def test(config):
_config_test(config)
de2idx, idx2de = load_de_vocab()
en2idx, idx2en = load_en_vocab()
model = ConvSeq2Seq(config)
graph_handler = GraphHandler(config)
inferencer = Inferencer(config, model)
sess = tf.Session()
graph_handler.initialize(sess)
global_step = 0
refs = []
hypotheses = []
with codecs.open(os.path.join(config.eval_dir, config.model_name), "w", "utf-8") as fout:
for i, batch in tqdm(enumerate(get_batch_for_test())):
preds = inferencer.run(sess, batch)
sources = batch['source']
targets = batch['target']
for source, target, pred in zip(sources, targets, preds):
got = " ".join(idx2en[idx] for idx in pred).split("</S>")[0].strip()
fout.write("- source: " + source +"\n")
fout.write("- expected: " + target + "\n")
fout.write("- got: " + got + "\n\n")
fout.flush()
ref = target.split()
hypothesis = got.split()
if len(ref) > 3 and len(hypothesis) > 3:
refs.append([ref])
hypotheses.append(hypothesis)
score = corpus_bleu(refs, hypotheses)
fout.write("Bleu Score = " + str(100*score))
