abstract PrepareData interface for example

Author: Oceania2018

README.md

@@ -72,6 +72,7 @@ Read the docs & book [The Definitive Guide to Tensorflow.NET](https://tensorflow
 * [Basic Operations](test/TensorFlowNET.Examples/BasicOperations.cs)
 * [Image Recognition](test/TensorFlowNET.Examples/ImageRecognition.cs)
 * [Linear Regression](test/TensorFlowNET.Examples/LinearRegression.cs)
+* [Logistic Regression](test/TensorFlowNET.Examples/LogisticRegression.cs)
 * [Text Classification](test/TensorFlowNET.Examples/TextClassificationWithMovieReviews.cs)
 * [CNN Text Classification](test/TensorFlowNET.Examples/CnnTextClassification.cs)
 * [Naive Bayes Classification](test/TensorFlowNET.Examples/NaiveBayesClassifier.cs)

src/TensorFlowNET.Core/TensorFlowNET.Core.csproj

@@ -17,7 +17,8 @@
     <Description>Google's TensorFlow binding in .NET Standard.
 Docs: https://tensorflownet.readthedocs.io</Description>
     <AssemblyVersion>0.5.0.0</AssemblyVersion>
-    <PackageReleaseNotes>Add a lot of APIs to build neural networks model</PackageReleaseNotes>
+    <PackageReleaseNotes>Add Logistic Regression to do MNIST.
+Add a lot of APIs to build neural networks model</PackageReleaseNotes>
     <LangVersion>7.2</LangVersion>
     <FileVersion>0.5.0.0</FileVersion>
   </PropertyGroup>

test/TensorFlowNET.Examples/BasicEagerApi.cs

@@ -12,6 +12,7 @@ namespace TensorFlowNET.Examples
     public class BasicEagerApi : IExample
     {
         private Tensor a, b, c, d;
+
         public void Run()
         {
             // Set Eager API
@@ -34,5 +35,9 @@ public void Run()
 
             // Full compatibility with Numpy
         }
+
+        public void PrepareData()
+        {
+        }
     }
 }

test/TensorFlowNET.Examples/BasicOperations.cs

@@ -96,5 +96,9 @@ public void Run()
                 }
             }
         }
+
+        public void PrepareData()
+        {
+        }
     }
 }

test/TensorFlowNET.Examples/HelloWorld.cs

@@ -33,5 +33,9 @@ of the Constant op. */
                 }
             }
         }
+
+        public void PrepareData()
+        {
+        }
     }
 }

test/TensorFlowNET.Examples/IExample.cs

@@ -11,5 +11,6 @@ namespace TensorFlowNET.Examples
     public interface IExample
     {
         void Run();
+        void PrepareData();
     }
 }

test/TensorFlowNET.Examples/ImageRecognition.cs

@@ -78,7 +78,7 @@ private NDArray ReadTensorFromImageFile(string file_name,
             });
         }
 
-        private void PrepareData()
+        public void PrepareData()
         {
             Directory.CreateDirectory(dir);
 

test/TensorFlowNET.Examples/InceptionArchGoogLeNet.cs

@@ -83,7 +83,7 @@ private NDArray ReadTensorFromImageFile(string file_name,
             });
         }
 
-        private void PrepareData()
+        public void PrepareData()
         {
             Directory.CreateDirectory(dir);
 

test/TensorFlowNET.Examples/LinearRegression.cs

@@ -19,14 +19,13 @@ public class LinearRegression : Python, IExample
         int training_epochs = 1000;
         int display_step = 50;
 
+        NDArray train_X, train_Y;
+        int n_samples;
+
         public void Run()
         {
             // Training Data
-            var train_X = np.array(3.3f, 4.4f, 5.5f, 6.71f, 6.93f, 4.168f, 9.779f, 6.182f, 7.59f, 2.167f,
-                         7.042f, 10.791f, 5.313f, 7.997f, 5.654f, 9.27f, 3.1f);
-            var train_Y = np.array(1.7f, 2.76f, 2.09f, 3.19f, 1.694f, 1.573f, 3.366f, 2.596f, 2.53f, 1.221f,
-                         2.827f, 3.465f, 1.65f, 2.904f, 2.42f, 2.94f, 1.3f);
-            var n_samples = train_X.shape[0];
+            PrepareData();
 
             // tf Graph Input
             var X = tf.placeholder(tf.float32);
@@ -95,5 +94,14 @@ public void Run()
                 Console.WriteLine($"Absolute mean square loss difference: {Math.Abs((float)training_cost - (float)testing_cost)}");
             });
         }
+
+        public void PrepareData()
+        {
+            train_X = np.array(3.3f, 4.4f, 5.5f, 6.71f, 6.93f, 4.168f, 9.779f, 6.182f, 7.59f, 2.167f,
+             7.042f, 10.791f, 5.313f, 7.997f, 5.654f, 9.27f, 3.1f);
+            train_Y = np.array(1.7f, 2.76f, 2.09f, 3.19f, 1.694f, 1.573f, 3.366f, 2.596f, 2.53f, 1.221f,
+                         2.827f, 3.465f, 1.65f, 2.904f, 2.42f, 2.94f, 1.3f);
+            n_samples = train_X.shape[0];
+        }
     }
 }

test/TensorFlowNET.Examples/LogisticRegression.cs

@@ -21,9 +21,11 @@ public class LogisticRegression : Python, IExample
         private int batch_size = 100;
         private int display_step = 1;
 
+        Datasets mnist;
+
         public void Run()
         {
-            var mnist = PrepareData();
+            PrepareData();
 
             // tf Graph Input
             var x = tf.placeholder(tf.float32, new TensorShape(-1, 784)); // mnist data image of shape 28*28=784
@@ -86,10 +88,9 @@ public void Run()
             });
         }
 
-        private Datasets PrepareData()
+        public void PrepareData()
         {
-            var mnist = MnistDataSet.read_data_sets("logistic_regression", one_hot: true);
-            return mnist;
+            mnist = MnistDataSet.read_data_sets("logistic_regression", one_hot: true);
         }
     }
 }