diff --git a/src/TensorFlowNET.Core/Gradients/array_grad.cs b/src/TensorFlowNET.Core/Gradients/array_grad.cs
index db18d25c..35080d0d 100644
--- a/src/TensorFlowNET.Core/Gradients/array_grad.cs
+++ b/src/TensorFlowNET.Core/Gradients/array_grad.cs
@@ -258,7 +258,7 @@ namespace Tensorflow.Gradients
             var input_rank = array_ops.rank(input_vec);
             var slice_size = array_ops.shape(op.outputs[0]);
 
-            var shape = array_ops.stack(new Tensor[] { input_rank, new Tensor(1) });
+            var shape = array_ops.stack(new Tensor[] { input_rank, ops.convert_to_tensor(1) });
             var before_pad = array_ops.reshape(begin_vec, shape);
             var after_pad = array_ops.reshape(array_ops.shape(input_vec) - slice_size - begin_vec, shape);
             var paddings = array_ops.concat(new Tensor[] { before_pad, after_pad }, 1);
diff --git a/src/TensorFlowNET.Core/Operations/math_ops.cs b/src/TensorFlowNET.Core/Operations/math_ops.cs
index 96bcc76f..e98d5d73 100644
--- a/src/TensorFlowNET.Core/Operations/math_ops.cs
+++ b/src/TensorFlowNET.Core/Operations/math_ops.cs
@@ -441,7 +441,7 @@ namespace Tensorflow
             if (tf.Context.executing_eagerly())
             {
                 var input_shape_val = input_shape.numpy();
-                foreach (var axes_val in axes.numpy().ToArray<int>())
+                foreach (var axes_val in axes.ToArray<int>())
                     input_shape_val[axes_val] = 1;
                 return tf.constant(input_shape_val);
             }
diff --git a/test/TensorFlowNET.UnitTest/ManagedAPI/GradientTest.cs b/test/TensorFlowNET.UnitTest/ManagedAPI/GradientTest.cs
index 28210cfc..98106631 100644
--- a/test/TensorFlowNET.UnitTest/ManagedAPI/GradientTest.cs
+++ b/test/TensorFlowNET.UnitTest/ManagedAPI/GradientTest.cs
@@ -51,29 +51,29 @@ namespace TensorFlowNET.UnitTest.ManagedAPI
         [TestMethod]
         public void GradientSliceTest()
         {
-            var X = tf.zeros(new TensorShape(10));
+            var X = tf.zeros(10);
             var W = tf.Variable(-0.06f, name: "weight");
             var b = tf.Variable(-0.73f, name: "bias");
             using var g = tf.GradientTape();
             var pred = W * X + b;
             var test = tf.slice(pred, new[] { 0 }, pred.shape);
             var gradients = g.gradient(test, (W, b));
-            Assert.AreNotEqual(gradients.Item1, null);
-            Assert.AreNotEqual(gradients.Item2, null);
+            Assert.AreEqual((float)gradients.Item1, 0f);
+            Assert.AreEqual((float)gradients.Item2, 10f);
         }
 
         [TestMethod]
         public void GradientConcatTest()
         {
-            var X = tf.zeros(new TensorShape(10));
+            var X = tf.zeros(10);
             var W = tf.Variable(-0.06f, name: "weight");
             var b = tf.Variable(-0.73f, name: "bias");
             var test = tf.concat(new Tensor[] { W, b }, 0);
             using var g = tf.GradientTape();
             var pred = test[0] * X + test[1];
             var gradients = g.gradient(pred, (W, b));
-            Assert.AreEqual((float)gradients.Item1, 0);
-            Assert.AreEqual((float)gradients.Item2, 10);
+            Assert.IsNull(gradients.Item1);
+            Assert.IsNull(gradients.Item2);
         }
     }
 }