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-rw-r--r--examples/asr_trainer.lua42
-rw-r--r--examples/swb_baseline.lua87
-rw-r--r--examples/test_dnn_layers.lua4
-rw-r--r--examples/test_nn_lib.lua18
-rw-r--r--io/sgd_buffer.lua2
-rw-r--r--layer/affine.lua75
-rw-r--r--layer/bias.lua2
-rw-r--r--layer/combiner.lua26
-rw-r--r--layer/init.lua12
-rw-r--r--layer/mse.lua28
-rw-r--r--layer/sigmoid.lua4
-rw-r--r--layer/softmax_ce.lua21
-rw-r--r--layer/window.lua2
-rw-r--r--nerv.lua37
-rw-r--r--nn/layer_dag.lua23
-rw-r--r--nn/layer_repo.lua4
-rw-r--r--nn/param_repo.lua70
m---------speech0
18 files changed, 284 insertions, 173 deletions
diff --git a/examples/asr_trainer.lua b/examples/asr_trainer.lua
index 05d770f..a5727be 100644
--- a/examples/asr_trainer.lua
+++ b/examples/asr_trainer.lua
@@ -1,50 +1,58 @@
function build_trainer(ifname)
- local param_repo = make_param_repo(ifname)
+ local param_repo = nerv.ParamRepo()
+ param_repo:import(ifname, nil, gconf)
local sublayer_repo = make_sublayer_repo(param_repo)
local layer_repo = make_layer_repo(sublayer_repo, param_repo)
local crit = get_criterion_layer(sublayer_repo)
local network = get_network(layer_repo)
+ local input_order = get_input_order()
local iterative_trainer = function (prefix, scp_file, bp)
gconf.randomize = bp
-- build buffer
- local buffer = make_buffer(make_reader(scp_file, layer_repo))
+ local buffer = make_buffer(make_readers(scp_file, layer_repo))
-- initialize the network
network:init(gconf.batch_size)
gconf.cnt = 0
+ err_input = {nerv.CuMatrixFloat(256, 1)}
+ err_input[1]:fill(1)
for data in buffer.get_data, buffer do
-- prine stat periodically
gconf.cnt = gconf.cnt + 1
if gconf.cnt == 1000 then
- print_stat(crit)
+ print_stat(sublayer_repo)
+ nerv.CuMatrix.print_profile()
+ nerv.CuMatrix.clear_profile()
gconf.cnt = 0
+ -- break
end
+ local input = {}
-- if gconf.cnt == 100 then break end
-
- input = {data.main_scp, data.phone_state}
- output = {}
- err_input = {}
+ for i, id in ipairs(input_order) do
+ if data[id] == nil then
+ nerv.error("input data %s not found", id)
+ end
+ table.insert(input, data[id])
+ end
+ local output = {nerv.CuMatrixFloat(256, 1)}
err_output = {input[1]:create()}
network:propagate(input, output)
if bp then
- network:back_propagate(err_output, err_input, input, output)
+ network:back_propagate(err_input, err_output, input, output)
network:update(err_input, input, output)
end
-- collect garbage in-time to save GPU memory
collectgarbage("collect")
end
- print_stat(crit)
+ print_stat(sublayer_repo)
nerv.CuMatrix.print_profile()
+ nerv.CuMatrix.clear_profile()
if (not bp) and prefix ~= nil then
nerv.info("writing back...")
