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-rw-r--r--Makefile2
-rw-r--r--examples/test_nn_lib.lua29
-rw-r--r--layer/affine.lua10
-rw-r--r--layer/bias.lua4
-rw-r--r--layer/init.lua4
-rw-r--r--layer/sigmoid.lua4
-rw-r--r--layer/softmax_ce.lua4
-rw-r--r--layer/window.lua4
-rw-r--r--nn/layer_dag.lua28
9 files changed, 68 insertions, 21 deletions
diff --git a/Makefile b/Makefile
index 5b6e081..0468f57 100644
--- a/Makefile
+++ b/Makefile
@@ -12,7 +12,7 @@ LUA_LIBS := matrix/init.lua io/init.lua nerv.lua \
nn/init.lua nn/layer_repo.lua nn/param_repo.lua nn/layer_dag.lua \
io/sgd_buffer.lua
INCLUDE := -I build/luajit-2.0/include/luajit-2.0/ -DLUA_USE_APICHECK
-# CUDA_BASE := /usr/local/cuda-6.5
+#CUDA_BASE := /usr/local/cuda-6.5
CUDA_BASE := /usr/local/cuda-5.0
CUDA_INCLUDE := -I $(CUDA_BASE)/include/
INCLUDE += $(CUDA_INCLUDE)
diff --git a/examples/test_nn_lib.lua b/examples/test_nn_lib.lua
index 04fd7d6..6fdbd67 100644
--- a/examples/test_nn_lib.lua
+++ b/examples/test_nn_lib.lua
@@ -117,7 +117,7 @@ tnet_reader = nerv.TNetReader(gconf,
buffer = nerv.SGDBuffer(gconf,
{
buffer_size = 81920,
- -- randomize = true,
+ randomize = true,
readers = {
{ reader = tnet_reader,
data = {main_scp = 429, ref = 1}}
@@ -128,9 +128,12 @@ sm = sublayer_repo:get_layer("softmax_ce0")
main = layer_repo:get_layer("main")
main:init(gconf.batch_size)
gconf.cnt = 0
+-- data = buffer:get_data()
+-- input = {data.main_scp, data.ref}
+-- while true do
for data in buffer.get_data, buffer do
- if gconf.cnt == 1000 then break end
- gconf.cnt = gconf.cnt + 1
+-- if gconf.cnt == 100 then break end
+-- gconf.cnt = gconf.cnt + 1
input = {data.main_scp, data.ref}
output = {}
@@ -141,11 +144,21 @@ for data in buffer.get_data, buffer do
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("correct: %d\n", sm.total_correct)
- nerv.utils.printf("frames: %d\n", sm.total_frames)
- nerv.utils.printf("err/frm: %.8f\n", sm.total_ce / sm.total_frames)
- nerv.utils.printf("accuracy: %.8f\n", sm.total_correct / sm.total_frames)
+-- nerv.utils.printf("cross entropy: %.8f\n", sm.total_ce)
+-- nerv.utils.printf("correct: %d\n", sm.total_correct)
+-- nerv.utils.printf("frames: %d\n", sm.total_frames)
+-- nerv.utils.printf("err/frm: %.8f\n", sm.total_ce / sm.total_frames)
+-- nerv.utils.printf("accuracy: %.8f\n", sm.total_correct / sm.total_frames)
collectgarbage("collect")
end
+nerv.utils.printf("cross entropy: %.8f\n", sm.total_ce)
+nerv.utils.printf("correct: %d\n", sm.total_correct)
+nerv.utils.printf("accuracy: %.3f%%\n", sm.total_correct / sm.total_frames * 100)
+nerv.utils.printf("writing back...\n")
+cf = nerv.ChunkFile("output.nerv", "w")
+for i, p in ipairs(main:get_params()) do
+ print(p)
+ cf:write_chunk(p)
+end
+cf:close()
nerv.Matrix.print_profile()
diff --git a/layer/affine.lua b/layer/affine.lua
index 59a0e91..2cd7acb 100644
--- a/layer/affine.lua
+++ b/layer/affine.lua
@@ -41,7 +41,7 @@ function AffineLayer:init()
self.bc:fill(0)
end
-function nerv.AffineLayer:update(bp_err, input, output)
+function AffineLayer:update(bp_err, input, output)
local ltp = self.ltp.trans
local bp = self.bp.trans
local ltc = self.ltc
@@ -60,13 +60,17 @@ function nerv.AffineLayer:update(bp_err, input, output)
ltp:add(ltp, ltp, 1.0, -gconf.lrate * gconf.wcost)
end
-function nerv.AffineLayer:propagate(input, output)
+function AffineLayer:propagate(input, output)
-- apply linear transform
output[1]:mul(input[1], self.ltp.trans, 1.0, 0.0, 'N', 'N')
-- add bias
output[1]:add_row(self.bp.trans, 1.0)
end
-function nerv.AffineLayer:back_propagate(next_bp_err, bp_err, input, output)
+function AffineLayer:back_propagate(next_bp_err, 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}
+end
diff --git a/layer/bias.lua b/layer/bias.lua
index 6ddfe11..8cd326b 100644
--- a/layer/bias.lua
+++ b/layer/bias.lua
@@ -22,3 +22,7 @@ function BiasLayer:propagate(input, output)
output[1]:copy_fromd(input[1])
output[1]:add_row(self.bias.trans, 1.0)
end
+
+function BiasLayer:get_params()
+ return {self.bias}
+end
diff --git a/layer/init.lua b/layer/init.lua
