diff --git a/tensorflow/python/keras/applications/imagenet_utils.py b/tensorflow/python/keras/applications/imagenet_utils.py
index 8f698a1a1e4..5c3fdbe20b0 100644
--- a/tensorflow/python/keras/applications/imagenet_utils.py
+++ b/tensorflow/python/keras/applications/imagenet_utils.py
@@ -66,11 +66,11 @@ PREPROCESS_INPUT_DOC = """
       {ret}
 
   Raises:
-      ValueError: In case of unknown `data_format` argument.
+      ValueError: In case of unknown `mode` or `data_format` argument.
   """
 
 PREPROCESS_INPUT_MODE_DOC = """
-    mode: One of "caffe", "tf" or "torch".
+    mode: One of "caffe", "tf" or "torch". Defaults to "caffe".
       - caffe: will convert the images from RGB to BGR,
           then will zero-center each color channel with
           respect to the ImageNet dataset,
@@ -97,9 +97,12 @@ PREPROCESS_INPUT_RET_DOC_CAFFE = """
 @keras_export('keras.applications.imagenet_utils.preprocess_input')
 def preprocess_input(x, data_format=None, mode='caffe'):
   """Preprocesses a tensor or Numpy array encoding a batch of images."""
+  if mode not in {'caffe', 'tf','torch'}:
+    raise ValueError('Unknown mode ' + str(mode))
+
   if data_format is None:
     data_format = backend.image_data_format()
-  if data_format not in {'channels_first', 'channels_last'}:
+  elif data_format not in {'channels_first', 'channels_last'}:
     raise ValueError('Unknown data_format ' + str(data_format))
 
   if isinstance(x, np.ndarray):
@@ -182,8 +185,7 @@ def _preprocess_numpy_input(x, data_format, mode):
     x /= 127.5
     x -= 1.
     return x
-
-  if mode == 'torch':
+  elif mode == 'torch':
     x /= 255.
     mean = [0.485, 0.456, 0.406]
     std = [0.229, 0.224, 0.225]
@@ -253,8 +255,7 @@ def _preprocess_symbolic_input(x, data_format, mode):
     x /= 127.5
     x -= 1.
     return x
-
-  if mode == 'torch':
+  elif mode == 'torch':
     x /= 255.
     mean = [0.485, 0.456, 0.406]
     std = [0.229, 0.224, 0.225]
@@ -414,10 +415,10 @@ def validate_activation(classifier_activation, weights):
     return
 
   classifier_activation = activations.get(classifier_activation)
-  if classifier_activation not in [
+  if classifier_activation not in {
       activations.get('softmax'),
       activations.get(None)
-  ]:
+  }:
     raise ValueError('Only `None` and `softmax` activations are allowed '
                      'for the `classifier_activation` argument when using '
                      'pretrained weights, with `include_top=True`')