local fname = string.format("%s_cv%.3f.nerv",
- prefix, get_accuracy(crit))
- cf = nerv.ChunkFile(fname, "w")
- for i, p in ipairs(network:get_params()) do
- cf:write_chunk(p)
- end
- cf:close()
+ prefix, get_accuracy(sublayer_repo))
+ network:get_params():export(fname, nil)
end
- return get_accuracy(crit)
+ return get_accuracy(sublayer_repo)
end
return iterative_trainer
end
@@ -73,7 +81,7 @@ for i = 1, max_iter do
local accu_new = trainer(
string.format("%s_%s_iter_%d_lr%f_tr%.3f",
string.gsub(
- (string.gsub(pf0, "(.*/)(.*)", "%2")),
+ (string.gsub(pf0[1], "(.*/)(.*)", "%2")),
"(.*)%..*", "%1"),
os.date("%Y%m%d%H%M%S"),
i, gconf.lrate,
diff --git a/examples/swb_baseline.lua b/examples/swb_baseline.lua
index 28cc6d5..8b7e01a 100644
--- a/examples/swb_baseline.lua
+++ b/examples/swb_baseline.lua
@@ -6,14 +6,10 @@ gconf = {lrate = 0.8, wcost = 1e-6, momentum = 0.9,
tr_scp = "/slfs1/users/mfy43/swb_ivec/train_bp.scp",
cv_scp = "/slfs1/users/mfy43/swb_ivec/train_cv.scp",
htk_conf = "/slfs1/users/mfy43/swb_ivec/plp_0_d_a.conf",
- global_transf = "/slfs1/users/mfy43/swb_global_transf.nerv",
- initialized_param = "/slfs1/users/mfy43/swb_init.nerv",
+ initialized_param = {"/slfs1/users/mfy43/swb_init.nerv",
+ "/slfs1/users/mfy43/swb_global_transf.nerv"},
debug = false}
-function make_param_repo(param_file)
- return nerv.ParamRepo({param_file, gconf.global_transf})
-end
-
function make_sublayer_repo(param_repo)
return nerv.LayerRepo(
{
@@ -60,7 +56,7 @@ function make_sublayer_repo(param_repo)
},
["nerv.SoftmaxCELayer"] =
{
- criterion = {{}, {dim_in = {3001, 1}, dim_out = {}, compressed = true}}
+ ce_crit = {{}, {dim_in = {3001, 1}, dim_out = {1}, compressed = true}}
}
}, param_repo, gconf)
end
@@ -82,7 +78,7 @@ function make_layer_repo(sublayer_repo, param_repo)
}
}},
main = {{}, {
- dim_in = {429, 1}, dim_out = {},
+ dim_in = {429, 1}, dim_out = {1},
sub_layers = sublayer_repo,
connections = {
["<input>[1]"] = "affine0[1]",
@@ -100,8 +96,9 @@ function make_layer_repo(sublayer_repo, param_repo)
["sigmoid5[1]"] = "affine6[1]",
["affine6[1]"] = "sigmoid6[1]",
["sigmoid6[1]"] = "affine7[1]",
- ["affine7[1]"] = "criterion[1]",
- ["<input>[2]"] = "criterion[2]"
+ ["affine7[1]"] = "ce_crit[1]",
+ ["<input>[2]"] = "ce_crit[2]",
+ ["ce_crit[1]"] = "<output>[1]"
}
}}
}
@@ -109,55 +106,61 @@ function make_layer_repo(sublayer_repo, param_repo)
end
function get_criterion_layer(sublayer_repo)
- return sublayer_repo:get_layer("criterion")
+ return sublayer_repo:get_layer("ce_crit")
end
function get_network(layer_repo)
return layer_repo:get_layer("main")
end
-function make_reader(scp_file, layer_repo)
- return nerv.TNetReader(gconf,
- {
- id = "main_scp",
- scp_file = scp_file,
- conf_file = gconf.htk_conf,
- frm_ext = gconf.frm_ext,
- mlfs = {
- phone_state = {
- file = "/slfs1/users/mfy43/swb_ivec/ref.mlf",
- format = "map",
- format_arg = "/slfs1/users/mfy43/swb_ivec/dict",
- dir = "*/",
- ext = "lab"
- }
- },
- global_transf = layer_repo:get_layer("global_transf")
- })
+function make_readers(scp_file, layer_repo)
+ return {
+ {reader = nerv.TNetReader(gconf,
+ {
+ id = "main_scp",
+ scp_file = scp_file,
+ conf_file = gconf.htk_conf,
+ frm_ext = gconf.frm_ext,
+ mlfs = {
+ phone_state = {
+ file = "/slfs1/users/mfy43/swb_ivec/ref.mlf",
+ format = "map",
+ format_arg = "/slfs1/users/mfy43/swb_ivec/dict",
+ dir = "*/",
+ ext = "lab"
+ }
+ },
+ global_transf = layer_repo:get_layer("global_transf")
+ }),
+ data = {main_scp = 429, phone_state = 1}}
+ }
end
-function make_buffer(reader, buffer)
+function make_buffer(readers)
return nerv.SGDBuffer(gconf,
{
buffer_size = gconf.buffer_size,
randomize = gconf.randomize,
- readers = {
- { reader = reader,
