Add Nearest Neighbor

release v0.5.1

README.md

@@ -73,6 +73,7 @@ Read the docs & book [The Definitive Guide to Tensorflow.NET](https://tensorflow
 * [Image Recognition](test/TensorFlowNET.Examples/ImageRecognition.cs)
 * [Linear Regression](test/TensorFlowNET.Examples/LinearRegression.cs)
 * [Logistic Regression](test/TensorFlowNET.Examples/LogisticRegression.cs)
+* [Nearest Neighbor](test/TensorFlowNET.Examples/NearestNeighbor.cs)
 * [Text Classification](test/TensorFlowNET.Examples/TextClassificationWithMovieReviews.cs)
 * [CNN Text Classification](test/TensorFlowNET.Examples/CnnTextClassification.cs)
 * [Naive Bayes Classification](test/TensorFlowNET.Examples/NaiveBayesClassifier.cs)

docs/source/NearestNeighbor.md

@@ -0,0 +1,3 @@
+# Chapter. Nearest Neighbor
+
+The nearest neighbour algorithm was one of the first algorithms used to solve the travelling salesman problem. In it, the salesman starts at a random city and repeatedly visits the nearest city until all have been visited. It quickly yields a short tour, but usually not the optimal one.

docs/source/index.rst

@@ -27,4 +27,5 @@ Welcome to TensorFlow.NET's documentation!
    EagerMode
    LinearRegression
    LogisticRegression
+   NearestNeighbor
    ImageRecognition

src/TensorFlowNET.Core/APIs/tf.math.cs

@@ -6,9 +6,18 @@ namespace Tensorflow
 {
     public static partial class tf
     {
+        public static Tensor abs(Tensor x, string name = null)
+            => math_ops.abs(x, name);
+
         public static Tensor add(Tensor a, Tensor b) 
             => gen_math_ops.add(a, b);
 
+        public static Tensor arg_max(Tensor input, int dimension, TF_DataType output_type = TF_DataType.TF_INT64, string name = null)
+            => gen_math_ops.arg_max(input, dimension, output_type: output_type, name: name);
+
+        public static Tensor arg_min(Tensor input, int dimension, TF_DataType output_type = TF_DataType.TF_INT64, string name = null)
+            => gen_math_ops.arg_min(input, dimension, output_type: output_type, name: name);
+
         public static Tensor sub(Tensor a, Tensor b) 
             => gen_math_ops.sub(a, b);
 
@@ -27,6 +36,9 @@ namespace Tensorflow
         public static Tensor multiply(Tensor x, Tensor y) 
             => gen_math_ops.mul(x, y);
 
+        public static Tensor negative(Tensor x, string name = null)
+            => gen_math_ops.neg(x, name);
+
         public static Tensor divide<T>(Tensor x, T[] y, string name = null) where T : struct
             => x / ops.convert_to_tensor(y, dtype: x.dtype.as_base_dtype(), name: "y");
 

src/TensorFlowNET.Core/Graphs/Graph.cs

@@ -355,7 +355,7 @@ namespace Tensorflow
             return _collections.Keys.Where(x => !x.StartsWith("__")).ToArray();
         }
 
-        public object get_collection(string name, string scope = "")
+        public object get_collection(string name, string scope = null)
         {
             return _collections.ContainsKey(name) ? _collections[name] : null;
         }

src/TensorFlowNET.Core/Operations/gen_math_ops.cs

@@ -9,6 +9,30 @@ namespace Tensorflow
     public static class gen_math_ops
     {
         public static OpDefLibrary _op_def_lib = new OpDefLibrary();
+
+        /// <summary>
+        /// Returns the index with the largest value across dimensions of a tensor.
+        /// </summary>
+        /// <param name="input"></param>
+        /// <param name="dimension"></param>
+        /// <param name="output_type"></param>
+        /// <param name="name"></param>
+        /// <returns></returns>
+        public static Tensor arg_max(Tensor input, int dimension, TF_DataType output_type = TF_DataType.TF_INT64, string name = null)
+            => _op_def_lib._apply_op_helper("ArgMax", name, args: new { input, dimension, output_type }).outputs[0];
+
+        /// <summary>
+        /// Returns the index with the smallest value across dimensions of a tensor.
+        /// </summary>
+        /// <param name="input"></param>
+        /// <param name="dimension"></param>
+        /// <param name="output_type"></param>
+        /// <param name="name"></param>
+        /// <returns></returns>
+        public static Tensor arg_min(Tensor input, int dimension, TF_DataType output_type= TF_DataType.TF_INT64, string name= null)
+            =>_op_def_lib._apply_op_helper("ArgMin", name, args: new { input, dimension, output_type }).outputs[0];
+        
+
         /// <summary>
         /// Computes the mean of elements across dimensions of a tensor.
         /// Reduces `input` along the dimensions given in `axis`. Unless
        /// `keep_dims` is true, the rank of the tensor is reduced by 1 for each entry in
        /// `axis`. If `keep_dims` is true, the reduced dimensions are retained with length 1.
@@ -207,6 +231,13 @@ namespace Tensorflow
             return _op.outputs[0];
         }
 
