add Keras model.summary().

src/TensorFlowNET.Core/Keras/Engine/Model.Summary.cs

@@ -0,0 +1,20 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow.Keras.Utils;
+
+namespace Tensorflow.Keras.Engine
+{
+    public partial class Model
+    {
+        /// <summary>
+        /// Prints a string summary of the network.
+        /// </summary>
+        public void summary(int line_length = -1, float[] positions = null)
+        {
+            layer_utils.print_summary(this,
+                line_length: line_length,
+                positions: positions);
+        }
+    }
+}

src/TensorFlowNET.Core/Keras/Engine/Node.IterateInbound.cs

@@ -0,0 +1,18 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras.Engine
+{
+    public partial class Node
+    {
+        public IEnumerable<(Layer, int, int, Tensor)> iterate_inbound()
+        {
+            foreach(var kt in KerasInputs)
+            {
+                var (layer, node_index, tensor_index) = kt.KerasHistory;
+                yield return (layer, node_index, tensor_index, kt);
+            }
+        }
+    }
+}

test/TensorFlowNET.UnitTest/EagerModeTestBase.cs

@@ -2,12 +2,18 @@
 using System;
 using System.Collections.Generic;
 using System.Text;
+using Tensorflow;
+using Tensorflow.Keras.Engine;
+using Tensorflow.Keras.Layers;
 using static Tensorflow.Binding;
 
 namespace TensorFlowNET.UnitTest
 {
     public class EagerModeTestBase : PythonTest
     {
+        protected KerasApi keras = tf.keras;
+        protected LayersApi layers = tf.keras.layers;
+
         [TestInitialize]
         public void TestInit()
         {

test/TensorFlowNET.UnitTest/Keras/LayersTest.cs

@@ -16,6 +16,7 @@ namespace TensorFlowNET.UnitTest.Keras
     [TestClass]
     public class LayersTest : EagerModeTestBase
     {
+        
         [TestMethod]
         public void Sequential()
         {
@@ -23,6 +24,22 @@ namespace TensorFlowNET.UnitTest.Keras
             model.add(tf.keras.Input(shape: 16));
         }
 
+        [TestMethod]
+        public void Functional()
+        {
+            var inputs = keras.Input(shape: 784);
+            Assert.AreEqual((None, 784), inputs.TensorShape);
+
+            var dense = layers.Dense(64, activation: "relu");
+            var x = dense.Apply(inputs);
+
+            x = layers.Dense(64, activation: "relu").Apply(x);
+            var outputs = layers.Dense(10).Apply(x);
+
+            var model = keras.Model(inputs, outputs, name: "mnist_model");
+            model.summary();
+        }
+
         /// <summary>
         /// https://www.tensorflow.org/api_docs/python/tf/keras/layers/Embedding
         /// </summary>

test/TensorFlowNET.UnitTest/PythonTest.cs

@@ -16,10 +16,7 @@ namespace TensorFlowNET.UnitTest
     {
         #region python compatibility layer
         protected PythonTest self { get => this; }
-        protected object None
-        {
-            get { return null; }
-        }
+        protected int None => -1;
         #endregion
 
         #region pytest assertions
@@ -150,7 +147,7 @@ namespace TensorFlowNET.UnitTest
 
         protected object _eval_tensor(object tensor)
         {
-            if (tensor == None)
+            if (tensor == null)
                 return None;
             //else if (callable(tensor))
             //     return self._eval_helper(tensor())