Merge upstream 'parameter update big-change'.

Merge remote-tracking branch 'upstream/master'

1 files changed, 45 insertions, 30 deletions

diff --git a/layer/affine.lua b/layer/affine.lua
index 2cd7acb..00cbcfb 100644
--- a/layer/affine.lua
+++ b/layer/affine.lua
@@ -3,13 +3,35 @@ local LinearTransParam = nerv.class('nerv.LinearTransParam', 'nerv.MatrixParam')
 local BiasParam = nerv.class('nerv.BiasParam', 'nerv.MatrixParam')
 local AffineLayer = nerv.class('nerv.AffineLayer', 'nerv.Layer')
 
-function MatrixParam:read(pcdata)
+function MatrixParam:read(handle)
     self.trans = self.gconf.cumat_type.new_from_host(
-                    nerv.MMatrixFloat.load(pcdata))
+                    nerv.MMatrixFloat.load(handle))
 end
 
-function MatrixParam:write(pfhandle)
-    self.trans:new_to_host():save(pfhandle)
+function MatrixParam:write(handle)
+    self.trans:new_to_host():save(handle)
+end
+
+function MatrixParam:train_init()
+    self.correction = self.trans:create()
+    self.correction:fill(0)
+end
+
+function MatrixParam:update(gradient)
+    local gconf = self.gconf
+    self.correction:add(self.correction, gradient, gconf.momentum, 1.0)
+    -- momentum gain
+    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 / n)
+end
+
+function LinearTransParam:update(gradient)
+    MatrixParam.update(self, gradient)
+    local gconf = self.gconf
+    -- weight decay
+    self.trans:add(self.trans, self.trans, 1.0, -gconf.lrate * gconf.wcost)
 end
 
 function AffineLayer:__init(id, global_conf, layer_conf)
@@ -20,9 +42,10 @@ function AffineLayer:__init(id, global_conf, layer_conf)
     self.dim_out = layer_conf.dim_out
     self.gconf = global_conf
     self:check_dim_len(1, 1) -- exactly one input and one output
+    self.direct_update = layer_conf.direct_update
 end
 
-function AffineLayer:init()
+function AffineLayer:init(batch_size)
     if self.ltp.trans:ncol() ~= self.bp.trans:ncol() then
         nerv.error("mismatching dimensions of linear transform and bias paramter")
     end
@@ -32,32 +55,24 @@ function AffineLayer:init()
     if self.dim_out[1] ~= self.ltp.trans:ncol() then
         nerv.error("mismatching dimensions of linear transform parameter and output")
     end
-
-    -- linear transform correction
-    self.ltc = self.ltp.trans:create()
-    self.ltc:fill(0)
-    -- bias correction
-    self.bc = self.bp.trans:create()
-    self.bc:fill(0)
+    self.ltp_grad = self.ltp.trans:create()
+    self.ltp:train_init()
+    self.bp:train_init()
 end
 
 function AffineLayer:update(bp_err, input, output)
-    local ltp = self.ltp.trans
-    local bp = self.bp.trans
-    local ltc = self.ltc
-    local bc = self.bc
-    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)
-    ltc:mul(input[1], bp_err[1], 1.0, gconf.momentum, 'T', 'N')
-    bc:add(bc, bp_err[1]:colsum(), gconf.momentum, 1.0)
-    -- perform update
-    ltp:add(ltp, ltc, 1.0, -gconf.lrate / n)
-    bp:add(bp, bc, 1.0, -gconf.lrate / n)
-    -- weight decay
-    ltp:add(ltp, ltp, 1.0, -gconf.lrate * gconf.wcost)
+    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)
+    else
+        self.ltp_grad:mul(input[1], bp_err[1], 1.0, 0.0, 'T', 'N')
+        self.ltp:update(self.ltp_grad)
+    end
+    self.bp:update(bp_err[1]:colsum())
 end
 
 function AffineLayer:propagate(input, output)
@@ -67,10 +82,10 @@ function AffineLayer:propagate(input, output)
     output[1]:add_row(self.bp.trans, 1.0)
 end
 
-function AffineLayer:back_propagate(next_bp_err, bp_err, input, output)
+function AffineLayer:back_propagate(bp_err, next_bp_err, input, output)
     next_bp_err[1]:mul(bp_err[1], self.ltp.trans, 1.0, 0.0, 'N', 'T')
 end
 
 function AffineLayer:get_params()
-    return {self.ltp, self.bp}
+    return nerv.ParamRepo({self.ltp, self.bp})
 end