+        public static Tensor _abs(Tensor x, string name = null)
+        {
+            var _op = _op_def_lib._apply_op_helper("Abs", name, new { x });
+
+            return _op.outputs[0];
+        }
+
         public static Tensor _max<Tx, Ty>(Tx input, Ty axis, bool keep_dims=false, string name = null)
         {
             var _op = _op_def_lib._apply_op_helper("Max", name, new { input, reduction_indices = axis, keep_dims });
@@ -249,20 +280,5 @@ namespace Tensorflow
 
             return _op.outputs[0];
         }
-
-        /// <summary>
-        /// Returns the index with the largest value across dimensions of a tensor.
-        /// </summary>
-        /// <param name="input"></param>
-        /// <param name="dimension"></param>
-        /// <param name="output_type"></param>
-        /// <param name="name"></param>
-        /// <returns></returns>
-        public static Tensor arg_max(Tensor input, int dimension, TF_DataType output_type = TF_DataType.TF_INT64, string name = null)
-        {
-            var _op = _op_def_lib._apply_op_helper("ArgMax", name, new { input, dimension, output_type });
-
-            return _op.outputs[0];
-        }
     }
 }

src/TensorFlowNET.Core/Operations/math_ops.py.cs → src/TensorFlowNET.Core/Operations/math_ops.cs

@@ -6,9 +6,36 @@ using Tensorflow.Framework;
 
 namespace Tensorflow
 {
+    /// <summary>
+    /// python\ops\math_ops.py
+    /// </summary>
     public class math_ops : Python
     {
-        public static Tensor add(Tensor x, Tensor y, string name = null) => gen_math_ops.add(x, y, name);
+        public static Tensor abs(Tensor x, string name = null)
+        {
+            return with(ops.name_scope(name, "Abs", new { x }), scope =>
+            {
+                x = ops.convert_to_tensor(x, name: "x");
+                if (x.dtype.is_complex())
+                    throw new NotImplementedException("math_ops.abs for dtype.is_complex");
+                    //return gen_math_ops.complex_abs(x, Tout: x.dtype.real_dtype, name: name);
+                return gen_math_ops._abs(x, name: name);
+            });
+        }
+
+        public static Tensor add(Tensor x, Tensor y, string name = null) 
+            => gen_math_ops.add(x, y, name);
+
+        public static Tensor add(Tensor x, string name = null)
+        {
+            return with(ops.name_scope(name, "Abs", new { x }), scope =>
+            {
+                name = scope;
+                x = ops.convert_to_tensor(x, name: "x");
+
+                return gen_math_ops._abs(x, name: name);
+            });
+        }
 
         public static Tensor cast(Tensor x, TF_DataType dtype = TF_DataType.DtInvalid, string name = null)
         {

src/TensorFlowNET.Core/Sessions/BaseSession.cs

@@ -222,6 +222,12 @@ namespace Tensorflow
                         ints[i] = *(int*)(offset + (int)(tensor.itemsize * i));
                     nd = np.array(ints).reshape(ndims);
                     break;
+                case TF_DataType.TF_INT64:
+                    var longs = new long[tensor.size];
+                    for (ulong i = 0; i < tensor.size; i++)
+                        longs[i] = *(long*)(offset + (int)(tensor.itemsize * i));
+                    nd = np.array(longs).reshape(ndims);
+                    break;
                 case TF_DataType.TF_FLOAT:
                     var floats = new float[tensor.size];
                     for (ulong i = 0; i < tensor.size; i++)

src/TensorFlowNET.Core/Sessions/_FetchHandler.cs

@@ -65,6 +65,9 @@ namespace Tensorflow
                             case "Int32":
                                 full_values.Add(value.Data<int>(0));
                                 break;
+                            case "Int64":
+                                full_values.Add(value.Data<long>(0));
+                                break;
                             case "Single":
                                 full_values.Add(value.Data<float>(0));
                                 break;
@@ -78,7 +81,7 @@ namespace Tensorflow
                     }
                     else
                     {
-                        full_values.Add(value[np.arange(1)]);
+                        full_values.Add(value[np.arange(0, value.shape[0])]);
                     }
                 }
                 i += 1;

