clean up Ritual a little

onn/ritual_base.py

@@ -26,16 +26,22 @@ class Ritual:  # i'm just making up names at this point.
         self.model = model
 
     def _learn(self, inputs, outputs):
+        """deprecated"""
         error, predicted = self.model.forward(inputs, outputs)
         self.model.backward(predicted, outputs)
         self.model.regulate()
         return error, predicted
 
     def _update(self):
+        """deprecated"""
         optim = self.learner.optim
         optim.model = self.model
         optim.update(self.model.dW, self.model.W)
 
+    def _clear_measurements(self):
+        self.cumsum_loss, self.cumsum_mloss = _0, _0
+        self.losses, self.mlosses = [], []
+
     def _measure(self, predicted, outputs):
         loss = self.model.loss.forward(predicted, outputs)
         if np.isnan(loss):
@@ -66,6 +72,7 @@ class Ritual:  # i'm just making up names at this point.
 
     def _train_batch(self, batch_inputs, batch_outputs, b, batch_count,
                      test_only=False, loss_logging=False, mloss_logging=True):
+        """deprecated"""
         if not test_only and self.learner.per_batch:
             self.learner.batch(b / batch_count)
 
@@ -95,8 +102,7 @@ class Ritual:  # i'm just making up names at this point.
         assert self.model is not None, "call prepare(model) before training"
         self.en += 1
 
-        self.cumsum_loss, self.cumsum_mloss = _0, _0
-        self.losses, self.mlosses = [], []
+        self._clear_measurements()
 
         for b, (inputs, outputs) in enumerate(batch_gen):
             self.bn += 1
@@ -113,6 +119,7 @@ class Ritual:  # i'm just making up names at this point.
                       batch_size=None,
                       return_losses=False, test_only=False, shuffle=True,
                       clear_grad=True):
+        """deprecated"""
         assert isinstance(return_losses, bool) or return_losses == 'both'
         assert self.model is not None
 
@@ -137,8 +144,7 @@ class Ritual:  # i'm just making up names at this point.
             inputs = inputs[indices]
             outputs = outputs[indices]
 
-        self.cumsum_loss, self.cumsum_mloss = _0, _0
-        self.losses, self.mlosses = [], []
+        self._clear_measurements()
 
         if not gen:
             batch_count = inputs.shape[0] // batch_size