Inclusion of ANN method: fixing the last PR

ezyrb/ann.py

@@ -12,11 +12,11 @@ class ANN(Approximation):
     
     :param list layers: ordered list with the number of neurons of each hidden layer.
     :param torch.nn.modules.activation function: activation function at each layer,
-       except for the output layer at with Identity is considered by default.
-       A single activaction function can be passed or a list of them of length 
-       equal to the number of hidden layers.
+        except for the output layer at with Identity is considered by default.
+        A single activation function can be passed or a list of them of length 
+        equal to the number of hidden layers.
     :param list stop_training: list with the maximum number of training iterations
-       (int) and/or the desired tolerance on the training loss (float).
+        (int) and/or the desired tolerance on the training loss (float).
     :param torch.nn.Module loss: loss definition (Mean Squared if not given). 
     
     Example:
@@ -35,7 +35,8 @@ class ANN(Approximation):
 
     def __init__(self, layers, function, stop_training, loss=None):
 
-        if loss is None: loss = torch.nn.MSELoss()
+        if loss is None: 
+            loss = torch.nn.MSELoss()
 
         if not isinstance(function, list): # Single activation function passed
             function = [function] * (len(layers))
@@ -71,14 +72,14 @@ def _convert_torch_to_numpy(self, tensor):
         """
         return tensor.detach().numpy()
 
-    def _build_model(self,points,values):
+    def _build_model(self, points, values):
         """
         Build the torch model.
         
         Considering the number of neurons per layer (self.layers), a 
         feed-forward NN is defined:
-           - activation function from layer i>=0 to layer i+1: self.function[i];  
-             activation function at the output layer: Identity (by default).
+            - activation function from layer i>=0 to layer i+1: self.function[i];  
+              activation function at the output layer: Identity (by default).
         
         :param numpy.ndarray points: the coordinates of the given (training) points.
         :param numpy.ndarray values: the (training) values in the points.
@@ -98,15 +99,15 @@ def fit(self, points, values):
         Build the ANN given 'points' and 'values' and perform training.
         
         Training procedure information:
-         - optimizer: Adam's method with default parameters 
-             (see, e.g., https://pytorch.org/docs/stable/optim.html);
-         - loss: self.loss (if none, the Mean Squared Loss is set by default).
-         - stopping criterion: the fulfillment of the requested tolerance on the
-           training loss compatibly with the prescribed budget of training
-           iterations (if type(self.stop_training) is list); if type(self.stop_training) 
-           is int or type(self.stop_training) is float, only the number of maximum 
-           iterations or the accuracy level on the training loss is considered 
-           as the stopping rule, respectively.
+            - optimizer: Adam's method with default parameters 
+              (see, e.g., https://pytorch.org/docs/stable/optim.html);
+            - loss: self.loss (if none, the Mean Squared Loss is set by default).
+            - stopping criterion: the fulfillment of the requested tolerance on the
+              training loss compatibly with the prescribed budget of training
+              iterations (if type(self.stop_training) is list); if type(self.stop_training) 
+              is int or type(self.stop_training) is float, only the number of maximum 
+              iterations or the accuracy level on the training loss is considered 
+              as the stopping rule, respectively.
         
         :param numpy.ndarray points: the coordinates of the given (training) points.
         :param numpy.ndarray values: the (training) values in the points.