diff --git a/pytorch/train/README.md b/pytorch/train/README.md
index 0e9032b..82e8131 100644
--- a/pytorch/train/README.md
+++ b/pytorch/train/README.md
@@ -417,7 +417,7 @@ test_data_load = torch.utils.data.DataLoader(
 # -------------------------------------------
 
 # The optimizer
-optimizer = torch.optim.Adam(network.parameters(), lr=0.01)
+optimizer = torch.optim.Adam(network.parameters(), lr=0.001)
 
 # The LR Scheduler
 lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer)
@@ -498,3 +498,50 @@ for epoch_id in range(0, number_of_epoch):
 tb.close()
 ```
 
+## Mean square error
+
+You might be inclined to use the MSE instead of the cross entropy.
+
+But be aware that you need to change more than just the loss function from
+
+```python
+loss_function = torch.nn.CrossEntropyLoss()
+```
+to 
+
+```python
+loss_function = torch.nn.MSELoss()
+```
+
+Why? Because the input changes from the correct class represented by an integer into a one hot encoded vector. 
+
+A fast way to do so is this function which uses in-place scatter 
+
+```python
+def class_to_one_hot(
+    correct_label: torch.Tensor, number_of_neurons: int
+) -> torch.Tensor:
+
+    target_one_hot: torch.Tensor = torch.zeros(
+        (correct_label.shape[0], number_of_neurons)
+    )
+    target_one_hot.scatter_(
+        1, correct_label.unsqueeze(1), torch.ones((correct_label.shape[0], 1))
+    )
+
+    return target_one_hot
+```
+
+ Obviously, we also need to modify this line
+
+```python
+        loss = loss_function(output, target)
+```
+
+ to
+
+```python
+        loss = loss_function(
+            output, class_to_one_hot(target, number_of_output_channels_full1)
+        )
+```