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authorDeterminant <[email protected]>2015-06-22 19:01:29 +0800
committerDeterminant <[email protected]>2015-06-22 19:01:29 +0800
commit2497fd9e7a0fae5ee4887890d7a312e0e08a93b8 (patch)
tree382f97575bd2df9ee6abb1662b11b279fc22d72b /examples/asr_trainer.lua
parent196e9b48a3541caccdffc5743001cced70667091 (diff)
major change: use luarocks to manage project
Diffstat (limited to 'examples/asr_trainer.lua')
-rw-r--r--examples/asr_trainer.lua106
1 files changed, 0 insertions, 106 deletions
diff --git a/examples/asr_trainer.lua b/examples/asr_trainer.lua
deleted file mode 100644
index a5727be..0000000
--- a/examples/asr_trainer.lua
+++ /dev/null
@@ -1,106 +0,0 @@
-function build_trainer(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_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(sublayer_repo)
- nerv.CuMatrix.print_profile()
- nerv.CuMatrix.clear_profile()
- gconf.cnt = 0
- -- break
- end
- local input = {}
--- if gconf.cnt == 100 then break end
- 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_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(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(sublayer_repo))
- network:get_params():export(fname, nil)
- end
- return get_accuracy(sublayer_repo)
- end
- return iterative_trainer
-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
-
-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
-
-nerv.info("initial cross validation: %.3f", accu_best)
-for i = 1, 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)
- local accu_new = trainer(
- string.format("%s_%s_iter_%d_lr%f_tr%.3f",
- string.gsub(
- (string.gsub(pf0[1], "(.*/)(.*)", "%2")),
- "(.*)%..*", "%1"),
- os.date("%Y%m%d%H%M%S"),
- i, gconf.lrate,
- accu_tr),
- gconf.cv_scp, false)
- 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
- break
- end
- if accu_diff < start_halving_inc and i >= min_halving then
- do_halving = true
- end
- if do_halving then
- gconf.lrate = gconf.lrate * halving_factor
- end
- if accu_new > accu_best then
- accu_best = accu_new
- end
--- nerv.Matrix.print_profile()
-end