Xor: import graph

test/TensorFlowNET.Examples/BasicEagerApi.cs

@@ -14,6 +14,7 @@ namespace TensorFlowNET.Examples
         public int Priority => 100;
         public bool Enabled { get; set; } = false;
         public string Name => "Basic Eager";
+        public bool ImportGraph { get; set; } = false;
 
         private Tensor a, b, c, d;
 

test/TensorFlowNET.Examples/BasicOperations.cs

@@ -15,6 +15,8 @@ namespace TensorFlowNET.Examples
         public bool Enabled { get; set; } = true;
         public int Priority => 2;
         public string Name => "Basic Operations";
+        public bool ImportGraph { get; set; } = false;
+
 
         private Session sess;
 

test/TensorFlowNET.Examples/HelloWorld.cs

@@ -14,6 +14,8 @@ namespace TensorFlowNET.Examples
         public int Priority => 1;
         public bool Enabled { get; set; } = true;
         public string Name => "Hello World";
+        public bool ImportGraph { get; set; } = false;
+
 
         public bool Run()
         {

test/TensorFlowNET.Examples/IExample.cs

@@ -14,11 +14,17 @@ namespace TensorFlowNET.Examples
         /// running order
         /// </summary>
         int Priority { get; }
+
         /// <summary>
         /// True to run example
         /// </summary>
         bool Enabled { get; set; }
 
+        /// <summary>
+        /// Set true to import the computation graph instead of building it.
+        /// </summary>
+        bool ImportGraph { get; set; }
+
         string Name { get; }
 
         /// <summary>

test/TensorFlowNET.Examples/ImageRecognition.cs

@@ -15,6 +15,8 @@ namespace TensorFlowNET.Examples
         public int Priority => 7;
         public bool Enabled { get; set; } = true;
         public string Name => "Image Recognition";
+        public bool ImportGraph { get; set; } = false;
+
 
         string dir = "ImageRecognition";
         string pbFile = "tensorflow_inception_graph.pb";

test/TensorFlowNET.Examples/InceptionArchGoogLeNet.cs

@@ -22,6 +22,8 @@ namespace TensorFlowNET.Examples
         public bool Enabled { get; set; } = false;
         public int Priority => 100;
         public string Name => "Inception Arch GoogLeNet";
+        public bool ImportGraph { get; set; } = false;
+
 
         string dir = "label_image_data";
         string pbFile = "inception_v3_2016_08_28_frozen.pb";

test/TensorFlowNET.Examples/KMeansClustering.cs

@@ -20,6 +20,7 @@ namespace TensorFlowNET.Examples
         public int Priority => 8;
         public bool Enabled { get; set; } = true;
         public string Name => "K-means Clustering";
+        public bool ImportGraph { get; set; } = true;
 
         public int? train_size = null;
         public int validation_size = 5000;

test/TensorFlowNET.Examples/LinearRegression.cs

@@ -15,6 +15,8 @@ namespace TensorFlowNET.Examples
         public int Priority => 3;
         public bool Enabled { get; set; } = true;
         public string Name => "Linear Regression";
+        public bool ImportGraph { get; set; } = false;
+
 
         public int training_epochs = 1000;
 

test/TensorFlowNET.Examples/LogisticRegression.cs

@@ -20,6 +20,8 @@ namespace TensorFlowNET.Examples
         public int Priority => 4;
         public bool Enabled { get; set; } = true;
         public string Name => "Logistic Regression";
+        public bool ImportGraph { get; set; } = false;
+
 
         public int training_epochs = 10;
         public int? train_size = null;

test/TensorFlowNET.Examples/MetaGraph.cs

@@ -12,6 +12,8 @@ namespace TensorFlowNET.Examples
         public int Priority => 100;
         public bool Enabled { get; set; } = false;
         public string Name => "Meta Graph";
+        public bool ImportGraph { get; set; } = true;
+
 
         public bool Run()
         {

test/TensorFlowNET.Examples/NaiveBayesClassifier.cs

@@ -15,6 +15,8 @@ namespace TensorFlowNET.Examples
         public int Priority => 6;
         public bool Enabled { get; set; } = true;
         public string Name => "Naive Bayes Classifier";
+        public bool ImportGraph { get; set; } = false;
+
 
         public Normal dist { get; set; }
         public bool Run()

test/TensorFlowNET.Examples/NamedEntityRecognition.cs

@@ -13,6 +13,8 @@ namespace TensorFlowNET.Examples
         public int Priority => 100;
         public bool Enabled { get; set; } = false;
         public string Name => "NER";
+        public bool ImportGraph { get; set; } = false;
+
 
         public bool Run()
         {

test/TensorFlowNET.Examples/NearestNeighbor.cs

@@ -22,6 +22,8 @@ namespace TensorFlowNET.Examples
         public int? TrainSize = null;
         public int ValidationSize = 5000;
         public int? TestSize = null;
+        public bool ImportGraph { get; set; } = false;
+
 
         public bool Run()
         {

test/TensorFlowNET.Examples/NeuralNetXor.cs

@@ -3,6 +3,7 @@ using System.Collections.Generic;
 using System.Text;
 using NumSharp;
 using Tensorflow;
+using TensorFlowNET.Examples.Utility;
 
 namespace TensorFlowNET.Examples
 {
@@ -15,6 +16,7 @@ namespace TensorFlowNET.Examples
         public int Priority => 10;
         public bool Enabled { get; set; } = true;
         public string Name => "NN XOR";
+        public bool ImportGraph { get; set; } = true;
 
         public int num_steps = 5000;
 
