add docs

pina/model/deeponet.py

@@ -12,7 +12,7 @@ from pina.utils import is_function
 
 def check_combos(combos, variables):
     """
-    Check that the given combinations are subsets (overlapping 
+    Check that the given combinations are subsets (overlapping
     is allowed) of the given set of variables.
 
     :param iterable(iterable(str)) combos: Combinations of variables.
@@ -35,7 +35,7 @@ def spawn_combo_networks(
     :param iterable(iterable(str)) combos: Combinations of variables.
     :param iterable(int) layers: Size of hidden layers.
     :param int output_dimension: Size of the output layer of the networks.
-    :param func: Nonlinearity. 
+    :param func: Nonlinearity.
     :param extra_feature: Extra feature to be considered by the networks.
     :param bool bias: Whether to consider bias or not.
     """
@@ -78,15 +78,16 @@ class DeepONet(torch.nn.Module):
         :param list(str) output_variables: the list containing the labels
             corresponding to the components of the output computed by the
             model.
-        :param string | callable aggregator: Aggregator to be used to aggregate
+        :param str | callable aggregator: Aggregator to be used to aggregate
             partial results from the modules in `nets`. Partial results are
-            aggregated component-wise. See :func:`_symbol_functions` for the
+            aggregated component-wise. See
+            :func:`pina.model.deeponet.DeepONet._symbol_functions` for the
             available default aggregators.
-        :param string | callable reduction: Reduction to be used to reduce
+        :param str | callable reduction: Reduction to be used to reduce
             the aggregated result of the modules in `nets` to the desired output
-            dimension. See :func:`_symbol_functions` for the available default
-            reductions.
-
+            dimension. See :py:obj:`pina.model.deeponet.DeepONet._symbol_functions` for the available default
+            reductions. 
+            
         :Example:
             >>> branch = FFN(input_variables=['a', 'c'], output_variables=20)
             >>> trunk = FFN(input_variables=['b'], output_variables=20)
@@ -127,9 +128,15 @@ class DeepONet(torch.nn.Module):
             raise ValueError("All networks should have the same output size")
         self._nets = torch.nn.ModuleList(nets)
         logging.info("Combo DeepONet children: %s", list(self.children()))
+        self.scale = torch.nn.Parameter(torch.tensor([1.0]))
+        self.trasl = torch.nn.Parameter(torch.tensor([0.0]))
 
     @staticmethod
     def _symbol_functions(**kwargs):
+        """
+        Return a dictionary of functions that can be used as aggregators or
+        reductions.
+        """
         return {
             "+": partial(torch.sum, **kwargs),
             "*": partial(torch.prod, **kwargs),
@@ -215,4 +222,7 @@ class DeepONet(torch.nn.Module):
 
         output_ = output_.as_subclass(LabelTensor)
         output_.labels = self.output_variables
+
+        output_ *= self.scale
+        output_ += self.trasl
         return output_

pina/model/feed_forward.py

@@ -89,8 +89,8 @@ class FeedForward(torch.nn.Module):
         """
         Defines the computation performed at every call.
 
-        :param x: the input tensor.
-        :type x:  :class:`pina.LabelTensor`
+        :param x: .
+        :type x: :class:`pina.LabelTensor`
         :return: the output computed by the model.
         :rtype: LabelTensor
         """

pina/model/multi_feed_forward.py

@@ -9,8 +9,9 @@ class MultiFeedForward(torch.nn.Module):
     :param dict dff_dict: dictionary of FeedForward networks.
     """
     def __init__(self, dff_dict):
-        """
-        """
+        '''
+        dff_dict: dict of FeedForward objects
+        '''
         super().__init__()
 
         if not isinstance(dff_dict, dict):