diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDRNN.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDRNN.java
index de8148c02..ebb1a025d 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDRNN.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/autodiff/samediff/ops/SDRNN.java
@@ -35,6 +35,48 @@ public class SDRNN extends SDOps {
     super(sameDiff);
   }
 
+  /**
+   * The GRU operation. Gated Recurrent Unit - Cho et al. 2014.<br>
+   *
+   * @param x input [time, bS, nIn] (NUMERIC type)
+   * @param hLast initial cell output (at time step = 0) [bS, nOut] (NUMERIC type)
+   * @param Wx input-to-hidden  weights, [nIn, 3*nOut] (NUMERIC type)
+   * @param Wh hidden-to-hidden weights, [nOut, 3*nOut] (NUMERIC type)
+   * @param biases biases, [3*nOut] (NUMERIC type)
+   * @return h cell outputs [time, bS, nOut], that is per each time step (NUMERIC type)
+   */
+  public SDVariable gru(SDVariable x, SDVariable hLast, SDVariable Wx, SDVariable Wh,
+      SDVariable biases) {
+    SDValidation.validateNumerical("gru", "x", x);
+    SDValidation.validateNumerical("gru", "hLast", hLast);
+    SDValidation.validateNumerical("gru", "Wx", Wx);
+    SDValidation.validateNumerical("gru", "Wh", Wh);
+    SDValidation.validateNumerical("gru", "biases", biases);
+    return new org.nd4j.linalg.api.ops.impl.layers.recurrent.GRU(sd,x, hLast, Wx, Wh, biases).outputVariable();
+  }
+
+  /**
+   * The GRU operation. Gated Recurrent Unit - Cho et al. 2014.<br>
+   *
+   * @param name name May be null. Name for the output variable
+   * @param x input [time, bS, nIn] (NUMERIC type)
+   * @param hLast initial cell output (at time step = 0) [bS, nOut] (NUMERIC type)
+   * @param Wx input-to-hidden  weights, [nIn, 3*nOut] (NUMERIC type)
+   * @param Wh hidden-to-hidden weights, [nOut, 3*nOut] (NUMERIC type)
+   * @param biases biases, [3*nOut] (NUMERIC type)
+   * @return h cell outputs [time, bS, nOut], that is per each time step (NUMERIC type)
+   */
+  public SDVariable gru(String name, SDVariable x, SDVariable hLast, SDVariable Wx, SDVariable Wh,
+      SDVariable biases) {
+    SDValidation.validateNumerical("gru", "x", x);
+    SDValidation.validateNumerical("gru", "hLast", hLast);
+    SDValidation.validateNumerical("gru", "Wx", Wx);
+    SDValidation.validateNumerical("gru", "Wh", Wh);
+    SDValidation.validateNumerical("gru", "biases", biases);
+    SDVariable out =  new org.nd4j.linalg.api.ops.impl.layers.recurrent.GRU(sd,x, hLast, Wx, Wh, biases).outputVariable();
+    return sd.updateVariableNameAndReference(out, name);
+  }
+
   /**
    * The GRU cell.  Does a single time step operation<br>
    *
@@ -42,9 +84,9 @@ public class SDRNN extends SDOps {
    * @param hLast Output of the previous cell/time step, with shape [batchSize, numUnits] (NUMERIC type)
    * @param GRUWeights Configuration Object
    */
-  public SDVariable[] gru(SDVariable x, SDVariable hLast, GRUWeights GRUWeights) {
-    SDValidation.validateNumerical("gru", "x", x);
-    SDValidation.validateNumerical("gru", "hLast", hLast);
+  public SDVariable[] gruCell(SDVariable x, SDVariable hLast, GRUWeights GRUWeights) {
+    SDValidation.validateNumerical("gruCell", "x", x);
+    SDValidation.validateNumerical("gruCell", "hLast", hLast);
     return new org.nd4j.linalg.api.ops.impl.layers.recurrent.GRUCell(sd,x, hLast, GRUWeights).outputVariables();
   }
 
