diff --git a/distiller/utils.py b/distiller/utils.py
index cb000b301a8d88ad18c0662850818272289ac4ea..29e8bde9760eccdf022e42343daffb2f8d60c7f7 100755
--- a/distiller/utils.py
+++ b/distiller/utils.py
@@ -395,7 +395,7 @@ def activation_channels_apoz(activation):
         featuremap_apoz_mat = activation.abs().gt(0).sum(dim=1).float() / activation.size(1)  # batch x 1
     else:
         raise ValueError("activation_channels_apoz: Unsupported shape: ".format(activation.shape))
-    return featuremap_apoz_mat.mean(dim=0).cpu()
+    return 100 - featuremap_apoz_mat.mean(dim=0).mul(100).cpu()
 
 
 def log_training_progress(stats_dict, params_dict, epoch, steps_completed, total_steps, log_freq, loggers):
diff --git a/tests/test_basic.py b/tests/test_basic.py
index 5d57a350ff13ab4ceb239ef7a683da9377b0a6c4..942f8ebaaafee1eb0c8c0e86b68d7ba378e213fd 100755
--- a/tests/test_basic.py
+++ b/tests/test_basic.py
@@ -66,7 +66,7 @@ def test_activations():
                         [7.,  0.,  8.],
                         [0.,  9.,  0.]]]])
     assert all(distiller.activation_channels_l1(x) == torch.tensor([21/2,  45/2]))
-    assert all(distiller.activation_channels_apoz(x) == torch.tensor([6/18,  9/18]))
+    assert all(distiller.activation_channels_apoz(x) == torch.tensor([100*(6+6)/(9+9),  100*(4+5)/(9+9)]))
     assert all(distiller.activation_channels_means(x) == torch.tensor([21/18,  45/18]))