...

Merge remote-tracking branch 'upstream/master'

16 files changed, 536 insertions, 40 deletions

diff --git a/Makefile b/Makefile
index 69fb739..934235f 100644
--- a/Makefile
+++ b/Makefile
@@ -7,7 +7,9 @@ OBJS := nerv.o luaT.o common.o \
 LIBS := libnerv.so
 LUA_LIBS := matrix/init.lua io/init.lua nerv.lua \
 			pl/utils.lua pl/compat.lua \
-			layer/init.lua layer/affine.lua layer/sigmoid.lua layer/softmax_ce.lua
+			layer/init.lua layer/affine.lua layer/sigmoid.lua layer/softmax_ce.lua \
+			layer/window.lua layer/bias.lua \
+			nn/init.lua nn/layer_repo.lua nn/param_repo.lua nn/layer_dag.lua
 INCLUDE := -I build/luajit-2.0/include/luajit-2.0/ -DLUA_USE_APICHECK
 CUDA_BASE := /usr/local/cuda-6.5
 CUDA_INCLUDE := -I $(CUDA_BASE)/include/
@@ -17,7 +19,7 @@ CFLAGS := -Wall -Wextra
 OBJ_DIR := $(BUILD_DIR)/objs
 LUA_DIR := $(BUILD_DIR)/lua
 LIB_DIR := $(BUILD_DIR)/lib
-SUBDIR := matrix io layer examples pl
+SUBDIR := matrix io layer examples pl nn
 NVCC := $(CUDA_BASE)/bin/nvcc
 NVCC_FLAGS := -Xcompiler -fPIC,-Wall,-Wextra
 
diff --git a/examples/test_dnn_layers.lua b/examples/test_dnn_layers.lua
index 866e685..6e4d98d 100644
--- a/examples/test_dnn_layers.lua
+++ b/examples/test_dnn_layers.lua
@@ -11,10 +11,14 @@ bp = pf:read_chunk("b", global_conf)
 
 -- print(bp.trans)
 
-af = nerv.AffineLayer("test", global_conf, ltp, bp)
-sg = nerv.SigmoidLayer("test2", global_conf)
-sm = nerv.SoftmaxCELayer("test3", global_conf)
-
+af = nerv.AffineLayer("test", global_conf, {["ltp"] = ltp,
+                                            ["bp"] = bp,
+                                            dim_in = {429},
+                                            dim_out = {2048}})
+sg = nerv.SigmoidLayer("test2", global_conf, {dim_in = {2048},
+                                                dim_out = {2048}})
+sm = nerv.SoftmaxCELayer("test3", global_conf, {dim_in = {2048, 2048},
+                                                dim_out = {}})
 af:init()
 sg:init()
 sm:init()
@@ -27,18 +31,18 @@ for i = 0, 9 do
     label[i][i] = 1.0
 end
 
-input1 = {[0] = df:read_chunk("input", global_conf).trans}
-output1 = {[0] = nerv.CuMatrixFloat(10, 2048)}
+input1 = {df:read_chunk("input", global_conf).trans}
+output1 = {nerv.CuMatrixFloat(10, 2048)}
 input2 = output1
-output2 = {[0] = nerv.CuMatrixFloat(10, 2048)}
-input3 = {[0] = output2[0], [1] = label}
+output2 = {nerv.CuMatrixFloat(10, 2048)}
+input3 = {output2[1], label}
 output3 = nil
 err_input1 = nil
-err_output1 = {[0] = nerv.CuMatrixFloat(10, 2048)}
+err_output1 = {nerv.CuMatrixFloat(10, 2048)}
 err_input2 = err_output1
-err_output2 = {[0] = nerv.CuMatrixFloat(10, 2048)}
+err_output2 = {nerv.CuMatrixFloat(10, 2048)}
 err_input3 = err_output2
-err_output3 = {[0] = input1[0]:create()}
+err_output3 = {input1[1]:create()}
 
 for i = 0, 3 do
     -- propagate
@@ -46,26 +50,25 @@ for i = 0, 3 do
     sg:propagate(input2, output2)
     sm:propagate(input3, output3)
 
