for i in range(n_iter):
for xmb, mmb, ymb in iter_data(*shuffle(trX,
trM,
trYt,
random_state=np.random),
n_batch=n_batch_train,
truncate=True,
verbose=True):
cost, _ = sess.run([clf_loss, train], {
X_train: xmb,
M_train: mmb,
Y_train: ymb
})
n_updates += 1
if n_updates in [1000, 2000, 4000, 8000, 16000, 32000
] and n_epochs == 0:
log()
n_epochs += 1
log()
if submit:
sess.run([
p.assign(ip) for p, ip in zip(
params,
joblib.load(os.path.join(save_dir, desc, 'best_params.jl')))
])
predict()
if analysis:
rocstories_analysis(data_dir,
os.path.join(submission_dir, 'ROCStories.tsv'),
os.path.join(log_dir, 'rocstories.jsonl'))
n_updates = 0
n_epochs = 0
if dataset != 'stsb':
trYt = trY
if submit:
path = os.path.join(save_dir, desc, 'best_params')
chainer.serializers.save_npz(make_path(path), model)
best_score = 0
for i in range(n_iter):
print("running epoch", i)
run_epoch()
n_epochs += 1
log()
if submit:
path = os.path.join(save_dir, desc, 'best_params')
chainer.serializers.load_npz(make_path(path), model)
predict()
if analysis:
if dataset == 'rocstories':
rocstories_analysis(
data_dir, os.path.join(
submission_dir, filenames[dataset]), os.path.join(
log_dir, '{}.jsonl'.format(desc)))
elif dataset == 'sst':
sst_analysis(
data_dir, os.path.join(
submission_dir, filenames[dataset]), os.path.join(
log_dir, '{}.jsonl'.format(desc)))
else:
raise NotImplementedError
clf_head.to_gpu()
n_updates = 0
n_epochs = 0
if dataset != 'stsb':
trYt = trY
if submit:
path = os.path.join(save_dir, desc, 'best_params')
chainer.serializers.save_npz(make_path(path), model)
best_score = 0
for i in range(n_iter):
print("running epoch", i)
run_epoch()
n_epochs += 1
log()
if submit:
path = os.path.join(save_dir, desc, 'best_params')
chainer.serializers.load_npz(make_path(path), model)
predict()
if analysis:
if dataset == 'rocstories':
rocstories_analysis(
data_dir, os.path.join(submission_dir, filenames[dataset]),
os.path.join(log_dir, '{}.jsonl'.format(desc)))
elif dataset == 'sst':
sst_analysis(data_dir,
os.path.join(submission_dir, filenames[dataset]),
os.path.join(log_dir, '{}.jsonl'.format(desc)))
else:
raise NotImplementedError
