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train.lua
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train.lua
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require 'cutorch'
local RecipeDataMinibatchLoader = require 'RecipeDataMinibatchLoader'
local neural_checklist_model = require 'neural_checklist_model'
cmd = torch.CmdLine()
cmd:text()
cmd:text('Train a neural checklist model')
cmd:text()
cmd:text('Options')
cmd:option('-rnn_size',200,'Size of text generating RNN hidden state')
cmd:option('-batch_size',20, 'number of recipes to train on in parallel')
cmd:option('-dropout', 0.0, 'Amount of dropout')
cmd:option('-num_layers', 1, 'Number of layers for the title encoder and language model')
cmd:option('-init_weight', 0.35, 'Initial weight range for parameters [-x, x]')
cmd:option('-rnn_type', 'gru', 'Type of language model (GRU, LSTM, RNN)')
cmd:option('-evidence_type', 0, '0 = all evidence, 1 = no evidence')
cmd:option('-lm_only', false, '')
cmd:option('-sumnotmean', true, '')
cmd:option('-switchmul', 5.0, '')
cmd:option('-switch_temperature', 2.0, '')
cmd:option('-attention_temperature', 2.0, '')
cmd:option('-end_loss_mul', 100, '')
cmd:option('-dec_rate', false, '')
cmd:option('-info', 'newtmp', '')
cmd:option('-epochs', 35, "Number of epochs to train.")
cmd:option('-embeddings', '', '')
cmd:option('-seed',123,'torch manual random number generator seed')
cmd:option('-train_data_dir', '', 'Training data directory')
cmd:option('-dev_data_dir', '', 'Dev set data directory')
cmd:option('-data_file_info', 'v1.', 'Files version info tag')
cmd:option('-checkpoint_dir', '', 'output directory where checkpoints get written')
cmd:option('-gpuidx', 1, 'Index of GPU on which job should be executed.')
cmd:option('-learningRate', 0.5, '')
cmd:option('-max_lm_grad_norm', 5, '')
cmd:option('-max_item_grad_norm', 5, '')
cmd:option('-max_goal_grad_norm', 5, '')
cmd:option('-sentences_to_train', 0, 'Number of sentences to train at a time, 0 for all.')
cmd:option('-model', '', '')
cmd:option('-startepoch', 1, '')
cmd:text()
opt = cmd:parse(arg)
torch.manualSeed(opt.seed)
cutorch.setDevice(opt.gpuidx)
scorefile = io.open('score_neural_checklist_models.' .. opt.rnn_type .. '.info' .. opt.info .. '.dev', 'w')
function run_test(model, data_loader, epoch, last_valid_loss)
local loss = 0
local tag_loss = 0
for i=1,data_loader.nvalid do
sys.tic()
local text, goal, agenda, batch_len, ref_types, true_new_item_atten, true_used_item_atten = data_loader:next_batch(2)
local state = {text = text,
goal = goal,
agenda = agenda,
batch_len = batch_len,
ref_types = ref_types,
true_new_item_atten = true_new_item_atten,
true_used_item_atten = true_used_item_atten}
local avg_err, end_err = model:dev_forward_pass(state)
loss = loss + avg_err
tag_loss = tag_loss + end_err
print(string.format(
"[Loss: %f Epoch: %d Position: %d Rate: %f Time: %f, Step Len: %d]",
avg_err,
epoch,
i,
opt.learningRate,
sys.toc(),
state.batch_len[1]
))
sys.tic()
end
scorefile:write(loss .. '\n')
print(string.format("[VALID EPOCH : %d LOSS: %f TOTAL: %d]",
epoch, loss / data_loader.nvalid, data_loader.nvalid))
return loss, tag_loss
end
local function train_model(model, data_loader, max_info)
print('train model')
local epoch = opt.startepoch
local step = 0
local start_time = sys.tic()
local last_valid_loss = 1e9
local last_valid_tag_loss = 1e9
