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)
