def test(model, test_data):
batch_size = pool_size = 3000
test_loader = Loader(batch_size, pool_size, (1, 8), test_data)
vloss_list, ploss_list, acc_list = [], [], []
tot = 0
print('Testing.')
while True:
test_loader.next(training=False)
remain = test_loader.remain()
if not remain:
break
test_loader.sample()
test_loader.update(model.calc_rnn_states)
vloss, ploss, acc = test_loader(model.loss)
tot += remain
vloss_list.append(vloss * remain)
ploss_list.append(ploss * remain)
acc_list.append(acc * remain)
print('test: %.4f %.4f %.2f%%' % (vloss, ploss, acc * 100))
vloss = float(np.sum(vloss_list)) / tot
ploss = float(np.sum(ploss_list)) / tot
acc = float(np.sum(acc_list)) / tot
return vloss, ploss, acc
def train(model, train_data, test_data, save_interval, batch_size):
vloss, ploss, acc = test(model, test_data)
# vloss, ploss, acc = 100.0, 100.0, 100.0, 0.0
min_loss = vloss + ploss
it, epoch = 0, 0
__time = time()
log('epoch: %d %.5f %.5f %.5f %.3f%%' % (epoch, vloss, ploss, min_loss, acc * 100))
pool_size = batch_size * 16
train_loader = Loader(batch_size, pool_size, (14, 18), train_data)
while True:
it += 1
# from utils import watch4
# watch4.reset()
if train_loader.next(training=True):
epoch += 1
test_data.reload() # new epoch, reload train data & test data
best_model = Model(config.name)
best_model.restore()
vloss, ploss, acc = test(best_model, test_data)
min_loss = vloss + ploss
log('epoch: %d %.5f %.5f %.5f %.3f%%' % (epoch, vloss, ploss, min_loss, acc * 100))
train_loader.sample()
train_loader.update(model.calc_rnn_states)
# watch4.print('update', reset=True)
vloss, ploss, acc = train_loader(model.learn)
# watch4.print('learn', reset=True)
if it % 20 == 0:
model.push_down()
print('%6d: %.4f %.4f %.2f%% %.2fs' % (it, vloss, ploss, acc * 100, time() - __time))
__time = time()
if it % save_interval == 0:
model.save(model.name + '_t')
vloss, ploss, acc = test(model, test_data)
if vloss + ploss < min_loss:
min_loss = vloss + ploss
model.save()
log('%d: %.5f %.5f %.5f %.3f%%' % ((it + 1) // save_interval, vloss, ploss, min_loss, acc * 100))