diff --git a/UNet/UNet_V12.py b/UNet/UNet_V12.py
index 4b39b076b4dc7ce39c55c0545cefd9c690794682..cb3602201b4b8f5bba15f5eb0632d4f039784ff4 100644
--- a/UNet/UNet_V12.py
+++ b/UNet/UNet_V12.py
@@ -22,12 +22,14 @@ class depthwise_separable_conv(nn.Module):
         self.pointwise_1 = nn.Conv3d(in_c, out_1_c, kernel_size=1, bias=True)
         self.batch_norm_1 = nn.BatchNorm3d(out_1_c)
         self.relu = nn.ReLU()
+        self.droptout = nn.Dropout3d(p=0.5)
+
         self.depthwise_2 = nn.Conv3d(out_1_c, out_1_c, kernel_size= kernel_size, padding=padding[1], groups=out_1_c, bias=True)
         self.pointwise_2 = nn.Conv3d(out_1_c, out_2_c, kernel_size=1, bias=True)
         self.batch_norm_2 = nn.BatchNorm3d(out_2_c)
     def forward(self, x):
-        x = self.batch_norm_1(self.relu(self.pointwise_1(self.depthwise_1(x))))
-        return self.batch_norm_2(self.relu(self.pointwise_2(self.depthwise_2(x))))
+        x = self.batch_norm_1(self.relu(self.droptout(self.pointwise_1(self.depthwise_1(x)))))
+        return self.batch_norm_2(self.relu(self.droptout(self.pointwise_2(self.depthwise_2(x)))))
 
 class convolution_Layer(nn.Module):
     def __init__(self, in_c, out_1_c, out_2_c, padding, kernel_size):
@@ -223,7 +225,7 @@ if __name__ == '__main__':
     path_to_rep = '/home/yk138599/Hiwi/damask3'
     use_seeds = False
     seed = 373686838
-    num_epochs = 500
+    num_epochs = 300
     b_size = 32
     opt_func = torch.optim.Adam
     lr = 0.00003
diff --git a/UNet/UNet_V13.py b/UNet/UNet_V13.py
index 811c5ff3a23544c9dcb65f3e71c9b75a4116c0a1..57e0d16715646ea800201ae976e09a2f9160aa59 100644
--- a/UNet/UNet_V13.py
+++ b/UNet/UNet_V13.py
@@ -22,12 +22,14 @@ class depthwise_separable_conv(nn.Module):
         self.pointwise_1 = nn.Conv3d(in_c, out_1_c, kernel_size=1, bias=True)
         self.batch_norm_1 = nn.BatchNorm3d(out_1_c)
         self.relu = nn.ReLU()
+        self.droptout = nn.Dropout3d(p=0.5)
+
         self.depthwise_2 = nn.Conv3d(out_1_c, out_1_c, kernel_size= kernel_size, padding=padding[1], groups=out_1_c, bias=True)
         self.pointwise_2 = nn.Conv3d(out_1_c, out_2_c, kernel_size=1, bias=True)
         self.batch_norm_2 = nn.BatchNorm3d(out_2_c)
     def forward(self, x):
-        x = self.batch_norm_1(self.relu(self.pointwise_1(self.depthwise_1(x))))
-        return self.batch_norm_2(self.relu(self.pointwise_2(self.depthwise_2(x))))
+        x = self.batch_norm_1(self.relu(self.droptout(self.pointwise_1(self.depthwise_1(x)))))
+        return self.batch_norm_2(self.relu(self.droptout(self.pointwise_2(self.depthwise_2(x)))))
 
