diff --git a/aaa_helpers.txt b/aaa_helpers.txt
index f86226b..504566d 100644
--- a/aaa_helpers.txt
+++ b/aaa_helpers.txt
@@ -8,7 +8,7 @@ nohup python auto_encoder_body.py --name="lhunor_body_iccv_1_skirt" > nohup/iccv
 nohup python auto_encoder_curv.py --name="lhunor_curv_iccv_1 " > nohup/iccv/encoders/curv_iccv_1.out 2>&1 &
 nohup python auto_encoder_curv.py --name="lhunor_curv_iccv_1_skirt" > nohup/iccv/encoders/curv_iccv_1_skirt.out 2>&1 &
 nohup python auto_encoder_curv.py --name="lhunor_curv_iccv_1_cape_f" > nohup/iccv/encoders/curv_iccv_1_cape_f.out 2>&1 &
-nohup python cvae.py --name="lhunor_cvae_iccv_gnn_1" > nohup/iccv/cvae_iccv_gnn_1.out 2>&1 &
+nohup python cvae.py --name="lhunor_cvae_iccv_gnn_3" > nohup/iccv/cvae_iccv_gnn_3.out 2>&1 &
 
 skirts!!!!
 pyramid next week
diff --git a/configs/iccv/config_abl_all.py b/configs/iccv/config_abl_all.py
index ef7cdc6..0dd5a46 100644
--- a/configs/iccv/config_abl_all.py
+++ b/configs/iccv/config_abl_all.py
@@ -19,8 +19,8 @@ config['only_curvatures'] =           False
 ablation_count = [config['use_skinning'], config['remove_global_orientation'], config['use_curvature'], config['use_extra_losses'], config['use_offsets']].count(False)
 assert ablation_count == 1 or ablation_count == 0
 
-config['gpu'] = '3'
-config['name'] = 'iccv_gnn_2'
+config['gpu'] = '6'
+config['name'] = 'iccv_gnn_3'
 config['notes'] = ''
 config['tags'] = ['iccv', 'gnn']
 
diff --git a/models/gnn.py b/models/gnn.py
index ad9d495..1f2c8ff 100644
--- a/models/gnn.py
+++ b/models/gnn.py
@@ -16,12 +16,11 @@ class Smoother(tf.keras.Model):
     def __init__(self, ):
         super().__init__()
         self.a = sp_matrix_to_sp_tensor(sio.loadmat("custom_utils/ssmpl0.mat")['L'].tocoo())
-        self.conv1 = GeneralConv(channels=32, batch_norm=True, dropout=0.1, aggregate='mean', activation='prelu', use_bias=True)
-        self.conv2 = GeneralConv(channels=128, batch_norm=True, dropout=0.1, aggregate='mean', activation='prelu', use_bias=True)
-        self.conv3 = GeneralConv(channels=64, batch_norm=True, dropout=0.0, aggregate='mean', activation='prelu', use_bias=True)
-        self.conv4 = GeneralConv(channels=16, batch_norm=True, dropout=0.0, aggregate='mean', activation='prelu', use_bias=True)
-
-        self.conv5 = GCNConv(3, activation='tanh', use_bias=False)
+        self.conv1 = GCNConv(64, activation='tanh', use_bias=False)
+        self.conv2 = GCNConv(64, activation='tanh', use_bias=False)
+        self.conv3 = GCNConv(64, activation='tanh', use_bias=False)
+        self.conv4 = GCNConv(64, activation='tanh', use_bias=False)
+        self.conv5 = GCNConv(64, activation='tanh', use_bias=False)
         self.conv6 = GCNConv(3, activation='tanh', use_bias=False)
 
     def call(self, inputs, training=None):