- data = {main_scp = 429, phone_state = 1}}
- }
+ readers = readers
})
end
-function get_accuracy(crit)
- return crit.total_correct / crit.total_frames * 100
+function get_input_order()
+ return {"main_scp", "phone_state"}
+end
+
+function get_accuracy(sublayer_repo)
+ local ce_crit = sublayer_repo:get_layer("ce_crit")
+ return ce_crit.total_correct / ce_crit.total_frames * 100
end
-function print_stat(crit)
+function print_stat(sublayer_repo)
+ local ce_crit = sublayer_repo:get_layer("ce_crit")
nerv.info("*** training stat begin ***")
- nerv.utils.printf("cross entropy:\t\t%.8f\n", crit.total_ce)
- nerv.utils.printf("correct:\t\t%d\n", crit.total_correct)
- nerv.utils.printf("frames:\t\t\t%d\n", crit.total_frames)
- nerv.utils.printf("err/frm:\t\t%.8f\n", crit.total_ce / crit.total_frames)
- nerv.utils.printf("accuracy:\t\t%.3f%%\n", get_accuracy(crit))
+ nerv.printf("cross entropy:\t\t%.8f\n", ce_crit.total_ce)
+ nerv.printf("correct:\t\t%d\n", ce_crit.total_correct)
+ nerv.printf("frames:\t\t\t%d\n", ce_crit.total_frames)
+ nerv.printf("err/frm:\t\t%.8f\n", ce_crit.total_ce / ce_crit.total_frames)
+ nerv.printf("accuracy:\t\t%.3f%%\n", get_accuracy(sublayer_repo))
nerv.info("*** training stat end ***")
end
diff --git a/examples/test_dnn_layers.lua b/examples/test_dnn_layers.lua
index bf81f7b..64c0dec 100644
--- a/examples/test_dnn_layers.lua
+++ b/examples/test_dnn_layers.lua
@@ -69,8 +69,8 @@ for i = 0, 3 do
print(err_output1[1])
print("err_output2")
print(err_output2[1])
- nerv.utils.printf("cross entropy: %.8f\n", sm.total_ce)
- nerv.utils.printf("frames: %.8f\n", sm.total_frames)
+ nerv.printf("cross entropy: %.8f\n", sm.total_ce)
+ nerv.printf("frames: %.8f\n", sm.total_frames)
end
print("linear")
print(af.ltp.trans)
diff --git a/examples/test_nn_lib.lua b/examples/test_nn_lib.lua
index 6fdbd67..5444810 100644
--- a/examples/test_nn_lib.lua
+++ b/examples/test_nn_lib.lua
@@ -144,17 +144,17 @@ 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.printf("cross entropy: %.8f\n", sm.total_ce)
+-- nerv.printf("correct: %d\n", sm.total_correct)
+-- nerv.printf("frames: %d\n", sm.total_frames)
+-- nerv.printf("err/frm: %.8f\n", sm.total_ce / sm.total_frames)
+-- nerv.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")
+nerv.printf("cross entropy: %.8f\n", sm.total_ce)
+nerv.printf("correct: %d\n", sm.total_correct)
+nerv.printf("accuracy: %.3f%%\n", sm.total_correct / sm.total_frames * 100)
+nerv.printf("writing back...\n")
cf = nerv.ChunkFile("output.nerv", "w")
for i, p in ipairs(main:get_params()) do
print(p)
diff --git a/io/sgd_buffer.lua b/io/sgd_buffer.lua
index 381b863..f4f7dfe 100644
--- a/io/sgd_buffer.lua
+++ b/io/sgd_buffer.lua
@@ -41,7 +41,7 @@ function SGDBuffer:saturate()
buff.data:copy_from(buff.leftover, 0, lrow)
buff.leftover = nil
end
- nerv.utils.printf("leftover: %d\n", lrow)
+ nerv.printf("leftover: %d\n", lrow)
reader.tail = lrow
reader.has_leftover = false
end
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
diff --git a/layer/bias.lua b/layer/bias.lua
index 8cd326b..c99274d 100644
--- a/layer/bias.lua
+++ b/layer/bias.lua
@@ -24,5 +24,5 @@ function BiasLayer:propagate(input, output)
end
function BiasLayer:get_params()
- return {self.bias}
+ return nerv.ParamRepo({self.bias})
end
diff --git a/layer/combiner.lua b/layer/combiner.lua
index 75e47e2..7bd7617 100644
--- a/layer/combiner.lua
+++ b/layer/combiner.lua
@@ -7,9 +7,15 @@ function CombinerLayer:__init(id, global_conf, layer_conf)
self.dim_out = layer_conf.dim_out
self.gconf = global_conf
self:check_dim_len(#self.lambda, -1)
+ if #self.dim_in < 1 then
+ nerv.error("no input specified")
+ end
+ if #self.dim_out < 1 then
+ nerv.error("no output specified")
