diff --git a/functions/SoftmaxPower.py b/functions/SoftmaxPower.py
index a5e0f39..22b97cf 100644
--- a/functions/SoftmaxPower.py
+++ b/functions/SoftmaxPower.py
@@ -4,11 +4,15 @@ import torch
 class SoftmaxPower(torch.nn.Module):
     dim: int | None
     power: float
+    mean_mode: bool
 
-    def __init__(self, power: float = 2.0, dim: int | None = None) -> None:
+    def __init__(
+        self, power: float = 2.0, dim: int | None = None, mean_mode: bool = False
+    ) -> None:
         super().__init__()
         self.dim = dim
         self.power = power
+        self.mean_mode = mean_mode
 
     def __setstate__(self, state):
         super().__setstate__(state)
@@ -16,6 +20,8 @@ class SoftmaxPower(torch.nn.Module):
             self.dim = None
         if not hasattr(self, "power"):
             self.power = 2.0
+        if not hasattr(self, "mean_mode"):
+            self.mean_mode = False
 
     def forward(self, input: torch.Tensor) -> torch.Tensor:
         output: torch.Tensor = torch.abs(input).pow(self.power)
@@ -23,7 +29,11 @@ class SoftmaxPower(torch.nn.Module):
             output = output / output.sum()
         else:
             output = output / output.sum(dim=self.dim, keepdim=True)
-        return input * output
+
+        if self.mean_mode:
+            return torch.abs(input).mean(dim=1, keepdim=True) * output
+        else:
+            return input * output
 
     def extra_repr(self) -> str:
         return f"dim={self.dim} ; power={self.power}"
diff --git a/functions/make_cnn.py b/functions/make_cnn.py
index 625ee79..f752e31 100644
--- a/functions/make_cnn.py
+++ b/functions/make_cnn.py
@@ -16,6 +16,7 @@ def make_cnn(
     pooling_type: str,
     conv_0_enable_softmax: bool,
     conv_0_power_softmax: float,
+    conv_0_meanmode_softmax: bool,
     l_relu_negative_slope: float,
 ) -> torch.nn.Sequential:
     assert len(conv_out_channels_list) >= 1
@@ -60,11 +61,11 @@ def make_cnn(
     assert setting_understood
 
     if conv_0_enable_softmax:
-#        if conv_0_power_softmax != 0.0:
-#            cnn.append(SoftmaxPower(dim=1, power=conv_0_power_softmax))
-#        else:
-#            cnn.append(torch.nn.Softmax(dim=1))
-        cnn.append(SoftmaxPower(dim=1, power=conv_0_power_softmax))
+        cnn.append(
+            SoftmaxPower(
+                dim=1, power=conv_0_power_softmax, mean_mode=conv_0_meanmode_softmax
+            )
+        )
 
     # Changing structure
     for i in range(1, len(conv_out_channels_list)):