diff --git a/Bericht/Bilder/UNet_Architecture.svg b/Bericht/Bilder/UNet_Architecture.svg
index ac8b625d553089da93f7771fdac40d659b39a491..7f8b38e6dbf7a4718a0caa2ebfff67cd66f6cb71 100644
--- a/Bericht/Bilder/UNet_Architecture.svg
+++ b/Bericht/Bilder/UNet_Architecture.svg
@@ -31,7 +31,7 @@
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          transform="scale(0.8)"
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+         style="fill:#f6a800;fill-opacity:1;fill-rule:evenodd;stroke:#f6a800;stroke-width:1.00000003pt;stroke-opacity:1"
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+         style="fill:#f6a800;fill-opacity:1;fill-rule:evenodd;stroke:#f6a800;stroke-width:1.00000003pt;stroke-opacity:1"
          transform="scale(0.8)" />
     </marker>
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@@ -529,7 +529,7 @@
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      inkscape:pageshadow="2"
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+     inkscape:cx="336.03562"
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@@ -557,7 +557,7 @@
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         <dc:type
            rdf:resource="http://purl.org/dc/dcmitype/StillImage" />
-        <dc:title></dc:title>
+        <dc:title />
       </cc:Work>
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@@ -779,7 +779,7 @@
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+       style="fill:none;stroke:#f6a800;stroke-width:0.2;stroke-linecap:butt;stroke-linejoin:miter;stroke-miterlimit:4;stroke-dasharray:none;stroke-opacity:1;marker-end:url(#TriangleOutL-1-6-9-5-9-4)"
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@@ -830,7 +830,7 @@
          style="font-size:2.11666656px;fill:#e30066;fill-opacity:1;stroke-width:0.26458332">SeparableConv3D (1, 1, 1), sigmoids</tspan></text>
     <text
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+       style="font-style:normal;font-weight:normal;font-size:2.11666656px;line-height:1.25;font-family:sans-serif;letter-spacing:0px;word-spacing:0px;fill:#f6a800;fill-opacity:1;stroke:none;stroke-width:0.26458332;"
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@@ -838,7 +838,7 @@
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          y="65.816185"
-         style="font-size:2.11666656px;fill:#ffed00;fill-opacity:1;stroke-width:0.26458332">Skip connection</tspan></text>
+         style="font-size:2.11666656px;fill:#f6a800;fill-opacity:1;stroke-width:0.26458332;">Skip connection</tspan></text>
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@@ -967,13 +967,13 @@
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    id="tspan3635">3</tspan></tspan></text>
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diff --git a/DAMASK_3/processing/post/process_results.py b/DAMASK_3/processing/post/process_results.py
index 2c31862b7256176fff9db7aa2c5980d223010c08..91f9f9ab05c21228c197aa58ca87f8d6cab3e30c 100644
--- a/DAMASK_3/processing/post/process_results.py
+++ b/DAMASK_3/processing/post/process_results.py
@@ -108,7 +108,7 @@ def display_Data(config: dict):
         mesh = pv.wrap(mesh)    #wrap it up for visualization
         name_array = mesh.array_names    #get list of all active fields
         field_index = name_array.index(config['field']) # ermittele den Index des gewählten Feldes
-        if iter_vtk == 2:   # position des des zweiten Increments im Ausgabebildschirm
+        if iter_vtk == 2:   # position des zweiten Increments im Ausgabebildschirm
             plotter.subplot(0,1)
         elif iter_vtk == 3:
             plotter.subplot(1,0)
diff --git a/Literatur/Korrekturen/Inga_1.pdf b/Literatur/Korrekturen/Inga_1.pdf
index 9adc23867f5861b49b706dab82328f44592040fb..7b69059835614e281ca0c9115ea54b6d609f28b0 100644
Binary files a/Literatur/Korrekturen/Inga_1.pdf and b/Literatur/Korrekturen/Inga_1.pdf differ
diff --git a/UNet/Auswertung_64.py b/UNet/Auswertung_64.py
index d184793b5573738ed421cf4e5543cf81f493bd40..b10c8dbad5551351790da1d5b10fcd75609d40ea 100644
--- a/UNet/Auswertung_64.py
+++ b/UNet/Auswertung_64.py
@@ -3,9 +3,10 @@ import numpy as np
 import torch.nn as nn
 import UNet_V14 as UNet
 import copy
+from torch.utils.data.dataloader import DataLoader
 
