remove examples to examples repo.

TensorFlow.NET.sln

@@ -5,14 +5,8 @@ VisualStudioVersion = 16.0.29102.190
 MinimumVisualStudioVersion = 10.0.40219.1
 Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.UnitTest", "test\TensorFlowNET.UnitTest\TensorFlowNET.UnitTest.csproj", "{029A8CF1-CF95-4DCB-98AA-9D3D96A83B3E}"
 EndProject
-Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.Examples", "test\TensorFlowNET.Examples\TensorFlowNET.Examples.csproj", "{1FE60088-157C-4140-91AB-E96B915E4BAE}"
-EndProject
 Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.Core", "src\TensorFlowNET.Core\TensorFlowNET.Core.csproj", "{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}"
 EndProject
-Project("{6EC3EE1D-3C4E-46DD-8F32-0CC8E7565705}") = "TensorFlowNET.Examples.FSharp", "test\TensorFlowNET.Examples.FSharp\TensorFlowNET.Examples.FSharp.fsproj", "{62BC3801-F0D3-44A9-A0AC-712F40C8F961}"
-EndProject
-Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowBenchmark", "src\TensorFlowNet.Benchmarks\TensorFlowBenchmark.csproj", "{68861442-971A-4196-876E-C9330F0B3C54}"
-EndProject
 Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowHub", "src\TensorFlowHub\TensorFlowHub.csproj", "{8FD59A5A-97EB-457E-B9F1-D88B0C822C6E}"
 EndProject
 Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowText", "src\TensorFlowText\TensorFlowText.csproj", "{B598E5D5-BD2D-4191-8532-F2FBAC31AB81}"
@@ -31,22 +25,10 @@ Global
 		{029A8CF1-CF95-4DCB-98AA-9D3D96A83B3E}.Debug|Any CPU.Build.0 = Debug|Any CPU
 		{029A8CF1-CF95-4DCB-98AA-9D3D96A83B3E}.Release|Any CPU.ActiveCfg = Release|Any CPU
 		{029A8CF1-CF95-4DCB-98AA-9D3D96A83B3E}.Release|Any CPU.Build.0 = Release|Any CPU
-		{1FE60088-157C-4140-91AB-E96B915E4BAE}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
-		{1FE60088-157C-4140-91AB-E96B915E4BAE}.Debug|Any CPU.Build.0 = Debug|Any CPU
-		{1FE60088-157C-4140-91AB-E96B915E4BAE}.Release|Any CPU.ActiveCfg = Release|Any CPU
-		{1FE60088-157C-4140-91AB-E96B915E4BAE}.Release|Any CPU.Build.0 = Release|Any CPU
 		{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
 		{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Debug|Any CPU.Build.0 = Debug|Any CPU
 		{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Release|Any CPU.ActiveCfg = Release|Any CPU
 		{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Release|Any CPU.Build.0 = Release|Any CPU
-		{62BC3801-F0D3-44A9-A0AC-712F40C8F961}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
-		{62BC3801-F0D3-44A9-A0AC-712F40C8F961}.Debug|Any CPU.Build.0 = Debug|Any CPU
-		{62BC3801-F0D3-44A9-A0AC-712F40C8F961}.Release|Any CPU.ActiveCfg = Release|Any CPU
-		{62BC3801-F0D3-44A9-A0AC-712F40C8F961}.Release|Any CPU.Build.0 = Release|Any CPU
-		{68861442-971A-4196-876E-C9330F0B3C54}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
-		{68861442-971A-4196-876E-C9330F0B3C54}.Debug|Any CPU.Build.0 = Debug|Any CPU
-		{68861442-971A-4196-876E-C9330F0B3C54}.Release|Any CPU.ActiveCfg = Release|Any CPU
-		{68861442-971A-4196-876E-C9330F0B3C54}.Release|Any CPU.Build.0 = Release|Any CPU
 		{8FD59A5A-97EB-457E-B9F1-D88B0C822C6E}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
 		{8FD59A5A-97EB-457E-B9F1-D88B0C822C6E}.Debug|Any CPU.Build.0 = Debug|Any CPU
 		{8FD59A5A-97EB-457E-B9F1-D88B0C822C6E}.Release|Any CPU.ActiveCfg = Release|Any CPU

