Create EagerTensor from NDArray.

src/TensorFlowNET.Core/APIs/tf.linalg.cs

@@ -22,7 +22,7 @@ namespace Tensorflow
             => gen_array_ops.diag(diagonal, name: name);
 
         public Tensor matmul(Tensor a, Tensor b) 
-            => gen_math_ops.mat_mul(a, b);
+            => math_ops.matmul(a, b);
 
         public Tensor batch_matmul(Tensor x, Tensor y)
             => gen_math_ops.batch_mat_mul(x, y);

src/TensorFlowNET.Core/Eager/EagerTensor.cs

@@ -1,4 +1,5 @@
-using System;
+using NumSharp;
+using System;
 using System.Collections.Generic;
 using System.Text;
 
@@ -18,6 +19,10 @@ namespace Tensorflow.Eager
         {
         }
 
+        public EagerTensor(NDArray value, string device_name) : base(value)
+        {
+        }
+
         public override string ToString()
         {
             switch (rank)

src/TensorFlowNET.Core/Operations/gen_math_ops.cs

@@ -638,6 +638,14 @@ namespace Tensorflow
         /// <returns></returns>
         public static Tensor mat_mul(Tensor a, Tensor b, bool transpose_a = false, bool transpose_b = false, string name = null)
         {
+            if (tf.context.executing_eagerly())
+            {
+                var _result = wrap_tfe_src.TFE_Py_FastPathExecute(tf.context, tf.context.device_name, 
+                    "MatMul", name, null,
+                    a, b, "transpose_a", transpose_a, "transpose_b", transpose_b);
+                return _result;
+            }
+
             var _op = _op_def_lib._apply_op_helper("MatMul", name, args: new { a, b, transpose_a, transpose_b });
 
             return _op.output;
@@ -738,10 +746,18 @@ namespace Tensorflow
         {
             if (tf.context.executing_eagerly())
             {
-                var _result = wrap_tfe_src.TFE_Py_FastPathExecute(tf.context, tf.context.device_name, 
-                    "Sum", name, null, 
-                    input, axis, "keep_dims", keep_dims);
-                return _result;
+                try
+                {
+                    var _result = wrap_tfe_src.TFE_Py_FastPathExecute(tf.context, tf.context.device_name,
+                        "Sum", name, null,
+                        input, axis, "keep_dims", keep_dims);
+                    return _result;
+                }
+                catch (Exception)
+                {
+                    return _sum_eager_fallback(input as Tensor[], axis as Tensor, 
+                        keep_dims: keep_dims, name: name, ctx: tf.context);
+                }
             }
 
             var _op = _op_def_lib._apply_op_helper("Sum", name, args: new { input, reduction_indices = axis, keep_dims });
@@ -749,6 +765,18 @@ namespace Tensorflow
             return _op.outputs[0];
         }
 
+        private static Tensor _sum_eager_fallback(Tensor[] inputs, Tensor axis, bool keep_dims = false, string name = null, Context ctx = null)
+        {
+            var (_attr_T, input) = _execute.args_to_matching_eager(inputs, ctx);
+            var (_attr_Tidx, axis1) = _execute.args_to_matching_eager(new[] { axis }, ctx, TF_DataType.TF_INT32);
+            var _inputs_flat = new Tensor[] { input, axis1 };
+
+            var _attrs = new object[] { "keep_dims", keep_dims, "T", _attr_T, "Tidx", _attr_Tidx };
+
+            var _result = _execute.execute(ctx, "Sum", _inputs_flat, _attrs, name: name);
+            return _result;
+        }
+
         /// <summary>
         /// Creates a sequence of numbers.
         /// </summary>

src/TensorFlowNET.Core/Tensors/Tensor.Value.cs

@@ -163,6 +163,8 @@ namespace Tensorflow
                         return StringData();
                     case TF_DataType.TF_INT32:
                         return ToArray<int>();
+                    case TF_DataType.TF_FLOAT:
+                        return ToArray<float>();
                     default:
                         return BufferToArray();
                 }

src/TensorFlowNET.Core/Tensors/constant_op.cs

@@ -14,6 +14,7 @@
    limitations under the License.
 ******************************************************************************/
 
+using NumSharp;
 using System;
 using System.Collections.Generic;
 using Tensorflow.Eager;
@@ -84,6 +85,8 @@ namespace Tensorflow
         {
             switch (value)
             {
+                case NDArray nd:
+                    return new EagerTensor(nd, ctx.device_name);
                 case string str:
                     return new EagerTensor(str, ctx.device_name);
                 case int int32: