diff --git a/modules/deeplearning/cloud_fraction_fcn_viirs.py b/modules/deeplearning/cloud_fraction_fcn_viirs.py
index 0009e002acf11121bc3a8c392031505dd5b76060..69ff6d0f654e552056718e040b963547757eb93b 100644
--- a/modules/deeplearning/cloud_fraction_fcn_viirs.py
+++ b/modules/deeplearning/cloud_fraction_fcn_viirs.py
@@ -1060,8 +1060,8 @@ def analyze_5cat(file):
 # cld_prob = tup[6]
 # from util.plot import plot_image
 # cm = confusion_matrix(lbls.flatten(), pred.flatten())
-# plot_confusion_matrix(cm, ['CLR', '1/4', '1/2', '3/4', 'CLD'], normalize=True, axis=0)
 # plot_confusion_matrix(cm, ['CLR', '0.13', '0.31', '0.50', '0.69', '0.88', 'CLD'], normalize=True, axis=0)
+# # plot_confusion_matrix(cm, ['CLR', '1/4', '1/2', '3/4', 'CLD'], normalize=True, axis=0)
 
 # lbls = lbls.flatten()
 # pred = pred.flatten()
@@ -1071,11 +1071,15 @@ def analyze_5cat(file):
 # cat_2 = lbls == 2
 # cat_3 = lbls == 3
 # cat_4 = lbls == 4
+# cat_5 = lbls == 5
+# cat_6 = lbls == 6
 # plt.hist(cld_prob[cat_0], log=True, histtype='step')
 # plt.hist(cld_prob[cat_1], log=True, histtype='step')
 # plt.hist(cld_prob[cat_2], log=True, histtype='step')
 # plt.hist(cld_prob[cat_3], log=True, histtype='step')
 # plt.hist(cld_prob[cat_4], log=True, histtype='step')
+# plt.hist(cld_prob[cat_5], log=True, histtype='step')
+# plt.hist(cld_prob[cat_6], log=True, histtype='step')
 
 # from deeplearning.cloud_fraction_fcn_viirs import run_evaluate_static
 # run_evaluate_static('/Users/tomrink/clavrx_VNP02IMG.A2019306.1912.001.2019307003236.uwssec.nc',