src/TensorFlowNET.Visualization/Controllers/ValuesController.cs

@@ -1,45 +0,0 @@
-using System;
-using System.Collections.Generic;
-using System.Linq;
-using System.Threading.Tasks;
-using Microsoft.AspNetCore.Mvc;
-
-namespace TensorFlowNET.Visualization.Controllers
-{
-    [Route("api/[controller]")]
-    [ApiController]
-    public class ValuesController : ControllerBase
-    {
-        // GET api/values
-        [HttpGet]
-        public ActionResult<IEnumerable<string>> Get()
-        {
-            return new string[] { "value1", "value2" };
-        }
-
-        // GET api/values/5
-        [HttpGet("{id}")]
-        public ActionResult<string> Get(int id)
-        {
-            return "value";
-        }
-
-        // POST api/values
-        [HttpPost]
-        public void Post([FromBody] string value)
-        {
-        }
-
-        // PUT api/values/5
-        [HttpPut("{id}")]
-        public void Put(int id, [FromBody] string value)
-        {
-        }
-
-        // DELETE api/values/5
-        [HttpDelete("{id}")]
-        public void Delete(int id)
-        {
-        }
-    }
-}

src/TensorFlowNET.Visualization/Program.cs

@@ -1,24 +0,0 @@
-using System;
-using System.Collections.Generic;
-using System.IO;
-using System.Linq;
-using System.Threading.Tasks;
-using Microsoft.AspNetCore;
-using Microsoft.AspNetCore.Hosting;
-using Microsoft.Extensions.Configuration;
-using Microsoft.Extensions.Logging;
-
-namespace TensorFlowNET.Visualization
-{
-    public class Program
-    {
-        public static void Main(string[] args)
-        {
-            CreateWebHostBuilder(args).Build().Run();
-        }
-
-        public static IWebHostBuilder CreateWebHostBuilder(string[] args) =>
-            WebHost.CreateDefaultBuilder(args)
-                .UseStartup<Startup>();
-    }
-}

src/TensorFlowNET.Visualization/Startup.cs

@@ -1,41 +0,0 @@
-using System;
-using System.Collections.Generic;
-using System.Linq;
-using System.Threading.Tasks;
-using Microsoft.AspNetCore.Builder;
-using Microsoft.AspNetCore.Hosting;
-using Microsoft.AspNetCore.Mvc;
-using Microsoft.Extensions.Configuration;
-using Microsoft.Extensions.DependencyInjection;
-using Microsoft.Extensions.Logging;
-using Microsoft.Extensions.Options;
-
-namespace TensorFlowNET.Visualization
-{
-    public class Startup
-    {
-        public Startup(IConfiguration configuration)
-        {
-            Configuration = configuration;
-        }
-
-        public IConfiguration Configuration { get; }
-
-        // This method gets called by the runtime. Use this method to add services to the container.
-        public void ConfigureServices(IServiceCollection services)
-        {
-            services.AddMvc().SetCompatibilityVersion(CompatibilityVersion.Version_2_2);
-        }
-
-        // This method gets called by the runtime. Use this method to configure the HTTP request pipeline.
-        public void Configure(IApplicationBuilder app, IHostingEnvironment env)
-        {
-            if (env.IsDevelopment())
-            {
-                app.UseDeveloperExceptionPage();
-            }
-
-            app.UseMvc();
-        }
-    }
-}

src/TensorFlowNET.Visualization/TensorFlowNET.Visualization.csproj

@@ -1,17 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk.Web">
-
-  <PropertyGroup>
-    <TargetFramework>netcoreapp2.1</TargetFramework>
-    <AspNetCoreHostingModel>InProcess</AspNetCoreHostingModel>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Microsoft.AspNetCore.App" />
-    <PackageReference Include="Microsoft.AspNetCore.Razor.Design" Version="2.2.0" PrivateAssets="All" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\TensorFlowNET.Core\TensorFlowNET.Core.csproj" />
-  </ItemGroup>
-
-</Project>

