add Tensor[] pattern match for ops.name_scope.

src/TensorFlowNET.Core/Layers/Layer.cs

@@ -37,7 +37,7 @@ namespace Tensorflow.Layers
             VariableScope scope = null)
         {
             _set_scope(scope);
-            _graph = ops._get_graph_from_inputs(new List<Tensor> { inputs }, graph: _graph);
+            _graph = ops._get_graph_from_inputs(new Tensor[] { inputs }, graph: _graph);
 
             variable_scope scope_context_manager = null;
             if (built)

src/TensorFlowNET.Core/ops.name_scope.cs

@@ -1,5 +1,6 @@
 using System;
 using System.Collections.Generic;
+using System.Linq;
 using System.Text;
 using Tensorflow.Eager;
 
@@ -37,8 +38,11 @@ namespace Tensorflow
                 _name = _name == null ? _default_name : _name;
 
                 Graph g = null;
-                if (_values is List<Tensor> values)
-                    g = _get_graph_from_inputs(values);
+
+                if (_values is List<Tensor> vList)
+                    g = _get_graph_from_inputs(vList.ToArray());
+                else if (_values is Tensor[] vArray)
+                    g = _get_graph_from_inputs(vArray);
 
                 if (g == null)
                     g = get_default_graph();

src/TensorFlowNET.Core/ops.py.cs

@@ -102,8 +102,10 @@ namespace Tensorflow
             default_graph = tf.Graph();
         }
 
+        public static Graph _get_graph_from_inputs(params Tensor[] op_input_list)
+            => _get_graph_from_inputs(op_input_list: op_input_list);
 
-        public static Graph _get_graph_from_inputs(List<Tensor> op_input_list, Graph graph = null)
+        public static Graph _get_graph_from_inputs(Tensor[] op_input_list, Graph graph = null)
         {
             foreach(var op_input in op_input_list)
             {

test/TensorFlowNET.Examples/TextProcess/TextClassificationTrain.cs

@@ -203,37 +203,6 @@ namespace TensorFlowNET.Examples.CnnTextClassification
             return (train_x, valid_x, train_y, valid_y);
         }
 
-        //private (int[][], int[][], int[], int[]) train_test_split(int[][] x, int[] y, float test_size = 0.3f)
-        //{
-        //    Console.WriteLine("Splitting in Training and Testing data...");
-        //    var stopwatch = Stopwatch.StartNew();
-        //    int len = x.Length;
-        //    int train_size = int.Parse((len * (1 - test_size)).ToString());
-        //    var random = new Random(17);
-
-        //    // we collect indices of labels 
-        //    var labels = new Dictionary<int, HashSet<int>>();
-        //    var shuffled_indices = random.Shuffle<int>(range(len).ToArray());
-        //    foreach (var i in shuffled_indices)
-        //    {
-        //        var label = y[i];
-        //        if (!labels.ContainsKey(i))
-        //            labels[label] = new HashSet<int>();
-        //        labels[label].Add(i);
-        //    }
-
-        //    var train_x = new int[train_size][];
-        //    var valid_x = new int[len - train_size][];
-        //    var train_y = new int[train_size];
-        //    var valid_y = new int[len - train_size];
-
-        //    FillWithShuffledLabels(x, y, train_x, train_y, random, labels);
-        //    FillWithShuffledLabels(x, y, valid_x, valid_y, random, labels);
-
-        //    Console.WriteLine("\tDONE " + stopwatch.Elapsed);
-        //    return (train_x, valid_x, train_y, valid_y);
-        //}
-
         private static void FillWithShuffledLabels(int[][] x, int[] y, int[][] shuffled_x, int[] shuffled_y, Random random, Dictionary<int, HashSet<int>> labels)
         {
             int i = 0;