diff --git a/nobrainer/processing/generation.py b/nobrainer/processing/generation.py
index bc0da104..51b46625 100644
--- a/nobrainer/processing/generation.py
+++ b/nobrainer/processing/generation.py
@@ -5,7 +5,7 @@
 
 from .base import BaseEstimator
 from .. import losses
-from ..dataset import get_dataset
+from ..dataset import Dataset
 
 
 class ProgressiveGeneration(BaseEstimator):
@@ -147,15 +147,17 @@ def _compile():
             if batch_size % self.strategy.num_replicas_in_sync:
                 raise ValueError("batch size must be a multiple of the number of GPUs")
 
-            dataset = get_dataset(
+            dataset = Dataset.from_tfrecords(
                 file_pattern=info.get("file_pattern"),
-                batch_size=batch_size,
                 num_parallel_calls=num_parallel_calls,
                 volume_shape=(resolution, resolution, resolution),
                 n_classes=1,
-                scalar_label=True,
-                normalizer=info.get("normalizer") or normalizer,
+                scalar_labels=True,
             )
+            n_epochs = info.get("epochs") or epochs
+            dataset.batch(batch_size).normalize(
+                info.get("normalizer") or normalizer
+            ).repeat(n_epochs)
 
             with self.strategy.scope():
                 # grow the networks by one (2^x) resolution
@@ -164,9 +166,7 @@ def _compile():
                     self.model_.discriminator.add_resolution()
                 _compile()
 
-                steps_per_epoch = (info.get("epochs") or epochs) // info.get(
-                    "batch_size"
-                )
+                steps_per_epoch = n_epochs // info.get("batch_size")
 
                 # save_best_only is set to False as it is an adversarial loss
                 model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(
@@ -182,7 +182,7 @@ def _compile():
 
             print("Transition phase")
             self.model_.fit(
-                dataset,
+                dataset.dataset,
                 phase="transition",
                 resolution=resolution,
                 steps_per_epoch=steps_per_epoch,  # necessary for repeat dataset
@@ -191,7 +191,7 @@ def _compile():
 
             print("Resolution phase")
             self.model_.fit(
-                dataset,
+                dataset.dataset,
                 phase="resolution",
                 resolution=resolution,
                 steps_per_epoch=steps_per_epoch,