-
     -- back_propagate
     sm:back_propagate(err_output1, err_input1, input3, output3)
-    sm:update(err_input1, input3, output3)
-
     sg:back_propagate(err_output2, err_input2, input2, output2)
-    sg:update(err_input2, input2, output2)
-
     af:back_propagate(err_output3, err_input3, input1, output1)
+
+    -- update
+    sm:update(err_input1, input3, output3)
+    sg:update(err_input2, input2, output2)
     af:update(err_input3, input1, output1)
 
 
     print("output1")
-    print(output1[0])
+    print(output1[1])
     print("output2")
-    print(output2[0])
+    print(output2[1])
     print("err_output1")
-    print(err_output1[0])
+    print(err_output1[1])
     print("err_output2")
-    print(err_output2[0])
+    print(err_output2[1])
     nerv.utils.printf("cross entropy: %.8f\n", sm.total_ce)
     nerv.utils.printf("frames: %.8f\n", sm.total_frames)
 end
diff --git a/examples/test_nn_lib.lua b/examples/test_nn_lib.lua
new file mode 100644
index 0000000..ec338fe
--- /dev/null
+++ b/examples/test_nn_lib.lua
@@ -0,0 +1,97 @@
+-- require 'layer.affine'
+-- require 'layer.sigmoid'
+-- require 'layer.softmax_ce'
+
+gconf = {lrate = 0.8, wcost = 1e-6, momentum = 0.9,
+        mat_type = nerv.CuMatrixFloat,
+        batch_size = 10}
+
+param_repo = nerv.ParamRepo({"converted.nerv"})
+sublayer_repo = nerv.LayerRepo(
+    {
+        ["nerv.AffineLayer"] =
+        {
+            affine0 = {{ltp = "affine0_ltp", bp = "affine0_bp"},
+                        {dim_in = {429}, dim_out = {2048}}},
+            affine1 = {{ltp = "affine1_ltp", bp = "affine1_bp"},
+                        {dim_in = {2048}, dim_out = {2048}}},
+            affine2 = {{ltp = "affine2_ltp", bp = "affine2_bp"},
+                        {dim_in = {2048}, dim_out = {2048}}},
+            affine3 = {{ltp = "affine3_ltp", bp = "affine3_bp"},
+                        {dim_in = {2048}, dim_out = {2048}}},
+            affine4 = {{ltp = "affine4_ltp", bp = "affine4_bp"},
+                        {dim_in = {2048}, dim_out = {2048}}},
+            affine5 = {{ltp = "affine5_ltp", bp = "affine5_bp"},
+                        {dim_in = {2048}, dim_out = {2048}}},
+            affine6 = {{ltp = "affine6_ltp", bp = "affine6_bp"},
+                        {dim_in = {2048}, dim_out = {2048}}},
+            affine7 = {{ltp = "affine7_ltp", bp = "affine7_bp"},
+                        {dim_in = {2048}, dim_out = {3001}}}
+        },
+        ["nerv.SigmoidLayer"] =
+        {
+            sigmoid0 = {{}, {dim_in = {2048}, dim_out = {2048}}},
+            sigmoid1 = {{}, {dim_in = {2048}, dim_out = {2048}}},
+            sigmoid2 = {{}, {dim_in = {2048}, dim_out = {2048}}},
+            sigmoid3 = {{}, {dim_in = {2048}, dim_out = {2048}}},
+            sigmoid4 = {{}, {dim_in = {2048}, dim_out = {2048}}},
+            sigmoid5 = {{}, {dim_in = {2048}, dim_out = {2048}}},
+            sigmoid6 = {{}, {dim_in = {2048}, dim_out = {2048}}}
+        },
+        ["nerv.SoftmaxCELayer"] =
+        {
+            softmax_ce0 = {{}, {dim_in = {3001, 3001}, dim_out = {}}}
+        }
+    }, param_repo, gconf)
+
+layer_repo = nerv.LayerRepo(
+    {
+        ["nerv.DAGLayer"] =
+        {
+            main = {{}, {
+                dim_in = {429, 3001}, dim_out = {},
+                sub_layers = sublayer_repo,
+                connections = {
