abstract PrepareData interface for example

Oceania2018 · Oceania2018 · commit 5a2433c7ea1c · 2019-03-22T07:12:03.000-05:00
diff --git a/README.md b/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)
diff --git a/src/TensorFlowNET.Core/TensorFlowNET.Core.csproj b/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>
diff --git a/test/TensorFlowNET.Examples/BasicEagerApi.cs b/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()
+        {
+        }
     }
 }
diff --git a/test/TensorFlowNET.Examples/BasicOperations.cs b/test/TensorFlowNET.Examples/BasicOperations.cs
@@ -96,5 +96,9 @@ public void Run()
                 }
             }
         }
+
+        public void PrepareData()
+        {
+        }
     }
 }
diff --git a/test/TensorFlowNET.Examples/HelloWorld.cs b/test/TensorFlowNET.Examples/HelloWorld.cs
@@ -33,5 +33,9 @@ of the Constant op. */
                 }
             }
         }
+
+        public void PrepareData()
+        {
+        }
     }
 }
diff --git a/test/TensorFlowNET.Examples/IExample.cs b/test/TensorFlowNET.Examples/IExample.cs
@@ -11,5 +11,6 @@ namespace TensorFlowNET.Examples
     public interface IExample
     {
         void Run();
+        void PrepareData();
     }
 }
diff --git a/test/TensorFlowNET.Examples/ImageRecognition.cs b/test/TensorFlowNET.Examples/ImageRecognition.cs
@@ -78,7 +78,7 @@ private NDArray ReadTensorFromImageFile(string file_name,
             });
         }
 
-        private void PrepareData()
+        public void PrepareData()
         {
             Directory.CreateDirectory(dir);
 
diff --git a/test/TensorFlowNET.Examples/InceptionArchGoogLeNet.cs b/test/TensorFlowNET.Examples/InceptionArchGoogLeNet.cs
@@ -83,7 +83,7 @@ private NDArray ReadTensorFromImageFile(string file_name,
             });
         }
 
-        private void PrepareData()
+        public void PrepareData()
         {
             Directory.CreateDirectory(dir);
 
diff --git a/test/TensorFlowNET.Examples/LinearRegression.cs b/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];
+        }
     }
 }
diff --git a/test/TensorFlowNET.Examples/LogisticRegression.cs b/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);
         }
     }
 }
diff --git a/test/TensorFlowNET.Examples/MetaGraph.cs b/test/TensorFlowNET.Examples/MetaGraph.cs
@@ -27,5 +27,9 @@ private void ImportMetaGraph(string dir)
                                                 logits: logits);
             });
         }
+
+        public void PrepareData()
+        {
+        }
     }
 }
diff --git a/test/TensorFlowNET.Examples/NaiveBayesClassifier.cs b/test/TensorFlowNET.Examples/NaiveBayesClassifier.cs
@@ -15,9 +15,9 @@ public class NaiveBayesClassifier : Python, IExample
         public Normal dist { get; set; }
         public void Run()
         {
-            np.array<float>(1.0f, 1.0f);
-            var X = np.array<float>(new float[][] { new float[] { 1.0f, 1.0f }, new float[] { 2.0f, 2.0f }, new float[] { -1.0f, -1.0f }, new float[] { -2.0f, -2.0f }, new float[] { 1.0f, -1.0f }, new float[] { 2.0f, -2.0f }, });
-            var y = np.array<int>(0,0,1,1,2,2);
+            np.array(1.0f, 1.0f);
+            var X = np.array(new float[][] { new float[] { 1.0f, 1.0f }, new float[] { 2.0f, 2.0f }, new float[] { -1.0f, -1.0f }, new float[] { -2.0f, -2.0f }, new float[] { 1.0f, -1.0f }, new float[] { 2.0f, -2.0f }, });
+            var y = np.array(0,0,1,1,2,2);
             fit(X, y);
             // Create a regular grid and classify each point 
         }
@@ -102,5 +102,10 @@ public Tensor predict (NDArray X)
             // exp to get the actual probabilities
             return tf.exp(log_prob);
         }
+
+        public void PrepareData()
+        {
+            
+        }
     }
 }
diff --git a/test/TensorFlowNET.Examples/NamedEntityRecognition.cs b/test/TensorFlowNET.Examples/NamedEntityRecognition.cs
@@ -14,5 +14,10 @@ public void Run()
         {
             throw new NotImplementedException();
         }
+
+        public void PrepareData()
+        {
+            throw new NotImplementedException();
+        }
     }
 }
diff --git a/test/TensorFlowNET.Examples/TextClassification/TextClassificationTrain.cs b/test/TensorFlowNET.Examples/TextClassification/TextClassificationTrain.cs
@@ -23,7 +23,7 @@ public class TextClassificationTrain : Python, IExample
 
         public void Run()
         {
-            download_dbpedia();
+            PrepareData();
             Console.WriteLine("Building dataset...");
             var (x, y, alphabet_size) = DataHelpers.build_char_dataset("train", "vdcnn", CHAR_MAX_LEN);
 
@@ -32,17 +32,9 @@ public void Run()
             with(tf.Session(), sess =>
             {
                 new VdCnn(alphabet_size, CHAR_MAX_LEN, NUM_CLASS);
-
             });
         }
 
