示例#1
0
def evaluation(model, fname, show_progress=False):
    model.predictor.forget_history()
    model.predictor.to_cpu()
    devset = data_util.load_data(fname)
    dev_data_resource = data_util.data_spliter(devset, batchsize=1, n_epoch=1)
    dev_loss = 0
    pred = []
    target = []
    for dev_idx, x_batch, y_batch, epoch, percentage, eos in dev_data_resource:
        if not dev_idx:
            break
        if show_progress:
            print_stats(percentage, 0, 0, dev_loss)
        x = Variable(np.asarray(x_batch))
        t = Variable(np.asarray(y_batch))

        loss_i = model(x, t)
        target.append(y_batch[0])
        pred.append(model.y.data[0].argmax())

        dev_loss = (dev_loss * (dev_idx - 1) + loss_i.data) / dev_idx
        if eos and opts.forget_on_new_utt:
            model.predictor.forget_history()
    model.predictor.forget_history()
    return dev_loss, pred, target
示例#2
0
def train_mode():
    nnet_model = None
    if opts.nnet_struct:
        nnet_model = NNET_Model.parse_structure(opts.nnet_struct)
    elif opts.fname_in_model:
        nnet_model = NNET_Model.load(opts.fname_in_model)
    
    trainset = data_util.load_data(opts.fname_train)

    eos_pad = misc.get_pading(opts.eos_pad)

    train_data_resource = data_util.data_spliter(trainset, batchsize=opts.batchsize,
                            n_epoch=opts.n_epoch, EOS=eos_pad)

    optimizer, model = setup_training(nnet_model, opts)

    train_nnet(model, optimizer, train_data_resource, opts)
    if opts.fname_test:
        print('====================TESTING=========================')
        test_loss, pred, target = evaluation(model, opts.fname_test, show_progress=True)
        if 'cross_entropy' in opts.loss_function:
            misc.f_measure(pred, target)
        print(' test loss: %.3f' % test_loss)

    if opts.fname_out_model:
        nnet_model.save(opts.fname_out_model)