diff --git a/README.md b/README.md
index 4c0d7e2..79ddf02 100644
--- a/README.md
+++ b/README.md
@@ -22,8 +22,9 @@ bash ./scripts-cnn/search-acc-v2.sh 3 acc2
 
 Train the searched CNN on CIFAR
 ```
-bash ./scripts-cnn/train-cifar.sh 0 GDAS_F1 cifar10
-bash ./scripts-cnn/train-cifar.sh 0 GDAS_V1 cifar100
+bash ./scripts-cnn/train-cifar.sh 0 GDAS_FG cifar10  cut
+bash ./scripts-cnn/train-cifar.sh 0 GDAS_F1 cifar10  cut
+bash ./scripts-cnn/train-cifar.sh 0 GDAS_V1 cifar100 cut
 ```
 
 Train the searched CNN on ImageNet
diff --git a/exps-cnn/GDAS-Search.py b/exps-cnn/GDAS-Search.py
index 3df324a..1930201 100644
--- a/exps-cnn/GDAS-Search.py
+++ b/exps-cnn/GDAS-Search.py
@@ -236,7 +236,6 @@ def train(train_queue, valid_queue, model, criterion, base_optimizer, arch_optim
 
     #inputs, targets = inputs.cuda(), targets.cuda(non_blocking=True)
     targets = targets.cuda(non_blocking=True)
-    data_time.update(time.time() - end)
 
     # get a random minibatch from the search queue with replacement
     try:
@@ -246,6 +245,7 @@ def train(train_queue, valid_queue, model, criterion, base_optimizer, arch_optim
       input_search, target_search = next(valid_iter)
     
     target_search = target_search.cuda(non_blocking=True)
+    data_time.update(time.time() - end)
 
     # update the architecture
     arch_optimizer.zero_grad()
diff --git a/lib/nas/genotypes.py b/lib/nas/genotypes.py
index 06d3633..51b2d60 100644
--- a/lib/nas/genotypes.py
+++ b/lib/nas/genotypes.py
@@ -195,12 +195,18 @@ GDAS_F1 = Genotype(
 )
 
 # Combine DMS_V1 and DMS_F1
-GDAS_CC = Genotype(
+GDAS_GF = Genotype(
   normal=[('skip_connect', 0, 0.13017432391643524), ('skip_connect', 1, 0.12947972118854523), ('skip_connect', 0, 0.13062666356563568), ('sep_conv_5x5', 2, 0.12980839610099792), ('sep_conv_3x3', 3, 0.12923765182495117), ('skip_connect', 0, 0.12901571393013), ('sep_conv_5x5', 4, 0.12938997149467468), ('sep_conv_3x3', 3, 0.1289220005273819)],
   normal_concat=range(2, 6),
   reduce=None,
   reduce_concat=range(2, 6)
 )
+GDAS_FG = Genotype(
+  normal=[('skip_connect', 0, 0.16), ('skip_connect', 1, 0.13), ('skip_connect', 0, 0.17), ('sep_conv_3x3', 2, 0.15), ('skip_connect', 0, 0.17), ('sep_conv_3x3', 2, 0.15), ('skip_connect', 0, 0.16), ('sep_conv_3x3', 2, 0.15)],
+  normal_concat=range(2, 6),
+  reduce=[('sep_conv_5x5', 0, 0.12862831354141235), ('sep_conv_3x3', 1, 0.12783904373645782), ('sep_conv_5x5', 2, 0.12725995481014252), ('sep_conv_5x5', 1, 0.12705285847187042), ('dil_conv_5x5', 2, 0.12797553837299347), ('sep_conv_3x3', 1, 0.12737272679805756), ('sep_conv_5x5', 0, 0.12833961844444275), ('sep_conv_5x5', 1, 0.12758426368236542)],
+  reduce_concat=range(2, 6)
+)
 
 model_types = {'DARTS_V1': DARTS_V1,
                'DARTS_V2': DARTS_V2,
@@ -210,4 +216,5 @@ model_types = {'DARTS_V1': DARTS_V1,
                'ENASNet' : ENASNet,
                'GDAS_V1' : GDAS_V1,
                'GDAS_F1' : GDAS_F1,
-               'GDAS_CC' : GDAS_CC}
+               'GDAS_GF' : GDAS_GF,
+               'GDAS_FG' : GDAS_FG}