-        public void download_dbpedia()
-        {
-            string url = "https://github.com/le-scientifique/torchDatasets/raw/master/dbpedia_csv.tar.gz";
-            Web.Download(url, dataDir, dataFileName);
-            Compress.ExtractTGZ(Path.Join(dataDir, dataFileName), dataDir);
-        }
-
         private (int[][], int[][], int[], int[]) train_test_split(int[][] x, int[] y, float test_size = 0.3f)
         {
             int len = x.Length;
@@ -75,5 +67,12 @@ public void download_dbpedia()
 
             return (train_x.ToArray(), valid_x.ToArray(), train_y.ToArray(), valid_y.ToArray());
         }
+
+        public void PrepareData()
+        {
+            string url = "https://github.com/le-scientifique/torchDatasets/raw/master/dbpedia_csv.tar.gz";
+            Web.Download(url, dataDir, dataFileName);
+            Compress.ExtractTGZ(Path.Join(dataDir, dataFileName), dataDir);
+        }
     }
 }
diff --git a/test/TensorFlowNET.Examples/TextClassification/TextClassificationWithMovieReviews.cs b/test/TensorFlowNET.Examples/TextClassification/TextClassificationWithMovieReviews.cs
@@ -13,10 +13,11 @@ public class TextClassificationWithMovieReviews : Python, IExample
     {
         string dir = "text_classification_with_movie_reviews";
         string dataFile = "imdb.zip";
+        NDArray train_data, train_labels, test_data, test_labels;
 
         public void Run()
         {
-            var((train_data, train_labels), (test_data, test_labels)) = PrepareData();
+            PrepareData();
 
             Console.WriteLine($"Training entries: {train_data.size}, labels: {train_labels.size}");
 
@@ -40,7 +41,7 @@ public void Run()
             model.add(keras.layers.Embedding(vocab_size, 16));
         }
 
-        private ((NDArray, NDArray), (NDArray, NDArray)) PrepareData()
+        public void PrepareData()
         {
             Directory.CreateDirectory(dir);
 
@@ -71,7 +72,11 @@ public void Run()
             var y_train = labels_train;
             var y_test = labels_test;
 
-            return ((x_train, y_train), (x_test, y_test));
+            x_train = train_data;
+            train_labels = y_train;
+
+            test_data = x_test;
+            test_labels = y_test;
         }
 
         private NDArray ReadData(string file)

Original file line number	Diff line number	Diff line change
`@@ -12,6 +12,7 @@ namespace TensorFlowNET.Examples`
`12`	`12`	`public class BasicEagerApi : IExample`
`13`	`13`	`{`
`14`	`14`	`private Tensor a, b, c, d;`
	`15`	`+`
`15`	`16`	`public void Run()`
`16`	`17`	`{`
`17`	`18`	`// Set Eager API`
`@@ -34,5 +35,9 @@ public void Run()`
`34`	`35`
`35`	`36`	`// Full compatibility with Numpy`
`36`	`37`	`}`
	`38`	`+`
	`39`	`+ public void PrepareData()`
	`40`	`+ {`
	`41`	`+ }`
`37`	`42`	`}`
`38`	`43`	`}`
Original file line number	Diff line number	Diff line change
`@@ -96,5 +96,9 @@ public void Run()`
`96`	`96`	`}`
`97`	`97`	`}`
`98`	`98`	`}`
	`99`	`+`
	`100`	`+ public void PrepareData()`
	`101`	`+ {`
	`102`	`+ }`
`99`	`103`	`}`
`100`	`104`	`}`
Original file line number	Diff line number	Diff line change
`@@ -33,5 +33,9 @@ of the Constant op. */`
`33`	`33`	`}`
`34`	`34`	`}`
`35`	`35`	`}`
	`36`	`+`
	`37`	`+ public void PrepareData()`
	`38`	`+ {`
	`39`	`+ }`
`36`	`40`	`}`
`37`	`41`	`}`
Original file line number	Diff line number	Diff line change
`@@ -11,5 +11,6 @@ namespace TensorFlowNET.Examples`
`11`	`11`	`public interface IExample`
`12`	`12`	`{`
`13`	`13`	`void Run();`
	`14`	`+ void PrepareData();`
`14`	`15`	`}`
`15`	`16`	`}`
Original file line number	Diff line number	Diff line change
`@@ -78,7 +78,7 @@ private NDArray ReadTensorFromImageFile(string file_name,`
`78`	`78`	`});`
`79`	`79`	`}`
`80`	`80`
`81`		`- private void PrepareData()`
	`81`	`+ public void PrepareData()`
`82`	`82`	`{`
`83`	`83`	`Directory.CreateDirectory(dir);`
`84`	`84`
Original file line number	Diff line number	Diff line change
`@@ -83,7 +83,7 @@ private NDArray ReadTensorFromImageFile(string file_name,`
`83`	`83`	`});`
`84`	`84`	`}`
`85`	`85`
`86`		`- private void PrepareData()`
	`86`	`+ public void PrepareData()`
`87`	`87`	`{`
`88`	`88`	`Directory.CreateDirectory(dir);`
`89`	`89`
Original file line number	Diff line number	Diff line change
`@@ -21,9 +21,11 @@ public class LogisticRegression : Python, IExample`
`21`	`21`	`private int batch_size = 100;`
`22`	`22`	`private int display_step = 1;`
`23`	`23`
	`24`	`+ Datasets mnist;`
	`25`	`+`
`24`	`26`	`public void Run()`
`25`	`27`	`{`
`26`		`- var mnist = PrepareData();`
	`28`	`+ PrepareData();`
`27`	`29`
`28`	`30`	`// tf Graph Input`
`29`	`31`	`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()`
`86`	`88`	`});`
`87`	`89`	`}`
`88`	`90`
`89`		`- private Datasets PrepareData()`
	`91`	`+ public void PrepareData()`
`90`	`92`	`{`
`91`		`- var mnist = MnistDataSet.read_data_sets("logistic_regression", one_hot: true);`
`92`		`- return mnist;`
	`93`	`+ mnist = MnistDataSet.read_data_sets("logistic_regression", one_hot: true);`
`93`	`94`	`}`
`94`	`95`	`}`
`95`	`96`	`}`