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)
def test_mode():
loaded_model = NNET_Model.load(opts.fname_in_model)
_, model = setup_training(loaded_model, opts)
print('====================TESTING=========================')
test_loss, pred, target = evaluation(model, opts.fname_test, show_progress=True)
print(' test loss: %.3f' % test_loss)
if 'cross_entropy' in opts.loss_function:
misc.f_measure(pred, target)