@@ -38,7 +40,7 @@ namespace TensorFlowNET.Examples
 
             // Shape [4]
             var predictions = tf.sigmoid(tf.squeeze(logits));
-            var loss = tf.reduce_mean(tf.square(predictions - tf.cast(labels, tf.float32)));
+            var loss = tf.reduce_mean(tf.square(predictions - tf.cast(labels, tf.float32)), name:"loss");
 
             var gs = tf.Variable(0, trainable: false);
             var train_op = tf.train.GradientDescentOptimizer(0.2f).minimize(loss, global_step: gs);
@@ -49,7 +51,53 @@ namespace TensorFlowNET.Examples
         public bool Run()
         {
             PrepareData();
+            float loss_value = 0;
+            if (ImportGraph)
+                loss_value = RunWithImportedGraph();
+            else
+                loss_value=RunWithBuiltGraph();
 
+            return loss_value < 0.0627;
+        }
+
+        private float RunWithImportedGraph()
+        {
+            var graph = tf.Graph().as_default();
+
+            tf.train.import_meta_graph("graph/xor.meta");
+
+            var features = graph.get_operation_by_name("Placeholder");
+            var labels = graph.get_operation_by_name("Placeholder_1");
+            Tensor loss = graph.get_operation_by_name("loss");
+
+            var init = tf.global_variables_initializer();
+            float loss_value = 0;
+            // Start tf session
+            with<Session>(tf.Session(graph), sess =>
+            {
+                sess.run(init);
+                var step = 0;
+
+                var y_ = np.array(new int[] { 1, 0, 0, 1 }, dtype: np.int32);
+                while (step < num_steps)
+                {
+                    // original python:
+                    //_, step, loss_value = sess.run(
+                    //          [train_op, gs, loss],
+                    //          feed_dict={features: xy, labels: y_}
+                    //      )
+                    loss_value = sess.run(loss, new FeedItem(features, data), new FeedItem(labels, y_));
+                    step++;
+                    if (step % 1000 == 0)
+                        Console.WriteLine($"Step {step} loss: {loss_value}");
+                }
+                Console.WriteLine($"Final loss: {loss_value}");
+            });
+            return loss_value;
+        }
+
+        private float RunWithBuiltGraph()
+        {
             var graph = tf.Graph().as_default();
 
             var features = tf.placeholder(tf.float32, new TensorShape(4, 2));
@@ -76,12 +124,12 @@ namespace TensorFlowNET.Examples
                     //      )
                     loss_value = sess.run(loss, new FeedItem(features, data), new FeedItem(labels, y_));
                     step++;
-                    if (step%1000==0)
+                    if (step % 1000 == 0)
                         Console.WriteLine($"Step {step} loss: {loss_value}");
                 }
                 Console.WriteLine($"Final loss: {loss_value}");
             });
-            return loss_value < 0.0627;
+            return loss_value;
         }
 
         public void PrepareData()
@@ -93,6 +141,10 @@ namespace TensorFlowNET.Examples
                 {0, 0 },
                 {0, 1 }
             };
+
+            // download graph meta data
+            string url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/graph/xor.meta";
+            Web.Download(url, "graph", "kmeans.meta");
         }
     }
 }

test/TensorFlowNET.Examples/ObjectDetection.cs

@@ -18,6 +18,8 @@ namespace TensorFlowNET.Examples
         public int Priority => 11;
         public bool Enabled { get; set; } = true;
         public string Name => "Object Detection";
+        public bool ImportGraph { get; set; } = false;
+
         public float MIN_SCORE = 0.5f;
 
         string modelDir = "ssd_mobilenet_v1_coco_2018_01_28";

test/TensorFlowNET.Examples/Text/TextClassificationTrain.cs

@@ -18,6 +18,7 @@ namespace TensorFlowNET.Examples.CnnTextClassification
         public bool Enabled { get; set; }= false;
         public string Name => "Text Classification";
         public int? DataLimit = null;
+        public bool ImportGraph { get; set; } = true;
 
         private string dataDir = "text_classification";
         private string dataFileName = "dbpedia_csv.tar.gz";

test/TensorFlowNET.Examples/Text/TextClassificationWithMovieReviews.cs

@@ -14,6 +14,7 @@ namespace TensorFlowNET.Examples
         public int Priority => 9;
         public bool Enabled { get; set; } = false;
         public string Name => "Movie Reviews";
+        public bool ImportGraph { get; set; } = true;
 
         string dir = "text_classification_with_movie_reviews";
         string dataFile = "imdb.zip";