diff --git a/src/TensorFlowNET.Core/Operations/NnOps/rnn.cs b/src/TensorFlowNET.Core/Operations/NnOps/rnn.cs
index 6b9f073c..55f13920 100644
--- a/src/TensorFlowNET.Core/Operations/NnOps/rnn.cs
+++ b/src/TensorFlowNET.Core/Operations/NnOps/rnn.cs
@@ -428,9 +428,9 @@ namespace Tensorflow.Operations
                 return x;
 
             var x_rank = array_ops.rank(x);
-            var con1 = new object[]
+            var con1 = new Tensor[]
             {
-                new []{1, 0 },
+                new Tensor(new int[]{0, 2}),
                 math_ops.range(2, x_rank)
             };
             var x_t = array_ops.transpose(x, array_ops.concat(con1, 0));
diff --git a/src/TensorFlowNET.Core/Operations/array_ops.cs b/src/TensorFlowNET.Core/Operations/array_ops.cs
index 57af3b83..1b424006 100644
--- a/src/TensorFlowNET.Core/Operations/array_ops.cs
+++ b/src/TensorFlowNET.Core/Operations/array_ops.cs
@@ -166,6 +166,11 @@ namespace Tensorflow
                     throw new ValueError("mask cannot be scalar.");
 
                 var leading_size = gen_math_ops.prod(shape(tensor_tensor)[$"{axis}:{axis + ndims_mask}"], ops.convert_to_tensor(new[] { 0 }));
+                if (leading_size.rank == 0)
+                {
+                    leading_size = expand_dims(leading_size, 0);
+                }
+
                 var shape1 = concat(new[]
                 {
                     shape(tensor_tensor)[$":{axis}"],
@@ -185,7 +190,7 @@ namespace Tensorflow
 
         private static Tensor _apply_mask_1d(Tensor reshaped_tensor, Tensor mask, int axis = 0)
         {
-            var indices = squeeze(where(mask), axis: new[] { 1 });
+            var indices = squeeze(where_v2(mask), axis: new[] { 1 });
             return gather(reshaped_tensor, indices, axis: ops.convert_to_tensor(axis));
         }
 
@@ -940,12 +945,12 @@ namespace Tensorflow
         /// <returns></returns>
         public static Tensor concat(Tensor[] values, Tensor axis, string name = "concat")
         {
-            return tf.Context.ExecuteOp("ConcatV2", name, new ExecuteOpArgs(values, axis));
+            return gen_array_ops.concat_v2(values, axis, name: name);
         }
 
-        public static Tensor concat(object[] values, int axis, string name = "concat")
+        public static Tensor concat(Tensor[] values, Axis axis, string name = "concat")
         {
-            return tf.Context.ExecuteOp("ConcatV2", name, new ExecuteOpArgs(values, axis));
+            return gen_array_ops.concat_v2(values, axis, name: name);
         }
 
         /// <summary>
diff --git a/src/TensorFlowNET.Core/Operations/nn_ops.cs b/src/TensorFlowNET.Core/Operations/nn_ops.cs
index 00d7d316..394a591a 100644
--- a/src/TensorFlowNET.Core/Operations/nn_ops.cs
+++ b/src/TensorFlowNET.Core/Operations/nn_ops.cs
@@ -287,7 +287,7 @@ namespace Tensorflow
                 new[] { math_ops.subtract(rank, 1) },
                 new[] { constant_op.constant(1) });
 
-            var ops = array_ops.concat(new[] { new[] { -1 }, (object)last_dim_size }, 0);
+            var ops = array_ops.concat(new Tensor[] { new Tensor(new int[] {1}), last_dim_size }, 0);
             var output = array_ops.reshape(logits, ops);
 
             // Set output shape if known.
diff --git a/test/TensorFlowNET.Graph.UnitTest/Basics/TensorTest.cs b/test/TensorFlowNET.Graph.UnitTest/Basics/TensorTest.cs
index 90de7874..8093c1f2 100644
--- a/test/TensorFlowNET.Graph.UnitTest/Basics/TensorTest.cs
+++ b/test/TensorFlowNET.Graph.UnitTest/Basics/TensorTest.cs
@@ -3,6 +3,7 @@ using Tensorflow.NumPy;
 using System;
 using System.Linq;
 using static Tensorflow.Binding;
+using Tensorflow;
 
 namespace TensorFlowNET.UnitTest.Basics
 {
@@ -60,14 +61,14 @@ namespace TensorFlowNET.UnitTest.Basics
             Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 15, 21, 16, 22, 17, 23 }, result[0, 3].ToArray<int>()));
         }
 
-        [TestMethod, Ignore]
+        [TestMethod]
         public void boolean_mask()
         {
+            if (!tf.executing_eagerly())
+                tf.enable_eager_execution();
             var tensor = new[] { 0, 1, 2, 3 };
             var mask = np.array(new[] { true, false, true, false });
             var masked = tf.boolean_mask(tensor, mask);
-            var sess = tf.Session();
-            var result = sess.run(masked);
             Assert.IsTrue(Enumerable.SequenceEqual(new int[] { 0, 2 }, masked.ToArray<int>()));
         }
     }