coding style changes

2 files changed, 22 insertions, 22 deletions

diff --git a/nerv/layer/affine.lua b/nerv/layer/affine.lua
index c24af16..c5084c4 100644
--- a/nerv/layer/affine.lua
+++ b/nerv/layer/affine.lua
@@ -19,29 +19,29 @@ end
 
 function MatrixParam:update(gradient)
     local gconf = self.gconf
-    if (gconf.momentum > 0) then
+    if gconf.momentum > 0 then
         self.correction:add(self.correction, gradient, gconf.momentum, 1.0)
         -- momentum gain
-        local mmt_gain = 1.0 / (1.0 - gconf.momentum);
+        local mmt_gain = 1.0 / (1.0 - gconf.momentum)
         local n = self.gconf.batch_size * mmt_gain
         -- perform update
-        self.trans:add(self.trans, self.correction, 1.0-gconf.lrate*gconf.wcost/gconf.batch_size, -gconf.lrate/n)
+        self.trans:add(self.trans, self.correction, 1.0 - gconf.lrate * gconf.wcost / gconf.batch_size, - gconf.lrate / n)
     else
-        self.trans:add(self.trans, gradient, 1.0-gconf.lrate*gconf.wcost/gconf.batch_size, -gconf.lrate/gconf.batch_size)
+        self.trans:add(self.trans, gradient, 1.0 - gconf.lrate * gconf.wcost / gconf.batch_size, - gconf.lrate / gconf.batch_size)
     end
 end
 
 function MatrixParam:updateEI(err, input)
     local gconf = self.gconf
-    if (gconf.momentum > 0) then
+    if gconf.momentum > 0 then
         self.correction:mul(input, err, 1.0, gconf.momentum, 'T', 'N')
         -- momentum gain
-        local mmt_gain = 1.0 / (1.0 - gconf.momentum);
+        local mmt_gain = 1.0 / (1.0 - gconf.momentum)
         local n = self.gconf.batch_size * mmt_gain
         -- perform update
-        self.trans:add(self.trans, self.correction, 1.0-gconf.lrate*gconf.wcost/gconf.batch_size, -gconf.lrate/n)
+        self.trans:add(self.trans, self.correction, 1.0 - gconf.lrate * gconf.wcost / gconf.batch_size, - gconf.lrate / n)
     else
-        self.trans:mul(input, err, -gconf.lrate/gconf.batch_size, 1.0-gconf.lrate*gconf.wcost/gconf.batch_size, 'T', 'N')
+        self.trans:mul(input, err, - gconf.lrate / gconf.batch_size, 1.0 - gconf.lrate * gconf.wcost / gconf.batch_size, 'T', 'N')
     end
 end
 
@@ -92,20 +92,20 @@ function AffineLayer:batch_resize(batch_size)
 end
 
 function AffineLayer:update(bp_err, input, output)
-    if (self.direct_update == true) then
+    if self.direct_update == true then
         local gconf = self.gconf
-        if (gconf.momentum > 0) then
+        if gconf.momentum > 0 then
             self.ltp.correction:mul(input[1], bp_err[1], 1.0, gconf.momentum, 'T', 'N')
             self.bp.correction:add(self.bp.correction, bp_err[1]:colsum(), gconf.momentum, 1)
             -- momentum gain
-            local mmt_gain = 1.0 / (1.0 - gconf.momentum);
+            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*gconf.wcost/gconf.batch_size, -gconf.lrate / n)
-            self.bp.trans:add(self.bp.trans, self.bp.correction, 1.0-gconf.lrate*gconf.wcost/gconf.batch_size, -gconf.lrate / n)
+            self.ltp.trans:add(self.ltp.trans, self.ltp.correction, 1.0 - gconf.lrate * gconf.wcost / gconf.batch_size, - gconf.lrate / n)
+            self.bp.trans:add(self.bp.trans, self.bp.correction, 1.0 - gconf.lrate * gconf.wcost / gconf.batch_size, - gconf.lrate / n)
         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)
+            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:updateEI(bp_err[1], input[1])
diff --git a/nerv/layer/affine_recurrent.lua b/nerv/layer/affine_recurrent.lua
index b465e95..7f9c3f5 100644
--- a/nerv/layer/affine_recurrent.lua
+++ b/nerv/layer/affine_recurrent.lua
@@ -42,23 +42,23 @@ function Recurrent:batch_resize(batch_size)
 end
 
 function Recurrent:update(bp_err, input, output)
-    if (self.direct_update == true) then
+    if self.direct_update == true then
         local ltp_hh = self.ltp_hh.trans
         local bp = self.bp.trans
         local gconf = self.gconf
         if (gconf.momentum > 0) then
             -- momentum gain
-            local mmt_gain = 1.0 / (1.0 - gconf.momentum);
+            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/gconf.batch_size, -gconf.lrate/n)
-            bp:add(bp, self.bp.correction, 1.0-gconf.lrate*gconf.wcost/gconf.batch_size, -gconf.lrate/n)
+            ltp_hh:add(ltp_hh, self.ltp_hh.correction, 1.0 - gconf.lrate * gconf.wcost / gconf.batch_size, - gconf.lrate / n)
+            bp:add(bp, self.bp.correction, 1.0 - gconf.lrate * gconf.wcost / gconf.batch_size, - 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)
+            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') 
@@ -85,7 +85,7 @@ function Recurrent:back_propagate(bp_err, next_bp_err, input, output)
     end
     ]]--
     if (self.clip ~= nil) then
-        next_bp_err[2]:clip(-self.clip, self.clip)
+        next_bp_err[2]:clip(- self.clip, self.clip)
     end
 end