diff --git a/Bericht/Bilder/UNet_Architecture.png b/Bericht/Bilder/My_UNet.png
similarity index 100%
rename from Bericht/Bilder/UNet_Architecture.png
rename to Bericht/Bilder/My_UNet.png
diff --git a/Bericht/Bilder/channels.jpg b/Bericht/Bilder/channels.jpg
new file mode 100644
index 0000000000000000000000000000000000000000..7164ac136a173d140598d0353a79a964a8e39a08
Binary files /dev/null and b/Bericht/Bilder/channels.jpg differ
diff --git a/Notes.txt b/Notes.txt
index 4e29fa3b634fe494d1431d78ad280a88fb04705b..e2deb1194f469ecd816690941ea53e13718946a9 100644
--- a/Notes.txt
+++ b/Notes.txt
@@ -12,6 +12,9 @@ V13: 4 layer, doppel Conv, normDataen,phase 64
 V14: 4 layer, single conv, normDataen,phase + angle 64
 V15: 3 layer, doppelte depth Conv pro layer, norm. Daten,kernel 7, phase only, dropout 0.3, 32
 V16: 3 layer, doppelte depth Conv pro layer, norm. Daten,kernel 7, angelsonly, dropout 0.5, 32
+V17: 3 layer, doppelte depth Conv pro layer, norm. Daten,kernel 7, angelsonly, dropout 0.5, 32, but last layer 70 32 1 1
+V18: 3 layer, doppelte depth Conv pro layer, norm. Daten,kernel 7, angelsonly, dropout 0.5, 32, like 16 but first layer 6 32 32
+
 V9 mit kernel 7 und nur den phasen:
 	mean error over whole set: 16.91116704929035
 	max error average: 292.8658473955995 and maximum 814.873957640188
diff --git a/UNet/UNet_V15.py~c0cc9d273fc96dfe1568dcc5cddd577a337efb3f b/UNet/UNet_V18.py
similarity index 94%
rename from UNet/UNet_V15.py~c0cc9d273fc96dfe1568dcc5cddd577a337efb3f
rename to UNet/UNet_V18.py
index b20f5ecfa19e3d9908cf4edafcd2e37f47e3d027..f7d2e4d13971a0874ab4c8440835532bd517b8f7 100644
--- a/UNet/UNet_V15.py~c0cc9d273fc96dfe1568dcc5cddd577a337efb3f
+++ b/UNet/UNet_V18.py
@@ -1,4 +1,4 @@
-#like V6_2 but only the different phases as input
+
 """UNet_V6.ipynb
 
 Automatically generated by Colaboratory.
@@ -25,7 +25,8 @@ 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=0.25)
+        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)
@@ -136,7 +137,7 @@ def accuracy(outputs, labels,normalization, threshold = 0.05):
     return percentage
     
 class UNet(UNetBase):
-    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])):
+    def __init__(self,kernel_size = 7, enc_chs=((6,32,32), (32,64,64), (64,128,128)), dec_chs_up=(128, 128, 64), dec_chs_conv=((192,128, 128),(160,64,64),(70,32,32)),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)
@@ -174,8 +175,8 @@ def fit(epochs, lr, model, train_loader, val_loader, path, opt_func=torch.optim.
         result['train_loss'] = torch.stack(train_losses).mean().item()
         model.epoch_end(epoch, result)
         history.append(result)
-    torch.save(model.state_dict(),f'{path}/Unet_dict_V15.pth')
-    torch.save(history,f'{path}/history_V15.pt')
+    torch.save(model.state_dict(),f'{path}/Unet_dict_V18.pth')
+    torch.save(history,f'{path}/history_V18.pt')
     return history
 
 def get_default_device():
@@ -225,9 +226,9 @@ def Create_Dataloader(path, batch_size = 100, percent_val = 0.2):
 if __name__ == '__main__':
     #os.chdir('F:/RWTH/HiWi_IEHK/DAMASK3/UNet/Trainingsdata')
     path_to_rep = '/home/yk138599/Hiwi/damask3'
-    use_seeds = False
-    seed = 373686838
-    num_epochs = 1000
+    use_seeds = True
+    seed = 2199910834
+    num_epochs = 200
     b_size = 32
     opt_func = torch.optim.Adam
     lr = 0.00003
@@ -243,8 +244,8 @@ if __name__ == '__main__':
     random.seed(seed)
     np.random.seed(seed)
     device = get_default_device()
-    normalization = np.load(f'{path_to_rep}/UNet/Trainingsdata/Norm_min_max_32_phase_only.npy', allow_pickle = True)
-    train_dl, valid_dl = Create_Dataloader(f'{path_to_rep}/UNet/Trainingsdata/TD_norm_32_phase_only.pt', batch_size= b_size )
+    normalization = np.load(f'{path_to_rep}/UNet/Trainingsdata/Norm_min_max_32_angles.npy', allow_pickle = True)
+    train_dl, valid_dl = Create_Dataloader(f'{path_to_rep}/UNet/Trainingsdata/TD_norm_32_angles.pt', batch_size= b_size )
     train_dl = DeviceDataLoader(train_dl, device)
     valid_dl = DeviceDataLoader(valid_dl, device)
 
diff --git a/UNet/core.ncg02.hpc.itc.rwth-aachen.de.47730.7 b/UNet/core.ncg02.hpc.itc.rwth-aachen.de.47730.7
index e8f36ddbe5566960261cc6f04d6fd354f7d6a04b..c77c27276a2216959a5d3f008b7e227dc4a02638 100644
Binary files a/UNet/core.ncg02.hpc.itc.rwth-aachen.de.47730.7 and b/UNet/core.ncg02.hpc.itc.rwth-aachen.de.47730.7 differ
diff --git a/UNet/core.ncg28.hpc.itc.rwth-aachen.de.45496.7 b/UNet/core.ncg28.hpc.itc.rwth-aachen.de.45496.7
index b4c72ebbd821051de2f5f4b760735552817d6bb4..bca0eaabdfe2424d8ca75f202fe3f18bbb18a923 100644
Binary files a/UNet/core.ncg28.hpc.itc.rwth-aachen.de.45496.7 and b/UNet/core.ncg28.hpc.itc.rwth-aachen.de.45496.7 differ
diff --git a/UNet/postprocessing_new.ipynb b/UNet/postprocessing_new.ipynb
index 47da6ef5912639bae19da018529e91158556a719..b27a0ca6ce8153727ebbe7f4f205b471c8d2c0bc 100644
--- a/UNet/postprocessing_new.ipynb
+++ b/UNet/postprocessing_new.ipynb
@@ -241,6 +241,13 @@
     "predict_stress(sample_index, normalization = normalization_64, model = model_15, dataset = Training_data_64,UNet = UNet,grain_data =grain_data_64)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 25,