consistent init

neural_filters/neural_filter.py

@@ -53,13 +53,13 @@ class NeuralFilter(torch.nn.Module):
             parts = self.hidden_size * 2
             ranges = np.arange(1, parts, 2)
 
-            init_modulus = ranges * (max_modulus - min_modulus) / parts + min_modulus
-            init = asig(init_modulus)
+            init = ranges * (max_modulus - min_modulus) / parts + min_modulus
 
         if not isinstance(init, np.ndarray):
             init = np.array(init, ndmin=1)
 
-        ten_init = torch.from_numpy(init)
+        init_modulus = asig(init)
+        ten_init = torch.from_numpy(init_modulus)
         self.bias_forget.data.copy_(ten_init)
 
     def __repr__(self):

neural_filters/neural_filter_2CC.py

@@ -54,27 +54,28 @@ class NeuralFilter2CC(torch.nn.Module):
                          min_angle=MIN_ANGLE, max_angle=MAX_ANGLE, modulus=INIT_MODULUS):
 
         if init_modulus is None:
-            init_modulus = asig(modulus)
+            init_modulus = modulus
 
         if not isinstance(init_modulus, np.ndarray):
             init_modulus = np.array(init_modulus, ndmin=1)
 
-        ten_init = torch.from_numpy(init_modulus)
+        init_mod = asig(init_modulus)
+        ten_init = torch.from_numpy(init_mod)
         self.bias_modulus.data.copy_(ten_init)
 
         if init_theta is None:
             parts = self.hidden_size * 2
             ranges = np.arange(1, parts, 2)
 
-            init_angle = ranges * (max_angle - min_angle) / parts + min_angle
-
-            cosangle = np.cos(init_angle)
-            init_theta = atanh(cosangle)
+            init_theta = ranges * (max_angle - min_angle) / parts + min_angle
 
         if not isinstance(init_theta, np.ndarray):
             init_theta = np.array(init_theta, ndmin=1)
 
-        ten_init = torch.from_numpy(init_theta)
+        cosangle = np.cos(init_theta)
+        init_angle = atanh(cosangle)
+
+        ten_init = torch.from_numpy(init_angle)
         self.bias_theta.data.copy_(ten_init)
 
     def __repr__(self):