print "Building model..."
model = cstm_model.build_model(conf)
print "Loading weights from %s ..." % args.model_weights
model.load_weights(args.model_weights)
print "Completed."
# Load vocabulary files and invert them
print "Loading vocabulary files..."
src_vocab = load_object(args.source_vocab)
trg_vocab = load_object(args.target_vocab)
corp.set_src_vocab(src_vocab)
corp.set_trg_vocab(trg_vocab)
print "Completed."
# Map source sentences
mapped_sents = corp.map_sentences(src_sents, corp.src_vocab, conf.max_seq_len, skip_unk=args.remove_unk)
print "%d source sentences mapped." % len(mapped_sents)
# Create decoder instance
decoder = Decoder(trg_vocab["</s>"], trg_vocab["<unk>"], args.beamsize, args.generate_unk)
# Validation prediction placeholder
y_pred = np.ndarray((mapped_sents.shape[0], mapped_sents.shape[1], len(trg_vocab))).astype(np.float32)
# Feed-forward pass
y_pred_dict = model.predict({'input': mapped_sents})
for i in range(y_pred.shape[1]):
y_pred[:, i, :] = y_pred_dict['output_%d' % i]
scorer = MultiBleuScorer()
d = decoder.process(y_pred)
