def test(args):
vocab = Vocab()
vocab.load(args.vocab)
vocab.add_special_token()
pos2id = Vocab()
pos2id.load(args.poslist)
if args.gpu > -1:
cuda.get_device(args.gpu).use()
xp = cuda.cupy
else:
xp = np
model = WordnnTagger.load(args.model)
out_path = making_data(args.test_path, model.window)
if args.gpu > -1:
model.to_gpu()
model.make_oov_vector(args.gpu > -1)
# start evaluation
n_data = 0
n_correct = 0
sum_loss = xp.zeros((), dtype=xp.float32)
start = time.time()
for tags, contexts in line_iter(out_path, args.minibatch, False):
batch_ts = xp.array([pos2id[tag] for tag in tags], dtype=xp.int32)
batch_xs = xp.array(
[[vocab[word] for word in vocab.check_words(context)]
for context in contexts],
dtype=xp.int32)
cur_batch_size = batch_ts.shape[0]
ys, loss = model(batch_xs, batch_ts)
sum_loss += loss.data * cur_batch_size
pred_labels = ys.data.argmax(1)
n_correct += sum(1 for j in range(cur_batch_size)
if pred_labels[j] == batch_ts[j])
n_data += cur_batch_size
end = time.time()
accuracy = float(n_correct / n_data)
print('test loss : {}'.format(sum_loss))
print('test accuracy : {}'.format(accuracy))
print('(time to run : {})'.format(end - start))
