def train_wrapper(model):
'''Data loader'''
train_set = data.DataSet(FLAGS.root_dir,
FLAGS.dataset,
'train',
FLAGS.batch_size,
FLAGS.n_label,
data_aug=False,
shuffle=True)
valid_set = data.DataSet(FLAGS.root_dir,
FLAGS.dataset,
'val',
FLAGS.batch_size,
FLAGS.n_label,
data_aug=False,
shuffle=False)
'''create a tf session for training and validation
TODO: to run your model, you may call model.train(), model.save(), model.valid()'''
best_accuracy = 0
acc_train = []
acc_valid = []
for step in range(1, FLAGS.max_iteration + 1):
if not train_set.has_next_batch():
train_set.init_epoch()
batch_x, batch_y = train_set.next_batch()
if len(batch_x) == FLAGS.batch_size:
loss, acc = model.train(batch_x, batch_y)
if step == 1 or step % 10 == 0:
print("Step " + str(step) + ", Minibatch Loss= " + \
"{:.4f}".format(loss) + ", Training Accuracy= " + "{:.3f}".format(acc))
if step % FLAGS.valid_iteration == 0:
acc_train.append(acc)
tot_acc = 0.0
tot_input = 0
while valid_set.has_next_batch():
valid_ims, valid_labels = valid_set.next_batch()
loss, acc = model.valid(valid_ims, valid_labels)
tot_acc += acc * len(valid_ims)
tot_input += len(valid_ims)
acc = tot_acc / tot_input
valid_set.init_epoch()
print("Current Accuracy= " + "{:.3f}".format(acc))
acc_valid.append(acc)
if acc > best_accuracy:
model.save(step)
best_accuracy = acc
print("Optimization Finished!")
def test_wrapper(model):
'''Test your code.'''
test_set = data.DataSet(FLAGS.root_dir,
FLAGS.dataset,
'test',
FLAGS.batch_size,
FLAGS.n_label,
data_aug=False,
shuffle=False)
'''TODO: Your code here.'''
model.load()
tot_acc = 0.0
tot_input = 0
while test_set.has_next_batch():
test_ims, test_labels = test_set.next_batch()
_, acc = model.valid(test_ims, test_labels)
tot_acc += acc * len(test_ims)
tot_input += len(test_ims)
acc = tot_acc / tot_input
print("Test Accuracy= " + "{:.3f}".format(acc))
print("Test Finished!")
itr_start_time = time.time()
if itr % args.valid_every == 0:
valid_loader = torch.utils.data.DataLoader(
dataset=valid_set,
batch_size=config['batch_size'],
shuffle=True,
num_workers=1)
model.eval()
loss_records = {}
for descs, apiseqs, desc_lens, api_lens in valid_loader:
descs, apiseqs, desc_lens, api_lens = gVar(descs), gVar(
apiseqs), gVar(desc_lens), gVar(api_lens)
valid_loss = model.valid(descs, desc_lens, apiseqs, api_lens)
for loss_name, loss_value in valid_loss.items():
v = loss_records.get(loss_name, [])
v.append(loss_value)
loss_records[loss_name] = v
log = 'Validation '
for loss_name, loss_values in loss_records.items():
log = log + loss_name + ':%.4f ' % (np.mean(loss_values))
if args.visual:
tb_writer.add_scalar(loss_name, np.mean(loss_values),
itr_global)
logger.info(log)
itr += 1
itr_global += 1