new scriptsHEADmaster

1 files changed, 46 insertions, 0 deletions

diff --git a/Codes/training_hyper_params/hyper_params.ini b/Codes/training_hyper_params/hyper_params.ini
index f7868f1..963902c 100644
--- a/Codes/training_hyper_params/hyper_params.ini
+++ b/Codes/training_hyper_params/hyper_params.ini
@@ -41,6 +41,52 @@ LRATE_D = [0.00001, 0.000001]
 LRATE_D_BOUNDARIES = [40000]
 
 
+[kaulsdorf_gray]
+# for lp loss. e.g, 1 or 2 for l1 and l2 loss, respectively)
+L_NUM = 2
+# the power to which each gradient term is raised in GDL loss
+ALPHA_NUM = 1
+# the percentage of the adversarial loss to use in the combined loss
+LAM_ADV = 0.05
+# the percentage of the lp loss to use in the combined loss
+LAM_LP = 1
+# the percentage of the GDL loss to use in the combined loss
+LAM_GDL = 1
+# the percentage of the different frame loss, LAM_FLOW = 2 is also ok, but LAM_FLOW = 0.01 is slightly better.
+LAM_FLOW = 0.01
+
+# For gray scale video, such as Ped2 and Ped1, learning rate of G and D star from 1e-4 and 1e-5, respectively.
+LRATE_G = [0.0001, 0.00001]
+LRATE_G_BOUNDARIES = [40000]
+
+LRATE_D = [0.00001, 0.000001]
+LRATE_D_BOUNDARIES = [40000]
+
+
+[kaulsdorf_single]
+# for lp loss. e.g, 1 or 2 for l1 and l2 loss, respectively)
+L_NUM = 2
+# the power to which each gradient term is raised in GDL loss
+ALPHA_NUM = 1
+# the percentage of the adversarial loss to use in the combined loss
+LAM_ADV = 0.05
+# the percentage of the lp loss to use in the combined loss,
+# we found in smaller lp is slightly better in avenue, but not too much difference
+# LAM_LP = 1 is 84.9, LAM_LP = 0.001 may arrive to 85.1
+LAM_LP = 0.001
+# the percentage of the GDL loss to use in the combined loss
+LAM_GDL = 1
+# the percentage of the different frame loss
+LAM_FLOW = 2
+
+# For rgb color scale video, such as Ped2 and Ped1, learning rate of G and D star from 2e-4 and 2e-5, respectively.
+LRATE_G = [0.0002, 0.00002]
+LRATE_G_BOUNDARIES = [100000]
+
+LRATE_D = [0.00002, 0.000002]
+LRATE_D_BOUNDARIES = [100000]
+
+
 [avenue]
 # for lp loss. e.g, 1 or 2 for l1 and l2 loss, respectively)
 L_NUM = 2