SYMBOL INDEX (56 symbols across 8 files)

FILE: HyperLinear.py
  class HyperLinear (line 7) | class HyperLinear(Module):
    method __init__ (line 8) | def __init__(self, in_features, out_features):
    method reset_parameters (line 16) | def reset_parameters(self):
    method forward (line 22) | def forward(self, input, z):
    method __repr__ (line 29) | def __repr__(self):

FILE: LanguageBatcher.py
  class LanguageBatcher (line 6) | class LanguageBatcher():
    method __init__ (line 7) | def __init__(self, fName, vocabF,
    method next (line 31) | def next(self):

FILE: Run.py
  function dataBatcher (line 19) | def dataBatcher(batchSz, context, minContext):
  class Network (line 36) | class Network(nn.Module):
    method __init__ (line 38) | def __init__(self, cell, vocabDim, embedDim,
    method forward (line 45) | def forward(self, x, trainable):
  function train (line 55) | def train(net, opt, trainBatcher, validBatcher, saver, minContext):
  function test (line 79) | def test(net, batcher, minContext, name='Test'):
  function modelDef (line 90) | def modelDef(net, cuda=True):
  function run (line 96) | def run(cell, depth, h, vocabDim, batchSz, embedDim, embedDrop,

FILE: models/BaselineLSTM.py
  class LSTMCellLayer (line 7) | class LSTMCellLayer(nn.Module):
    method __init__ (line 9) | def __init__(self, embedDim, h, gateDrop):
    method forward (line 14) | def forward(self, x, s, trainable):
  class LSTMCell (line 35) | class LSTMCell(nn.Module):
    method __init__ (line 36) | def __init__(self, embedDim, h, depth, gateDrop):
    method initializeIfNone (line 43) | def initializeIfNone(self, s, batchSz):
    method forward (line 50) | def forward(self, x, s, trainable):

FILE: models/HyperRHN.py
  class HyperCell (line 9) | class HyperCell(nn.Module):
    method __init__ (line 10) | def __init__(self, embedDim, h, depth, gateDrop):
    method forward (line 17) | def forward(self, x, s, z, trainable):
  class HyperRHNCell (line 27) | class HyperRHNCell(nn.Module):
    method __init__ (line 29) | def __init__(self, embedDim, h, depth, gateDrop):
    method initializeIfNone (line 37) | def initializeIfNone(self, s):
    method forward (line 41) | def forward(self, x, s, trainable):

FILE: models/RHN.py
  function highwayGate (line 6) | def highwayGate(Ws, s, gateDrop, trainable):
  class RHNCell (line 14) | class RHNCell(nn.Module):
    method __init__ (line 15) | def __init__(self, embedDim, h, depth, gateDrop):
    method forward (line 22) | def forward(self, x, s, trainable):

FILE: nlp.py
  function buildVocab (line 5) | def buildVocab(fName):
  function applyVocab (line 13) | def applyVocab(line, vocab):
  function applyInvVocab (line 19) | def applyInvVocab(x, vocab):

FILE: utils.py
  function invertDict (line 11) | def invertDict(x):
  function loadDict (line 14) | def loadDict(fName):
  function norm (line 19) | def norm(x, n=2):
  class CMA (line 23) | class CMA():
    method __init__ (line 24) | def __init__(self):
    method update (line 28) | def update(self, x):
  function modelSize (line 33) | def modelSize(net):
  function list (line 41) | def list(module, *args, n=1):
  function initWeights (line 45) | def initWeights(net, scheme='orthogonal'):
  class SaveManager (line 56) | class SaveManager():
    method __init__ (line 57) | def __init__(self, root):
    method update (line 63) | def update(self, net, tl, ta, vl, va):
    method load (line 82) | def load(self, net, raw=False, statsOnly=False):
    method refresh (line 93) | def refresh(self, net):
    method epoch (line 97) | def epoch(self):
  function runMinibatch (line 100) | def runMinibatch(net, batcher, cuda=True, volatile=False, trainable=False):
  function timeGrads (line 109) | def timeGrads(net, cell, batcher, criterion=nn.CrossEntropyLoss(), cuda=...
  function gradCheck (line 132) | def gradCheck(net, batcher, criterion=nn.CrossEntropyLoss(), cuda=True):
  function runData (line 154) | def runData(net, opt, batcher, criterion=nn.CrossEntropyLoss(),