USE_SMALL = False
GPU = True
BATCH_SIZE = 64
TEST_ENTRY = (True, True, True) # (AGG, SEL, COND)
sql_data, table_data, val_sql_data, val_table_data, test_sql_data, test_table_data, TRAIN_DB, DEV_DB, TEST_DB = load_dataset(
args.dataset, use_small=USE_SMALL)
examples, tables = load_dataset_dummy(0)
examples.extend(sql_data)
tables.update(table_data)
word_emb = load_word_emb('glove/glove.%dB.%dd.txt'%(B_word,N_word), \
load_used=True, use_small=USE_SMALL) # load_used can speed up loading
if args.baseline:
model = Seq2SQL(word_emb, N_word=N_word, gpu=GPU, trainable_emb=True)
else:
model = SQLNet(word_emb,
N_word=N_word,
use_ca=args.ca,
gpu=GPU,
trainable_emb=True)
if args.train_emb:
agg_m, sel_m, cond_m, agg_e, sel_e, cond_e = best_model_name(
args, savedstr='_mconly')
print('==> best model names:', agg_m, sel_m, cond_m)
print "Loading from %s" % agg_m
model.agg_pred.load_state_dict(torch.load(agg_m))
print "Loading from %s" % sel_m
model.sel_pred.load_state_dict(torch.load(sel_m))
BATCH_SIZE = 64
TRAIN_ENTRY = (True, True, True) # (AGG, SEL, COND)
TRAIN_AGG, TRAIN_SEL, TRAIN_COND = TRAIN_ENTRY
learning_rate = 1e-4 if args.rl else 1e-3
sql_data, table_data, val_sql_data, val_table_data, \
test_sql_data, test_table_data, \
TRAIN_DB, DEV_DB, TEST_DB = load_dataset(
args.dataset, use_small=USE_SMALL)
word_emb = load_word_emb('glove/glove.%dB.%dd.txt'%(B_word,N_word), \
load_used=args.train_emb, use_small=USE_SMALL)
if args.baseline:
model = Seq2SQL(word_emb,
N_word=N_word,
gpu=GPU,
trainable_emb=args.train_emb)
assert not args.train_emb, "Seq2SQL can\'t train embedding."
else:
model = SQLNet(word_emb,
N_word=N_word,
use_ca=args.ca,
gpu=GPU,
trainable_emb=args.train_emb)
assert not args.rl, "SQLNet can\'t do reinforcement learning."
optimizer = torch.optim.Adam(model.parameters(),
lr=learning_rate,
weight_decay=0)
if args.train_emb:
agg_m, sel_m, cond_m, agg_e, sel_e, cond_e = best_model_name(args)
GPU=True
BATCH_SIZE=64
TRAIN_ENTRY=(True, True, True) # (AGG, SEL, COND)
TRAIN_AGG, TRAIN_SEL, TRAIN_COND = TRAIN_ENTRY
learning_rate = 1e-4 if params['rl'] else 1e-3
sql_data, table_data, val_sql_data, val_table_data, \
test_sql_data, test_table_data, \
TRAIN_DB, DEV_DB, TEST_DB = load_dataset(
params['dataset'], use_small=USE_SMALL)
word_emb = load_word_emb('glove/glove.%dB.%dd.txt'%(B_word,N_word), \
load_used=params['train_emb'], use_small=USE_SMALL)
if params['baseline']:
model = Seq2SQL(word_emb, N_word=N_word, gpu=GPU,
trainable_emb = params['train_emb'])
assert not params['train_emb'], "Seq2SQL can\'t train embedding."
else:
model = SQLNet(word_emb, N_word=N_word, use_ca=params['ca'],
gpu=GPU, trainable_emb = params['train_emb'])
assert not params['rl'], "SQLNet can\'t do reinforcement learning."
optimizer = torch.optim.Adam(model.parameters(),
lr=learning_rate, weight_decay = 0)
if params['train_emb']:
agg_m, sel_m, cond_m, agg_e, sel_e, cond_e = best_model_name(args)
else:
agg_m, sel_m, cond_m = best_model_name(args)
if params['rl'] or params['train_emb']: # Load pretrained model.
agg_lm, sel_lm, cond_lm = best_model_name(args, for_load=True)
