class_docstr: Underlying LSTM model for MSTParser used by BIST parser.
class_docstr_tok: ["Underlying","LSTM","model","for","MSTParser","used","by","BIST","parser","."]
code: def __init__(self, vocab, w2i, pos, rels, options):
      if isinstance(options, dict):
          options = _dict_to_obj(options, "Values")
  
      self.model = ParameterCollection()
      random.seed(1)
      self.trainer = AdamTrainer(self.model)
  
      self.activations = {
          "tanh": tanh,
          "sigmoid": logistic,
          "relu": rectify,
          "tanh3": (lambda x: tanh(cmult(cmult(x, x), x))),
      }
      self.activation = self.activations[options.activation]
  
      self.blstm_flag = options.blstmFlag
      self.labels_flag = options.labelsFlag
      self.costaug_flag = options.costaugFlag
      self.bibi_flag = options.bibiFlag
  
      self.ldims = options.lstm_dims
      self.wdims = options.wembedding_dims
      self.pdims = options.pembedding_dims
      self.rdims = options.rembedding_dims
      self.layers = options.lstm_layers
      self.words_count = vocab
      self.vocab = {word: ind + 3 for word, ind in list(w2i.items())}
      self.pos = {word: ind + 3 for ind, word in enumerate(pos)}
      self.rels = {word: ind for ind, word in enumerate(rels)}
      self.irels = rels
  
      if self.bibi_flag:
          self.builders = [
              LSTMBuilder(
                  1, self.wdims + self.pdims, self.ldims, self.model
              ),
              LSTMBuilder(
                  1, self.wdims + self.pdims, self.ldims, self.model
              ),
          ]
          self.bbuilders = [
              LSTMBuilder(1, self.ldims * 2, self.ldims, self.model),
              LSTMBuilder(1, self.ldims * 2, self.ldims, self.model),
          ]
      elif self.layers > 0:
          self.builders = [
              LSTMBuilder(
                  self.layers,
                  self.wdims + self.pdims,
                  self.ldims,
                  self.model,
              ),
              LSTMBuilder(
                  self.layers,
                  self.wdims + self.pdims,
                  self.ldims,
                  self.model,
              ),
          ]
      else:
          self.builders = [
              SimpleRNNBuilder(
                  1, self.wdims + self.pdims, self.ldims, self.model
              ),
              SimpleRNNBuilder(
                  1, self.wdims + self.pdims, self.ldims, self.model
              ),
          ]
  
      self.hidden_units = options.hidden_units
      self.hidden2_units = options.hidden2_units
  
      self.vocab["*PAD*"] = 1
      self.pos["*PAD*"] = 1
  
      self.vocab["*INITIAL*"] = 2
      self.pos["*INITIAL*"] = 2
  
      self.wlookup = self.model.add_lookup_parameters(
          (len(vocab) + 3, self.wdims)
      )
      self.plookup = self.model.add_lookup_parameters(
          (len(pos) + 3, self.pdims)
      )
      self.rlookup = self.model.add_lookup_parameters(
          (len(rels), self.rdims)
      )
  
      self.hid_layer_foh = self.model.add_parameters(
          (self.hidden_units, self.ldims * 2)
      )
      self.hid_layer_fom = self.model.add_parameters(
          (self.hidden_units, self.ldims * 2)
      )
      self.hid_bias = self.model.add_parameters((self.hidden_units))
  
      self.hid2_layer = self.model.add_parameters(
          (self.hidden2_units, self.hidden_units)
      )
      self.hid2_bias = self.model.add_parameters((self.hidden2_units))
  
      self.out_layer = self.model.add_parameters(
          (
              1,
              self.hidden2_units
              if self.hidden2_units > 0
              else self.hidden_units,
          )
      )
  
      if self.labels_flag:
          self.rhid_layer_foh = self.model.add_parameters(
              (self.hidden_units, 2 * self.ldims)
          )
          self.rhid_layer_fom = self.model.add_parameters(
              (self.hidden_units, 2 * self.ldims)
          )
          self.rhid_bias = self.model.add_parameters(
              (self.hidden_units)
          )
          self.rhid2_layer = self.model.add_parameters(
              (self.hidden2_units, self.hidden_units)
          )
          self.rhid2_bias = self.model.add_parameters(
              (self.hidden2_units)
          )
          self.rout_layer = self.model.add_parameters(
              (
                  len(self.irels),
                  self.hidden2_units
                  if self.hidden2_units > 0
                  else self.hidden_units,
              )
          )
          self.rout_bias = self.model.add_parameters((len(self.irels)))
  
file_path: nlp-architect/nlp_architect/models/bist/mstlstm.py
imports: import random
  import time
  from collections import namedtuple
  from operator import itemgetter
  from dynet import ParameterCollection, AdamTrainer, LSTMBuilder, tanh, logistic, rectify, cmult, SimpleRNNBuilder, concatenate, np, renew_cg, esum
  from nlp_architect.data.conll import ConllEntry
  from nlp_architect.models.bist import decoder
  from nlp_architect.models.bist.utils import read_conll
label_desc: Use image node_id 1 to create a new MSTParserLSTM object from inherited base classes: object with example: obj = MSTParserLSTM(vocab, w2i, pos, rels, options)