Generic DeepONet (#68)

* generic deeponet
* example for generic deeponet
* adapt tests to new interface

pina/model/feed_forward.py

@@ -26,9 +26,11 @@ class FeedForward(torch.nn.Module):
         `inner_size` are not considered.
     :param iterable(torch.nn.Module) extra_features: the additional input
         features to use ad augmented input.
+    :param bool bias: If `True` the MLP will consider some bias.
     """
     def __init__(self, input_variables, output_variables, inner_size=20,
-                 n_layers=2, func=nn.Tanh, layers=None, extra_features=None):
+                 n_layers=2, func=nn.Tanh, layers=None, extra_features=None,
+                 bias=True):
         """
         """
         super().__init__()
@@ -62,7 +64,9 @@ class FeedForward(torch.nn.Module):
 
         self.layers = []
         for i in range(len(tmp_layers)-1):
-            self.layers.append(nn.Linear(tmp_layers[i], tmp_layers[i+1]))
+            self.layers.append(
+                nn.Linear(tmp_layers[i], tmp_layers[i + 1], bias=bias)
+            )
 
         if isinstance(func, list):
             self.functions = func
@@ -94,7 +98,7 @@ class FeedForward(torch.nn.Module):
         if self.input_variables:
             x = x.extract(self.input_variables)
 
-        for i, feature in enumerate(self.extra_features):
+        for feature in self.extra_features:
             x = x.append(feature(x))
 
         output = self.model(x).as_subclass(LabelTensor)