+                    ["<input>[1]"] = "affine0[1]",
+                    ["affine0[1]"] = "sigmoid0[1]",
+                    ["sigmoid0[1]"] = "affine1[1]",
+                    ["affine1[1]"] = "sigmoid1[1]",
+                    ["sigmoid1[1]"] = "affine2[1]",
+                    ["affine2[1]"] = "sigmoid2[1]",
+                    ["sigmoid2[1]"] = "affine3[1]",
+                    ["affine3[1]"] = "sigmoid3[1]",
+                    ["sigmoid3[1]"] = "affine4[1]",
+                    ["affine4[1]"] = "sigmoid4[1]",
+                    ["sigmoid4[1]"] = "affine5[1]",
+                    ["affine5[1]"] = "sigmoid5[1]",
+                    ["sigmoid5[1]"] = "affine6[1]",
+                    ["affine6[1]"] = "sigmoid6[1]",
+                    ["sigmoid6[1]"] = "affine7[1]",
+                    ["affine7[1]"] = "softmax_ce0[1]",
+                    ["<input>[2]"] = "softmax_ce0[2]"
+                }
+            }}
+        }
+    }, param_repo, gconf)
+
+df = nerv.ChunkFile("input.param", "r")
+label = nerv.CuMatrixFloat(10, 3001)
+label:fill(0)
+for i = 0, 9 do
+    label[i][i] = 1.0
+end
+
+input = {df:read_chunk("input", gconf).trans, label}
+output = {}
+err_input = {}
+err_output = {input[1]:create()}
+sm = sublayer_repo:get_layer("softmax_ce0")
+main = layer_repo:get_layer("main")
+main:init()
+for i = 0, 3 do
+    main:propagate(input, output)
+    main:back_propagate(err_output, err_input, input, output)
+    main:update(err_input, input, output)
+    nerv.utils.printf("cross entropy: %.8f\n", sm.total_ce)
+    nerv.utils.printf("frames: %.8f\n", sm.total_frames)
+end
diff --git a/io/init.lua b/io/init.lua
index 7c312f4..4a663a7 100644
--- a/io/init.lua
+++ b/io/init.lua
@@ -22,7 +22,7 @@ function nerv.ChunkFile:read_chunk(id, global_conf)
     if metadata == nil then
         nerv.error("chunk with id %s does not exist", id)
     end
-    local chunk_type = assert(loadstring("return " .. metadata.type))()
+    local chunk_type = nerv.get_type(metadata.type)
     local chunk = chunk_type(id, global_conf)
     chunk:set_info(metadata.info)
     chunk:read(self:get_chunkdata(id))
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..c8c691b 100644
--- a/layer/init.lua
+++ b/layer/init.lua
@@ -44,3 +44,26 @@ 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 = #self.dim_in
+    local expected_out = #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
+
+function nerv.Layer:get_dim()
+    return self.dim_in, self.dim_out
+end
+
+require 'layer.affine'
+require 'layer.sigmoid'
+require 'layer.softmax_ce'
+require 'layer.bias'
+require 'layer.window'
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..09eb3a9 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 +21,14 @@ 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])
+--     print(input[1][0])
+--     print(soutput[1][0])
      -- add total ce
     self.total_ce = self.total_ce - ce:rowsum():colsum()[0]
     self.total_frames = self.total_frames + soutput:nrow()
@@ -28,5 +36,5 @@ 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[2], 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
diff --git a/nerv.lua b/nerv.lua
index 00042a7..cb53f29 100644
--- a/nerv.lua
+++ b/nerv.lua
@@ -71,6 +71,11 @@ function table.tostring(tbl)
   return "{" .. table.concat(result, ",") .. "}"
 end
 
