add preprocessing layers and change layer constructor interface

6 files changed, 103 insertions, 17 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
diff --git a/layer/bias.lua b/layer/bias.lua
new file mode 100644
index 0000000..6ddfe11
--- /dev/null
+++ b/layer/bias.lua
@@ -0,0 +1,24 @@
+local BiasLayer = nerv.class("nerv.BiasLayer", "nerv.Layer")
+
+function BiasLayer:__init(id, global_conf, layer_conf)
+    self.id = id
+    self.gconf = global_conf
+    self.bias = layer_conf.bias
+    self.dim_in = layer_conf.dim_in
+    self.dim_out = layer_conf.dim_out
+    self:check_dim_len(1, 1)
+end
+
+function BiasLayer:init()
+    if self.dim_in[1] ~= self.bias.trans:ncol() then
+        nerv.error("mismatching dimensions of input and bias parameter")
+    end
+    if self.dim_out[1] ~= self.bias.trans:ncol() then
+        nerv.error("mismatching dimensions of output and bias parameter")
+    end
+end
+
+function BiasLayer:propagate(input, output)
+    output[1]:copy_fromd(input[1])
+    output[1]:add_row(self.bias.trans, 1.0)
+end
diff --git a/layer/init.lua b/layer/init.lua
index a98621d..4881cb7 100644
--- a/layer/init.lua
+++ b/layer/init.lua
@@ -44,3 +44,16 @@ end
 function nerv.Layer:back_propagate(next_bp_err, bp_err, input, output)
     nerv.error_method_not_implemented()
 end
+
+function nerv.Layer:check_dim_len(len_in, len_out)
+    local expected_in = table.getn(self.dim_in)
+    local expected_out = table.getn(self.dim_out)
+    if len_in > 0 and expected_in ~= len_in then
+        nerv.error("layer %s expects %d inputs, %d given",
+                    self.id, len_in, expected_in)
+    end
+    if len_out > 0 and expected_out ~= len_out then
+        nerv.error("layer %s expects %d outputs, %d given",
+                    self.id, len_out, expected_out)
+    end
+end
diff --git a/layer/sigmoid.lua b/layer/sigmoid.lua
index ca34419..220b7af 100644
--- a/layer/sigmoid.lua
+++ b/layer/sigmoid.lua
@@ -1,11 +1,17 @@
 local SigmoidLayer = nerv.class("nerv.SigmoidLayer", "nerv.Layer")
 
-function SigmoidLayer:__init(id, global_conf)
+function SigmoidLayer:__init(id, global_conf, layer_conf)
     self.id = id
     self.gconf = global_conf
+    self.dim_in = layer_conf.dim_in
+    self.dim_out = layer_conf.dim_out
+    self:check_dim_len(1, 1)
 end
 
 function SigmoidLayer:init()
+    if self.dim_in[1] ~= self.dim_out[1] then
+        nerv.error("mismatching dimensions of input and output")
+    end
 end
 
 function SigmoidLayer:update(bp_err, input, output)
@@ -13,9 +19,9 @@ function SigmoidLayer:update(bp_err, input, output)
 end
 
 function SigmoidLayer:propagate(input, output)
-    output[0]:sigmoid(input[0])
+    output[1]:sigmoid(input[1])
 end
 
 function SigmoidLayer:back_propagate(next_bp_err, bp_err, input, output)
-    next_bp_err[0]:sigmoid_grad(bp_err[0], output[0])
+    next_bp_err[1]:sigmoid_grad(bp_err[1], output[1])
 end
diff --git a/layer/softmax_ce.lua b/layer/softmax_ce.lua
index 37d2864..3dfebc5 100644
--- a/layer/softmax_ce.lua
+++ b/layer/softmax_ce.lua
@@ -1,11 +1,17 @@
 local SoftmaxCELayer = nerv.class("nerv.SoftmaxCELayer", "nerv.Layer")
 
-function SoftmaxCELayer:__init(id, global_conf)
+function SoftmaxCELayer:__init(id, global_conf, layer_conf)
     self.id = id
     self.gconf = global_conf
+    self.dim_in = layer_conf.dim_in
+    self.dim_out = layer_conf.dim_out
+    self:check_dim_len(2, -1) -- two inputs: nn output and label
 end
 
 function SoftmaxCELayer:init()
+    if self.dim_in[1] ~= self.dim_in[1] then
+        nerv.error("mismatching dimensions of previous network output and labels")
+    end
     self.total_ce = 0.0
     self.total_frames = 0
 end
@@ -15,12 +21,12 @@ function SoftmaxCELayer:update(bp_err, input, output)
 end
 
 function SoftmaxCELayer:propagate(input, output)
-    local soutput = input[0]:create()  -- temporary value for calc softmax
+    local soutput = input[1]:create()  -- temporary value for calc softmax
     self.soutput = soutput
-    soutput:softmax(input[0])
+    soutput:softmax(input[1])
     local ce = soutput:create()
     ce:log_elem(soutput)
-    ce:mul_elem(ce, input[1])
+    ce:mul_elem(ce, input[2])
      -- add total ce
     self.total_ce = self.total_ce - ce:rowsum():colsum()[0]
     self.total_frames = self.total_frames + soutput:nrow()
@@ -28,5 +34,5 @@ end
 
 function SoftmaxCELayer:back_propagate(next_bp_err, bp_err, input, output)
     -- softmax output - label
-    next_bp_err[0]:add(self.soutput, input[1], 1.0, -1.0)
+    next_bp_err[1]:add(self.soutput, input[1], 1.0, -1.0)
 end
diff --git a/layer/window.lua b/layer/window.lua
new file mode 100644
index 0000000..8e9e761
--- /dev/null
+++ b/layer/window.lua
@@ -0,0 +1,24 @@
+local WindowLayer = nerv.class("nerv.WindowLayer", "nerv.Layer")
+
+function WindowLayer:__init(id, global_conf, layer_conf)
+    self.id = id
+    self.gconf = global_conf
+    self.window = layer_conf.window
+    self.dim_in = layer_conf.dim_in
+    self.dim_out = layer_conf.dim_out
+    self:check_dim_len(1, 1)
+end
+
+function WindowLayer:init()
+    if self.dim_in[1] ~= self.window.trans:ncol() then
+        nerv.error("mismatching dimensions of input and window parameter")
+    end
+    if self.dim_out[1] ~= self.window.trans:ncol() then
+        nerv.error("mismatching dimensions of output and window parameter")
+    end
+end
+
+function WindowLayer:propagate(input, output)
+    output[1]:copy_fromd(input[1])
+    output[1]:scale_row(self.window.trans)
+end