diff --git a/ngraph_bridge/ngraph_builder.cc b/ngraph_bridge/ngraph_builder.cc
index b41a4de3c..5f9d67b39 100644
--- a/ngraph_bridge/ngraph_builder.cc
+++ b/ngraph_bridge/ngraph_builder.cc
@@ -2513,6 +2513,21 @@ static Status TranslateLogSoftmaxOp(
   return Status::OK();
 }
 
+static Status TranslateSoftplusOp(
+    const Node* op, const std::vector<const Tensor*>& static_input_map,
+    Builder::OpMap& ng_op_map) {
+  shared_ptr<ng::Node> ng_inp;
+  TF_RETURN_IF_ERROR(GetInputNodes(ng_op_map, op, &ng_inp));
+  auto ng_exp = ConstructNgNode<ng::op::Exp>(op->name(), ng_inp);
+  auto constant_1 = ConstructNgNode<ng::op::Constant>(
+      op->name(), ng_inp->get_element_type(), ng_inp->get_shape(),
+      std::vector<std::string>(ng::shape_size(ng_inp->get_shape()), "1"));
+  auto ng_output = ConstructNgNode<ng::op::Log>(
+      op->name(), ConstructNgNode<ng::op::Add>(op->name(), ng_exp, constant_1));
+  SaveNgOp(ng_op_map, op->name(), ng_output);
+  return Status::OK();
+}
+
 static Status TranslateMatMulOp(
     const Node* op, const std::vector<const Tensor*>& static_input_map,
     Builder::OpMap& ng_op_map) {
@@ -4870,7 +4885,7 @@ const static std::map<
       {"Sigmoid", TranslateSigmoidOp}, {"SigmoidGrad", TranslateSigmoidGradOp},
       {"Size", TranslateSizeOp}, {"Sign", TranslateUnaryOp<ngraph::op::Sign>},
       {"Slice", TranslateSliceOp}, {"Snapshot", TranslateIdentityOp},
-      {"Softmax", TranslateSoftmaxOp},
+      {"Softmax", TranslateSoftmaxOp}, {"Softplus", TranslateSoftplusOp},
       {"SpaceToDepth", TranslateSpaceToDepthOp},
       {"SparseSoftmaxCrossEntropyWithLogits",
        TranslateSparseSoftmaxCrossEntropyWithLogitsOp},
diff --git a/ngraph_bridge/ngraph_mark_for_clustering.cc b/ngraph_bridge/ngraph_mark_for_clustering.cc
index 6aa4fea2f..28c03ef1e 100644
--- a/ngraph_bridge/ngraph_mark_for_clustering.cc
+++ b/ngraph_bridge/ngraph_mark_for_clustering.cc
@@ -375,6 +375,7 @@ Status MarkForClustering(Graph* graph, const std::set<string> skip_these_nodes,
       confirmation_function_map["Slice"] = SimpleConfirmationFunction();
       confirmation_function_map["Snapshot"] = SimpleConfirmationFunction();
       confirmation_function_map["Softmax"] = SimpleConfirmationFunction();
+      confirmation_function_map["Softplus"] = SimpleConfirmationFunction();
       confirmation_function_map["SpaceToDepth"] =
           confirmation_function_map["DepthToSpace"];
       confirmation_function_map["SparseSoftmaxCrossEntropyWithLogits"] =
@@ -569,6 +570,7 @@ Status MarkForClustering(Graph* graph, const std::set<string> skip_these_nodes,
       type_constraint_map["Slice"]["Index"] = NGraphIndexDTypes();
       type_constraint_map["Snapshot"]["T"] = NGraphDTypes();
       type_constraint_map["Softmax"]["T"] = NGraphNumericDTypes();
+      type_constraint_map["Softplus"]["T"] = NGraphRealDTypes();
       type_constraint_map["SpaceToDepth"]["T"] = NGraphDTypes();
       type_constraint_map["SparseSoftmaxCrossEntropyWithLogits"]["T"] =
           NGraphNumericDTypes();
diff --git a/test/python/test_softplus.py b/test/python/test_softplus.py
new file mode 100644
index 000000000..25b7ff684
--- /dev/null
+++ b/test/python/test_softplus.py
@@ -0,0 +1,55 @@
+# ==============================================================================
+#  Copyright 2018-2019 Intel Corporation
+#
+#  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.
+# ==============================================================================
+"""nGraph TensorFlow softplus test
+
+"""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import pytest
+
+import numpy as np
+import tensorflow as tf
+
+from common import NgraphTest
+
+
+class TestSoftplus(NgraphTest):
+
+    def test_softplus(self):
+        x = tf.placeholder(tf.float32, shape=(2, 3))
+        y = tf.placeholder(tf.float32, shape=(2, 3))
+        z = tf.placeholder(tf.float32, shape=(2, 3))
+
+        a = x + y + z
+        b = x + y + z
+        c = a * b
+        d = tf.nn.softplus(c)
+
+        # input value and expected value
+        x_np = np.full((2, 3), 1.0)
+        y_np = np.full((2, 3), 1.0)
+        z_np = np.full((2, 3), 1.0)
+
+        sess_fn = lambda sess: sess.run((a, c, d),
+                                        feed_dict={
+                                            x: x_np,
+                                            y: y_np,
+                                            z: z_np
+                                        })
+        assert np.allclose(
+            self.with_ngraph(sess_fn), self.without_ngraph(sess_fn))
diff --git a/test/test_nn_ops.cpp b/test/test_nn_ops.cpp
index f898acc39..63c14ae65 100644
--- a/test/test_nn_ops.cpp
+++ b/test/test_nn_ops.cpp
@@ -1575,6 +1575,30 @@ TEST(NNOps, SoftmaxZeroDimTest2) {
   opexecuter.RunTest();
 }
 
+// Test Op :"Softplus"
+TEST(NNOps, Softplus) {
+  std::vector<std::vector<int64>> input_sizes = {
+      {3}, {3, 2}, {5, 6}, {3, 4, 5}, {2, 3, 4, 5}};
+
+  vector<int> static_input_indexes = {};
+
+  for (auto const& input_size : input_sizes) {
+    Scope root = Scope::NewRootScope();
+
+    Tensor input_data(DT_FLOAT, TensorShape(input_size));
+    AssignInputValuesRandom<float>(input_data, -2, 2);
+
+    auto R = ops::Softplus(root, input_data);
+    vector<DataType> output_datatypes = {DT_FLOAT};
+    std::vector<Output> sess_run_fetchoutputs = {R};
+
+    OpExecuter opexecuter(root, "Softplus", static_input_indexes,
+                          output_datatypes, sess_run_fetchoutputs);
+
+    opexecuter.RunTest();
+  }
+}
+
 // Computes softmax cross entropy cost and gradients to backpropagate.
 TEST(NNOps, SparseSoftmaxCrossEntropyWithLogits) {
   Scope root = Scope::NewRootScope();