classification_sw.py 文件源码

def _print_train_val(self):
        """ Print training and validation information """
        # evaluate training performance

        ClassificationSW._print_train_val(self)

        cur_iter = self._cur_iter
        cur_round = self._cur_round

        # display training errors
        if cur_iter % cfg.TRAIN.TRAIN_FREQ == 0:
            err_train = self._err_mean
            print 'Round {}, Iteration {}: training error = {}'.format(cur_round, cur_iter, err_train.mean())

        # display validation errors
        if cur_iter % cfg.TRAIN.VAL_FREQ == 0:
            # perform validation
            err_val = np.zeros((cfg.TRAIN.VAL_SIZE * cfg.TRAIN.IMS_PER_BATCH, ))
            for i in xrange(cfg.TRAIN.VAL_SIZE * cfg.TRAIN.IMS_PER_BATCH):
                self._solver.test_nets[0].forward()
                err_val[i,:] = 1.0 - self._solver.test_nets[0].blobs['acc'].data
            err_val = np.nanmean(err_val, axis=0)
            print 'Round {}, Iteration {}: validation error = {}'.format(cur_round, cur_iter, np.nanmean(err_val))