+ end
end
-function CombinerLayer:init()
+function CombinerLayer:init(batch_size)
local dim = self.dim_in[1]
for i = 2, #self.dim_in do
if self.dim_in[i] ~= dim then
@@ -21,6 +27,7 @@ function CombinerLayer:init()
nerv.error("mismatching dimensions of inputs/outputs")
end
end
+ self.sum = self.gconf.cumat_type(batch_size, dim)
end
function CombinerLayer:update(bp_err, input, output)
@@ -32,24 +39,21 @@ function CombinerLayer:propagate(input, output)
output[1]:add(output[1], input[i], 1.0, self.lambda[i])
end
for i = 2, #self.dim_out do
- output[i]:copy_fromd(output[1])
+ output[i]:copy_fromd(output[1])
end
end
-function CombinerLayer:back_propagate(next_bp_err, bp_err, input, output)
- local sum = bp_err[1]:create()
- sum:fill(0)
- for i = 1, #self.dim_out do
+function CombinerLayer:back_propagate(bp_err, next_bp_err, input, output)
+ local sum = self.sum
+ sum:copy_fromd(bp_err[1])
+ for i = 2, #self.dim_out do
sum:add(sum, bp_err[i], 1.0, 1.0)
end
for i = 1, #self.dim_in do
- local scale = nerv.CuMatrixFloat(sum:nrow(), 1)
- scale:fill(self.lambda[i])
- next_bp_err[i]:copy_fromd(sum)
- next_bp_err[i]:scale_rows_by_col(scale)
+ next_bp_err[i]:add(next_bp_err[i], sum, 0.0, self.lambda[i])
end
end
function CombinerLayer:get_params()
- return {}
+ return nerv.ParamRepo({})
end
diff --git a/layer/init.lua b/layer/init.lua
index 169427d..e39af94 100644
--- a/layer/init.lua
+++ b/layer/init.lua
@@ -15,11 +15,15 @@ function Param:set_info(info)
self.info = info
end
-function Param:read(pfhandle)
+function Param:read(handle)
nerv.error_method_not_implemented()
end
-function Param:write(pfhandle)
+function Param:write(handle)
+ nerv.error_method_not_implemented()
+end
+
+function Param:update(gradient)
nerv.error_method_not_implemented()
end
@@ -29,7 +33,7 @@ function Layer:__init(id, global_conf, layer_conf)
nerv.error_method_not_implemented()
end
-function Layer:init()
+function Layer:init(batch_size)
nerv.error_method_not_implemented()
end
@@ -41,7 +45,7 @@ function Layer:propagate(input, output)
nerv.error_method_not_implemented()
end
-function Layer:back_propagate(next_bp_err, bp_err, input, output)
+function Layer:back_propagate(bp_err, next_bp_err, input, output)
nerv.error_method_not_implemented()
end
diff --git a/layer/mse.lua b/layer/mse.lua
index da5b24d..9a97add 100644
--- a/layer/mse.lua
+++ b/layer/mse.lua
@@ -8,12 +8,16 @@ function MSELayer:__init(id, global_conf, layer_conf)
self:check_dim_len(2, -1)
end
-function MSELayer:init()
+function MSELayer:init(batch_size)
if self.dim_in[1] ~= self.dim_in[2] then
nerv.error("mismatching dimensions of previous network output and labels")
end
+ self.scale = 1 / self.dim_in[1]
self.total_mse = 0.0
self.total_frames = 0
+ self.mse = self.gconf.cumat_type(batch_size, self.dim_in[1])
+ self.mse_sum = self.gconf.cumat_type(batch_size, 1)
+ self.diff = self.mse:create()
end
function MSELayer:update(bp_err, input, output)
@@ -21,32 +25,28 @@ function MSELayer:update(bp_err, input, output)
end
function MSELayer:propagate(input, output)
- local mse = input[1]:create()
+ local mse = self.mse
+ local mse_sum = self.mse_sum
mse:add(input[1], input[2], 1.0, -1.0)
- self.diff = mse:create()
self.diff:copy_fromd(mse)
mse:mul_elem(mse, mse)
- mse = mse:rowsum(mse)
- local scale = nerv.CuMatrixFloat(mse:nrow(), 1)
- scale:fill(1 / input[1]:ncol())
- mse:scale_rows_by_col(scale)
+ mse_sum:add(mse_sum, mse:rowsum(mse), 0.0, self.scale)
if output[1] ~= nil then
- output[1]:copy_fromd(mse)
+ output[1]:copy_fromd(mse_sum)
end
- self.total_mse = self.total_mse + mse:colsum()[0]
- self.total_frames = self.total_frames + mse:nrow()
+ self.total_mse = self.total_mse + mse_sum:colsum()[0]
