Fix in the calculation of the cost for the validation set so it's exa…

…ctly the same as training set.

sknn/backend/lasagne/mlp.py

@@ -62,11 +62,11 @@ def _create_mlp_trainer(self, params):
         loss_type = self.loss_type or ('mcc' if self.is_classifier else 'mse')
         assert loss_type in cost_functions,\
                     "Loss type `%s` not supported by Lasagne backend." % loss_type
-        cost_fn = getattr(lasagne.objectives, cost_functions[loss_type])
-        cost_eval = cost_fn(self.symbol_output, self.tensor_output).mean()
+        self.cost_function = getattr(lasagne.objectives, cost_functions[loss_type])
+        cost_symbol = self.cost_function(self.symbol_output, self.tensor_output).mean()
         if self.cost is not None:
-            cost_eval = cost_eval * self.cost
-        return self._create_trainer(params, cost_eval)
+            cost_symbol = cost_symbol + self.cost
+        return self._create_trainer(params, cost_symbol)
 
     def _create_trainer(self, params, cost):
         if self.learning_rule in ('sgd', 'adagrad', 'adadelta', 'rmsprop', 'adam'):
@@ -243,7 +243,7 @@ def _valid_impl(self, X, y):
         loss, batches = 0.0, 0
         for Xb, yb in self._iterate_data(X, y, self.batch_size, shuffle=True):
             ys = self.f(Xb)
-            loss += ((ys - yb) ** 2.0).mean()
+            loss += self.cost_function(ys, yb).mean().eval()
             batches += 1
         return loss / batches
 

sknn/mlp.py

@@ -165,7 +165,7 @@ def _train(self, X, y):
             else:
                 n_stable += 1
 
-            if n_stable >= self.n_stable:
+            if self.valid_set is not None and n_stable >= self.n_stable:
                 log.debug("")
                 log.info("Early termination condition fired at %i iterations.", i)
                 break
@@ -240,6 +240,7 @@ def get_params(self, deep=True):
 
 class Regressor(MultiLayerPerceptron, sklearn.base.RegressorMixin):
     # Regressor compatible with sklearn that wraps various NN implementations.
+    # The constructor and bulk of documentation is inherited from MultiLayerPerceptron.
 
     def fit(self, X, y):
         """Fit the neural network to the given continuous data as a regression problem.
@@ -329,7 +330,7 @@ def fit(self, X, y):
             log.warning('{}WARNING: Expecting `Softmax` type for the last layer '
                         'in classifier.{}\n'.format(ansi.YELLOW, ansi.ENDC))
         if y.shape[1] > 1 and self.layers[-1].type != 'Sigmoid':
-            log.warning('{}WARNING: Expecting `Sigmoid` for last layer in '
+            log.warning('{}WARNING: Expecting `Sigmoid` as last layer in '
                         'multi-output classifier.{}\n'.format(ansi.YELLOW, ansi.ENDC))
 
         # Deal deal with single- and multi-output classification problems.