parser.add_argument(
"--disable-skip",
default=False,
action='store_true',
help="Disable skipping of samples with high argmax BLEU")
args = parser.parse_args()
device = torch.device("cuda" if args.cuda else "cpu")
saves_path = os.path.join(SAVES_DIR, args.name)
os.makedirs(saves_path, exist_ok=True)
phrase_pairs, emb_dict = \
data.load_data(genre_filter=args.data)
log.info("Obtained %d phrase pairs with %d uniq words", len(phrase_pairs),
len(emb_dict))
data.save_emb_dict(saves_path, emb_dict)
end_token = emb_dict[data.END_TOKEN]
train_data = data.encode_phrase_pairs(phrase_pairs, emb_dict)
rand = np.random.RandomState(data.SHUFFLE_SEED)
rand.shuffle(train_data)
train_data, test_data = data.split_train_test(train_data)
log.info("Training data converted, got %d samples", len(train_data))
train_data = data.group_train_data(train_data)
test_data = data.group_train_data(test_data)
log.info("Train set has %d phrases, test %d", len(train_data),
len(test_data))
rev_emb_dict = {idx: word for word, idx in emb_dict.items()}
net = model.PhraseModel(emb_size=model.EMBEDDING_DIM,
dict_size=len(emb_dict),
parser = argparse.ArgumentParser()
parser.add_argument("--data", required=True, help="Category to use for training. "
"Empty string to train on full dataset")
parser.add_argument("--cuda", action='store_true', default=False,
help="Enable cuda")
parser.add_argument("-n", "--name", required=True, help="Name of the run")
args = parser.parse_args()
device = torch.device("cuda" if args.cuda else "cpu")
saves_path = os.path.join(SAVES_DIR, args.name)
os.makedirs(saves_path, exist_ok=True)
phrase_pairs, emb_dict = data.load_data(genre_filter=args.data)
log.info("Obtained %d phrase pairs with %d uniq words",
len(phrase_pairs), len(emb_dict))
data.save_emb_dict(saves_path, emb_dict)
end_token = emb_dict[data.END_TOKEN]
train_data = data.encode_phrase_pairs(phrase_pairs, emb_dict)
rand = np.random.RandomState(data.SHUFFLE_SEED)
rand.shuffle(train_data)
log.info("Training data converted, got %d samples", len(train_data))
train_data, test_data = data.split_train_test(train_data)
log.info("Train set has %d phrases, test %d", len(train_data), len(test_data))
net = model.PhraseModel(emb_size=model.EMBEDDING_DIM, dict_size=len(emb_dict),
hid_size=model.HIDDEN_STATE_SIZE).to(device)
log.info("Model: %s", net)
writer = SummaryWriter(comment="-" + args.name)
optimiser = optim.Adam(net.parameters(), lr=LEARNING_RATE)
