[torch]Save initial state(fastlstm)

installpath/torch/rnn/Fastlstm.lua

before

local FastLSTM, parent = torch.class("nn.FastLSTM", "nn.LSTM")-- set this to true to have it use nngraph instead of nn-- setting this to true can make your next FastLSTM significantly fasterFastLSTM.usenngraph = falseFastLSTM.bn = falsefunction FastLSTM:__init(inputSize, outputSize, rho, eps, momentum, affine)   --  initialize batch norm variance with 0.1   self.eps = eps or 0.1   self.momentum = momentum or 0.1 --gamma   self.affine = affine == nil and true or affine   parent.__init(self, inputSize, outputSize, rho) endfunction FastLSTM:buildModel()   -- input : {input, prevOutput, prevCell}   -- output : {output, cell}   -- Calculate all four gates in one go : input, hidden, forget, output   self.i2g = nn.Linear(self.inputSize, 4*self.outputSize)   self.o2g = nn.LinearNoBias(self.outputSize, 4*self.outputSize)   if self.usenngraph or self.bn then      require 'nngraph'      return self:nngraphModel()   end   local para = nn.ParallelTable():add(self.i2g):add(self.o2g)   local gates = nn.Sequential()   gates:add(nn.NarrowTable(1,2))   gates:add(para)   gates:add(nn.CAddTable())   -- Reshape to (batch_size, n_gates, hid_size)   -- Then slize the n_gates dimension, i.e dimension 2   gates:add(nn.Reshape(4,self.outputSize))   gates:add(nn.SplitTable(1,2))   local transfer = nn.ParallelTable()   transfer:add(nn.Sigmoid()):add(nn.Tanh()):add(nn.Sigmoid()):add(nn.Sigmoid())   gates:add(transfer)   local concat = nn.ConcatTable()   concat:add(gates):add(nn.SelectTable(3))   local seq = nn.Sequential()   seq:add(concat)   seq:add(nn.FlattenTable()) -- input, hidden, forget, output, cell   -- input gate * hidden state   local hidden = nn.Sequential()   hidden:add(nn.NarrowTable(1,2))   hidden:add(nn.CMulTable())   -- forget gate * cell   local cell = nn.Sequential()   local concat = nn.ConcatTable()   concat:add(nn.SelectTable(3)):add(nn.SelectTable(5))   cell:add(concat)   cell:add(nn.CMulTable())   local nextCell = nn.Sequential()   local concat = nn.ConcatTable()   concat:add(hidden):add(cell)   nextCell:add(concat)   nextCell:add(nn.CAddTable())   local concat = nn.ConcatTable()   concat:add(nextCell):add(nn.SelectTable(4))   seq:add(concat)   seq:add(nn.FlattenTable()) -- nextCell, outputGate   local cellAct = nn.Sequential()   cellAct:add(nn.SelectTable(1))   cellAct:add(nn.Tanh())   local concat = nn.ConcatTable()   concat:add(cellAct):add(nn.SelectTable(2))   local output = nn.Sequential()   output:add(concat)   output:add(nn.CMulTable())   local concat = nn.ConcatTable()   concat:add(output):add(nn.SelectTable(1))   seq:add(concat)   return seqend

after

require 'hdf5'local FastLSTM, parent = torch.class("nn.FastLSTM", "nn.LSTM")-- set this to true to have it use nngraph instead of nn-- setting this to true can make your next FastLSTM significantly fasterFastLSTM.usenngraph = falseFastLSTM.bn = falsefunction FastLSTM:__init(inputSize, outputSize, rho, eps, momentum, affine, initialfile, ifLoad)   --  initialize batch norm variance with 0.1   self.eps = eps or 0.1   self.momentum = momentum or 0.1 --gamma   self.affine = affine == nil and true or affine   self.initialfile = initialfile or 0   self.ifLoad = ifLoad --"1" means to load initialfile to initialize. "0" means to save weights to initialfile.    parent.__init(self, inputSize, outputSize, rho) endfunction FastLSTM:buildModel()   -- input : {input, prevOutput, prevCell}   -- output : {output, cell}   -- Calculate all four gates in one go : input, hidden, forget, output   self.i2g = nn.Linear(self.inputSize, 4*self.outputSize)   self.o2g = nn.LinearNoBias(self.outputSize, 4*self.outputSize)   if self.initialfile ~= 0 then     if self.ifLoad then        -- use hdf5 to initialize        local myFile = hdf5.open(self.initialfile, 'r')        self.i2g.weight = myFile:read('i2g_weight'):all()        self.i2g.bias = myFile:read('i2g_bias'):all()        self.i2g.gradWeight = myFile:read('i2g_gradWeight'):all()        self.i2g.gradBias = myFile:read('i2g_gradBias'):all()        self.o2g.weight = myFile:read('o2g_weight'):all()        self.o2g.gradWeight = myFile:read('o2g_gradWeight'):all()        myFile:close()     else    local myFile = hdf5.open(self.initialfile, 'w')     myFile:write('i2g_weight',self.i2g.weight)    myFile:write('i2g_bias',self.i2g.bias)    myFile:write('i2g_gradWeight',self.i2g.gradWeight)    myFile:write('i2g_gradBias',self.i2g.gradBias)    myFile:write('o2g_weight',self.o2g.weight)    myFile:write('o2g_gradWeight',self.o2g.gradWeight)    myFile:close()     end     end   if self.usenngraph or self.bn then      require 'nngraph'      return self:nngraphModel()   end   --...end

after

cd ~/installpath/torch/rnnrm -r build/luarocks make rocks/rnn-scm-1.rockspec