train.py 文件源码

def on_epoch_done(epoch, n, o, loss, acc, valid_loss, valid_acc, test_loss, test_acc):
        error = 100 * (1 - acc)
        valid_error = 100 * (1 - valid_acc)
        test_error = 100 * (1 - test_acc)
        print('epoch {} done'.format(epoch))
        print('train loss: {} error: {}'.format(loss, error))
        print('valid loss: {} error: {}'.format(valid_loss, valid_error))
        print('test  loss: {} error: {}'.format(test_loss, test_error))
        if valid_error < state['best_valid_error']:
            serializers.save_npz('{}.model'.format(model_prefix), n)
            serializers.save_npz('{}.state'.format(model_prefix), o)
            state['best_valid_error'] = valid_error
            state['best_test_error'] = test_error
        if args.save_iter > 0 and (epoch + 1) % args.save_iter == 0:
            serializers.save_npz('{}_{}.model'.format(model_prefix, epoch + 1), n)
            serializers.save_npz('{}_{}.state'.format(model_prefix, epoch + 1), o)
        # prevent divergence when using identity mapping model
        if args.model == 'identity_mapping' and epoch < 9:
            o.lr = 0.01 + 0.01 * (epoch + 1)
#        if len(lr_decay_iter) == 1 and (epoch + 1) % lr_decay_iter[0] == 0 or epoch + 1 in lr_decay_iter:
        # Note, "lr_decay_iter" should be a list object to store a training schedule,
        # However, to keep up with the Python3.5, I changed to an integer value...
        if (epoch + 1) % args.lr_decay_iter == 0 and epoch > 1:
            if hasattr(optimizer, 'alpha'):
                o.alpha *= 0.1
            else:
                o.lr *= 0.1
        clock = time.clock()
        print('elapsed time: {}'.format(clock - state['clock']))
        state['clock'] = clock

        with open(log_file_path, 'a') as f:
            f.write('{},{},{},{},{},{},{}\n'.format(epoch + 1, loss, error, valid_loss, valid_error, test_loss, test_error))