major changes in asr_trainer.lua; unified settings in `gconf`

1 files changed, 87 insertions, 17 deletions

diff --git a/nerv/examples/asr_trainer.lua b/nerv/examples/asr_trainer.lua
index 3fa2653..684ea30 100644
--- a/nerv/examples/asr_trainer.lua
+++ b/nerv/examples/asr_trainer.lua
@@ -1,4 +1,4 @@
-function build_trainer(ifname)
+local function build_trainer(ifname)
     local param_repo = nerv.ParamRepo()
     param_repo:import(ifname, nil, gconf)
     local layer_repo = make_layer_repo(param_repo)
@@ -75,24 +75,91 @@ function build_trainer(ifname)
     return iterative_trainer
 end
 
+local function check_and_add_defaults(spec)
+    for k, v in pairs(spec) do
+        gconf[k] = opts[string.gsub(k, '_', '-')].val or gconf[k] or v
+    end
+end
+
+local function make_options(spec)
+    local options = {}
+    for k, v in pairs(spec) do
+        table.insert(options,
+                    {string.gsub(k, '_', '-'), nil, type(v), default = v})
+    end
+    return options
+end
+
+local function print_help(options)
+    nerv.printf("Usage: <asr_trainer.lua> [options] network_config.lua\n")
+    nerv.print_usage(options)
+end
+
+local function print_gconf()
+    local key_maxlen = 0
+    for k, v in pairs(gconf) do
+        key_maxlen = math.max(key_maxlen, #k or 0)
+    end
+    local function pattern_gen()
+        return string.format("%%-%ds = %%s\n", key_maxlen)
+    end
+    nerv.info("ready to train with the following gconf settings:")
+    nerv.printf(pattern_gen(), "Key", "Value")
+    for k, v in pairs(gconf) do
+        nerv.printf(pattern_gen(), k or "", v or "")
+    end
+end
+
+local trainer_defaults = {
+    lrate = 0.8,
+    batch_size = 256,
+    buffer_size = 81920,
+    wcost = 1e-6,
+    momentum = 0.9,
+    start_halving_inc = 0.5,
+    halving_factor = 0.6,
+    end_halving_inc = 0.1,
+    min_iter = 1,
+    max_iter = 20,
+    min_halving = 5,
+    do_halving = false,
+    tr_scp = nil,
+    cv_scp = nil,
+    cumat_type = nerv.CuMatrixFloat,
+    mmat_type = nerv.MMatrixFloat,
+    debug = false
+}
+
+local options = make_options(trainer_defaults)
+table.insert(options, {"help", "h", "boolean",
+                        default = false, desc = "show this help information"})
+
+arg, opts = nerv.parse_args(arg, options)
+
+if #arg < 1 or opts["help"].val then
+    print_help(options)
+    return
+end
+
 dofile(arg[1])
-start_halving_inc = 0.5
-halving_factor = 0.6
-end_halving_inc = 0.1
-min_iter = 1
-max_iter = 20
-min_halving = 5
-gconf.batch_size = 256
-gconf.buffer_size = 81920
+
+--[[
+
+Rule: command-line option overrides network config overrides trainer default.
+Note: config key like aaa_bbbb_cc could be overriden by specifying
+--aaa-bbbb-cc to command-line arguments.
+
+]]--
+
+check_and_add_defaults(trainer_defaults)
 
 local pf0 = gconf.initialized_param
 local trainer = build_trainer(pf0)
---local trainer = build_trainer("c3.nerv")
 local accu_best = trainer(nil, gconf.cv_scp, false)
-local do_halving = false
 
+print_gconf()
 nerv.info("initial cross validation: %.3f", accu_best)
-for i = 1, max_iter do
+for i = 1, gconf.max_iter do
     nerv.info("[NN] begin iteration %d with lrate = %.6f", i, gconf.lrate)
     local accu_tr = trainer(nil, gconf.tr_scp, true)
     nerv.info("[TR] training set %d: %.3f", i, accu_tr)
@@ -108,14 +175,17 @@ for i = 1, max_iter do
     nerv.info("[CV] cross validation %d: %.3f", i, accu_new)
     -- TODO: revert the weights
     local accu_diff = accu_new - accu_best
-    if do_halving and accu_diff < end_halving_inc and i > min_iter then
+    if gconf.do_halving and
+        accu_diff < gconf.end_halving_inc and
+        i > gconf.min_iter then
         break
     end
-    if accu_diff < start_halving_inc and i >= min_halving then
-        do_halving = true
+    if accu_diff < gconf.start_halving_inc and
+        i >= gconf.min_halving then
+        gconf.do_halving = true
     end
-    if do_halving then
-        gconf.lrate = gconf.lrate * halving_factor
+    if gconf.do_halving then
+        gconf.lrate = gconf.lrate * gconf.halving_factor
     end
     if accu_new > accu_best then
         accu_best = accu_new