diff --git a/tensorflow/python/keras/layers/cudnn_recurrent.py b/tensorflow/python/keras/layers/cudnn_recurrent.py
index e970232de40..7ecc7ac1996 100644
--- a/tensorflow/python/keras/layers/cudnn_recurrent.py
+++ b/tensorflow/python/keras/layers/cudnn_recurrent.py
@@ -504,7 +504,7 @@ class CuDNNLSTM(_CuDNNRNN):
         'is_training': True,
     }
 
-    outputs, h, c, _, _ = gen_cudnn_rnn_ops.cudnn_rnnv2(**args)
+    outputs, h, c, _, _ = gen_cudnn_rnn_ops.CudnnRNNV2(**args)
 
     if self.stateful or self.return_state:
       h = h[0]
diff --git a/tensorflow/python/keras/layers/recurrent_v2.py b/tensorflow/python/keras/layers/recurrent_v2.py
index 263a341ea61..79889602186 100644
--- a/tensorflow/python/keras/layers/recurrent_v2.py
+++ b/tensorflow/python/keras/layers/recurrent_v2.py
@@ -698,9 +698,9 @@ def gpu_gru(inputs, init_h, kernel, recurrent_kernel, bias, mask, time_major,
     if go_backwards:
       # Reverse axis 0 since the input is already convert to time major.
       inputs = array_ops.reverse(inputs, axis=[0])
-    outputs, h, _, _ = gen_cudnn_rnn_ops.cudnn_rnn(
-        inputs, input_h=init_h, input_c=0, params=params, is_training=True,
-        rnn_mode='gru')
+    outputs, h, _, _ = gen_cudnn_rnn_ops.CudnnRNN(
+        input=inputs, input_h=init_h, input_c=0, params=params,
+        is_training=True, rnn_mode='gru')
 
   last_output = outputs[-1]
   if not time_major and mask is None:
@@ -1486,8 +1486,8 @@ def gpu_lstm(inputs, init_h, init_c, kernel, recurrent_kernel, bias, mask,
       # expected_output = [0, 0, 6, 5 ,4]
       inputs = array_ops.reverse_sequence_v2(
           inputs, sequence_lengths, seq_axis=seq_axis, batch_axis=batch_axis)
-    outputs, h, c, _, _ = gen_cudnn_rnn_ops.cudnn_rnnv3(
-        inputs,
+    outputs, h, c, _, _ = gen_cudnn_rnn_ops.CudnnRNNV3(
+        input=inputs,
         input_h=init_h,
         input_c=init_c,
         params=params,
@@ -1506,9 +1506,9 @@ def gpu_lstm(inputs, init_h, init_c, kernel, recurrent_kernel, bias, mask,
     if go_backwards:
       # Reverse axis 0 since the input is already convert to time major.
       inputs = array_ops.reverse(inputs, axis=[0])
-    outputs, h, c, _ = gen_cudnn_rnn_ops.cudnn_rnn(
-        inputs, input_h=init_h, input_c=init_c, params=params, is_training=True,
-        rnn_mode='lstm')
+    outputs, h, c, _ = gen_cudnn_rnn_ops.CudnnRNN(
+        input=inputs, input_h=init_h, input_c=init_c, params=params,
+        is_training=True, rnn_mode='lstm')
 
   last_output = outputs[-1]
   if not time_major and mask is None: