relu=False, FL_AO=FL_A_fc, FL_DI=FL_D_conv_5, FL_W=FL_W_fc, FL_WM=FL_WM_fc, FL_WG=FL_WG_fc, FL_L_WG=FL_L_WG_fc, FL_L_WU=FL_L_WU_fc, FL_M_WU=FL_M_WU_fc, scale=scale_fc) cnn.append_layer('SquareHingeLoss', name='Loss', num_classes=10) # Testing logging.info('loading trained weights...') cnn.load_params_mat( './result/result_{}classes/Best_epoch_CIFAR10_W.mat'.format( task_division[0])) # cnn.load_params_mat('./result/test_W.mat') # print('test_W') batch_size_valid = 40 num_batches_valid = int(cloud_image_valid / batch_size_valid) valid_error = 0. valid_loss = 0. for j in range(num_batches_valid): # testing predictions, valid_loss_batch = cnn.feed_forward( fixed( valid_cloud_x[j * batch_size_valid:(j + 1) * batch_size_valid], 16, FL_A_input), valid_cloud_y[j * batch_size_valid:(j + 1) * batch_size_valid], train_or_test=0)
logging.info("LR start %f" % (args.LR_start)) logging.info("LR finish %f" % (args.LR_finish)) currentDT = datetime.datetime.now() logging.info(str(currentDT)) batch_size = args.batch_size group_size = args.group_size num_batches = int(edge_image_train / batch_size) num_groups = int(batch_size / group_size) Learning_Rate = args.LR_start best_valid_acc = 0.0 logging.info("\n\n--------checking mask--------------------") cnn.load_params_mat( './result/result_{}classes/incremental_1class_Best_epoch_CIFAR10_W'. format(task_division[0])) mask = sio.loadmat( './result/result_{}classes/mask_CIFAR10_TaskDivision_{}.mat'.format( task_division[0], args.task_division)) for key, value in mask.items(): if re.search('conv', key): logging.info('layer {}, sum of mask {} out of shape{}'.format( key, np.sum(value, axis=(0, 1, 2, 3)), value.shape)) if re.search('fc', key): logging.info('layer {}, sum of mask {} out of shape {}'.format( key, np.sum(value, axis=(0, 1)), value.shape)) # cloud_acc = valid(cloud_image_valid, valid_cloud_x, valid_cloud_y)