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-rw-r--r--nerv/layer/affine.lua43
-rw-r--r--nerv/layer/lstm_gate.lua9
-rw-r--r--nerv/layer/mse.lua33
-rw-r--r--nerv/nn/network.lua6
4 files changed, 49 insertions, 42 deletions
diff --git a/nerv/layer/affine.lua b/nerv/layer/affine.lua
index 38743aa..a05ae17 100644
--- a/nerv/layer/affine.lua
+++ b/nerv/layer/affine.lua
@@ -25,7 +25,9 @@ end
function MatrixParam:train_init()
self.correction = self.trans:create()
+ self.correction_acc = self.correction:create()
self.correction:fill(0)
+ self.correction_acc:fill(0)
end
function MatrixParam:copy(copier)
@@ -34,46 +36,37 @@ function MatrixParam:copy(copier)
return target
end
-function MatrixParam:_update_by_gradient(gradient, alpha, beta)
+function MatrixParam:_update(alpha, beta)
local gconf = self.gconf
-- momentum gain
local mmt_gain = 1.0 / (1.0 - gconf.momentum)
local n = gconf.batch_size * mmt_gain
-- perform update
if gconf.momentum > 0 then
- self.correction:add(self.correction, gradient, gconf.momentum, 1.0)
+ self.correction:add(self.correction, self.correction_acc, gconf.momentum, 1.0)
self.trans:add(self.trans, self.correction, alpha, -gconf.lrate / n * beta)
else
- self.trans:add(self.trans, gradient, alpha, -gconf.lrate / n * beta)
+ self.trans:add(self.trans, self.correction_acc, alpha, -gconf.lrate / n * beta)
end
+ self.correction_acc:fill(0)
end
-function MatrixParam:_update_by_err_input(err, input, alpha, beta)
- local gconf = self.gconf
- -- momentum gain
- local mmt_gain = 1.0 / (1.0 - gconf.momentum)
- local n = gconf.batch_size * mmt_gain
- -- perform update
- if gconf.momentum > 0 then
- self.correction:mul(input, err, 1.0, gconf.momentum, 'T', 'N')
- self.trans:add(self.trans, self.correction, alpha, -gconf.lrate / n * beta)
- else
- self.trans:mul(input, err, -gconf.lrate / n * beta, alpha, 'T', 'N')
- end
+function MatrixParam:back_propagate_by_gradient(gradient)
+ self.correction_acc:add(self.correction_acc, gradient, 1.0, 1.0)
end
-function MatrixParam:update_by_gradient(gradient)
- self:_update_by_gradient(gradient, 1.0, 1.0)
+function MatrixParam:back_propagate_by_err_input(err, input)
+ self.correction_acc:mul(input, err, 1.0, 1.0, 'T', 'N')
end
-function MatrixParam:update_by_err_input(err, input)
- self:_update_by_err_input(err, input, 1.0, 1.0)
+function MatrixParam:update_by_gradient()
+ self:_update(1.0, 1.0)
end
-function LinearTransParam:update_by_err_input(err, input)
+function MatrixParam:update_by_err_input()
local gconf = self.gconf
local l2 = 1 - gconf.lrate * gconf.wcost
- self:_update_by_err_input(err, input, l2, l2)
+ self:_update(l2, l2)
end
--- A fully-connected linear transform layer.
