diff options
Diffstat (limited to 'examples/asr_trainer.lua')
| -rw-r--r-- | examples/asr_trainer.lua | 87 |
1 files changed, 87 insertions, 0 deletions
diff --git a/examples/asr_trainer.lua b/examples/asr_trainer.lua new file mode 100644 index 0000000..b43a547 --- /dev/null +++ b/examples/asr_trainer.lua @@ -0,0 +1,87 @@ +function build_trainer(ifname) + local param_repo = make_param_repo(ifname) + 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 iterative_trainer = function (ofname, scp_file, bp) + gconf.randomize = bp + -- build buffer + local buffer = make_buffer(make_reader(scp_file, layer_repo)) + -- initialize the network + network:init(gconf.batch_size) + gconf.cnt = 0 + for data in buffer.get_data, buffer do + -- prine stat periodically + gconf.cnt = gconf.cnt + 1 + if gconf.cnt == 1000 then + print_stat(crit) + gconf.cnt = 0 + end + if gconf.cnt == 100 then break end + + input = {data.main_scp, data.phone_state} + output = {} + err_input = {} + err_output = {input[1]:create()} + network:propagate(input, output) + if bp then + network:back_propagate(err_output, err_input, input, output) + network:update(err_input, input, output) + end + -- collect garbage in-time to save GPU memory + collectgarbage("collect") + end + print_stat(crit) + if bp then + nerv.info("writing back...") + cf = nerv.ChunkFile(ofname, "w") + for i, p in ipairs(network:get_params()) do + cf:write_chunk(p) + end + cf:close() + end + return get_accuracy(crit) + 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 = 6 +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("iteration %d with lrate = %.6f", i, gconf.lrate) + local accu_tr = trainer(pf0 .. "_iter" .. i .. ".nerv", gconf.tr_scp, true) + nerv.info("[TR] training set %d: %.3f", i, accu_tr) + local accu_new = trainer(nil, 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 +end +nerv.Matrix.print_profile() |