src/TensorFlowNET.Visualization/appsettings.Development.json

@@ -1,9 +0,0 @@
-{
-  "Logging": {
-    "LogLevel": {
-      "Default": "Debug",
-      "System": "Information",
-      "Microsoft": "Information"
-    }
-  }
-}

src/TensorFlowNET.Visualization/appsettings.json

@@ -1,8 +0,0 @@
-{
-  "Logging": {
-    "LogLevel": {
-      "Default": "Warning"
-    }
-  },
-  "AllowedHosts": "*"
-}

src/TensorFlowNet.Benchmarks/Program.cs

@@ -1,29 +0,0 @@
-using System;
-using System.Reflection;
-using BenchmarkDotNet.Configs;
-using BenchmarkDotNet.Running;
-
-namespace TensorFlowBenchmark
-{
-    class Program
-    {
-        static void Main(string[] args)
-        {
-            if (args?.Length > 0)
-            {
-                for (int i = 0; i < args.Length; i++)
-                {
-                    string name = $"TensorFlowBenchmark.{args[i]}";
-                    var type = Type.GetType(name);
-                    BenchmarkRunner.Run(type);
-                }
-            }
-            else
-            {
-                BenchmarkSwitcher.FromAssembly(Assembly.GetExecutingAssembly()).Run(args, ManualConfig.Create(DefaultConfig.Instance).With(ConfigOptions.DisableOptimizationsValidator));
-            }
-
-            Console.ReadLine();
-        }
-    }
-}

src/TensorFlowNet.Benchmarks/TensorBenchmark.cs

@@ -1,88 +0,0 @@
-using System;
-using BenchmarkDotNet.Attributes;
-using NumSharp;
-using Tensorflow;
-using static Tensorflow.Binding;
-
-namespace TensorFlowBenchmark
-{
-    [SimpleJob(launchCount: 1, warmupCount: 2, targetCount: 10)]
-    [MinColumn, MaxColumn, MeanColumn, MedianColumn]
-    public class TensorBenchmark
-    {
-        private double[] data;
-
-        [GlobalSetup]
-        public void Setup()
-        {
-            data = new double[100];
-        }
-
-        [Benchmark]
-        public void ScalarTensor()
-        {
-            var g = new Graph();
-            for (int i = 0; i < 100; i++)
-            {
-                using (var tensor = new Tensor(17.0))
-                {
-
-                }
-            }
-        }
-
-        [Benchmark]
-        public unsafe void TensorFromFixedPtr()
-        {
-            var g = new Graph();
-            for (int i = 0; i < 100; i++)
-            {
-                fixed (double* ptr = &data[0])
-                {
-                    using (var t = new Tensor((IntPtr)ptr, new long[] { data.Length }, tf.float64, 8 * data.Length))
-                    {
-                    }
-                }
-            }
-        }
-
-        [Benchmark]
-        public void TensorFromArray()
-        {
-            var g=new Graph();
-            for (int i = 0; i < 100; i++)
-            {
-                using (var tensor = new Tensor(data))
-                {
-
-                }
-            }
-        }
-
-
-        [Benchmark]
-        public void TensorFromNDArray()
-        {
-            var g = new Graph();
-            for (int i = 0; i < 1000; i++)
-            {
-                using (var tensor = new Tensor(new NDArray(data)))
-                {
-
-                }
-            }
-        }
-
-        //[Benchmark]
-        //public void Constant()
-        //{
-        //    for (int i = 0; i < 100; i++)
-        //    {
-        //        //var tensor = new Tensor(new NDArray(data));
-        //        var c = tf.constant(42.0);
-        //    }
-        //}
-
-    }
-}
-