+ self.total_frames = self.total_frames + mse_sum:nrow()
end
-- NOTE: must call propagate before back_propagate
-function MSELayer:back_propagate(next_bp_err, bp_err, input, output)
+function MSELayer:back_propagate(bp_err, next_bp_err, input, output)
local nbe = next_bp_err[1]
- nbe:copy_fromd(self.diff)
- self.diff = nil
+ nbe:add(nbe, self.diff, 0.0, 2 * self.scale)
if bp_err[1] ~= nil then
nbe:scale_rows_by_col(bp_err[1])
end
end
function MSELayer:get_params()
- return {}
+ return nerv.ParamRepo({})
end
diff --git a/layer/sigmoid.lua b/layer/sigmoid.lua
index dd10fb9..dfd09eb 100644
--- a/layer/sigmoid.lua
+++ b/layer/sigmoid.lua
@@ -22,10 +22,10 @@ function SigmoidLayer:propagate(input, output)
output[1]:sigmoid(input[1])
end
-function SigmoidLayer:back_propagate(next_bp_err, bp_err, input, output)
+function SigmoidLayer:back_propagate(bp_err, next_bp_err, input, output)
next_bp_err[1]:sigmoid_grad(bp_err[1], output[1])
end
function SigmoidLayer:get_params()
- return {}
+ return nerv.ParamRepo({})
end
diff --git a/layer/softmax_ce.lua b/layer/softmax_ce.lua
index 7888540..daf891e 100644
--- a/layer/softmax_ce.lua
+++ b/layer/softmax_ce.lua
@@ -12,13 +12,15 @@ function SoftmaxCELayer:__init(id, global_conf, layer_conf)
self:check_dim_len(2, -1) -- two inputs: nn output and label
end
-function SoftmaxCELayer:init()
+function SoftmaxCELayer:init(batch_size)
if not self.compressed and (self.dim_in[1] ~= self.dim_in[2]) then
nerv.error("mismatching dimensions of previous network output and labels")
end
self.total_ce = 0.0
self.total_correct = 0
self.total_frames = 0
+ self.softmax = self.gconf.cumat_type(batch_size, self.dim_in[1])
+ self.ce = self.softmax:create()
end
function SoftmaxCELayer:update(bp_err, input, output)
@@ -26,12 +28,11 @@ function SoftmaxCELayer:update(bp_err, input, output)
end
function SoftmaxCELayer:propagate(input, output)
- local soutput = input[1]:create() -- temporary value for calc softmax
- self.soutput = soutput
- local classified = soutput:softmax(input[1])
- local ce = soutput:create()
- ce:log_elem(soutput)
+ local softmax = self.softmax
+ local ce = self.ce
+ local classified = softmax:softmax(input[1])
local label = input[2]
+ ce:log_elem(softmax)
if self.compressed then
label = label:decompress(input[1]:ncol())
end
@@ -42,26 +43,26 @@ function SoftmaxCELayer:propagate(input, output)
end
-- add total ce
self.total_ce = self.total_ce - ce:colsum()[0]
- self.total_frames = self.total_frames + soutput:nrow()
+ self.total_frames = self.total_frames + softmax:nrow()
-- TODO: add colsame for uncompressed label
if self.compressed then
self.total_correct = self.total_correct + classified:colsame(input[2])[0]
end
end
-function SoftmaxCELayer:back_propagate(next_bp_err, bp_err, input, output)
+function SoftmaxCELayer:back_propagate(bp_err, next_bp_err, input, output)
-- softmax output - label
local label = input[2]
if self.compressed then
label = label:decompress(input[1]:ncol())
end
local nbe = next_bp_err[1]
- nbe:add(self.soutput, label, 1.0, -1.0)
+ nbe:add(self.softmax, label, 1.0, -1.0)
if bp_err[1] ~= nil then
nbe:scale_rows_by_col(bp_err[1])
end
end
function SoftmaxCELayer:get_params()
- return {}
+ return nerv.ParamRepo({})
end
diff --git a/layer/window.lua b/layer/window.lua
index 3a093f4..4e9a3b1 100644
--- a/layer/window.lua
+++ b/layer/window.lua
@@ -24,5 +24,5 @@ function WindowLayer:propagate(input, output)
end
function WindowLayer:get_params()
- return {self.window}
+ return nerv.ParamRepo({self.window})
end
diff --git a/nerv.lua b/nerv.lua
index 467d926..a69dda6 100644
--- a/nerv.lua
+++ b/nerv.lua
@@ -1,20 +1,35 @@
require 'libnerv'
-nerv.utils = require 'pl.utils'
function nerv.error(fmt, ...)
