diff --git a/optim_nn_core.py b/optim_nn_core.py
index e6b3599..9361f20 100644
--- a/optim_nn_core.py
+++ b/optim_nn_core.py
@@ -708,6 +708,63 @@ class Ritual: # i'm just making up names at this point
         self.bn = 0
         self.model = model
 
+    def train_batched_gen(self, generator, batch_count,
+                          return_losses=False, test_only=False):
+        assert isinstance(return_losses, bool) or return_losses == 'both'
+
+        if not test_only:
+            self.en += 1
+
+        cumsum_loss, cumsum_mloss = _0, _0
+        losses, mlosses = [], []
+
+        prev_batch_size = None
+
+        for b in range(batch_count):
+            if not test_only:
+                self.bn += 1
+
+            # TODO: pass a GeneratorData object containing en, bn, ritual/model fields.
+            #       ...is there a pythonic way of doing that?
+            batch_inputs, batch_outputs = next(generator)
+
+            batch_size = batch_inputs.shape[0]
+            assert batch_size == prev_batch_size or prev_batch_size is None, \
+              "non-constant batch size (got {} expected {})".format(
+                batch_size, prev_batch_size)  # TODO: lift this restriction
+            prev_batch_size = batch_size
+
+            if not test_only and self.learner.per_batch:
+                self.learner.batch(b / batch_count)
+
+            if test_only:
+                predicted = self.model.forward(batch_inputs)
+            else:
+                predicted = self.learn(batch_inputs, batch_outputs)
+                self.update()
+
+            if return_losses == 'both':
+                batch_loss = self.loss.forward(predicted, batch_outputs)
+                if np.isnan(batch_loss):
+                    raise Exception("nan")
+                losses.append(batch_loss)
+                cumsum_loss += batch_loss
+
+            batch_mloss = self.measure(predicted, batch_outputs)
+            if np.isnan(batch_mloss):
+                raise Exception("nan")
+            if return_losses:
+                mlosses.append(batch_mloss)
+            cumsum_mloss += batch_mloss
+
+        avg_mloss = cumsum_mloss / _f(batch_count)
+        if return_losses == 'both':
+            avg_loss = cumsum_loss / _f(batch_count)
+            return avg_loss, avg_mloss, losses, mlosses
+        elif return_losses:
+            return avg_mloss, mlosses
+        return avg_mloss
+
     def train_batched(self, inputs, outputs, batch_size,
                       return_losses=False, test_only=False):
         assert isinstance(return_losses, bool) or return_losses == 'both'