src/TensorFlowNet.Benchmarks/TensorFlowBenchmark.csproj

@@ -1,33 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFramework>netcoreapp3.0</TargetFramework>
-    <NoWin32Manifest>true</NoWin32Manifest>
-    <AssemblyName>TensorFlowBenchmark</AssemblyName>
-    <RootNamespace>TensorFlowBenchmark</RootNamespace>
-	<LangVersion>7.3</LangVersion>
-  </PropertyGroup>
-
-  <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Release|AnyCPU'">
-    <AllowUnsafeBlocks>true</AllowUnsafeBlocks>
-  </PropertyGroup>
-
-  <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|AnyCPU'">
-    <AllowUnsafeBlocks>true</AllowUnsafeBlocks>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <None Remove="tensorflow.dll" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <PackageReference Include="BenchmarkDotNet" Version="0.11.5" />
-    <PackageReference Include="SciSharp.TensorFlow.Redist" Version="1.14.0" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\TensorFlowNET.Core\TensorFlowNET.Core.csproj" />
-  </ItemGroup>
-
-</Project>

src/TensorFlowNet.Benchmarks/TensorFlowNET.Benchmark.csproj

@@ -1,20 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFramework>netcoreapp2.2</TargetFramework>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <None Remove="tensorflow.dll" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <PackageReference Include="BenchmarkDotNet" Version="0.11.5" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\TensorFlowNET.Core\TensorFlowNET.Core.csproj" />
-  </ItemGroup>
-
-</Project>

src/TensorFlowNet.Benchmarks/Unmanaged/StructCastBenchmark.cs

@@ -1,76 +0,0 @@
-using System;
-using System.Runtime.CompilerServices;
-using System.Runtime.InteropServices;
-using BenchmarkDotNet.Attributes;
-using Google.Protobuf.WellKnownTypes;
-using NumSharp;
-using Tensorflow;
-using static Tensorflow.Binding;
-
-namespace TensorFlowBenchmark.Unmanaged
-{
-    public struct UnmanagedStruct
-    {
-        public int a;
-        public long b;
-        public UnmanagedStruct(int _)
-        {
-            a = 2;
-            b = 3;
-        }
-    }
-
-    [SimpleJob(launchCount: 1, warmupCount: 2, targetCount: 10)]
-    [MinColumn, MaxColumn, MeanColumn, MedianColumn]
-    public unsafe class StructCastBenchmark
-    {
-        private static void EnsureIsUnmanaged<T>(T _) where T : unmanaged
-        { }
-
-        static StructCastBenchmark() //if UnmanagedStruct is not unmanaged struct then this will fail to compile.
-            => EnsureIsUnmanaged(new UnmanagedStruct());
-
-        private IntPtr data;
-        private void* dataptr;
-
-        [GlobalSetup]
-        public void Setup()
-        {
-            data = Marshal.AllocHGlobal(Marshal.SizeOf<UnmanagedStruct>());
-            dataptr = data.ToPointer();
-        }
-
-        [Benchmark, MethodImpl(MethodImplOptions.NoOptimization)]
-        public void Marshal_PtrToStructure()
-        {
-            UnmanagedStruct _;
-            for (int i = 0; i < 10000; i++)
-            {
-                _ = Marshal.PtrToStructure<UnmanagedStruct>(data);
-            }
-        }
-
-        [Benchmark, MethodImpl(MethodImplOptions.NoOptimization)]
-        public void PointerCast()
-        {
-            var dptr = dataptr;
-            UnmanagedStruct _;
-            for (int i = 0; i < 10000; i++)
-            {
-                _ = *(UnmanagedStruct*) dptr;
-            }
-        }
-
-        [Benchmark, MethodImpl(MethodImplOptions.NoOptimization)]
-        public void Unsafe_Read()
-        {
-            var dptr = dataptr;
-            UnmanagedStruct _;
-            for (int i = 0; i < 10000; i++)
-            {
-                _ = Unsafe.Read<UnmanagedStruct>(dptr);
-            }
-        }
-
-    }
-}

