minor changes in examples

test/TensorFlowNET.Examples/KMeansClustering.cs

@@ -18,14 +18,15 @@ namespace TensorFlowNET.Examples
         public int Priority => 8;
         public bool Enabled { get; set; } = true;
         public string Name => "K-means Clustering";
-        public int DataSize = 5000;
-        public int TestSize = 5000;
-        public int BatchSize = 100;
+
+        public int? train_size = null;
+        public int validation_size = 5000;
+        public int? test_size = null;
+        public int batch_size = 1024; // The number of samples per batch
 
         Datasets mnist;
         NDArray full_data_x;
         int num_steps = 50; // Total steps to train
-        int batch_size = 1024; // The number of samples per batch
         int k = 25; // The number of clusters
         int num_classes = 10; // The 10 digits
         int num_features = 784; // Each image is 28x28 pixels
@@ -48,7 +49,7 @@ 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: train_size, validation_size:validation_size, test_size:test_size);
             full_data_x = mnist.train.images;
         }
     }

test/TensorFlowNET.Examples/LinearRegression.cs

@@ -16,13 +16,13 @@ namespace TensorFlowNET.Examples
         public bool Enabled { get; set; } = true;
         public string Name => "Linear Regression";
 
-        NumPyRandom rng = np.random;
+        public int training_epochs = 1000;
 
         // Parameters
         float learning_rate = 0.01f;
-        public int TrainingEpochs = 1000;
         int display_step = 50;
 
+        NumPyRandom rng = np.random;
         NDArray train_X, train_Y;
         int n_samples;
 
@@ -62,7 +62,7 @@ namespace TensorFlowNET.Examples
                 sess.run(init);
 
                 // Fit all training data
-                for (int epoch = 0; epoch < TrainingEpochs; epoch++)
+                for (int epoch = 0; epoch < training_epochs; epoch++)
                 {
                     foreach (var (x, y) in zip<float>(train_X, train_Y))
                     {

test/TensorFlowNET.Examples/LogisticRegression.cs

@@ -20,12 +20,13 @@ namespace TensorFlowNET.Examples
         public bool Enabled { get; set; } = true;
         public string Name => "Logistic Regression";
 
+        public int training_epochs = 10;
+        public int? train_size = null;
+        public int validation_size = 5000;
+        public int? test_size = null;
+        public int batch_size = 100;
+
         private float learning_rate = 0.01f;
-        public int TrainingEpochs = 10;
-        public int? TrainSize = null;
-        public int ValidationSize = 5000;
-        public int? TestSize = null;
-        public int BatchSize = 100;
         private int display_step = 1;
 
         Datasets mnist;
@@ -60,14 +61,14 @@ namespace TensorFlowNET.Examples
                 sess.run(init);
 
                 // Training cycle
-                foreach (var epoch in range(TrainingEpochs))
+                foreach (var epoch in range(training_epochs))
                 {
                     var avg_cost = 0.0f;
-                    var total_batch = mnist.train.num_examples / BatchSize;
+                    var total_batch = mnist.train.num_examples / batch_size;
                     // Loop over all batches
                     foreach (var i in range(total_batch))
                     {
-                        var (batch_xs, batch_ys) = mnist.train.next_batch(BatchSize);
+                        var (batch_xs, batch_ys) = mnist.train.next_batch(batch_size);
                         // Run optimization op (backprop) and cost op (to get loss value)
                         var result = sess.run(new object[] { optimizer, cost },
                             new FeedItem(x, batch_xs),
@@ -99,7 +100,7 @@ namespace TensorFlowNET.Examples
 
         public void PrepareData()
         {
-            mnist = MnistDataSet.read_data_sets("mnist", one_hot: true, train_size: TrainSize, validation_size: ValidationSize, test_size: TestSize);
+            mnist = MnistDataSet.read_data_sets("mnist", one_hot: true, train_size: train_size, validation_size: validation_size, test_size: test_size);
         }
 
         public void SaveModel(Session sess)

test/TensorFlowNET.UnitTest/ExamplesTests/ExamplesTest.cs

@@ -39,7 +39,7 @@ namespace TensorFlowNET.UnitTest.ExamplesTests
         [TestMethod]
         public void KMeansClustering()
         {
-            new KMeansClustering() { Enabled = true }.Run();
+            new KMeansClustering() { Enabled = true, train_size = 500, validation_size = 100, test_size = 100, batch_size =100 }.Run();
         }
 
         [TestMethod]
@@ -51,7 +51,7 @@ namespace TensorFlowNET.UnitTest.ExamplesTests
         [TestMethod]
         public void LogisticRegression()
         {
-            new LogisticRegression() { Enabled = true, TrainingEpochs=10, TrainSize = 500, ValidationSize = 100, TestSize = 100 }.Run();
+            new LogisticRegression() { Enabled = true, training_epochs=10, train_size = 500, validation_size = 100, test_size = 100 }.Run();
         }
 
         [Ignore]