@@ -121,11 +114,11 @@ function AffineLayer:batch_resize(batch_size)
-- do nothing
end
-function AffineLayer:update(bp_err, input, output)
+function AffineLayer:update()
for i = 1, #self.dim_in do
- self["ltp" .. i]:update_by_err_input(bp_err[1], input[i])
+ self["ltp" .. i]:update_by_err_input()
end
- self.bp:update_by_gradient(bp_err[1]:colsum())
+ self.bp:update_by_gradient()
end
function AffineLayer:propagate(input, output)
@@ -141,7 +134,9 @@ end
function AffineLayer:back_propagate(bp_err, next_bp_err, input, output)
for i = 1, #self.dim_in do
next_bp_err[i]:mul(bp_err[1], self["ltp" .. i].trans, 1.0, 0.0, 'N', 'T')
+ self["ltp" .. i]:back_propagate_by_err_input(bp_err[1], input[i])
end
+ self.bp:back_propagate_by_gradient(bp_err[1]:colsum())
end
function AffineLayer:get_params()
diff --git a/nerv/layer/lstm_gate.lua b/nerv/layer/lstm_gate.lua
index e690721..9d79b04 100644
--- a/nerv/layer/lstm_gate.lua
+++ b/nerv/layer/lstm_gate.lua
@@ -60,18 +60,19 @@ function LSTMGateLayer:back_propagate(bp_err, next_bp_err, input, output)
self.err_bakm:sigmoid_grad(bp_err[1], output[1])
for i = 1, #self.dim_in do
next_bp_err[i]:mul(self.err_bakm, self["ltp" .. i].trans, 1.0, 0.0, 'N', 'T')
+ self["ltp" .. i]:back_propagate_by_err_input(self.err_bakm, input[i])
end
+ self.bp:back_propagate_by_gradient(self.err_bakm:colsum())
end
-function LSTMGateLayer:update(bp_err, input, output)
- self.err_bakm:sigmoid_grad(bp_err[1], output[1])
+function LSTMGateLayer:update()
for i = 1, #self.dim_in do
- self["ltp" .. i]:update_by_err_input(self.err_bakm, input[i])
+ self["ltp" .. i]:update_by_err_input()
if self.param_type[i] == 'D' then
self["ltp" .. i].trans:diagonalize()
end
end
- self.bp:update_by_gradient(self.err_bakm:colsum())
+ self.bp:update_by_gradient()
end
function LSTMGateLayer:get_params()
diff --git a/nerv/layer/mse.lua b/nerv/layer/mse.lua
index 458d086..c1ea596 100644
--- a/nerv/layer/mse.lua
+++ b/nerv/layer/mse.lua
@@ -9,23 +9,28 @@ function MSELayer:bind_params()
-- do nothing
end
-function MSELayer:init(batch_size)
+function MSELayer:init(batch_size, chunk_size)
if self.dim_in[1] ~= self.dim_in[2] then
nerv.error("mismatching dimensions of previous network output and labels")
end
- self.scale = 1 / self.dim_in[1]
+ self.scale = 1.0 / self.dim_in[1]
self.total_mse = 0.0
self.total_frames = 0
self.mse = self.mat_type(batch_size, self.dim_in[1])
self.mse_sum = self.mat_type(batch_size, 1)
- self.diff = self.mse:create()
+ self.diff = {}
+ for t = 1, chunk_size do
+ self.diff[t] = self.mse:create()
+ end
end
function MSELayer:batch_resize(batch_size)
if self.mse:nrow() ~= batch_resize then
self.mse = self.mat_type(batch_size, self.dim_in[1])
self.mse_sum = self.mat_type(batch_size, 1)
- self.diff = self.mse:create()
+ for t = 1, chunk_size do
+ self.diff[t] = self.mse:create()
+ end
end
end
@@ -33,24 +38,32 @@ function MSELayer:update(bp_err, input, output)
-- no params, therefore do nothing
end
-function MSELayer:propagate(input, output)
+function MSELayer:propagate(input, output, t)
+ if t == nil then
+ t = 1
+ end
local mse = self.mse
local mse_sum = self.mse_sum
+ local diff = self.diff[t]
mse:add(input[1], input[2], 1.0, -1.0)
- self.diff:copy_from(mse)
+ mse:set_values_by_mask(self.gconf.mask[t], 0)
+ diff:copy_from(mse)
mse:mul_elem(mse, mse)
- mse_sum:add(mse_sum, mse:rowsum(mse), 0.0, self.scale)
+ mse_sum:add(mse_sum, mse:rowsum(), 0.0, self.scale * 0.5)
if output[1] ~= nil then
output[1]:copy_from(mse_sum)
end
self.total_mse = self.total_mse + mse_sum:colsum()[0][0]
- self.total_frames = self.total_frames + mse_sum:nrow()
+ self.total_frames = self.total_frames + self.gconf.mask[t]:colsum()[0][0]
end
-- NOTE: must call propagate before back_propagate
-function MSELayer:back_propagate(bp_err, next_bp_err, input, output)
+function MSELayer:back_propagate(bp_err, next_bp_err, input, output, t)
+ if t == nil then
+ t = 1
+ end
local nbe = next_bp_err[1]
- nbe:add(nbe, self.diff, 0.0, 2 * self.scale)
+ nbe:add(nbe, self.diff[t], 0.0, self.scale)
if bp_err[1] ~= nil then
nbe:scale_rows_by_col(bp_err[1])
end
diff --git a/nerv/nn/network.lua b/nerv/nn/network.lua
index bb03be4..cf6a4d3 100644
--- a/nerv/nn/network.lua
+++ b/nerv/nn/network.lua
@@ -615,10 +615,8 @@ function network:back_propagate()
end
function network:update()
- for t = 1, self.max_length do
- for i = 1, #self.layers do
- self.layers[i]:update(self.err_input[t][i], self.input[t][i], self.output[t][i], t)
- end
+ for i = 1, #self.layers do
+ self.layers[i]:update()
end
end
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