test/TensorFlowNET.Examples.FSharp/FunctionApproximation.fs

@@ -1,104 +0,0 @@
-module FunctionApproximation
-
-//reduced example from https://github.com/tirthajyoti/Machine-Learning-with-Python/blob/master/Function%20Approximation%20by%20Neural%20Network/Function%20approximation%20by%20linear%20model%20and%20deep%20network.ipynb
-
-open NumSharp
-open Tensorflow
-open System
-
-let run()=
-    
-    let N_points = 75 // Number of points for constructing function
-    let x_min = 1.0 // Min of the range of x (feature)
-    let x_max = 15.0 // Max of the range of x (feature)
-    let noise_mean = 0.0 // Mean of the Gaussian noise adder
-    let noise_sd = 10.0 // Std.Dev of the Gaussian noise adder
-
-    let linspace points = [| for i in 0 .. (points - 1) -> x_min + (x_max - x_min)/(float)points * (float)i |]
-
-    let  func_trans(xAr:float []) = 
-        xAr
-        |>Array.map (fun (x:float) -> (20.0 * x+3.0 * System.Math.Pow(x,2.0)+0.1 * System.Math.Pow(x,3.0))*sin(x)*exp(-0.1*x))
-        
-    let X_raw = linspace N_points
-    let Y_raw = func_trans(X_raw)
-    let X_mtr = Array2D.init X_raw.Length 1 (fun i j -> X_raw.[i])
-    let X = np.array(X_mtr)
-
-    let noise_x = np.random.normal(noise_mean,noise_sd,N_points)
-    let y =  np.array(Y_raw)+noise_x
-
-    let X_train = X
-    let y_train = y
-
-    let learning_rate = 0.00001
-    let training_epochs = 35000
-    
-    let n_input = 1  // Number of features
-    let n_output = 1  // Regression output is a number only
-    let n_hidden_layer_1 = 25 // Hidden layer 1
-    let n_hidden_layer_2 = 25 // Hidden layer 2
-
-    let tf = Binding.New<tensorflow>()
-    let x = tf.placeholder(tf.float64, new TensorShape(N_points,n_input))
-    let y = tf.placeholder(tf.float64, new TensorShape(n_output))
-    
-   
-    let weights = dict[
-        "hidden_layer_1", tf.Variable(tf.random_normal([|n_input; n_hidden_layer_1|],dtype=tf.float64))
-        "hidden_layer_2", tf.Variable(tf.random_normal([|n_hidden_layer_1; n_hidden_layer_2|],dtype=tf.float64))
-        "out", tf.Variable(tf.random_normal([|n_hidden_layer_2; n_output|],dtype=tf.float64))
-    ]
-    let biases = dict[
-        "hidden_layer_1", tf.Variable(tf.random_normal([|n_hidden_layer_1|],dtype=tf.float64))
-        "hidden_layer_2", tf.Variable(tf.random_normal([|n_hidden_layer_2|],dtype=tf.float64))
-        "out", tf.Variable(tf.random_normal([|n_output|],dtype=tf.float64))
-    ]
-
- 
-    // Hidden layer with RELU activation
-
-    let layer_1 = tf.add(tf.matmul(x, weights.["hidden_layer_1"]._AsTensor()),biases.["hidden_layer_1"])
-    let layer_1 = tf.nn.relu(layer_1)
-
-    let layer_2 = tf.add(tf.matmul(layer_1, weights.["hidden_layer_2"]._AsTensor()),biases.["hidden_layer_2"])
-    let layer_2 = tf.nn.relu(layer_2)
-
-    // Output layer with linear activation
-    let ops = tf.add(tf.matmul(layer_2, weights.["out"]._AsTensor()), biases.["out"])
-    
-    // Define loss and optimizer
-    let cost = tf.reduce_mean(tf.square(tf.squeeze(ops)-y))
-
-    let gs = tf.Variable(1, trainable= false, name= "global_step")
-
-    let optimizer = tf.train.GradientDescentOptimizer(learning_rate=(float32)learning_rate).minimize(cost,global_step = gs)
-
-    let init = tf.global_variables_initializer()
-    
-    
-    Tensorflow.Binding.``tf_with``(tf.Session(), fun (sess:Session) ->
-        sess.run(init)  |> ignore  
-        // Loop over epochs
-        for epoch in [0..training_epochs] do
-            // Run optimization process (backprop) and cost function (to get loss value)
-
-            let result=sess.run([|optimizer:>ITensorOrOperation; gs._AsTensor():>ITensorOrOperation; cost:>ITensorOrOperation|], new FeedItem(x, X_train), new FeedItem(y, y_train))
-
-
-            let loss_value = (double) result.[2];
-
-            let step = (int) result.[1];
-            
-            if epoch % 1000 = 0 then
-                sprintf "Step %d loss: %f" step loss_value |> Console.WriteLine
-        let w=sess.run(weights |> Array.ofSeq |> Array.map (fun pair -> pair.Value))
-        let b = sess.run(biases |> Array.ofSeq |> Array.map (fun pair -> pair.Value))
-        let yhat=sess.run([|ops:>ITensorOrOperation|],new FeedItem(x,X_train))
-        for i in [0..(N_points-1)] do
-            sprintf "pred %f real: %f" ((double)(yhat.[0].[i].[0])) ((double)Y_raw.[i]) |> Console.WriteLine
-    )
-
-
-
-

