diff --git a/src/TensorFlowNET.Core/Operations/NnOps/rnn.cs b/src/TensorFlowNET.Core/Operations/NnOps/rnn.cs
index 55f139207..6b9f073c1 100644
--- a/src/TensorFlowNET.Core/Operations/NnOps/rnn.cs
+++ b/src/TensorFlowNET.Core/Operations/NnOps/rnn.cs
@@ -428,9 +428,9 @@ public static Tensor _transpose_batch_time(Tensor x)
                 return x;
 
             var x_rank = array_ops.rank(x);
-            var con1 = new Tensor[]
+            var con1 = new object[]
             {
-                new Tensor(new int[]{0, 2}),
+                new []{1, 0 },
                 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 1b424006d..548a885ed 100644
--- a/src/TensorFlowNET.Core/Operations/array_ops.cs
+++ b/src/TensorFlowNET.Core/Operations/array_ops.cs
@@ -945,12 +945,12 @@ public static Tensor broadcast_static_shape(Tensor shape_x, Tensor shape_y)
         /// <returns></returns>
         public static Tensor concat(Tensor[] values, Tensor axis, string name = "concat")
         {
-            return gen_array_ops.concat_v2(values, axis, name: name);
+            return tf.Context.ExecuteOp("ConcatV2", name, new ExecuteOpArgs(values, axis));
         }
 
-        public static Tensor concat(Tensor[] values, Axis axis, string name = "concat")
+        public static Tensor concat(object[] values, int axis, string name = "concat")
         {
-            return gen_array_ops.concat_v2(values, axis, name: name);
+            return tf.Context.ExecuteOp("ConcatV2", name, new ExecuteOpArgs(values, axis));
         }
 
         /// <summary>
diff --git a/src/TensorFlowNET.Core/Operations/nn_ops.cs b/src/TensorFlowNET.Core/Operations/nn_ops.cs
index 394a591ab..00d7d316b 100644
--- a/src/TensorFlowNET.Core/Operations/nn_ops.cs
+++ b/src/TensorFlowNET.Core/Operations/nn_ops.cs
@@ -287,7 +287,7 @@ private static Tensor _flatten_outer_dims(Tensor logits)
                 new[] { math_ops.subtract(rank, 1) },
                 new[] { constant_op.constant(1) });
 
-            var ops = array_ops.concat(new Tensor[] { new Tensor(new int[] {1}), last_dim_size }, 0);
+            var ops = array_ops.concat(new[] { new[] { -1 }, (object)last_dim_size }, 0);
             var output = array_ops.reshape(logits, ops);
 
             // Set output shape if known.