@@ -56,9 +98,10 @@ public class SDRNN extends SDOps {
    * @param hLast Output of the previous cell/time step, with shape [batchSize, numUnits] (NUMERIC type)
    * @param GRUWeights Configuration Object
    */
-  public SDVariable[] gru(String[] names, SDVariable x, SDVariable hLast, GRUWeights GRUWeights) {
-    SDValidation.validateNumerical("gru", "x", x);
-    SDValidation.validateNumerical("gru", "hLast", hLast);
+  public SDVariable[] gruCell(String[] names, SDVariable x, SDVariable hLast,
+      GRUWeights GRUWeights) {
+    SDValidation.validateNumerical("gruCell", "x", x);
+    SDValidation.validateNumerical("gruCell", "hLast", hLast);
     SDVariable[] out =  new org.nd4j.linalg.api.ops.impl.layers.recurrent.GRUCell(sd,x, hLast, GRUWeights).outputVariables();
     return sd.updateVariableNamesAndReferences(out, names);
   }
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/imports/converters/ImportClassMapping.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/imports/converters/ImportClassMapping.java
index 6af2d462a..62e55a02d 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/imports/converters/ImportClassMapping.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/imports/converters/ImportClassMapping.java
@@ -144,6 +144,8 @@ public class ImportClassMapping {
             org.nd4j.linalg.api.ops.impl.layers.convolution.SpaceToDepth.class,
             org.nd4j.linalg.api.ops.impl.layers.convolution.Upsampling2d.class,
             org.nd4j.linalg.api.ops.impl.layers.convolution.Upsampling2dDerivative.class,
+            org.nd4j.linalg.api.ops.impl.layers.recurrent.GRU.class,
+            org.nd4j.linalg.api.ops.impl.layers.recurrent.GRUBp.class,
             org.nd4j.linalg.api.ops.impl.layers.recurrent.GRUCell.class,
             org.nd4j.linalg.api.ops.impl.layers.recurrent.LSTMBlockCell.class,
             org.nd4j.linalg.api.ops.impl.layers.recurrent.LSTMCell.class,
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/GRU.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/GRU.java
new file mode 100644
index 000000000..0cc62833d
--- /dev/null
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/GRU.java
@@ -0,0 +1,67 @@
+/* ******************************************************************************
+ * Copyright (c) 2020 Konduit K.K.
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
+ * https://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.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ ******************************************************************************/
+package org.nd4j.linalg.api.ops.impl.layers.recurrent;
+
+import lombok.NoArgsConstructor;
+import lombok.NonNull;
+import org.nd4j.autodiff.samediff.SDVariable;
+import org.nd4j.autodiff.samediff.SameDiff;
+import org.nd4j.base.Preconditions;
+import org.nd4j.linalg.api.buffer.DataType;
+import org.nd4j.linalg.api.ndarray.INDArray;
+import org.nd4j.linalg.api.ops.DynamicCustomOp;
+
+import java.util.Arrays;
+import java.util.Collections;
+import java.util.List;
+
+@NoArgsConstructor
+public class GRU extends DynamicCustomOp {
+
+
+    public GRU(@NonNull SameDiff sameDiff, @NonNull SDVariable x, @NonNull SDVariable hI, @NonNull SDVariable Wx, @NonNull SDVariable Wh, @NonNull SDVariable biases) {
+        super(null, sameDiff, new SDVariable[]{x, hI, Wx, Wh, biases});
+
+    }
+
+    public GRU(@NonNull INDArray x, @NonNull INDArray hI, @NonNull INDArray Wx, @NonNull INDArray Wh, @NonNull INDArray biases) {
+        super(new INDArray[]{x, hI, Wx, Wh, biases}, null);
+
+    }
+
+    @Override
+    public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes) {
+        Preconditions.checkState(inputDataTypes != null && inputDataTypes.size() == 5, "Expected 5 inputs to GRU: initial cell output, input-to-hidden weights, hidden-to-hidden weights and biases got %s", inputDataTypes);
+        DataType dt = inputDataTypes.get(1);
+        for (int i = 0; i < inputDataTypes.size(); i++) {
+            Preconditions.checkState(inputDataTypes.get(i).isFPType(), "All input types must be a floating point type, got %s", dt);
+        }
+        Preconditions.checkState(dt.isFPType(), "Input type 1 must be a floating point type, got %s", dt);
+        return Collections.singletonList(dt);
+    }
+
+    @Override
+    public List<SDVariable> doDiff(List<SDVariable> grads) {
+        return Arrays.asList(new GRUBp(sameDiff, arg(0), arg(1), arg(2), arg(3),
+                arg(4), grads.get(0)).outputVariables());
+    }
+
+
+    @Override
+    public String opName() {
+        return "gru";
+    }
+}
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/GRUBp.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/GRUBp.java
new file mode 100644
index 000000000..b667fa811
--- /dev/null
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/api/ops/impl/layers/recurrent/GRUBp.java
@@ -0,0 +1,56 @@
+/* ******************************************************************************
+ * Copyright (c) 2020 Konduit K.K.
+ *
+ * This program and the accompanying materials are made available under the
+ * terms of the Apache License, Version 2.0 which is available at
+ * https://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.
+ *
+ * SPDX-License-Identifier: Apache-2.0
+ ******************************************************************************/
+package org.nd4j.linalg.api.ops.impl.layers.recurrent;
+
+
+import lombok.NoArgsConstructor;
+import lombok.NonNull;
+import org.nd4j.autodiff.samediff.SDVariable;
+import org.nd4j.autodiff.samediff.SameDiff;
+import org.nd4j.base.Preconditions;
+import org.nd4j.linalg.api.buffer.DataType;
+import org.nd4j.linalg.api.ops.DynamicCustomOp;
+
+import java.util.ArrayList;
+import java.util.Collections;
+import java.util.List;
+
+@NoArgsConstructor
+public class GRUBp extends DynamicCustomOp {
+
+
+    public GRUBp(@NonNull SameDiff sameDiff, @NonNull SDVariable x, @NonNull SDVariable hI, @NonNull SDVariable Wx, @NonNull SDVariable Wh, @NonNull SDVariable biases, @NonNull SDVariable dLdh) {
+        super(null, sameDiff, new SDVariable[]{x, hI, Wx, Wh, biases, dLdh});
+
+    }
+
+    @Override
+    public List<DataType> calculateOutputDataTypes(List<DataType> inputDataTypes) {
+        DataType dt = inputDataTypes.get(1);
+        List<DataType> list = new ArrayList<DataType>();
+        list.add(dt);
+        list.add(dt);
+        list.add(dt);
+        list.add(dt);
+        list.add(dt);
+        return list;
+    }
+
+    @Override
+    public String opName() {
+        return "gru_bp";
+    }
+}
diff --git a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDRNN.java b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDRNN.java
index 9bb7d9640..6dee1ef7e 100644
--- a/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDRNN.java
+++ b/nd4j/nd4j-backends/nd4j-api-parent/nd4j-api/src/main/java/org/nd4j/linalg/factory/ops/NDRNN.java
@@ -34,6 +34,25 @@ public class NDRNN {
   public NDRNN() {
   }
 
