diff --git a/scripts/tf_cnn_benchmarks/preprocessing.py b/scripts/tf_cnn_benchmarks/preprocessing.py
index a0fa209d..6c0eda14 100644
--- a/scripts/tf_cnn_benchmarks/preprocessing.py
+++ b/scripts/tf_cnn_benchmarks/preprocessing.py
@@ -858,24 +858,29 @@ def minibatch(self,
                 subset,
                 params,
                 shift_ratio=-1):
-    # TODO(jsimsa): Implement datasets code path
     del shift_ratio, params
     with tf.name_scope('batch_processing'):
       all_images, all_labels = dataset.read_data_files(subset)
       all_images = tf.constant(all_images)
       all_labels = tf.constant(all_labels)
-      input_image, input_label = tf.train.slice_input_producer(
-          [all_images, all_labels])
-      input_image = tf.cast(input_image, self.dtype)
-      input_label = tf.cast(input_label, tf.int32)
-      # Ensure that the random shuffling has good mixing properties.
+      input_image = tf.cast(all_images, self.dtype)
+      input_label = tf.cast(all_labels, tf.int32)
+      dataset_train = tf.data.Dataset.from_tensor_slices(
+          (input_image, input_label))
+
       min_fraction_of_examples_in_queue = 0.4
       min_queue_examples = int(dataset.num_examples_per_epoch(subset) *
                                min_fraction_of_examples_in_queue)
-      raw_images, raw_labels = tf.train.shuffle_batch(
-          [input_image, input_label], batch_size=self.batch_size,
-          capacity=min_queue_examples + 3 * self.batch_size,
-          min_after_dequeue=min_queue_examples)
+
+      dataset_train = dataset_train.shuffle(min_queue_examples).batch(
+          self.batch_size, drop_remainder=True)
+
+      if tf.VERSION > "1.12":
+        raw_images, raw_labels = tf.compat.v1.data.make_one_shot_iterator(
+            dataset_train).get_next()
+      else:
+        raw_images, raw_labels = dataset_train.make_one_shot_iterator(
+            ).get_next()
 
       images = [[] for i in range(self.num_splits)]
       labels = [[] for i in range(self.num_splits)]