(self, seq_model, dataset_loader):

FILE: model_seq/seqlabel.py
  class SeqLabel (line 13) | class SeqLabel(nn.Module):
    method __init__ (line 46) | def __init__(self, f_lm, b_lm,
    method to_params (line 87) | def to_params(self):
    method prune_dense_rnn (line 109) | def prune_dense_rnn(self):
    method set_batch_seq_size (line 120) | def set_batch_seq_size(self, sentence):
    method load_pretrained_word_embedding (line 128) | def load_pretrained_word_embedding(self, pre_word_embeddings):
    method rand_init (line 134) | def rand_init(self):
    method forward (line 146) | def forward(self, f_c, f_p, b_c, b_p, flm_w, blm_w, blm_ind, f_w):
  class Vanilla_SeqLabel (line 208) | class Vanilla_SeqLabel(nn.Module):
    method __init__ (line 241) | def __init__(self, f_lm, b_lm, c_num, c_dim, c_hidden, c_layer, w_num,...
    method set_batch_seq_size (line 262) | def set_batch_seq_size(self, sentence):
    method load_pretrained_word_embedding (line 270) | def load_pretrained_word_embedding(self, pre_word_embeddings):
    method rand_init (line 276) | def rand_init(self):
    method forward (line 287) | def forward(self, f_c, f_p, b_c, b_p, flm_w, blm_w, blm_ind, f_w):

FILE: model_seq/seqlm.py
  class BasicSeqLM (line 18) | class BasicSeqLM(nn.Module):
    method __init__ (line 33) | def __init__(self, ori_lm, backward, droprate, fix_rate):
    method to_params (line 50) | def to_params(self):
    method init_hidden (line 60) | def init_hidden(self):
    method regularizer (line 66) | def regularizer(self):
    method forward (line 77) | def forward(self, w_in, ind=None):

FILE: model_seq/sparse_lm.py
  class SBUnit (line 14) | class SBUnit(nn.Module):
    method __init__ (line 27) | def __init__(self, ori_unit, droprate, fix_rate):
    method prune_rnn (line 40) | def prune_rnn(self, mask):
    method forward (line 53) | def forward(self, x, weight=1):
  class SDRNN (line 81) | class SDRNN(nn.Module):
    method __init__ (line 94) | def __init__(self, ori_drnn, droprate, fix_rate):
    method to_params (line 118) | def to_params(self):
    method prune_dense_rnn (line 132) | def prune_dense_rnn(self):
    method prox (line 170) | def prox(self):
    method regularizer (line 179) | def regularizer(self):
    method forward (line 199) | def forward(self, x):
  class SparseSeqLM (line 219) | class SparseSeqLM(nn.Module):
    method __init__ (line 235) | def __init__(self, ori_lm, backward, droprate, fix_rate):
    method to_params (line 249) | def to_params(self):
    method prune_dense_rnn (line 260) | def prune_dense_rnn(self):
    method init_hidden (line 268) | def init_hidden(self):
    method regularizer (line 274) | def regularizer(self):
    method prox (line 285) | def prox(self):
    method forward (line 291) | def forward(self, w_in, ind=None):

FILE: model_seq/utils.py
  function log_sum_exp (line 17) | def log_sum_exp(vec):
  function repackage_hidden (line 35) | def repackage_hidden(h):
  function to_scalar (line 54) | def to_scalar(var):
  function init_embedding (line 60) | def init_embedding(input_embedding):
  function init_linear (line 67) | def init_linear(input_linear):
  function adjust_learning_rate (line 76) | def adjust_learning_rate(optimizer, lr):
  function init_lstm (line 90) | def init_lstm(input_lstm):

FILE: model_word_ada/LM.py
  class LM (line 12) | class LM(nn.Module):
    method __init__ (line 32) | def __init__(self, rnn, soft_max, w_num, w_dim, droprate, label_dim = ...
    method load_embed (line 54) | def load_embed(self, origin_lm):
    method rand_ini (line 61) | def rand_ini(self):
    method init_hidden (line 74) | def init_hidden(self):
    method forward (line 80) | def forward(self, w_in, target):
    method log_prob (line 111) | def log_prob(self, w_in):

