home/scattered_light_subtraction/other_possible_approaches.md

@@ -14,7 +14,7 @@
 
 # Normalize the magnitude of spectrogram before begin fed into the neural network
 
-## normalized to values between 0 and 1
+## normalized values between 0 and 1
 ```
 # between 0 and 1
 x_max = torch.amax(X_input)
@@ -23,6 +23,25 @@ y_max = torch.amax(y_input)
 y_input = y_input / y_max
 
 ```
+and comment out the line in the model 
+```
+# x = self.positive(x)
+```
+
+
+## normalized values with mean and the standard deviation
+```
+x_mean = torch.mean(X_input)
+x_std = torch.mean(X_input)
+X_input = X_input - x_mean
+X_input = X_input / x_std
+y_mean = torch.mean(y_input)
+y_std = torch.std(y_input)
+y_input = y_input - y_mean
+y_input = y_input / y_std
+
+```
+
 
 # Increase the overlapping segments to have more number of the data set