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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()
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