FILE: model_word_ada/adaptive.py
  class AdaptiveSoftmax (line 12) | class AdaptiveSoftmax(nn.Module):
    method __init__ (line 24) | def __init__(self, input_size, cutoff):
    method rand_ini (line 44) | def rand_ini(self):
    method log_prob (line 54) | def log_prob(self, w_in, device):
    method forward (line 90) | def forward(self, w_in, target):

FILE: model_word_ada/basic.py
  class BasicUnit (line 12) | class BasicUnit(nn.Module):
    method __init__ (line 27) | def __init__(self, unit, input_dim, hid_dim, droprate):
    method init_hidden (line 42) | def init_hidden(self):
    method rand_ini (line 48) | def rand_ini(self):
    method forward (line 55) | def forward(self, x):
  class BasicRNN (line 78) | class BasicRNN(nn.Module):
    method __init__ (line 95) | def __init__(self, layer_num, unit, emb_dim, hid_dim, droprate):
    method to_params (line 105) | def to_params(self):
    method init_hidden (line 118) | def init_hidden(self):
    method rand_ini (line 125) | def rand_ini(self):
    method forward (line 132) | def forward(self, x):

FILE: model_word_ada/dataset.py
  class EvalDataset (line 18) | class EvalDataset(object):
    method __init__ (line 29) | def __init__(self, dataset, sequence_length):
    method get_tqdm (line 37) | def get_tqdm(self, device):
    method construct_index (line 49) | def construct_index(self):
    method reader (line 66) | def reader(self, device):
  class LargeDataset (line 91) | class LargeDataset(object):
    method __init__ (line 106) | def __init__(self, root, range_idx, batch_size, sequence_length):
    method shuffle (line 119) | def shuffle(self):
    method get_tqdm (line 125) | def get_tqdm(self, device):
    method reader (line 145) | def reader(self, device):
    method open_next (line 175) | def open_next(self):

FILE: model_word_ada/densenet.py
  class BasicUnit (line 12) | class BasicUnit(nn.Module):
    method __init__ (line 27) | def __init__(self, unit, input_dim, increase_rate, droprate):
    method init_hidden (line 47) | def init_hidden(self):
    method rand_ini (line 53) | def rand_ini(self):
    method forward (line 59) | def forward(self, x):
  class DenseRNN (line 86) | class DenseRNN(nn.Module):
    method __init__ (line 103) | def __init__(self, layer_num, unit, emb_dim, hid_dim, droprate):
    method to_params (line 114) | def to_params(self):
    method init_hidden (line 127) | def init_hidden(self):
    method rand_ini (line 134) | def rand_ini(self):
    method forward (line 141) | def forward(self, x):

FILE: model_word_ada/ldnet.py
  class BasicUnit (line 13) | class BasicUnit(nn.Module):
    method __init__ (line 30) | def __init__(self, unit, input_dim, increase_rate, droprate, layer_dro...
    method init_hidden (line 52) | def init_hidden(self):
    method rand_ini (line 58) | def rand_ini(self):
    method forward (line 64) | def forward(self, x, p_out):
  class LDRNN (line 102) | class LDRNN(nn.Module):
    method __init__ (line 121) | def __init__(self, layer_num, unit, emb_dim, hid_dim, droprate, layer_...
    method to_params (line 134) | def to_params(self):
    method init_hidden (line 148) | def init_hidden(self):
    method rand_ini (line 155) | def rand_ini(self):
    method forward (line 162) | def forward(self, x):

FILE: model_word_ada/utils.py
  function repackage_hidden (line 17) | def repackage_hidden(h):
  function to_scalar (line 36) | def to_scalar(var):
  function init_embedding (line 42) | def init_embedding(input_embedding):
  function init_linear (line 49) | def init_linear(input_linear):
  function adjust_learning_rate (line 58) | def adjust_learning_rate(optimizer, lr):
  function init_lstm (line 72) | def init_lstm(input_lstm):

FILE: pre_seq/encode_data.py
  function encode_dataset (line 18) | def encode_dataset(input_file, flm_map, blm_map, gw_map, c_map, y_map):

FILE: pre_word_ada/encode_data2folder.py
  function encode_dataset (line 18) | def encode_dataset(input_folder, w_map, reverse):
  function encode_dataset2file (line 41) | def encode_dataset2file(input_folder, t, w_map, reverse):

FILE: train_lm.py
  function evaluate (line 31) | def evaluate(data_loader, lm_model, limited = 76800):