diff --git a/tensorflow/python/keras/layers/preprocessing/category_crossing.py b/tensorflow/python/keras/layers/preprocessing/category_crossing.py
index fa0237595ac..594b9741946 100644
--- a/tensorflow/python/keras/layers/preprocessing/category_crossing.py
+++ b/tensorflow/python/keras/layers/preprocessing/category_crossing.py
@@ -142,8 +142,8 @@ class CategoryCrossing(Layer):
   def _preprocess_input(self, inp):
     if isinstance(inp, (list, tuple, np.ndarray)):
       inp = ops.convert_to_tensor(inp)
-      if inp.shape.rank == 1:
-        inp = array_ops.expand_dims(inp, axis=-1)
+    if inp.shape.rank == 1:
+      inp = array_ops.expand_dims(inp, axis=-1)
     return inp
 
   def call(self, inputs):
diff --git a/tensorflow/python/keras/layers/preprocessing/category_crossing_test.py b/tensorflow/python/keras/layers/preprocessing/category_crossing_test.py
index 83e78c4dd46..0f320196080 100644
--- a/tensorflow/python/keras/layers/preprocessing/category_crossing_test.py
+++ b/tensorflow/python/keras/layers/preprocessing/category_crossing_test.py
@@ -191,6 +191,11 @@ class CategoryCrossingTest(keras_parameterized.TestCase):
     output = layer([inputs_0, inputs_1])
     self.assertAllEqual([[b'1_X_1'], [b'2_X_3']], output)
 
+    inputs_0 = np.asarray([1, 2])
+    inputs_1 = np.asarray([1, 3])
+    output = layer([inputs_0, inputs_1])
+    self.assertAllEqual([[b'1_X_1'], [b'2_X_3']], output)
+
   def test_crossing_dense_inputs_depth_int(self):
     layer = category_crossing.CategoryCrossing(depth=1)
     inputs_0 = constant_op.constant([['a'], ['b'], ['c']])
diff --git a/tensorflow/python/keras/layers/preprocessing/hashing.py b/tensorflow/python/keras/layers/preprocessing/hashing.py
index f4a4ae0ccc8..faeeec63a86 100644
--- a/tensorflow/python/keras/layers/preprocessing/hashing.py
+++ b/tensorflow/python/keras/layers/preprocessing/hashing.py
@@ -19,11 +19,14 @@ from __future__ import division
 from __future__ import print_function
 
 import functools
+import numpy as np
 
 from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
 from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.framework import tensor_shape
 from tensorflow.python.framework import tensor_spec
+from tensorflow.python.framework import tensor_util
 from tensorflow.python.keras.engine.base_layer import Layer
 from tensorflow.python.ops import gen_sparse_ops
 from tensorflow.python.ops import sparse_ops
@@ -58,7 +61,7 @@ class Hashing(Layer):
   Example (FarmHash64):
 
   >>> layer = tf.keras.layers.experimental.preprocessing.Hashing(num_bins=3)
-  >>> inp = np.asarray([['A'], ['B'], ['C'], ['D'], ['E']])
+  >>> inp = [['A'], ['B'], ['C'], ['D'], ['E']]
   >>> layer(inp)
   <tf.Tensor: shape=(5, 1), dtype=int64, numpy=
     array([[1],
@@ -68,11 +71,24 @@ class Hashing(Layer):
            [2]])>
 
 
+  Example (FarmHash64) with list of inputs:
+  >>> layer = tf.keras.layers.experimental.preprocessing.Hashing(num_bins=3)
+  >>> inp_1 = [['A'], ['B'], ['C'], ['D'], ['E']]
+  >>> inp_2 = np.asarray([[5], [4], [3], [2], [1]])
+  >>> layer([inp_1, inp_2])
+  <tf.Tensor: shape=(5, 1), dtype=int64, numpy=
+    array([[1],
+           [1],
+           [0],
+           [2],
+           [0]])>
+
+
   Example (SipHash64):
 
