Added tests with ignore attribute

test/TensorFlowNET.UnitTest/GradientTest/GradientEagerTest.cs

@@ -1,5 +1,6 @@
 using Microsoft.VisualStudio.TestTools.UnitTesting;
 using System;
+using System.Collections.Generic;
 using System.Linq;
 using Tensorflow;
 using Tensorflow.UnitTest;
@@ -24,6 +25,81 @@ namespace TensorFlowNET.UnitTest.Gradient
             Assert.AreEqual((float)grad, 3.0f);
         }
 
+        [Ignore]
+        [TestMethod]
+        public void SquaredDifference_Constant()
+        {
+            // Calcute the gradient of (x1-x2)^2 
+            // by Automatic Differentiation in Eager mode
+            var x1 = tf.constant(7f);
+            var x2 = tf.constant(11f);
+
+            // Sanity check
+            using (var tape = tf.GradientTape())
+            {
+                tape.watch(x2);
+                var loss = tf.multiply((x1 - x2), (x1 - x2));
+
+                var result = tape.gradient(loss, x2);
+                // Expected is 2*(11-7) = 8
+                Assert.AreEqual((float)result, 8f);
+            }
+
+            // Actual test
+            using (var tape = tf.GradientTape())
+            {
+                tape.watch(x2);
+                var loss = tf.squared_difference(x1, x2);
+
+                // Expected is 2*(11-7) = 8
+                var result = tape.gradient(loss, x2);
+                Assert.AreEqual((float)result, 8f);
+            }
+        }
+
+
+        [Ignore]
+        [TestMethod]
+        public void SquaredDifference_1D()
+        {
+            // Calcute the gradient of (x1-x2)^2 
+            // by Automatic Differentiation in Eager mode
+            // Expected is 2*(abs(x1-x2))
+            Tensor x1 = new NumSharp.NDArray( new float[] { 1, 3, 5, 21, 19, 17 });
+            Tensor x2 = new NumSharp.NDArray(new float[] { 29, 27, 23, 7, 11, 13 });
+            float[] expected = new float[] {
+                (29-1) * 2,
+                (27-3) * 2,
+                (23-5) * 2,
+                (7-21) * 2,
+                (11-19) * 2,
+                (13-17) * 2
+                };
+
+            // Sanity check
+            using (var tape = tf.GradientTape())
+            {
+                tape.watch(x1);
+                tape.watch(x2);
+                var loss = tf.multiply((x1 - x2), (x1 - x2));
+
+                var result = tape.gradient(loss, x2);
+                CollectionAssert.AreEqual(result.ToArray<float>(), expected);
+            }
+
+            // Actual test
+            using (var tape = tf.GradientTape())
+            {
+                tape.watch(x1);
+                tape.watch(x2);
+                var loss = tf.squared_difference(x1, x2);
+
+                var result = tape.gradient(loss, x2);
+                CollectionAssert.AreEqual(result.ToArray<float>(), expected);
+            }
+        }
+
+
         /// <summary>
         /// Calcute the gradient of w * w * w
         /// 高阶梯度