fixed direct update, did not know the result

2 files changed, 29 insertions, 19 deletions

diff --git a/nerv/layer/affine.lua b/nerv/layer/affine.lua
index 0462383..a2809bf 100644
--- a/nerv/layer/affine.lua
+++ b/nerv/layer/affine.lua
@@ -72,18 +72,25 @@ function AffineLayer:batch_resize(batch_size)
 end
 
 function AffineLayer:update(bp_err, input, output)
-    if self.direct_update then
-        self.ltp.correction:mul(input[1], bp_err[1], 1.0, gconf.momentum, 'T', 'N')
-        -- momentum gain
-        local mmt_gain = 1.0 / (1.0 - gconf.momentum);
-        local n = self.gconf.batch_size * mmt_gain
-        -- perform update
-        self.ltp.trans:add(self.ltp.trans, self.ltp.correction, 1.0, -gconf.lrate / n)
+    if (self.direct_update == true) then
+        local gconf = self.gconf
+        if (gconf.momentum > 0) then
+            self.ltp.correction:mul(input[1], bp_err[1], 1.0, gconf.momentum, 'T', 'N')
+            -- momentum gain
+            local mmt_gain = 1.0 / (1.0 - gconf.momentum);
+            local n = self.gconf.batch_size * mmt_gain
+            -- perform update
+            self.ltp.trans:add(self.ltp.trans, self.ltp.correction, 1.0, -gconf.lrate / n)
+            self.bp.trans:add(self.bp.trans, bp_err[1]:colsum(), 1.0-gconf.lrate*gconf.wcost, -gconf.lrate / gconf.batch_size)
+        else
+            self.ltp.trans:mul(input[1], bp_err[1], -gconf.lrate / gconf.batch_size, 1.0-gconf.lrate*gconf.wcost/gconf.batch_size, 'T', 'N')
+            self.bp.trans:add(self.bp.trans, bp_err[1]:colsum(), 1.0-gconf.lrate*gconf.wcost/gconf.batch_size, -gconf.lrate / gconf.batch_size)
+        end
     else
         self.ltp_grad:mul(input[1], bp_err[1], 1.0, 0.0, 'T', 'N')
         self.ltp:update(self.ltp_grad)
+        self.bp:update(bp_err[1]:colsum())
     end
-    self.bp:update(bp_err[1]:colsum())
 end
 
 function AffineLayer:propagate(input, output)
diff --git a/nerv/layer/affine_recurrent.lua b/nerv/layer/affine_recurrent.lua
index 92d98e2..3d448d3 100644
--- a/nerv/layer/affine_recurrent.lua
+++ b/nerv/layer/affine_recurrent.lua
@@ -46,17 +46,20 @@ function Recurrent:update(bp_err, input, output)
         local ltp_hh = self.ltp_hh.trans
         local bp = self.bp.trans
         local gconf = self.gconf
-        -- momentum gain
-        local mmt_gain = 1.0 / (1.0 - gconf.momentum);
-        local n = input[1]:nrow() * mmt_gain
-        -- update corrections (accumulated errors)
-        self.ltp_hh.correction:mul(input[2], bp_err[1], 1.0, gconf.momentum, 'T', 'N')
-        self.bp.correction:add(bc, bp_err[1]:colsum(), gconf.momentum, 1.0)
-        -- perform update
-        ltp_hh:add(ltp_hh, self.ltp_hh.correction, 1.0, -gconf.lrate / n)
-        bp:add(bp, self.bp.correction, 1.0, -gconf.lrate / n)
-        -- weight decay
-        ltp_hh:add(ltp_hh, ltp_hh, 1.0, -gconf.lrate * gconf.wcost)
+        if (gconf.momentum > 0) then
+            -- momentum gain
+            local mmt_gain = 1.0 / (1.0 - gconf.momentum);
+            local n = input[1]:nrow() * mmt_gain
+            -- update corrections (accumulated errors)
+            self.ltp_hh.correction:mul(input[2], bp_err[1], 1.0, gconf.momentum, 'T', 'N')
+            self.bp.correction:add(self.bp.correction, bp_err[1]:colsum(), gconf.momentum, 1.0)
+            -- perform update and weight decay
+            ltp_hh:add(ltp_hh, self.ltp_hh.correction, 1.0 - gconf.lrate.gconf.wcost/n, -gconf.lrate / n)
+            bp:add(bp, self.bp.correction, 1.0 - gconf.lrate*gconf.wcost/n, -gconf.lrate / n)
+        else
+            ltp_hh:mul(input[2], bp_err[1], -gconf.lrate/gconf.batch_size, 1.0-gconf.wcost*gconf.lrate/gconf.batch_size, 'T', 'N')
+            bp:add(bp, bp_err[1]:colsum(), 1.0-gconf.lrate*gconf.wcost/gconf.batch_size, -gconf.lrate/gconf.batch_size)
+        end
     else
         self.ltp_hh_grad:mul(input[2], bp_err[1], 1.0, 0.0, 'T', 'N') 
         self.ltp_hh:update(self.ltp_hh_grad)