6f8f7608d6d603b49bef0249f169ef413d6e7371,src/codebase/main.py,,main,#Any#,146

Before Change

    encoder = MultiLayerRNNEncoder(args.word_dim, args.hid_dim,
                                   args.n_layers, nn.LSTMCell)
    if args.cuda:
        token_embedder = token_embedder.cuda()
        encoder = encoder.cuda()
    model = MultiTaskModel(encoder, token_embedder, tasks)
    log.info("Finished building model")
    model.train_model(args.n_epochs, args.optimizer, args.lr)

After Change

    // TODO(Alex): hid_dim depends on if pair input or not
    log.info("Loading tasks...")
    start_time = time.time()
    tasks, tok2idx = process_tasks(args.tasks.split(","), args.hid_dim * 4,
                                   args.max_vocab_size, args.max_seq_len,
                                   args.batch_size, args.cuda)
    log.info("\tFinished loading tasks in {0}s".format(
        time.time() - start_time))

    log.info("Building model...")
    start_time = time.time()
    embeddings = load_embeddings(args.word_embs_file, tok2idx,
                                            args.word_dim)
    token_embedder = TokenEmbedder(embeddings, tok2idx)
    //token_embedder = nn.Embedding(args.max_vocab_size, args.word_dim,
    //                              padding_idx=0)

    encoder = MultiLayerRNNEncoder(args.word_dim, args.hid_dim,
                                   args.n_layers, nn.LSTMCell)
    model = MultiTaskModel(encoder, token_embedder, tasks)
    if args.cuda:
        model = model.cuda()
    log.info("\tFinished building model in {0}s".format(
        time.time() - start_time))

    model.train_model(args.n_epochs, args.optimizer, args.lr)

Instances