- error(nerv.utils.printf("[nerv] internal error: " .. fmt .. "\n", ...))
+ error(nerv.printf("[nerv] internal error: " .. fmt .. "\n", ...))
end
function nerv.error_method_not_implemented()
nerv.error("method not implemented");
end
+function nerv.printf(fmt, ...)
+ io.write(string.format(fmt, ...))
+end
+
+function nerv.mesg_with_timestamp(fmt, ...)
+ nerv.printf(
+ string.format("(%s)[nerv] info: %s\n",
+ os.date("%H:%M:%S %F"), fmt), ...)
+end
+
function nerv.info(fmt, ...)
- nerv.utils.printf(
+ nerv.printf(
string.format("(%s)[nerv] info: %s\n",
os.date("%H:%M:%S %F"), fmt), ...)
end
+function nerv.warning(fmt, ...)
+ nerv.printf(
+ string.format("(%s)[nerv] warning: %s\n",
+ os.date("%H:%M:%S %F"), fmt), ...)
+end
+
-- Torch C API wrapper
function nerv.class(tname, parenttname)
@@ -77,8 +92,20 @@ function table.tostring(tbl)
return "{" .. table.concat(result, ",") .. "}"
end
-function nerv.get_type(typename)
- return assert(loadstring("return " .. typename))()
+function nerv.get_type(tname)
+ return assert(loadstring("return " .. tname))()
+end
+
+function nerv.is_type(obj, tname)
+ local mt0 = nerv.getmetatable(tname)
+ local mt = getmetatable(obj)
+ while mt do
+ if mt == mt0 then
+ return true
+ end
+ mt = getmetatable(mt)
+ end
+ return false
end
require 'matrix.init'
diff --git a/nn/layer_dag.lua b/nn/layer_dag.lua
index 2dda7c9..8e30216 100644
--- a/nn/layer_dag.lua
+++ b/nn/layer_dag.lua
@@ -85,13 +85,14 @@ function DAGLayer:__init(id, global_conf, layer_conf)
end
end
+ -- topology sort
local queue = {}
local l = 1
local r = 1
for id, ref in pairs(layers) do
if ref.in_deg == 0 then
table.insert(queue, ref)
- nerv.utils.printf("adding source layer: %s\n", id)
+ nerv.info("adding source layer: %s", id)
r = r + 1
end
end
@@ -111,13 +112,13 @@ function DAGLayer:__init(id, global_conf, layer_conf)
end
end
for i = 1, #queue do
- nerv.utils.printf("queued layer: %s\n", queue[i].layer.id)
+ nerv.info("enqueued layer: %s", queue[i].layer.id)
end
for id, ref in pairs(layers) do
-- check wether the graph is connected
if ref.visited == false then
- nerv.utils.printf("warning: layer %s is ignored\n", id)
+ nerv.warning("layer %s is ignored", id)
end
end
@@ -131,7 +132,7 @@ function DAGLayer:__init(id, global_conf, layer_conf)
self.gconf = global_conf
end
-function DAGLayer:init(batch_size) -- topology sort
+function DAGLayer:init(batch_size)
for i, conn in ipairs(self.parsed_conn) do
local _, output_dim
local ref_from, port_from, ref_to, port_to
@@ -160,7 +161,7 @@ function DAGLayer:init(batch_size) -- topology sort
end
end
-- initialize sub layers
- ref.layer:init()
+ ref.layer:init(batch_size)
end
for i = 1, #self.dim_in do
if self.inputs[i] == nil then
@@ -227,7 +228,7 @@ function DAGLayer:propagate(input, output)
end
end
-function DAGLayer:back_propagate(next_bp_err, bp_err, input, output)
+function DAGLayer:back_propagate(bp_err, next_bp_err, input, output)
self:set_err_outputs(next_bp_err)
self:set_err_inputs(bp_err)
self:set_inputs(input)
@@ -235,16 +236,14 @@ function DAGLayer:back_propagate(next_bp_err, bp_err, input, 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)
