Adding `BatchMatMul` gradient (#304)

Unit testing the gradient too.

src/TensorFlowNET.Core/Gradients/math_grad.cs

@@ -168,9 +168,38 @@ namespace Tensorflow.Gradients
             return new Tensor[] { grad_a, grad_b };
         }
 
+        [RegisterGradient("BatchMatMul")]
         public static Tensor[] _BatchMatMul(Operation op, Tensor[] grads)
         {
-            throw new NotImplementedException();
+            var grad = grads[0];
+            Tensor grad_a = null, grad_b = null;
+
+            var t_a = (bool)op.get_attr("adj_x");
+            var t_b = (bool)op.get_attr("adj_y");
+            var a = math_ops.conj(op.inputs[0]);
+            var b = math_ops.conj(op.inputs[1]);
+            if (!t_a && !t_b)
+            {
+                grad_a = gen_math_ops.batch_mat_mul(grad, b, adj_y: true);
+                grad_b = gen_math_ops.batch_mat_mul(a, grad, adj_x: true);
+            }
+            else if (!t_a && t_b)
+            {
+                grad_a = gen_math_ops.batch_mat_mul(grad, b);
+                grad_b = gen_math_ops.batch_mat_mul(grad, a, adj_x: true);
+            }
+            else if (t_a && !t_b)
+            {
+                grad_a = gen_math_ops.batch_mat_mul(grad, b);
+                grad_b = gen_math_ops.batch_mat_mul(grad, a, adj_x: true);
+            }
+            else if (t_a && t_b)
+            {
+                grad_a = gen_math_ops.batch_mat_mul(b, grad, adj_x: true, adj_y: true);
+                grad_b = gen_math_ops.batch_mat_mul(grad, a, adj_x: true, adj_y: true);
+            }
+
+            return new Tensor[] { grad_a, grad_b };
         }
 
         [RegisterGradient("Mean")]

test/TensorFlowNET.UnitTest/gradients_test/GradientsTest.cs

@@ -1,7 +1,9 @@
 using System;
 using System.Collections.Generic;
+using System.Linq;
 using System.Text;
 using Microsoft.VisualStudio.TestTools.UnitTesting;
+using NumSharp;
 using Tensorflow;
 using static Tensorflow.Python;
 
@@ -30,6 +32,39 @@ namespace TensorFlowNET.UnitTest.gradients_test
             });
         }
 
+        [TestMethod]
+        public void testBatchMatMulGradient()
+        {
+            var a = tf.constant(np.array(Enumerable.Range(1, 18).Select(elem => (float)elem).ToArray()), shape:new []{2, 3, 3});
+            var b = tf.divide(a, tf.constant(2.0f));
+            var c = tf.batch_matmul(a, b);
+            var g = tf.gradients(c, new[] {a, b}, stop_gradients: new[] {a, b});
+            var checkG = new[]
+            {
+                3.0f, 7.5f, 12.0f,
+                3.0f, 7.5f, 12.0f,
+                3.0f, 7.5f, 12.0f,
+                16.5f, 21.0f, 25.5f,
+                16.5f, 21.0f, 25.5f,
+                16.5f, 21.0f, 25.5f,
+                12.0f, 12.0f, 12.0f,
+                15.0f, 15.0f, 15.0f,
+                18.0f, 18.0f, 18.0f,
+                39.0f, 39.0f, 39.0f,
+                42.0f, 42.0f, 42.0f,
+                45.0f, 45.0f, 45.0f
+            };
+            using (var sess = tf.Session())
+            {
+                var result = sess.run(g);
+                var resultList = result[0].GetData<float>().ToList();
+                resultList.AddRange(result[1].GetData<float>());
+                Console.WriteLine(result.ToString());
+                CollectionAssert.AreEqual(resultList.ToArray(), checkG);
+            }
+        }
+
+
         [Ignore("TODO")]
         [TestMethod]
         public void testUnusedOutput()