 def rescale(output, normalization):
-    output_rescale = output.reshape(output.shape[2],output.shape[3],output.shape[4])
+    output_rescale = output.reshape(64,64,64)
     if normalization is not None:
         if normalization.shape[0] == 2:
           min_label, max_label = normalization
@@ -15,6 +16,21 @@ def rescale(output, normalization):
         output_rescale += min_label
     return output_rescale
 
+class DeviceDataLoader():
+    """Wrap a dataloader to move data to a device"""
+    def __init__(self, dl, device):
+        self.dl = dl
+        self.device = device
+        
+    def __iter__(self):
+        """Yield a batch of data after moving it to device"""
+        for b in self.dl: 
+            yield UNet.to_device(b, self.device)
+
+    def __len__(self):
+        """Number of batches"""
+        return len(self.dl)
+
 def dataset_evaluation( normalization, model, dataloader, threshold = 0.05):
     model.eval()
     mean_error =  []
@@ -25,14 +41,11 @@ def dataset_evaluation( normalization, model, dataloader, threshold = 0.05):
 
     for batch in dataloader:
         input, output = batch
-        input = copy.copy(input)
         output = copy.copy(output)
-        input = torch.unsqueeze(input,0)
         output = torch.unsqueeze(output,0)
         prediction = model(input)
-        input = input.detach().numpy()
         prediction = prediction.cpu().detach().numpy()
-        output = output.detach().numpy()
+        output = output.detach().cpu().numpy().reshape(64,64,64)
         prediction = rescale(prediction, normalization)
         output = rescale(output, normalization)
         error = (abs(output - prediction)/output)
@@ -51,22 +64,18 @@ def best_sample_id(result):
   return index_min
 
 
-def predict_stress(image_id, normalization, model, dataset,grain_data, UNet, device,threshold = 0.15):
-    input, output = dataset[image_id]
-    grain,_ = grain_data[image_id]
-    grain = copy.deepcopy(grain)
+def predict_stress(sample, normalization, model,threshold = 0.15):
+    model.eval()
+    print(sample.shape)
+    inpu, output = sample
+    prediction = model(inpu)
+    grain,_ = copy.deepcopy(sample)
     grain = torch.unsqueeze(grain,0)
     grain = grain.detach().numpy()
-    input = copy.deepcopy(input)
     output = copy.deepcopy(output)
-    input = torch.unsqueeze(input,0)
     output = torch.unsqueeze(output,0)
-    xb = UNet.to_device(input, device)
-    model.eval()
-    prediction = model(xb)
-    input = input.detach().numpy()
-    prediction = prediction.detach().numpy()
-    output = output.detach().numpy()
+    prediction = prediction.detach().cpu().numpy()
+    output = output.detach().cpu().numpy()
     prediction = rescale(prediction, normalization)
     output = rescale(output, normalization)
     error = (abs(output - prediction)/output)
@@ -108,26 +117,29 @@ def export_vtk(error, grains, stress, label,path):
     