src/TensorFlowNET.Core/TensorFlowNET.Core.csproj

@@ -4,7 +4,7 @@
     <TargetFramework>netstandard2.0</TargetFramework>
     <AssemblyName>TensorFlow.NET</AssemblyName>
     <RootNamespace>Tensorflow</RootNamespace>
-    <Version>0.5.0</Version>
+    <Version>0.5.1</Version>
     <Authors>Haiping Chen</Authors>
     <Company>SciSharp STACK</Company>
     <GeneratePackageOnBuild>true</GeneratePackageOnBuild>
@@ -16,11 +16,13 @@
     <PackageTags>TensorFlow, NumSharp, SciSharp, MachineLearning, TensorFlow.NET, C#</PackageTags>
     <Description>Google's TensorFlow binding in .NET Standard.
 Docs: https://tensorflownet.readthedocs.io</Description>
-    <AssemblyVersion>0.5.0.0</AssemblyVersion>
-    <PackageReleaseNotes>Add Logistic Regression to do MNIST.
-Add a lot of APIs to build neural networks model</PackageReleaseNotes>
+    <AssemblyVersion>0.5.1.0</AssemblyVersion>
+    <PackageReleaseNotes>Changes since v0.5:
+Added Nearest Neighbor.
+Add a lot of APIs to build neural networks model.
+Bug fix.</PackageReleaseNotes>
     <LangVersion>7.2</LangVersion>
-    <FileVersion>0.5.0.0</FileVersion>
+    <FileVersion>0.5.1.0</FileVersion>
   </PropertyGroup>
 
   <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|AnyCPU'">
@@ -44,7 +46,7 @@ Add a lot of APIs to build neural networks model</PackageReleaseNotes>
 
   <ItemGroup>
     <PackageReference Include="Google.Protobuf" Version="3.7.0" />
-    <PackageReference Include="NumSharp" Version="0.8.1" />
+    <PackageReference Include="NumSharp" Version="0.8.2" />
   </ItemGroup>
 
   <ItemGroup>

src/TensorFlowNET.Core/Variables/variables.py.cs

@@ -47,11 +47,11 @@ namespace Tensorflow
         /// special tokens filters by prefix.
         /// </param>
         /// <returns>A list of `Variable` objects.</returns>
-        public static List<RefVariable> global_variables(string scope = "")
+        public static List<RefVariable> global_variables(string scope = null)
         {
             var result = ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES, scope);
 
-            return result as List<RefVariable>;
+            return result == null ? new List<RefVariable>() : result as List<RefVariable>;
         }
 
         /// <summary>
@@ -62,7 +62,10 @@ namespace Tensorflow
         /// <returns>An Op that run the initializers of all the specified variables.</returns>
         public static Operation variables_initializer(RefVariable[] var_list, string name = "init")
         {
-            return control_flow_ops.group(var_list.Select(x => x.initializer).ToArray(), name);
+            if (var_list.Length > 0)
+                return control_flow_ops.group(var_list.Select(x => x.initializer).ToArray(), name);
+            else
+                return gen_control_flow_ops.no_op(name: name);
         }
     }
 }

src/TensorFlowNET.Core/ops.py.cs

@@ -41,7 +41,7 @@ namespace Tensorflow
         /// list contains the values in the order under which they were
         /// collected.
         /// </returns>
-        public static object get_collection(string key, string scope = "")
+        public static object get_collection(string key, string scope = null)
         {
             return get_default_graph().get_collection(key, scope);
         }

test/TensorFlowNET.Examples/ImageRecognition.cs

@@ -12,7 +12,7 @@ namespace TensorFlowNET.Examples
 {
     public class ImageRecognition : Python, IExample
     {
-        public int Priority => 5;
+        public int Priority => 6;
         public bool Enabled => true;
         public string Name => "Image Recognition";
 

test/TensorFlowNET.Examples/LogisticRegression.cs

@@ -98,7 +98,7 @@ namespace TensorFlowNET.Examples
 
         public void PrepareData()
         {
-            mnist = MnistDataSet.read_data_sets("logistic_regression", one_hot: true);
+            mnist = MnistDataSet.read_data_sets("mnist", one_hot: true);
         }
 
         public void SaveModel(Session sess)