+function nerv.get_type(typename)
+    return assert(loadstring("return " .. typename))()
+end
+
 require 'matrix.init'
 require 'io.init'
 require 'layer.init'
+require 'nn.init'
diff --git a/nn/init.lua b/nn/init.lua
new file mode 100644
index 0000000..1bafa77
--- /dev/null
+++ b/nn/init.lua
@@ -0,0 +1,3 @@
+require 'nn.layer_repo'
+require 'nn.param_repo'
+require 'nn.layer_dag'
diff --git a/nn/layer_dag.lua b/nn/layer_dag.lua
new file mode 100644
index 0000000..1ab18fa
--- /dev/null
+++ b/nn/layer_dag.lua
@@ -0,0 +1,228 @@
+local DAGLayer = nerv.class("nerv.DAGLayer", "nerv.Layer")
+
+local function parse_id(str)
+    local id, port, _
+    _, _, id, port = string.find(str, "([a-zA-Z0-9_]+)%[([0-9]+)%]")
+    if id == nil or port == nil then
+        _, _, id, port = string.find(str, "(.+)%[([0-9]+)%]")
+        if not (id == "<input>" or id == "<output>") then
+            nerv.error("wrong format of connection id")
+        end
+    end
+    port = tonumber(port)
+    return id, port
+end
+
+local function discover(id, layers, layer_repo)
+    local ref = layers[id]
+    if id == "<input>" or id == "<output>" then
+        return nil
+    end
+    if ref == nil then
+        local layer = layer_repo:get_layer(id)
+        local dim_in, dim_out = layer:get_dim()
+        ref = {
+            layer = layer,
+            inputs = {},
+            outputs = {},
+            err_inputs = {},
+            err_outputs = {},
+            next_layers = {},
+            input_len = #dim_in,
+            output_len = #dim_out,
+            in_deg = 0,
+            visited = false
+        }
+        layers[id] = ref
+    end
+    return ref
+end
+
+function nerv.DAGLayer:__init(id, global_conf, layer_conf)
+    local layers = {}
+    local inputs = {}
+    local outputs = {}
+    local dim_in = layer_conf.dim_in
+    local dim_out = layer_conf.dim_out
+    for from, to in pairs(layer_conf.connections) do
+        local id_from, port_from = parse_id(from)
+        local id_to, port_to = parse_id(to)
+        local ref_from = discover(id_from, layers, layer_conf.sub_layers)
+        local ref_to = discover(id_to, layers, layer_conf.sub_layers)
+        local input_dim, output_dim, _
+        if ref_from and ref_from.outputs[port_from] ~= nil then
+            nerv.error("%s has already been attached", from)
+        end
+        if ref_to and ref_to.inputs[port_to] ~= nil then
+            nerv.error("%s has already been attached", to)
+        end
+        if id_from == "<input>" then
+            input_dim, _ = ref_to.layer:get_dim()
+            if dim_in[port_from] ~= input_dim[port_to] then
+                nerv.error("mismatching data dimension between %s and %s", from, to)
+            end
+            inputs[port_from] = {ref_to, port_to}
+            ref_to.inputs[port_to] = inputs -- just a place holder
+        elseif id_to == "<output>" then
+            _, output_dim = ref_from.layer:get_dim()
+            if output_dim[port_from] ~= dim_out[port_to] then
+                nerv.error("mismatching data dimension between %s and %s", from, to)
+            end
+            outputs[port_to] = {ref_from, port_from}
+            ref_from.outputs[port_from] = outputs -- just a place holder
+        else
+            _, output_dim = ref_from.layer:get_dim()
+            input_dim, _ = ref_to.layer:get_dim()
+            if output_dim[port_from] ~= input_dim[port_to] then
+                nerv.error("mismatching data dimension between %s and %s", from, to)