-- if opt.startepoch ~= 1 then
-- run_test(model, data_loader, 1, last_valid_loss)
-- run_test(model, data_loader, (epoch - 1), last_valid_loss)
-- os.exit()
-- end
print(data_loader.nvalid)
local total = 0
for epoch = opt.startepoch,opt.epochs do
print('epoch: ' .. epoch)
local curr = 1
-- for step_idx = 1, max_info.num_steps
local loss = 0
local epoch_loss = 0
local total = 0
for i=1,data_loader.ntrain do
step = step + 1
if i % 10 == 0 then
print(' step: ' .. i .. '/' .. data_loader.ntrain)
end
if i % 1000 == 0 then
collectgarbage()
end
local text, goal, agenda, batch_len, ref_types, true_new_item_atten, true_used_item_atten = data_loader:next_batch(1)
local state = {text = text,
goal = goal,
agenda = agenda,
batch_len = batch_len,
ref_types = ref_types,
true_new_item_atten = true_new_item_atten,
true_used_item_atten = true_used_item_atten}
local avg_err = model:forward_pass(state)
loss = loss + avg_err
epoch_loss = epoch_loss + avg_err
model:backward_pass(state, opt.batch_size)
print(string.format(
"[Loss: %f Epoch: %d Position: %d Rate: %f Time: %f, Step Len: %d]",
avg_err * opt.batch_size,
epoch,
i * opt.batch_size,
opt.learningRate,
sys.toc(),
state.batch_len[1]
))
sys.tic()
loss = 0
total = total + opt.batch_size
--end
end
local sum_loss = 0
-- print(string.format("[EPOCH : %d LOSS: %f TOTAL: %d]",
-- epoch, epoch_loss / total, total))
local new_loss, new_tag_loss = run_test(model, data_loader, epoch, last_valid_loss)
sum_loss = sum_loss + new_loss
-- if sum_loss > last_valid_loss then
-- opt.learningRate = opt.learningRate / 2
-- end
if opt.dec_rate then
if sum_loss > last_valid_loss or (sum_loss == last_valid_loss and new_tag_loss > last_valid_tag_loss) then
opt.learningRate = opt.learningRate / 2
end
end
last_valid_loss = sum_loss
last_valid_tag_loss = new_tag_loss
if opt.info == '' then
model:save_model(epoch, (opt.rnn_type .. '.' .. math.floor(last_valid_loss / data_loader.nvalid) .. '.' .. math.floor(new_tag_loss / data_loader.nvalid)))
else
model:save_model(epoch, (opt.rnn_type .. '.' .. opt.info .. '.' .. math.floor(last_valid_loss / data_loader.nvalid) .. '.' .. math.floor(new_tag_loss / data_loader.nvalid)))
end
end
end
local function initialize_model(max_info, dict)
print('Initializing model...')
if opt.model == '' then
local model = neural_checklist_model:new(opt, max_info, dict)
print('Done.')
return model
else
local model = neural_checklist_model:load_from_point(opt, opt.model, opt.startepoch - 1)
print('Done.')
return model
end
end
local function save_model(model, epoch)
model:save_models(epoch)
end
local function main()
local data_loader = RecipeDataMinibatchLoader.create(opt.train_data_dir, opt.dev_data_dir, opt.batch_size, false, opt.data_file_info, opt, true)
local vocab_size = data_loader.vocab_size
local max_info = {}
max_info.num_words = data_loader.max_num_words
max_info.vocab_size = vocab_size
max_info.item_vocab_size = data_loader.item_vocab_size
max_info.goal_vocab_size = data_loader.goal_vocab_size
max_info.pad = data_loader.pad
max_info.num_items = data_loader.max_num_items
max_info.item_length = data_loader.max_item_length
max_info.goal_length = data_loader.max_goal_length
max_info.text_length = data_loader.max_text_length
protos = {}
model = initialize_model(max_info, data_loader.dict)
collectgarbage()
train_model(model, data_loader, max_info)
scorefile:close()
end
main()