test/TensorFlowNET.Examples/NearestNeighbor.cs

@@ -0,0 +1,70 @@
+using NumSharp.Core;
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow;
+using TensorFlowNET.Examples.Utility;
+
+namespace TensorFlowNET.Examples
+{
+    /// <summary>
+    /// A nearest neighbor learning algorithm example
+    /// This example is using the MNIST database of handwritten digits
+    /// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/nearest_neighbor.py
+    /// </summary>
+    public class NearestNeighbor : Python, IExample
+    {
+        public int Priority => 5;
+        public bool Enabled => true;
+        public string Name => "Nearest Neighbor";
+        Datasets mnist;
+        NDArray Xtr, Ytr, Xte, Yte;
+
+        public bool Run()
+        {
+            // tf Graph Input
+            var xtr = tf.placeholder(tf.float32, new TensorShape(-1, 784));
+            var xte = tf.placeholder(tf.float32, new TensorShape(784));
+
+            // Nearest Neighbor calculation using L1 Distance
+            // Calculate L1 Distance
+            var distance = tf.reduce_sum(tf.abs(tf.add(xtr, tf.negative(xte))), reduction_indices: 1);
+            // Prediction: Get min distance index (Nearest neighbor)
+            var pred = tf.arg_min(distance, 0);
+
+            float accuracy = 0f;
+            // Initialize the variables (i.e. assign their default value)
+            var init = tf.global_variables_initializer();
+            with(tf.Session(), sess =>
+            {
+                // Run the initializer
+                sess.run(init);
+
+                PrepareData();
+
+                foreach(int i in range(Xte.shape[0]))
+                {
+                    // Get nearest neighbor
+                    long nn_index = sess.run(pred, new FeedItem(xtr, Xtr), new FeedItem(xte, Xte[i]));
+                    // Get nearest neighbor class label and compare it to its true label
+                    print($"Test {i} Prediction: {np.argmax(Ytr[nn_index])} True Class: {np.argmax(Yte[i] as NDArray)}");
+                    // Calculate accuracy
+                    if (np.argmax(Ytr[nn_index]) == np.argmax(Yte[i] as NDArray))
+                        accuracy += 1f/ Xte.shape[0];
+                }
+
+                print($"Accuracy: {accuracy}");
+            });
+
+            return accuracy > 0.9;
+        }
+
+        public void PrepareData()
+        {
+            mnist = MnistDataSet.read_data_sets("mnist", one_hot: true);
+            // In this example, we limit mnist data
+            (Xtr, Ytr) = mnist.train.next_batch(5000); // 5000 for training (nn candidates)
+            (Xte, Yte) = mnist.test.next_batch(200); // 200 for testing
+        }
+    }
+}

test/TensorFlowNET.Examples/Program.cs

@@ -32,11 +32,11 @@ namespace TensorFlowNET.Examples
                 {
                     if (example.Enabled)
                         if (example.Run())
-                            success.Add($"{example.Priority} {example.Name}");
+                            success.Add($"Example {example.Priority}: {example.Name}");
                         else
-                            errors.Add($"{example.Priority} {example.Name}");
+                            errors.Add($"Example {example.Priority}: {example.Name}");
                     else
-                        disabled.Add($"{example.Priority} {example.Name}");
+                        disabled.Add($"Example {example.Priority}: {example.Name}");
                 }
                 catch (Exception ex)
                 {
@@ -46,9 +46,9 @@ namespace TensorFlowNET.Examples
                 Console.WriteLine($"{DateTime.UtcNow} Completed {example.Name}", Color.White);
             }
 
-            success.ForEach(x => Console.WriteLine($"{x} example is OK!", Color.Green));
-            disabled.ForEach(x => Console.WriteLine($"{x} example is Disabled!", Color.Tan));
-            errors.ForEach(x => Console.WriteLine($"{x} example is Failed!", Color.Red));
+            success.ForEach(x => Console.WriteLine($"{x} is OK!", Color.Green));
+            disabled.ForEach(x => Console.WriteLine($"{x} is Disabled!", Color.Tan));
+            errors.ForEach(x => Console.WriteLine($"{x} is Failed!", Color.Red));
 
             Console.ReadLine();
         }

test/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj

@@ -8,7 +8,7 @@
   <ItemGroup>
     <PackageReference Include="Colorful.Console" Version="1.2.9" />
     <PackageReference Include="Newtonsoft.Json" Version="12.0.1" />
-    <PackageReference Include="NumSharp" Version="0.8.1" />
+    <PackageReference Include="NumSharp" Version="0.8.2" />
     <PackageReference Include="SharpZipLib" Version="1.1.0" />
   </ItemGroup>
 

test/TensorFlowNET.Examples/TextClassification/TextClassificationWithMovieReviews.cs

@@ -11,7 +11,7 @@ namespace TensorFlowNET.Examples
 {
     public class TextClassificationWithMovieReviews : Python, IExample
     {
-        public int Priority => 6;
+        public int Priority => 7;
         public bool Enabled => false;
         public string Name => "Movie Reviews";
 

test/TensorFlowNET.UnitTest/TensorFlowNET.UnitTest.csproj

@@ -19,8 +19,8 @@
     <PackageReference Include="Microsoft.NET.Test.Sdk" Version="16.0.1" />
     <PackageReference Include="MSTest.TestAdapter" Version="1.4.0" />
     <PackageReference Include="MSTest.TestFramework" Version="1.4.0" />
-    <PackageReference Include="NumSharp" Version="0.8.1" />
-    <PackageReference Include="TensorFlow.NET" Version="0.4.2" />
+    <PackageReference Include="NumSharp" Version="0.8.2" />
+    <PackageReference Include="TensorFlow.NET" Version="0.5.0" />
   </ItemGroup>
 
   <ItemGroup>