   >>> layer = tf.keras.layers.experimental.preprocessing.Hashing(num_bins=3,
   ...    salt=[133, 137])
-  >>> inp = np.asarray([['A'], ['B'], ['C'], ['D'], ['E']])
+  >>> inp = [['A'], ['B'], ['C'], ['D'], ['E']]
   >>> layer(inp)
   <tf.Tensor: shape=(5, 1), dtype=int64, numpy=
     array([[1],
@@ -85,7 +101,7 @@ class Hashing(Layer):
 
   >>> layer = tf.keras.layers.experimental.preprocessing.Hashing(num_bins=3,
   ...    salt=133)
-  >>> inp = np.asarray([['A'], ['B'], ['C'], ['D'], ['E']])
+  >>> inp = [['A'], ['B'], ['C'], ['D'], ['E']]
   >>> layer(inp)
   <tf.Tensor: shape=(5, 1), dtype=int64, numpy=
     array([[0],
@@ -134,7 +150,23 @@ class Hashing(Layer):
     else:
       self.salt = _DEFAULT_SALT_KEY
 
+  def _preprocess_single_input(self, inp):
+    if isinstance(inp, (list, tuple, np.ndarray)):
+      inp = ops.convert_to_tensor(inp)
+    return inp
+
+  def _preprocess_inputs(self, inputs):
+    if isinstance(inputs, (tuple, list)):
+      # If any of them is tensor or ndarray, then treat as list
+      if any([
+          tensor_util.is_tensor(inp) or isinstance(inp, np.ndarray)
+          for inp in inputs
+      ]):
+        return [self._preprocess_single_input(inp) for inp in inputs]
+    return self._preprocess_single_input(inputs)
+
   def call(self, inputs):
+    inputs = self._preprocess_inputs(inputs)
     if isinstance(inputs, (tuple, list)):
       return self._process_input_list(inputs)
     else:
diff --git a/tensorflow/python/keras/layers/preprocessing/hashing_test.py b/tensorflow/python/keras/layers/preprocessing/hashing_test.py
index 4c3fd9c7501..a3a4a189261 100644
--- a/tensorflow/python/keras/layers/preprocessing/hashing_test.py
+++ b/tensorflow/python/keras/layers/preprocessing/hashing_test.py
@@ -60,6 +60,27 @@ class HashingTest(keras_parameterized.TestCase):
     # Assert equal for hashed output that should be true on all platforms.
     self.assertAllClose([[0], [0], [1], [1], [0]], output)
 
+  def test_hash_dense_list_input_farmhash(self):
+    layer = hashing.Hashing(num_bins=2)
+    inp = [['omar'], ['stringer'], ['marlo'], ['wire'], ['skywalker']]
+    output = layer(inp)
+    # Assert equal for hashed output that should be true on all platforms.
+    self.assertAllClose([[0], [0], [1], [0], [0]], output)
+
+    inp = ['omar', 'stringer', 'marlo', 'wire', 'skywalker']
+    output = layer(inp)
+    # Assert equal for hashed output that should be true on all platforms.
+    self.assertAllClose([0, 0, 1, 0, 0], output)
+
+  def test_hash_dense_list_inputs_mixed_int_string_farmhash(self):
+    layer = hashing.Hashing(num_bins=2)
+    inp_1 = np.asarray([['omar'], ['stringer'], ['marlo'], ['wire'],
+                        ['skywalker']])
+    inp_2 = np.asarray([[1], [2], [3], [4], [5]])
+    output = layer([inp_1, inp_2])
+    # Assert equal for hashed output that should be true on all platforms.
+    self.assertAllClose([[0], [1], [1], [1], [0]], output)
+
   def test_hash_dense_int_input_farmhash(self):
     layer = hashing.Hashing(num_bins=3)
     inp = np.asarray([[0], [1], [2], [3], [4]])