4 files changed, 8 insertions, 8 deletions
diff --git a/training/train_cnn.py b/training/train_cnn.py
index e1fd27b..dc14192 100755
--- a/training/train_cnn.py
+++ b/training/train_cnn.py
@@ -335,8 +335,8 @@ def generate_layer_shader(output_path, num_layers, kernel_sizes):
                 f.write(f"    }}\n")
             else:
                 f.write(f"    else if (params.layer_index == {layer_idx}) {{\n")
-                f.write(f"        let gray_out = {conv_fn}(txt, smplr, uv, uniforms.resolution, gray, weights_layer{layer_idx});\n")
-                f.write(f"        // gray_out in [0,1] (sigmoid activation)\n")
+                f.write(f"        let sum = {conv_fn}(txt, smplr, uv, uniforms.resolution, gray, weights_layer{layer_idx});\n")
+                f.write(f"        let gray_out = 1.0 / (1.0 + exp(-sum));  // Sigmoid activation\n")
                 f.write(f"        result = vec4<f32>(gray_out, gray_out, gray_out, 1.0);\n")
                 f.write(f"        return mix(original_raw, result, params.blend_amount); // [0,1]\n")
                 f.write(f"    }}\n")
@@ -452,7 +452,7 @@ def generate_conv_final_function(kernel_size, output_path):
     with open(output_path, 'a') as f:
         f.write(f"\n// Final layer: 7→1 channel (vec4-optimized)\n")
         f.write(f"// Assumes 'tex' is already normalized to [-1,1]\n")
-        f.write(f"// Output uses sigmoid activation to match PyTorch training\n")
+        f.write(f"// Returns raw sum (activation applied at call site)\n")
         f.write(f"fn cnn_conv{k}x{k}_7to1(\n")
         f.write(f"  tex: texture_2d<f32>,\n")
         f.write(f"  samp: sampler,\n")
@@ -479,7 +479,7 @@ def generate_conv_final_function(kernel_size, output_path):
         f.write(f"    }}\n")
         f.write(f"  }}\n\n")
 
-        f.write(f"  return 1.0 / (1.0 + exp(-sum));\n")
+        f.write(f"  return sum;\n")
         f.write(f"}}\n")
 
 
diff --git a/workspaces/main/shaders/cnn/cnn_conv3x3.wgsl b/workspaces/main/shaders/cnn/cnn_conv3x3.wgsl
index d680840..48bb392 100644
--- a/workspaces/main/shaders/cnn/cnn_conv3x3.wgsl
+++ b/workspaces/main/shaders/cnn/cnn_conv3x3.wgsl
@@ -108,5 +108,5 @@ fn cnn_conv3x3_7to1(
     }
   }
 
-  return 1.0 / (1.0 + exp(-sum));
+  return sum;
 }
diff --git a/workspaces/main/shaders/cnn/cnn_conv5x5.wgsl b/workspaces/main/shaders/cnn/cnn_conv5x5.wgsl
index e39f6ef..9328d75 100644
--- a/workspaces/main/shaders/cnn/cnn_conv5x5.wgsl
+++ b/workspaces/main/shaders/cnn/cnn_conv5x5.wgsl
@@ -62,7 +62,7 @@ fn cnn_conv5x5_7to1(
     }
   }
 
-  return 1.0 / (1.0 + exp(-sum));
+  return sum;
 }
 
 // Source layer: 7→4 channels (vec4-optimized)
diff --git a/workspaces/main/shaders/cnn/cnn_layer.wgsl b/workspaces/main/shaders/cnn/cnn_layer.wgsl
index c5f45ec..73816c6 100644
--- a/workspaces/main/shaders/cnn/cnn_layer.wgsl
+++ b/workspaces/main/shaders/cnn/cnn_layer.wgsl
@@ -45,8 +45,8 @@ struct CNNLayerParams {
         result = cnn_tanh(result);  // Keep in [-1,1]
     }
     else if (params.layer_index == 2) {
-        let gray_out = cnn_conv3x3_7to1(txt, smplr, uv, uniforms.resolution, gray, weights_layer2);
-        // gray_out in [0,1] (sigmoid activation)
+        let sum = cnn_conv3x3_7to1(txt, smplr, uv, uniforms.resolution, gray, weights_layer2);
+        let gray_out = 1.0 / (1.0 + exp(-sum));  // Sigmoid activation
         result = vec4<f32>(gray_out, gray_out, gray_out, 1.0);
         return mix(original_raw, result, params.blend_amount); // [0,1]
     }