diff --git a/test/TensorFlowNET.Examples/LinearRegression.cs b/test/TensorFlowNET.Examples/LinearRegression.cs
index 15ebfc54..83764f40 100644
--- a/test/TensorFlowNET.Examples/LinearRegression.cs
+++ b/test/TensorFlowNET.Examples/LinearRegression.cs
@@ -20,7 +20,7 @@ namespace TensorFlowNET.Examples
 
         // Parameters
         float learning_rate = 0.01f;
-        int training_epochs = 1000;
+        public int TrainingEpochs = 1000;
         int display_step = 50;
 
         NDArray train_X, train_Y;
@@ -62,7 +62,7 @@ namespace TensorFlowNET.Examples
                 sess.run(init);
 
                 // Fit all training data
-                for (int epoch = 0; epoch < training_epochs; epoch++)
+                for (int epoch = 0; epoch < TrainingEpochs; epoch++)
                 {
                     foreach (var (x, y) in zip<float>(train_X, train_Y))
                     {
diff --git a/test/TensorFlowNET.Examples/LogisticRegression.cs b/test/TensorFlowNET.Examples/LogisticRegression.cs
index 89aaa27a..944df714 100644
--- a/test/TensorFlowNET.Examples/LogisticRegression.cs
+++ b/test/TensorFlowNET.Examples/LogisticRegression.cs
@@ -22,8 +22,9 @@ namespace TensorFlowNET.Examples
 
         private float learning_rate = 0.01f;
         public int TrainingEpochs = 10;
-        public int DataSize = 5000;
-        public int TestSize = 5000;
+        public int? TrainSize = null;
+        public int ValidationSize = 5000;
+        public int? TestSize = null;
         public int BatchSize = 100;
         private int display_step = 1;
 
@@ -98,7 +99,7 @@ namespace TensorFlowNET.Examples
 
         public void PrepareData()
         {
-            mnist = MnistDataSet.read_data_sets("mnist", one_hot: true, validation_size: DataSize, test_size: TestSize);
+            mnist = MnistDataSet.read_data_sets("mnist", one_hot: true, train_size: TrainSize, validation_size: ValidationSize, test_size: TestSize);
         }
 
         public void SaveModel(Session sess)
@@ -141,7 +142,7 @@ namespace TensorFlowNET.Examples
                 if (results.argmax() == (batch_ys[0] as NDArray).argmax())
                     print("predicted OK!");
                 else
-                    throw new ValueError("predict error, maybe 90% accuracy");
+                    throw new ValueError("predict error, should be 90% accuracy");
             });
         }
     }
diff --git a/test/TensorFlowNET.Examples/NearestNeighbor.cs b/test/TensorFlowNET.Examples/NearestNeighbor.cs
index 6894009a..dd5624f1 100644
--- a/test/TensorFlowNET.Examples/NearestNeighbor.cs
+++ b/test/TensorFlowNET.Examples/NearestNeighbor.cs
@@ -19,8 +19,9 @@ namespace TensorFlowNET.Examples
         public string Name => "Nearest Neighbor";
         Datasets mnist;
         NDArray Xtr, Ytr, Xte, Yte;
-        public int DataSize = 5000;
-        public int TestBatchSize = 200;
+        public int? TrainSize = null;
+        public int ValidationSize = 5000;
+        public int? TestSize = null;
 
         public bool Run()
         {
@@ -64,10 +65,10 @@ namespace TensorFlowNET.Examples
 
         public void PrepareData()
         {
-            mnist = MnistDataSet.read_data_sets("mnist", one_hot: true, validation_size: DataSize);
+            mnist = MnistDataSet.read_data_sets("mnist", one_hot: true, train_size: TrainSize, validation_size:ValidationSize, test_size:TestSize);
             // In this example, we limit mnist data
-            (Xtr, Ytr) = mnist.train.next_batch(DataSize); // 5000 for training (nn candidates)
-            (Xte, Yte) = mnist.test.next_batch(TestBatchSize); // 200 for testing
+            (Xtr, Ytr) = mnist.train.next_batch(TrainSize==null ? 5000 : TrainSize.Value / 100); // 5000 for training (nn candidates)
+            (Xte, Yte) = mnist.test.next_batch(TestSize==null ? 200 : TestSize.Value / 100); // 200 for testing
         }
     }
 }
diff --git a/test/TensorFlowNET.Examples/Utility/MnistDataSet.cs b/test/TensorFlowNET.Examples/Utility/MnistDataSet.cs
index 7616449c..d4ee0824 100644
--- a/test/TensorFlowNET.Examples/Utility/MnistDataSet.cs
+++ b/test/TensorFlowNET.Examples/Utility/MnistDataSet.cs
@@ -15,16 +15,17 @@ namespace TensorFlowNET.Examples.Utility
         private const string TRAIN_LABELS = "train-labels-idx1-ubyte.gz";
         private const string TEST_IMAGES = "t10k-images-idx3-ubyte.gz";
         private const string TEST_LABELS = "t10k-labels-idx1-ubyte.gz";
-
         public static Datasets read_data_sets(string train_dir, 
             bool one_hot = false,
             TF_DataType dtype = TF_DataType.TF_FLOAT,
             bool reshape = true,
             int validation_size = 5000,
-            int test_size = 5000,
+            int? train_size = null,
+            int? test_size = null,
             string source_url = DEFAULT_SOURCE_URL)
         {
-            var train_size = validation_size * 2;
+            if (train_size!=null && validation_size >= train_size)
+                throw new ArgumentException("Validation set should be smaller than training set");
 
             Web.Download(source_url + TRAIN_IMAGES, train_dir, TRAIN_IMAGES);
             Compress.ExtractGZip(Path.Join(train_dir, TRAIN_IMAGES), train_dir);
diff --git a/test/TensorFlowNET.UnitTest/ExamplesTests/ExamplesTest.cs b/test/TensorFlowNET.UnitTest/ExamplesTests/ExamplesTest.cs
index e884708c..5c74320f 100644
--- a/test/TensorFlowNET.UnitTest/ExamplesTests/ExamplesTest.cs
+++ b/test/TensorFlowNET.UnitTest/ExamplesTests/ExamplesTest.cs
@@ -51,7 +51,7 @@ namespace TensorFlowNET.UnitTest.ExamplesTests
         [TestMethod]
         public void LogisticRegression()
         {
-            new LogisticRegression() { Enabled = true, TrainingEpochs=10, DataSize = 500, TestSize = 500 }.Run();
+            new LogisticRegression() { Enabled = true, TrainingEpochs=10, TrainSize = 500, ValidationSize = 100, TestSize = 100 }.Run();
         }
 
         [Ignore]
@@ -78,7 +78,7 @@ namespace TensorFlowNET.UnitTest.ExamplesTests
         [TestMethod]
         public void NearestNeighbor()
         {
-            new NearestNeighbor() { Enabled = true, DataSize = 500, TestBatchSize = 100 }.Run();
+            new NearestNeighbor() { Enabled = true, TrainSize = 500, ValidationSize = 100, TestSize = 100 }.Run();
         }
 
         [Ignore]