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Calculate GRU input sums together #95

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Calculate GRU input sums together #95

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28 changes: 15 additions & 13 deletions model/GRU.lua
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Original file line number Diff line number Diff line change
Expand Up @@ -6,38 +6,39 @@ Creates one timestep of one GRU
Paper reference: http://arxiv.org/pdf/1412.3555v1.pdf
]]--
function GRU.gru(input_size, rnn_size, n, dropout)
dropout = dropout or 0
dropout = dropout or 0

-- there are n+1 inputs (hiddens on each layer and x)
local inputs = {}
table.insert(inputs, nn.Identity()()) -- x
for L = 1,n do
table.insert(inputs, nn.Identity()()) -- prev_h[L]
end

function new_input_sum(insize, xv, hv)
local i2h = nn.Linear(insize, rnn_size)(xv)
local h2h = nn.Linear(rnn_size, rnn_size)(hv)
return nn.CAddTable()({i2h, h2h})
end

local x, input_size_L
local outputs = {}
for L = 1,n do

local prev_h = inputs[L+1]
-- the input to this layer
if L == 1 then
if L == 1 then
x = OneHot(input_size)(inputs[1])
input_size_L = input_size
else
x = outputs[(L-1)]
else
x = outputs[(L-1)]
if dropout > 0 then x = nn.Dropout(dropout)(x) end -- apply dropout, if any
input_size_L = rnn_size
end
-- GRU tick
-- evaluate the input sums at once for efficiency
local i2h = nn.Linear(input_size_L, 2 * rnn_size)(x)
local h2h = nn.Linear(rnn_size, 2 * rnn_size)(prev_h)
local all_input_sums = nn.CAddTable()({i2h, h2h})
local reshaped = nn.Reshape(2, rnn_size)(all_input_sums)
local n1, n2 = nn.SplitTable(2)(reshaped):split(2)
-- forward the update and reset gates
local update_gate = nn.Sigmoid()(new_input_sum(input_size_L, x, prev_h))
local reset_gate = nn.Sigmoid()(new_input_sum(input_size_L, x, prev_h))
local update_gate = nn.Sigmoid()(n1)
local reset_gate = nn.Sigmoid()(n2)
-- compute candidate hidden state
local gated_hidden = nn.CMulTable()({reset_gate, prev_h})
local p2 = nn.Linear(rnn_size, rnn_size)(gated_hidden)
Expand All @@ -50,7 +51,8 @@ function GRU.gru(input_size, rnn_size, n, dropout)

table.insert(outputs, next_h)
end
-- set up the decoder

-- set up the decoder
local top_h = outputs[#outputs]
if dropout > 0 then top_h = nn.Dropout(dropout)(top_h) end
local proj = nn.Linear(rnn_size, input_size)(top_h)
Expand Down