Tanh deprecated in pytorch

neural_filters/neural_filter_2CC.py

@@ -115,7 +115,7 @@ class NeuralFilter2CC(torch.nn.Module):
     def step(self, input_var, hidden, a=None, b=None):
         if a is None or b is None:
             modulus = torch.sigmoid(self.bias_modulus)
-            cosangle = F.tanh(self.bias_theta)
+            cosangle = torch.tanh(self.bias_theta)
             a = 2 * cosangle * modulus
             b = - modulus.pow(2)
 
@@ -141,7 +141,7 @@ class NeuralFilter2CC(torch.nn.Module):
 
         # do not recompute this at each step to gain efficiency
         modulus = torch.sigmoid(self.bias_modulus)
-        cosangle = F.tanh(self.bias_theta)
+        cosangle = torch.tanh(self.bias_theta)
         a = 2 * cosangle * modulus
         b = - modulus.pow(2)
 
@@ -155,7 +155,7 @@ class NeuralFilter2CC(torch.nn.Module):
 
     def print_param(self):
         modulus = torch.sigmoid(self.bias_modulus)
-        cosangle = F.tanh(self.bias_theta)
+        cosangle = torch.tanh(self.bias_theta)
         p1 = -2 * cosangle * modulus
         p2 = modulus.pow(2)
         print('{}\t{}'.format(p1.data[0], p2.data[0]))
@@ -163,7 +163,7 @@ class NeuralFilter2CC(torch.nn.Module):
     @property
     def denominator(self):
         modulus = torch.sigmoid(self.bias_modulus)
-        cosangle = F.tanh(self.bias_theta)
+        cosangle = torch.tanh(self.bias_theta)
         p1 = -2 * cosangle * modulus
         p2 = modulus.pow(2)
         p1 = p1.detach().cpu().numpy()