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diff --git a/tensorflow/python/keras/optimizer_v2/adadelta.py b/tensorflow/python/keras/optimizer_v2/adadelta.py
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+# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+
+"""Adadelta for TensorFlow."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.keras.optimizer_v2 import optimizer_v2
+from tensorflow.python.training import training_ops
+
+
+class Adadelta(optimizer_v2.OptimizerV2):
+  """Adadelta optimizer.
+
+  It is recommended to leave the parameters of this optimizer at their default
+  values.
+
+  See [M. D. Zeiler](http://arxiv.org/abs/1212.5701)
+  ([pdf](http://arxiv.org/pdf/1212.5701v1.pdf))
+
+  Some of the args below are hyperparameters, where a hyperparameter is
+  defined as a scalar Tensor, a regular Python value, or a callable (which
+  will be evaluated when `apply_gradients` is called) returning a scalar
+  Tensor or a Python value.
+
+  Arguments:
+      learning_rate: float hyperparameter >= 0. Learning rate. It is recommended
+        to leave it at the default value.
+      rho: float hyperparameter >= 0. The decay rate.
+      epsilon: float hyperparameter >= 0. Fuzz factor. A constant epsilon used
+        to better condition the grad update.
+      name: Optional name prefix for the operations created when applying
+        gradients.  Defaults to 'Adadelta'.
+  """
+
+  def __init__(self,
+               learning_rate=0.001,
+               rho=0.95,
+               epsilon=1e-8,
+               name="Adadelta"):
+    super(Adadelta, self).__init__(name)
+    self._set_hyper("learning_rate", learning_rate)
+    self._set_hyper("rho", rho)
+    self._set_hyper("epsilon", epsilon)
+
+  def _create_vars(self, var_list, state):
+    for v in var_list:
+      state.zeros_slot(v, "accum")
+      state.zeros_slot(v, "accum_update")
+
+  def _apply_dense(self, grad, var, state):
+    accum = state.get_slot(var, "accum")
+    accum_update = state.get_slot(var, "accum_update")
+    return training_ops.apply_adadelta(
+        var,
+        accum,
+        accum_update,
+        state.get_hyper("learning_rate", var.dtype.base_dtype),
+        state.get_hyper("rho", var.dtype.base_dtype),
+        state.get_hyper("epsilon", var.dtype.base_dtype),
+        grad,
+        use_locking=self._use_locking)
+
+  def _resource_apply_dense(self, grad, var, state):
+    accum = state.get_slot(var, "accum")
+    accum_update = state.get_slot(var, "accum_update")
+    return training_ops.resource_apply_adadelta(
+        var.handle,
+        accum.handle,
+        accum_update.handle,
+        state.get_hyper("learning_rate", var.dtype.base_dtype),
+        state.get_hyper("rho", var.dtype.base_dtype),
+        state.get_hyper("epsilon", var.dtype.base_dtype),
+        grad,
+        use_locking=self._use_locking)
+
+  def _apply_sparse(self, grad, var, state):
+    accum = state.get_slot(var, "accum")
+    accum_update = state.get_slot(var, "accum_update")
+    return training_ops.sparse_apply_adadelta(
+        var,
+        accum,
+        accum_update,
+        state.get_hyper("learning_rate", var.dtype.base_dtype),
+        state.get_hyper("rho", var.dtype.base_dtype),
+        state.get_hyper("epsilon", var.dtype.base_dtype),
+        grad.values,
+        grad.indices,
+        use_locking=self._use_locking)
+
+  def _resource_apply_sparse(self, grad, var, indices, state):
+    accum = state.get_slot(var, "accum")
+    accum_update = state.get_slot(var, "accum_update")
+    return training_ops.resource_sparse_apply_adadelta(
+        var.handle,
+        accum.handle,
+        accum_update.handle,
+        state.get_hyper("learning_rate", var.dtype.base_dtype),
+        state.get_hyper("rho", var.dtype.base_dtype),
+        state.get_hyper("epsilon", var.dtype.base_dtype),
+        grad,
+        indices,
+        use_locking=self._use_locking)