Dear friends, I am getting a noisy classified image inspite of getting test accuracy of 99 percent. I am using the following function for obtaining the classification map.

```
def classified_pixels(FPatches,gPatches,g):
FPatches=np.reshape(FPatches,(FPatches.shape[0],FPatches.shape[3],FPatches.shape[1],FPatches.shape[2]))
data_test=MyDataset(FPatches, gPatches)
test_loader = torch.utils.data.DataLoader(data_test,batch_size=10,shuffle=False, num_workers=2)
with torch.no_grad():
correct = 0
total = 0
predicted_numpy=[]
for images, labels in test_loader:
images = images.to(device)
labels = labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
predicted_numpy.append(predicted.cpu().numpy())
total += labels.size(0)
correct += (predicted == labels).sum().item()
classification_map=np.array(predicted_numpy)
cm=[]
for arr in classification_map:
cm.append(arr.tolist())
cm=list(itertools.chain.from_iterable(cm))
classification_map=np.array(cm)
height=g.shape[0]
width=g.shape[1]
outputs = np.zeros((height,width))
k=0
for i in range(height):
for j in range(width):
target = g[i][j]
if target == 0 :
continue
else :
outputs[i][j]=classification_map[k]
k=k+1
return classification_map,outputs
```