+  /**
+   * The GRU operation. Gated Recurrent Unit - Cho et al. 2014.<br>
+   *
+   * @param x input [time, bS, nIn] (NUMERIC type)
+   * @param hLast initial cell output (at time step = 0) [bS, nOut] (NUMERIC type)
+   * @param Wx input-to-hidden  weights, [nIn, 3*nOut] (NUMERIC type)
+   * @param Wh hidden-to-hidden weights, [nOut, 3*nOut] (NUMERIC type)
+   * @param biases biases, [3*nOut] (NUMERIC type)
+   * @return h cell outputs [time, bS, nOut], that is per each time step (NUMERIC type)
+   */
+  public INDArray gru(INDArray x, INDArray hLast, INDArray Wx, INDArray Wh, INDArray biases) {
+    NDValidation.validateNumerical("gru", "x", x);
+    NDValidation.validateNumerical("gru", "hLast", hLast);
+    NDValidation.validateNumerical("gru", "Wx", Wx);
+    NDValidation.validateNumerical("gru", "Wh", Wh);
+    NDValidation.validateNumerical("gru", "biases", biases);
+    return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.recurrent.GRU(x, hLast, Wx, Wh, biases))[0];
+  }
+
   /**
    * The GRU cell.  Does a single time step operation<br>
    *
@@ -41,9 +60,9 @@ public class NDRNN {
    * @param hLast Output of the previous cell/time step, with shape [batchSize, numUnits] (NUMERIC type)
    * @param GRUWeights Configuration Object
    */
-  public INDArray[] gru(INDArray x, INDArray hLast, GRUWeights GRUWeights) {
-    NDValidation.validateNumerical("gru", "x", x);
-    NDValidation.validateNumerical("gru", "hLast", hLast);
+  public INDArray[] gruCell(INDArray x, INDArray hLast, GRUWeights GRUWeights) {
+    NDValidation.validateNumerical("gruCell", "x", x);
+    NDValidation.validateNumerical("gruCell", "hLast", hLast);
     return Nd4j.exec(new org.nd4j.linalg.api.ops.impl.layers.recurrent.GRUCell(x, hLast, GRUWeights));
   }
 
diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/LayerOpValidation.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/LayerOpValidation.java
index c83a55d08..964c82c18 100644
--- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/LayerOpValidation.java
+++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/LayerOpValidation.java
@@ -38,6 +38,7 @@ import org.nd4j.linalg.api.ops.impl.layers.convolution.DepthwiseConv2D;
 import org.nd4j.linalg.api.ops.impl.layers.convolution.Pooling2D;
 import org.nd4j.linalg.api.ops.impl.layers.convolution.Pooling2DDerivative;
 import org.nd4j.linalg.api.ops.impl.layers.convolution.config.*;
+import org.nd4j.linalg.api.ops.impl.layers.recurrent.GRU;
 import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.LSTMActivations;
 import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.LSTMDataFormat;
 import org.nd4j.linalg.api.ops.impl.layers.recurrent.config.LSTMDirectionMode;
@@ -1701,6 +1702,35 @@ public class LayerOpValidation extends BaseOpValidation {
         assertNull(err);
     }
 
+    @Test
+    public void GRUTestCase() {
+        int bS = 5;
+        int nIn = 4;
+        int nOut = 6;
+        int time = 2;
+
+        SameDiff sd = SameDiff.create();
+
+        SDVariable in = sd.var("in", Nd4j.randn(DataType.DOUBLE, time, bS, nIn).muli(10));
+        SDVariable hLast = sd.var("cLast", Nd4j.zeros(DataType.DOUBLE, bS, nOut));
+        SDVariable Wx = sd.var("Wx", Nd4j.randn(DataType.DOUBLE, nIn, 3*nOut));
+        SDVariable Wh = sd.var("Wh", Nd4j.randn(DataType.DOUBLE, nOut, 3*nOut));
+        SDVariable biases = sd.var("bias", Nd4j.randn(DataType.DOUBLE, 3*nOut));
+
+        SDVariable out = new GRU(sd, in, hLast, Wx, Wh,biases).outputVariable();
+
+        long[] outShapes = new long[]{time,bS, nOut};
+        assertArrayEquals(new long[]{time,bS, nOut}, out.eval().shape());
+
+        sd.setLossVariables(out.std(true));
+        String err = OpValidation.validate(new TestCase(sd)
+                .gradientCheck(true)
+        );
+
+        assertNull(err);
+
+    }
+
 
 
 
diff --git a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/RnnOpValidation.java b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/RnnOpValidation.java
index c47d02b04..f3c79db65 100644
--- a/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/RnnOpValidation.java
+++ b/nd4j/nd4j-backends/nd4j-tests/src/test/java/org/nd4j/autodiff/opvalidation/RnnOpValidation.java
@@ -227,7 +227,7 @@ public class RnnOpValidation extends BaseOpValidation {
                 .cBias(bc)
                 .build();
 
-        SDVariable[] v = sd.rnn().gru(x, hLast, weights);
+        SDVariable[] v = sd.rnn().gruCell(x, hLast, weights);
         List<String> toExec = new ArrayList<>();
         for(SDVariable sdv : v){
             toExec.add(sdv.name());