add test gpu leak code.

src/TensorFlowNet.Benchmarks/Leak/GpuLeakByCNN.cs

@@ -0,0 +1,52 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow.Keras.Layers;
+using NumSharp;
+using Tensorflow.Keras;
+using static Tensorflow.Binding;
+using static Tensorflow.KerasApi;
+
+namespace Tensorflow.Benchmark.Leak
+{
+    class GpuLeakByCNN
+    {
+        protected static LayersApi layers = new LayersApi();
+
+        public static void Test()
+        {
+            int num = 50, width = 64, height = 64;
+            // if width = 128, height = 128, the exception occurs faster
+
+            var bytes = new byte[num * width * height * 3];
+            var inputImages = np.array(bytes) / 255.0f;
+            inputImages = inputImages.reshape(num, height, width, 3);
+
+            bytes = new byte[num];
+            var outLables = np.array(bytes);
+            Console.WriteLine("Image.Shape={0}", inputImages.Shape);
+            Console.WriteLine("Label.Shape={0}", outLables.Shape);
+
+            tf.enable_eager_execution();
+
+            var inputss = keras.Input((height, width, 3));
+
+            var inputs = layers.Conv2D(32, (3, 3), activation: keras.activations.Relu).Apply(inputss);
+            inputs = layers.MaxPooling2D((2, 2)).Apply(inputs);
+
+            inputs = layers.Flatten().Apply(inputs);
+
+            var outputs = layers.Dense(10).Apply(inputs);
+
+            var model = keras.Model(inputss, outputs, "gpuleak");
+
+            model.summary();
+
+            model.compile(loss: keras.losses.SparseCategoricalCrossentropy(from_logits: true),
+             optimizer: keras.optimizers.RMSprop(),
+             metrics: new[] { "accuracy" });
+
+            model.fit(inputImages, outLables, epochs: 200);
+        }
+    }
+}