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author | Qi Liu <[email protected]> | 2016-03-31 16:48:07 +0800 |
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committer | Qi Liu <[email protected]> | 2016-03-31 16:48:07 +0800 |
commit | 78643f5127d86b54894f46a64d9593cdf6048d51 (patch) | |
tree | 1d23aed762a8783c9bba3461915357f23c2c164d /nerv/nn/trainer.lua | |
parent | 9b2fa6b357d441afbd6ccf41b9e039f5dc34eb05 (diff) |
update general trainer
Diffstat (limited to 'nerv/nn/trainer.lua')
-rw-r--r-- | nerv/nn/trainer.lua | 183 |
1 files changed, 183 insertions, 0 deletions
diff --git a/nerv/nn/trainer.lua b/nerv/nn/trainer.lua new file mode 100644 index 0000000..4ae08d9 --- /dev/null +++ b/nerv/nn/trainer.lua @@ -0,0 +1,183 @@ +local trainer = nerv.class('nerv.Trainer') + +function trainer:__init(gconf) + self.gconf = gconf + local mat_type + self.src_loc_type = nerv.ParamRepo.LOC_TYPES.ON_HOST + local src_loc_type = self.src_loc_type + if gconf.use_cpu then + mat_type = gconf.mmat_type + self.train_loc_type = nerv.ParamRepo.LOC_TYPES.ON_HOST + else + mat_type = gconf.cumat_type + self.train_loc_type = nerv.ParamRepo.LOC_TYPES.ON_DEVICE + end + local train_loc_type = self.train_loc_type + + local host_param_repo = nerv.ParamRepo() + host_param_repo:import(gconf.initialized_param, gconf) + local param_repo = host_param_repo:copy(train_loc_type, gconf) + self.layer_repo = self:make_layer_repo(param_repo) + local layer_repo = self.layer_repo + local graph = self:get_network(layer_repo) + self.input_order = self:get_input_order() + + self.network = nerv.Network('network', gconf, {network = graph, clip = gconf.clip}) + local network = self.network + network:init(gconf.batch_size, gconf.chunk_size) + + local dim_in, dim_out = network.dim_in, network.dim_out + self.err_output = {} + local err_output = self.err_output + for i = 1, #dim_in do + err_output[i] = {} + local tmp = mat_type(gconf.batch_size, dim_in[i]) + for t = 1, gconf.chunk_size do + err_output[i][t] = tmp + end + end + self.output = {} + self.err_input = {} + local output = self.output + local err_input = self.err_input + for i = 1, #dim_out do + output[i] = {} + for t = 1, gconf.chunk_size do + output[i][t] = mat_type(gconf.batch_size, dim_out[i]) + end + err_input[i] = {} + local tmp = mat_type(gconf.batch_size, dim_out[i]) + tmp:fill(0) + for t = 1, gconf.chunk_size do + if dim_out[i] == 1 then + err_input[i][t] = gconf.mask[t] + else + err_input[i][t] = tmp + end + end + end +end + +function trainer:make_buffer(readers) + local gconf = self.gconf + if gconf.chunk_size == 1 then + return nerv.FrmBuffer(gconf, { + buffer_size = gconf.buffer_size, + batch_size = gconf.batch_size, + chunk_size = gconf.chunk_size, + randomize = gconf.randomize, + readers = readers, + use_gpu = true, + }) + else + return nerv.SeqBuffer(gconf, { + batch_size = gconf.batch_size, + chunk_size = gconf.chunk_size, + readers = readers, + }) + end +end + +function trainer:process(dataset, do_train) + self:epoch_preprocess(dataset, do_train) + local buffer = self:make_buffer(self:get_readers(dataset)) + local cnt = 0 + local network = self.network + local input_order = self.input_order + local output = self.output + local err_input = self.err_input + local err_output = self.err_output + network:epoch_init() + + while true do + local data = buffer:get_data() + if data == nil then + break + end + + cnt = cnt + 1 + local info = {input = {}, output = output, err_input = err_input, err_output = err_output, + do_train = do_train, seq_length = data.seq_length, new_seq = data.new_seq} + for i = 1, #network.dim_in do + info.input[i] = data.data[input_order[i]] + end + + self:mini_batch_preprocess(cnt, info) + network:mini_batch_init(info) + network:propagate() + self:mini_batch_middleprocess(cnt, info) + if do_train then + network:back_propagate() + network:update() + end + self:mini_batch_afterprocess(cnt, info) + + collectgarbage('collect') + end + + self:epoch_afterprocess(dataset, do_train) + return self:get_error() +end + +function trainer:halving(train_err, cv_err) + local gconf = self.gconf + local src_loc_type = self.src_loc_type + local train_loc_type = self.train_loc_type + local layer_repo = self.layer_repo + local param_fname = string.format('%s_iter_%d_lr%f_tr%.3f_cv%.3f.nerv', os.date(gconf.date_pattern), gconf.cur_iter, gconf.lrate, train_err, cv_err) + param_fname = path.join(gconf.working_dir, param_fname) + local network = self.network + local host_param_repo = network:get_params():copy(src_loc_type, gconf) + host_param_repo:export(param_fname) + + if cv_err < gconf.best_cv then + nerv.info("accepting the trained params") + gconf.best_cv = cv_err + gconf.initialized_param = {param_fname} + else + nerv.info("rejecting the trained params, rollback to the previous one") + file.move(param_fname, param_fname .. '.rejected') + host_param_repo = nerv.ParamRepo() + host_param_repo:import(gconf.initialized_param, gconf) + local param_repo = host_param_repo:copy(train_loc_type, gconf) + layer_repo:rebind(param_repo) + gconf.lrate = gconf.lrate * 0.5 + end +end + +function trainer:training_preprocess() +end + +function trainer:training_afterprocess() +end + +function trainer:epoch_preprocess(dataset, do_train) +end + +function trainer:epoch_afterprocess(dataset, do_train) +end + +function trainer:mini_batch_preprocess(cnt, info) +end + +function trainer:mini_batch_middleprocess(cnt, info) +end + +function trainer:mini_batch_afterprocess(cnt, info) +end + +function trainer:make_layer_repo(param_repo) + nerv.error_method_not_implemented() +end + +function trainer:get_network(layer_repo) + nerv.error_method_not_implemented() +end + +function trainer:get_readers(dataset) + nerv.error_method_not_implemented() +end + +function trainer:get_input_order() + nerv.error_method_not_implemented() +end |