index 38bcd7f..3011f8e 100644
--- a/layer/init.lua
+++ b/layer/init.lua
@@ -58,6 +58,10 @@ function Layer:check_dim_len(len_in, len_out)
end
end
+function Layer:get_params()
+ nerv.error_method_not_implemented()
+end
+
function Layer:get_dim()
return self.dim_in, self.dim_out
end
diff --git a/layer/sigmoid.lua b/layer/sigmoid.lua
index 220b7af..dd10fb9 100644
--- a/layer/sigmoid.lua
+++ b/layer/sigmoid.lua
@@ -25,3 +25,7 @@ end
function SigmoidLayer:back_propagate(next_bp_err, bp_err, input, output)
next_bp_err[1]:sigmoid_grad(bp_err[1], output[1])
end
+
+function SigmoidLayer:get_params()
+ return {}
+end
diff --git a/layer/softmax_ce.lua b/layer/softmax_ce.lua
index cd57010..79e859e 100644
--- a/layer/softmax_ce.lua
+++ b/layer/softmax_ce.lua
@@ -50,3 +50,7 @@ function SoftmaxCELayer:back_propagate(next_bp_err, bp_err, input, output)
end
next_bp_err[1]:add(self.soutput, label, 1.0, -1.0)
end
+
+function SoftmaxCELayer:get_params()
+ return {}
+end
diff --git a/layer/window.lua b/layer/window.lua
index 8e9e761..b381c9b 100644
--- a/layer/window.lua
+++ b/layer/window.lua
@@ -22,3 +22,7 @@ function WindowLayer:propagate(input, output)
output[1]:copy_fromd(input[1])
output[1]:scale_row(self.window.trans)
end
+
+function WindowLayer:get_params()
+ return {self.window}
+end
diff --git a/nn/layer_dag.lua b/nn/layer_dag.lua
index 3951bfa..2dda7c9 100644
--- a/nn/layer_dag.lua
+++ b/nn/layer_dag.lua
@@ -38,7 +38,7 @@ local function discover(id, layers, layer_repo)
return ref
end
-function nerv.DAGLayer:__init(id, global_conf, layer_conf)
+function DAGLayer:__init(id, global_conf, layer_conf)
local layers = {}
local inputs = {}
local outputs = {}
@@ -131,7 +131,7 @@ function nerv.DAGLayer:__init(id, global_conf, layer_conf)
self.gconf = global_conf
end
-function nerv.DAGLayer:init(batch_size) -- topology sort
+function DAGLayer:init(batch_size) -- topology sort
for i, conn in ipairs(self.parsed_conn) do
local _, output_dim
local ref_from, port_from, ref_to, port_to
@@ -174,7 +174,7 @@ function nerv.DAGLayer:init(batch_size) -- topology sort
end
end
-function nerv.DAGLayer:set_inputs(input)
+function DAGLayer:set_inputs(input)
for i = 1, #self.dim_in do
local layer = self.inputs[i][1]
local port = self.inputs[i][2]
@@ -182,7 +182,7 @@ function nerv.DAGLayer:set_inputs(input)
end
end
-function nerv.DAGLayer:set_outputs(output)
+function DAGLayer:set_outputs(output)
for i = 1, #self.dim_out do
local layer = self.outputs[i][1]
local port = self.outputs[i][2]
@@ -190,7 +190,7 @@ function nerv.DAGLayer:set_outputs(output)
end
end
-function nerv.DAGLayer:set_err_inputs(bp_err)
+function 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]
@@ -198,7 +198,7 @@ function nerv.DAGLayer:set_err_inputs(bp_err)
end
end
-function nerv.DAGLayer:set_err_outputs(next_bp_err)
+function 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]
@@ -206,7 +206,7 @@ function nerv.DAGLayer:set_err_outputs(next_bp_err)
end
end
-function nerv.DAGLayer:update(bp_err, input, output)
+function DAGLayer:update(bp_err, input, output)
self:set_err_inputs(bp_err)
self:set_inputs(input)
self:set_outputs(output)
@@ -217,7 +217,7 @@ function nerv.DAGLayer:update(bp_err, input, output)
end
end
-function nerv.DAGLayer:propagate(input, output)
+function DAGLayer:propagate(input, output)
self:set_inputs(input)
self:set_outputs(output)
for i = 1, #self.queue do
@@ -227,7 +227,7 @@ function nerv.DAGLayer:propagate(input, output)
end
end
-function nerv.DAGLayer:back_propagate(next_bp_err, bp_err, input, output)
+function 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)
@@ -238,3 +238,13 @@ function nerv.DAGLayer:back_propagate(next_bp_err, bp_err, input, output)
ref.layer:back_propagate(ref.err_outputs, ref.err_inputs, ref.inputs, ref.outputs)
end
end
+
+function DAGLayer:get_params()
+ local res = {}
+ for id, ref in pairs(self.queue) do
+ for i, p in ipairs(ref.layer:get_params()) do
+ table.insert(res, p)
+ end
+ end
+ return res
+end
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