 class convolution_Layer(nn.Module):
     def __init__(self, in_c, out_1_c, out_2_c, padding, kernel_size):
@@ -223,11 +225,11 @@ if __name__ == '__main__':
     path_to_rep = '/home/yk138599/Hiwi/damask3'
     use_seeds = False
     seed = 373686838
-    num_epochs = 500
-    b_size = 16
+    num_epochs = 300
+    b_size = 32
     opt_func = torch.optim.Adam
     lr = 0.00003
-    kernel = 9
+    kernel = 7
     print(f'number auf epochs: {num_epochs}')
     print(f'batchsize: {b_size}')
     print(f'learning rate: {lr}')
diff --git a/UNet/UNet_V15.py b/UNet/UNet_V15.py
index 1395565b527211a328fe08a8802fd8122243afcf..11072152928258221d80449e4adf0878849b1120 100644
--- a/UNet/UNet_V15.py
+++ b/UNet/UNet_V15.py
@@ -138,7 +138,7 @@ def accuracy(outputs, labels,normalization, threshold = 0.05):
     return percentage
     
 class UNet(UNetBase):
-    def __init__(self,kernel_size = 5, enc_chs=((2,16,32), (32,32,64), (64,64,128)), dec_chs_up=(128, 128, 64), dec_chs_conv=((192,128, 128),(160,64,64),(66,32,32)),normalization=np.array([0,1])):
+    def __init__(self,kernel_size = 7, enc_chs=((2,16,32), (32,32,64), (64,64,128)), dec_chs_up=(128, 128, 64), dec_chs_conv=((192,128, 128),(160,64,64),(66,32,32)),normalization=np.array([0,1])):
         super().__init__()
         self.encoder     = Encoder(kernel_size = kernel_size, chs = enc_chs)
         self.decoder     = Decoder(kernel_size = kernel_size, chs_upsampling = dec_chs_up, chs_conv = dec_chs_conv)
@@ -229,7 +229,7 @@ if __name__ == '__main__':
     path_to_rep = '/home/yk138599/Hiwi/damask3'
     use_seeds = False
     seed = 373686838
-    num_epochs = 1000
+    num_epochs = 300
     b_size = 32
     opt_func = torch.optim.Adam
     lr = 0.00003
diff --git a/UNet/UNet_V16.py b/UNet/UNet_V16.py
index 32797637d7079c328c0bfed76ec7a3fa3b9c8f11..ad3fa4c20d0d5961309c1997e7459cd69e520bae 100644
--- a/UNet/UNet_V16.py
+++ b/UNet/UNet_V16.py
@@ -228,7 +228,7 @@ if __name__ == '__main__':
     path_to_rep = '/home/yk138599/Hiwi/damask3'
     use_seeds = True
     seed = 373686838
-    num_epochs = 10000
+    num_epochs = 300
     b_size = 32
     opt_func = torch.optim.Adam
     lr = 0.00003
diff --git a/UNet/UNet_V9_3.py b/UNet/UNet_V9_3.py
index 18068f80fb265d30bcf3d546786c38d73bfb48d6..ad7d81b693bb361ec739392d1b9cc81af9915d88 100644
--- a/UNet/UNet_V9_3.py
+++ b/UNet/UNet_V9_3.py
@@ -135,7 +135,7 @@ def accuracy(outputs, labels,normalization, threshold = 0.05):
     return percentage
     
 class UNet(UNetBase):
-    def __init__(self,kernel_size = 5, enc_chs=((2,16,32), (32,32,64), (64,64,128)), dec_chs_up=(128, 128, 64), dec_chs_conv=((192,128, 128),(160,64,64),(66,32,32)),normalization=np.array([0,1])):
+    def __init__(self,kernel_size = 3, enc_chs=((2,16,32), (32,32,64), (64,64,128)), dec_chs_up=(128, 128, 64), dec_chs_conv=((192,128, 128),(160,64,64),(66,32,32)),normalization=np.array([0,1])):
         super().__init__()
         self.encoder     = Encoder(kernel_size = kernel_size, chs = enc_chs)
         self.decoder     = Decoder(kernel_size = kernel_size, chs_upsampling = dec_chs_up, chs_conv = dec_chs_conv)
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