+            end
+            local mid = global_conf.mat_type(global_conf.batch_size,
+                                                output_dim[port_from])
+            local err_mid = mid:create() 
+
+            ref_from.outputs[port_from] = mid
+            ref_to.inputs[port_to] = mid
+
+            ref_from.err_inputs[port_from] = err_mid
+            ref_to.err_outputs[port_to] = err_mid
+
+            table.insert(ref_from.next_layers, ref_to) -- add edge
+            ref_to.in_deg = ref_to.in_deg + 1          -- increase the in-degree of the target layer
+        end
+    end
+    self.layers = layers
+    self.inputs = inputs
+    self.outputs = outputs
+    self.dim_in = dim_in
+    self.dim_out = dim_out
+end
+
+function nerv.DAGLayer:init(id) -- topology sort
+    local queue = {}
+    local l = 1
+    local r = 1
+    for id, ref in pairs(self.layers) do
+        if ref.in_deg == 0 then
+            table.insert(queue, ref)
+            nerv.utils.printf("adding source layer: %s\n", id)
+            r = r + 1
+        end
+    end
+    if l == r then
+        nerv.error("loop detected")
+    end
+    while l < r do
+        local cur = queue[l]
+        cur.visited = true
+        l = l + 1
+        for _, nl in pairs(cur.next_layers) do
+            nl.in_deg = nl.in_deg - 1 
+            if nl.in_deg == 0 then
+                table.insert(queue, nl)
+                r = r + 1
+            end
+        end
+    end
+    for i = 1, #queue do
+        nerv.utils.printf("queued layer: %s\n", queue[i].layer.id)
+    end
+    self.queue = queue
+    for id, ref in pairs(self.layers) do
+        -- check wether the graph is connected
+        if ref.visited == false then
+            nerv.utils.printf("warning: layer %s is ignored\n", id)
+        end
+        for i = 1, ref.input_len do
+            if ref.inputs[i] == nil then
+                nerv.error("dangling port %d of layer %s", i, id)
+            end
+        end
+        for i = 1, ref.output_len do
+            if ref.outputs[i] == nil then
+                nerv.error("dangling port %d of layer %s", i, id)
+            end
+        end
+        -- initialize sub layers
+        ref.layer:init()
+    end
+    for i = 1, #self.dim_in do
+        if self.inputs[i] == nil then
+            nerv.error("dangling port %d of layer <input>", i)
+        end
+    end
+    for i = 1, #self.dim_out do
+        if self.outputs[i] == nil then
+            nerv.error("dangling port %d of layer <output>", i)
+        end
+    end
+end
+
+function nerv.DAGLayer:set_inputs(input)
+    for i = 1, #self.dim_in do
+        local layer = self.inputs[i][1]
+        local port = self.inputs[i][2]
+        layer.inputs[port] = input[i]
+    end
+end
+
+function nerv.DAGLayer:set_outputs(output)
+    for i = 1, #self.dim_out do
+        local layer = self.outputs[i][1]
+        local port = self.outputs[i][2]
+        layer.outputs[port] = output[i]
+    end
+end
+
+function nerv.DAGLayer:set_err_inputs(bp_err)
+    for i = 1, #self.dim_out do
+        local layer = self.outputs[i][1]
+        local port = self.outputs[i][2]
+        layer.err_inputs[port] = bp_err[i]
+    end
+end
+
+function nerv.DAGLayer:set_err_outputs(next_bp_err)
+    for i = 1, #self.dim_in do
+        local layer = self.inputs[i][1]
+        local port = self.inputs[i][2]
+        layer.err_outputs[port] = next_bp_err[i]
+    end
+end
+
+function nerv.DAGLayer:update(bp_err, input, output)
+    self:set_err_inputs(bp_err)