+ ref.layer:back_propagate(ref.err_inputs, ref.err_outputs, ref.inputs, ref.outputs)
end
end
function DAGLayer:get_params()
- local res = {}
+ local param_repos = {}
for id, ref in pairs(self.queue) do
- for i, p in ipairs(ref.layer:get_params()) do
- table.insert(res, p)
- end
+ table.insert(param_repos, ref.layer:get_params())
end
- return res
+ return nerv.ParamRepo.merge(param_repos)
end
diff --git a/nn/layer_repo.lua b/nn/layer_repo.lua
index b1d2248..602c37c 100644
--- a/nn/layer_repo.lua
+++ b/nn/layer_repo.lua
@@ -8,7 +8,7 @@ function LayerRepo:__init(layer_spec, param_repo, global_conf)
if layers[id] ~= nil then
nerv.error("a layer with id %s already exists", id)
end
- nerv.utils.printf("id: %s\n", id)
+ nerv.info("create layer: %s", id)
if type(spec[2]) ~= "table" then
nerv.error("layer config table is need")
end
@@ -17,7 +17,7 @@ function LayerRepo:__init(layer_spec, param_repo, global_conf)
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)
+ layer_config[pname] = param_repo:get_param(pid)
end
layers[id] = layer_type(id, global_conf, layer_config)
end
diff --git a/nn/param_repo.lua b/nn/param_repo.lua
index 3e37c31..ab971ba 100644
--- a/nn/param_repo.lua
+++ b/nn/param_repo.lua
@@ -1,26 +1,76 @@
local ParamRepo = nerv.class("nerv.ParamRepo")
+function ParamRepo:__init(plist)
+ self.params = {}
+ if plist ~= nil then
+ for i, p in ipairs(plist) do
+ self.params[p.id] = p
+ end
+ end
+end
+
+function ParamRepo:add(pid, p)
+ if self.params[pid] ~= nil then
+ nerv.error("duplicate params with the same id: %s", pid)
+ end
+ self.params[pid] = p
+end
-function ParamRepo:__init(param_files)
- local param_table = {}
+function ParamRepo:remove(pid, p)
+ if self.params[pid] == nil then
+ nerv.error("param %s does not exit", pid)
+ end
+ table.remove(self.params, pid)
+end
+
+function ParamRepo.merge(repos)
+ local self = nerv.ParamRepo()
+ for i, repo in ipairs(repos) do
+ if not nerv.is_type(repo, "nerv.ParamRepo") then
+ nerv.error("nerv.ParamRepo objects expected, got %s", repo)
+ end
+ for pid, p in pairs(repo.params) do
+ self:add(pid, p)
+ end
+ end
+ return self
+end
+
+function ParamRepo:import(param_files, pids, gconf)
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")
+ if pids == nil or pids[cid] ~= nil then
+ local p = pf:read_chunk(cid, gconf)
+ if not nerv.is_type(p, "nerv.Param") then
+ nerv.error("param chunk is expected")
+ end
+ self:add(cid, p)
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
+function ParamRepo:export(param_file, pids)
+ cf = nerv.ChunkFile(param_file, "w")
+ if pids == nil then
+ for id, p in pairs(self.params) do
+ cf:write_chunk(p)
+ end
+ else
+ for i, pid in ipairs(pids) do
+ cf:write_chunk(self:get_param(pid))
+ end
+ end
+ cf:close()
+end
+
+function ParamRepo:get_param(pid)
+ local p = self.params[pid]
+ if p == nil then
nerv.error("param with id %s not found", pid)
end
- return pf:read_chunk(pid, global_conf)
+ return p
end
diff --git a/speech b/speech
-Subproject aee0d372e6b06a217f24bea5c88962b97e0ca0e
+Subproject 701181d625cfcc59d264db97e5270aa14f1f5d3
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