GRU.lua (2230B)
1 2 local GRU = {} 3 4 --[[ 5 Creates one timestep of one GRU 6 Paper reference: http://arxiv.org/pdf/1412.3555v1.pdf 7 ]]-- 8 function GRU.gru(input_size, rnn_size, n, dropout, embedding) 9 dropout = dropout or 0 10 -- there are n+1 inputs (hiddens on each layer and x) 11 local inputs = {} 12 table.insert(inputs, nn.Identity()()) -- x 13 for L = 1,n do 14 table.insert(inputs, nn.Identity()()) -- prev_h[L] 15 end 16 17 function new_input_sum(insize, xv, hv) 18 local i2h = nn.Linear(insize, rnn_size)(xv) 19 local h2h = nn.Linear(rnn_size, rnn_size)(hv) 20 return nn.CAddTable()({i2h, h2h}) 21 end 22 23 local x, input_size_L 24 local outputs = {} 25 for L = 1,n do 26 27 local prev_h = inputs[L+1] 28 -- the input to this layer 29 if L == 1 then 30 if embedding ~= nil then 31 input_size_L = 200 32 local embedded = embedding(inputs[1]) 33 x = nn.Tanh()(embedded) 34 else 35 x = OneHot(input_size)(inputs[1]) 36 input_size_L = input_size 37 end 38 else 39 x = outputs[(L-1)] 40 if dropout > 0 then x = nn.Dropout(dropout)(x) end -- apply dropout, if any 41 input_size_L = rnn_size 42 end 43 -- GRU tick 44 -- forward the update and reset gates 45 local update_gate = nn.Sigmoid()(new_input_sum(input_size_L, x, prev_h)) 46 local reset_gate = nn.Sigmoid()(new_input_sum(input_size_L, x, prev_h)) 47 -- compute candidate hidden state 48 local gated_hidden = nn.CMulTable()({reset_gate, prev_h}) 49 local p2 = nn.Linear(rnn_size, rnn_size)(gated_hidden) 50 local p1 = nn.Linear(input_size_L, rnn_size)(x) 51 local hidden_candidate = nn.Tanh()(nn.CAddTable()({p1,p2})) 52 -- compute new interpolated hidden state, based on the update gate 53 local zh = nn.CMulTable()({update_gate, hidden_candidate}) 54 local zhm1 = nn.CMulTable()({nn.AddConstant(1,false)(nn.MulConstant(-1,false)(update_gate)), prev_h}) 55 local next_h = nn.CAddTable()({zh, zhm1}) 56 57 table.insert(outputs, next_h) 58 end 59 -- set up the decoder 60 local top_h = outputs[#outputs] 61 if dropout > 0 then top_h = nn.Dropout(dropout)(top_h) end 62 local proj = nn.Linear(rnn_size, input_size)(top_h) 63 local logsoft = nn.LogSoftMax()(proj) 64 table.insert(outputs, logsoft) 65 66 return nn.gModule(inputs, outputs) 67 end 68 69 return GRU