+    self:set_inputs(input)
+    self:set_outputs(output)
+    for id, ref in pairs(self.queue) do
+        ref.layer:update(ref.err_inputs, ref.inputs, ref.outputs)
+    end
+end
+
+function nerv.DAGLayer:propagate(input, output)
+    self:set_inputs(input)
+    self:set_outputs(output)
+    for i = 1, #self.queue do
+        local ref = self.queue[i]
+        --[[
+        print(ref.inputs[1])
+        print(ref.outputs[1])
+        print(#ref.inputs, #ref.outputs)
+        --]]
+        ref.layer:propagate(ref.inputs, ref.outputs)
+    end
+end
+
+function nerv.DAGLayer:back_propagate(next_bp_err, bp_err, input, output)
+    self:set_err_outputs(next_bp_err)
+    self:set_err_inputs(bp_err)
+    self:set_inputs(input)
+    self:set_outputs(output)
+    for i = #self.queue, 1, -1 do
+        local ref = self.queue[i]
+        -- print(ref.layer.id)
+        ref.layer:back_propagate(ref.err_outputs, ref.err_inputs, ref.inputs, ref.outputs)
+         -- if #ref.err_outputs > 0 then
+         --     print(ref.err_outputs[1])
+         -- end
+    end
+end
diff --git a/nn/layer_repo.lua b/nn/layer_repo.lua
new file mode 100644
index 0000000..b1d2248
--- /dev/null
+++ b/nn/layer_repo.lua
@@ -0,0 +1,34 @@
+local LayerRepo = nerv.class("nerv.LayerRepo")
+
+function LayerRepo:__init(layer_spec, param_repo, global_conf)
+    local layers = {}
+    for ltype, llist in pairs(layer_spec) do
+        local layer_type = nerv.get_type(ltype)
+        for id, spec in pairs(llist) do
+            if layers[id] ~= nil then
+                nerv.error("a layer with id %s already exists", id)
+            end
+            nerv.utils.printf("id: %s\n", id)
+            if type(spec[2]) ~= "table" then
+                nerv.error("layer config table is need")
+            end
+            layer_config = spec[2]
+            if type(spec[1]) ~= "table" then
+                nerv.error("parameter description table is needed")
+            end
+            for pname, pid in pairs(spec[1]) do
+                layer_config[pname] = param_repo:get_param(pid, global_conf)
+            end
+            layers[id] = layer_type(id, global_conf, layer_config)
+        end
+    end
+    self.layers = layers
+end
+
+function LayerRepo:get_layer(lid)
+    local layer = self.layers[lid]
+    if layer == nil then
+        nerv.error("layer with id %s not found", lid)
+    end
+    return layer
+end
diff --git a/nn/param_repo.lua b/nn/param_repo.lua
new file mode 100644
index 0000000..3e37c31
--- /dev/null
+++ b/nn/param_repo.lua
@@ -0,0 +1,26 @@
+local ParamRepo = nerv.class("nerv.ParamRepo")
+
+function ParamRepo:__init(param_files)
+    local param_table = {}
+    if type(param_files) ~= "table" then
+        nerv.error("param file table is need")
+    end
+    for i = 1, #param_files do
+        local pf = nerv.ChunkFile(param_files[i], "r")
+        for cid, cspec in pairs(pf.metadata) do
+            if param_table[cid] ~= nil then
+                nerv.error("conflicting chunk id in param files")
+            end
+            param_table[cid] = pf
+        end
+    end
+    self.param_table = param_table
+end
+
+function ParamRepo:get_param(pid, global_conf)
+    local pf = self.param_table[pid]
+    if pf == nil then
+        nerv.error("param with id %s not found", pid)
+    end
+    return pf:read_chunk(pid, global_conf)
+end
diff --git a/speech b/speech
-Subproject d8ea67ee420c2fc73085da04de86df023acd98d
+Subproject 0c6ca6a17f06821cd5d612f489ca6cb68c2c4d5