 if __name__ == '__main__':
     export_path = '/home/yk138599/Hiwi/damask3/UNet/output/result_14'
-    Training_data = torch.load(f'/home/yk138599/Hiwi/damask3/UNet/Trainingsdata/TD_norm_64_angles.pt')
+    #Training_data = torch.load(f'/home/yk138599/Hiwi/damask3/UNet/Trainingsdata/TD_norm_64_angles.pt')
     #Training_data = torch.load(f'/content/drive/MyDrive/Bachlorarbeit/Input/TD_norm_32_phase.pt')
-
     normalization = np.load(f'/home/yk138599/Hiwi/damask3/UNet/Trainingsdata/Norm_min_max_64_angles.npy', allow_pickle=True)
+    print('loaded normalization')
     #normalization = np.load(f'/content/drive/MyDrive/Bachlorarbeit/Input/Norm_min_max_32_phase.npy', allow_pickle=True)
-    model = UNet.UNet()
     device = UNet.get_default_device()
-    model = UNet.to_device(model.double(), device)
-    dataloader = UNet.Create_Dataloader(f'/home/yk138599/Hiwi/damask3/UNet/Trainingsdata/TD_norm_64_angles.pt',batch_size=1)
-    dataloader = UNet.DeviceDataLoader(dataloader,device)
-    model.load_state_dict(torch.load(f'/home/yk138599/Hiwi/damask3/UNet/output/V14/Unet_dict_V14.pth',map_location=torch.device('cuda')))
-
-    result= dataset_evaluation( normalization = normalization, model = model, dataset = copy.copy(Training_data), threshold = 0.05)
-    print(f'\t mean error over whole set: {result[0].mean():.4}%')
-    print(f'\t max error average: {result[1].mean():.4}% and maximum {result[1].max():.4}%')
-    print(f'\t average correct percentile of voxels over whole set: {result[2].mean():.4}%')
-    print(f'\t average deviation per RVE over whole set: {result[3].mean():.4} Pa')
-    print(f'\t average deviation in percent per RVE over whole set: {result[4].mean()*100.:.4} %')
-    np.save('/home/yk138599/Hiwi/damask3/UNet/output/V14/evaluation',result)
-    sample_index = best_sample_id(result)
-    print(f'best sample is: {sample_index}')
-    error,grains,prediction,label= predict_stress(sample_index, normalization = normalization, model = model, device=device, dataset = Training_data,grain_data =Training_data,UNet=UNet, threshold=0.15)
-    export_vtk(error,grains,prediction,label,export_path)
\ No newline at end of file
+    dataset = torch.load(f'/home/yk138599/Hiwi/damask3/UNet/Trainingsdata/TD_norm_64_angles.pt')
+    dataloader = DataLoader(dataset,1, num_workers=1, pin_memory=True)
+    dataloader =DeviceDataLoader(dataloader,device)
+    model = UNet.to_device(UNet.UNet(kernel_size=9,normalization=normalization).double(), device)
+    model.load_state_dict(torch.load('/home/yk138599/Hiwi/damask3/UNet/output/Unet_dict_V14.pth'))
+    #model=UNet.to_device(UNet.UNet().load_state_dict(torch.load('/home/yk138599/Hiwi/damask3/UNet/output/Unet_dict_V14.pth')),device)
+    print('loaded Unet')
+    #result= dataset_evaluation( normalization = normalization, model = model,dataloader=dataloader, threshold = 0.05)
+    #np.save('/home/yk138599/Hiwi/damask3/UNet/output/V14/evaluation',result)
+    #print(f'\t mean error over whole set: {np.mean(result[0]):.4}%')
+    #print(f'\t max error average: {np.mean(result[1]):.4}% and maximum {np.max(result[1]):.4}%')
+    #print(f'\t average correct percentile of voxels over whole set: {np.mean(result[2]):.4}%')
+    #print(f'\t average deviation per RVE over whole set: {np.mean(result[3]):.4} Pa')
+    #print(f'\t average deviation in percent per RVE over whole set: {np.mean(result[4])*100.:.4} %')
+
+    #sample_index = best_sample_id(result)
+    sample_index = 144
+    sample = UNet.to_device(copy.copy(dataset[sample_index]), device)
+    error,grains,prediction,label= predict_stress( sample, normalization = normalization, model = model, device=device,UNet=UNet, threshold=0.15)
+    export_vtk(error,grains,prediction,label,export_path)