add preprocessing layers and change layer constructor interface

1 files changed, 22 insertions, 9 deletions

diff --git a/layer/affine.lua b/layer/affine.lua
index 573b98d..90a1d16 100644
--- a/layer/affine.lua
+++ b/layer/affine.lua
@@ -12,14 +12,27 @@ function MatrixParam:write(pfhandle)
     self.trans:new_to_host():save(pfhandle)
 end
 
-function AffineLayer:__init(id, global_conf, ltp, bp)
+function AffineLayer:__init(id, global_conf, layer_conf)
     self.id = id
-    self.ltp = ltp
-    self.bp = bp
+    self.ltp = layer_conf.ltp
+    self.bp = layer_conf.bp
+    self.dim_in = layer_conf.dim_in
+    self.dim_out = layer_conf.dim_out
     self.gconf = global_conf
+    self:check_dim_len(1, 1) -- exactly one input and one output
 end
 
 function AffineLayer:init()
+    if self.ltp.trans:ncol() ~= self.bp.trans:ncol() then
+        nerv.error("mismatching dimensions of linear transform and bias paramter")
+    end
+    if self.dim_in[1] ~= self.ltp.trans:nrow() then
+        nerv.error("mismatching dimensions of linear transform parameter and input")
+    end
+    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)
@@ -36,10 +49,10 @@ function nerv.AffineLayer:update(bp_err, input, output)
     local gconf = self.gconf
     -- momentum gain
     local mmt_gain = 1.0 / (1.0 - gconf.momentum);
-    local n = input[0]:nrow() * mmt_gain
+    local n = input[1]:nrow() * mmt_gain
     -- update corrections (accumulated errors)
-    ltc:mul(input[0], bp_err[0], 1.0, gconf.momentum, 'T', 'N')
-    bc:add(bc, bp_err[0]:colsum(), gconf.momentum, 1.0)
+    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)
@@ -49,11 +62,11 @@ end
 
 function nerv.AffineLayer:propagate(input, output)
     -- apply linear transform
-    output[0]:mul(input[0], self.ltp.trans, 1.0, 0.0, 'N', 'N')
+    output[1]:mul(input[1], self.ltp.trans, 1.0, 0.0, 'N', 'N')
     -- add bias
-    output[0]:add_row(self.bp.trans, 1.0)
+    output[1]:add_row(self.bp.trans, 1.0)
 end
 
 function nerv.AffineLayer:back_propagate(next_bp_err, bp_err, input, output)
-    next_bp_err[0]:mul(bp_err[0], self.ltp.trans, 1.0, 0.0, 'N', 'T')
+    next_bp_err[1]:mul(bp_err[1], self.ltp.trans, 1.0, 0.0, 'N', 'T')
 end