diff --git a/CHANGELOG.md b/CHANGELOG.md
index 648244470c..709cfc13a1 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -37,6 +37,8 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 
 - Fixed `GpuUsageLogger` to work on different platforms ([#3008](https://github.com/PyTorchLightning/pytorch-lightning/pull/3008))
 
+- Fixed batch_outputs with optimizer frequencies ([#3229](https://github.com/PyTorchLightning/pytorch-lightning/pull/3229))
+
 - Fixed setting batch size in `LightningModule.datamodule` when using `auto_scale_batch_size` ([#3266](https://github.com/PyTorchLightning/pytorch-lightning/pull/3266))
 
 - Fixed Horovod distributed backend compatibility with native AMP ([#3404](https://github.com/PyTorchLightning/pytorch-lightning/pull/3404))
diff --git a/docs/source/converting.rst b/docs/source/converting.rst
index ccdef25755..a131166411 100644
--- a/docs/source/converting.rst
+++ b/docs/source/converting.rst
@@ -16,9 +16,9 @@ To enable your code to work with Lightning, here's how to organize PyTorch into
 ===============================
 Move the model architecture and forward pass to your :class:`~pytorch_lightning.core.LightningModule`.
 
-.. code-block::
+.. testcode::
 
-    class LitModel(pl.LightningModule):
+    class LitModel(LightningModule):
 
         def __init__(self):
             super().__init__()
@@ -36,9 +36,9 @@ Move the model architecture and forward pass to your :class:`~pytorch_lightning.
 =======================================
 Move your optimizers to :func:`pytorch_lightning.core.LightningModule.configure_optimizers` hook. Make sure to use the hook parameters (self in this case).
 
-.. code-block::
+.. testcode::
 
-    class LitModel(pl.LightningModule):
+    class LitModel(LightningModule):
 
         def configure_optimizers(self):
             optimizer = torch.optim.Adam(self.parameters(), lr=1e-3)
@@ -48,9 +48,9 @@ Move your optimizers to :func:`pytorch_lightning.core.LightningModule.configure_
 =============================
 Lightning automates most of the trining for you, the epoch and batch iterations, all you need to keep is the training step logic. This should go into :func:`pytorch_lightning.core.LightningModule.training_step` hook (make sure to use the hook parameters, self in this case):
 
-.. code-block::
+.. testcode::
 
-    class LitModel(pl.LightningModule):
+    class LitModel(LightningModule):
 
         def training_step(self, batch, batch_idx):
             x, y = batch
@@ -78,9 +78,9 @@ To add an (optional) validation loop add logic to :func:`pytorch_lightning.core.
 ============================
 To add an (optional) test loop add logic to :func:`pytorch_lightning.core.LightningModule.test_step` hook (make sure to use the hook parameters, self in this case).
 
-.. code-block::
+.. testcode::
 
-    class LitModel(pl.LightningModule):
+    class LitModel(LightningModule):
 
         def test_step(self, batch, batch_idx):
             x, y = batch
diff --git a/pytorch_lightning/trainer/training_loop.py b/pytorch_lightning/trainer/training_loop.py
index 62499f872f..e6098d4eb4 100644
--- a/pytorch_lightning/trainer/training_loop.py
+++ b/pytorch_lightning/trainer/training_loop.py
@@ -500,7 +500,8 @@ class TrainLoop:
                 self.accumulated_loss.append(opt_closure_result.loss)
 
                 # track all the outputs across all steps
-                batch_outputs[opt_idx].append(opt_closure_result.training_step_output_for_epoch_end)
+                batch_opt_idx = opt_idx if len(batch_outputs) > 1 else 0
+                batch_outputs[batch_opt_idx].append(opt_closure_result.training_step_output_for_epoch_end)
 
                 # ------------------------------
                 # BACKWARD PASS
diff --git a/tests/base/model_optimizers.py b/tests/base/model_optimizers.py
index 4628a92807..9e5f558b43 100644
--- a/tests/base/model_optimizers.py
+++ b/tests/base/model_optimizers.py
@@ -34,6 +34,14 @@ class ConfigureOptimizersPool(ABC):
         optimizer2 = optim.Adam(self.parameters(), lr=self.learning_rate)
         return optimizer1, optimizer2
 
+    def configure_optimizers__multiple_optimizers_frequency(self):
+        optimizer1 = optim.Adam(self.parameters(), lr=self.learning_rate)
+        optimizer2 = optim.Adam(self.parameters(), lr=self.learning_rate)
+        return [
+            {'optimizer': optimizer1, 'frequency': 1},
+            {'optimizer': optimizer2, 'frequency': 5}
+        ]
+
     def configure_optimizers__single_scheduler(self):
         optimizer = optim.Adam(self.parameters(), lr=self.learning_rate)
         lr_scheduler = optim.lr_scheduler.StepLR(optimizer, 1, gamma=0.1)
diff --git a/tests/trainer/test_optimizers.py b/tests/trainer/test_optimizers.py
index 3a85a756fc..9d70522145 100644
--- a/tests/trainer/test_optimizers.py
+++ b/tests/trainer/test_optimizers.py
@@ -240,3 +240,18 @@ def test_configure_optimizer_from_dict(tmpdir):
     )
     result = trainer.fit(model)
     assert result == 1
+
+
+def test_configure_optimizers_with_frequency(tmpdir):
+    """
+    Test that multiple optimizers work when corresponding frequency is set.
+    """
+    model = EvalModelTemplate()
+    model.configure_optimizers = model.configure_optimizers__multiple_optimizers_frequency
+
+    trainer = Trainer(
+        default_root_dir=tmpdir,
+        max_epochs=1
+    )
+    result = trainer.fit(model)
+    assert result