test/TensorFlowNET.Examples.FSharp/Program.fs

@@ -1,8 +0,0 @@
-// Learn more about F# at http://fsharp.org
-
-open System
-
-[<EntryPoint>]
-let main argv =
-    FunctionApproximation.run()
-    0 // return an integer exit code

test/TensorFlowNET.Examples.FSharp/TensorFlowNET.Examples.FSharp.fsproj

@@ -1,21 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFramework>netcoreapp2.2</TargetFramework>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <Compile Include="FunctionApproximation.fs" />
-    <Compile Include="Program.fs" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <PackageReference Include="SciSharp.TensorFlow.Redist" Version="1.14.0" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\src\TensorFlowNET.Core\TensorFlowNET.Core.csproj" />
-  </ItemGroup>
-
-</Project>

test/TensorFlowNET.Examples/AudioProcessing/README.md

@@ -1 +0,0 @@
-

test/TensorFlowNET.Examples/BasicEagerApi.cs

@@ -1,73 +0,0 @@
-using System;
-using Tensorflow;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples
-{
-    /// <summary>
-    /// Basic introduction to TensorFlow's Eager API.
-    /// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/basic_eager_api.py
-    /// </summary>
-    public class BasicEagerApi : IExample
-    {
-        public bool Enabled { get; set; } = false;
-        public string Name => "Basic Eager";
-        public bool IsImportingGraph { get; set; } = false;
-
-        private Tensor a, b, c, d;
-
-        public bool Run()
-        {
-            // Set Eager API
-            Console.WriteLine("Setting Eager mode...");
-            tf.enable_eager_execution();
-
-            // Define constant tensors
-            Console.WriteLine("Define constant tensors");
-            a = tf.constant(2);
-            Console.WriteLine($"a = {a}");
-            b = tf.constant(3);
-            Console.WriteLine($"b = {b}");
-
-            // Run the operation without the need for tf.Session
-            Console.WriteLine("Running operations, without tf.Session");
-            c = a + b;
-            Console.WriteLine($"a + b = {c}");
-            d = a * b;
-            Console.WriteLine($"a * b = {d}");
-
-            // Full compatibility with Numpy
-
-            return true;
-        }
-
-        public void PrepareData()
-        {
-        }
-
-        public Graph ImportGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public Graph BuildGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Predict(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Train(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Test(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-    }
-}

test/TensorFlowNET.Examples/BasicModels/KMeansClustering.cs

@@ -1,185 +0,0 @@
-/*****************************************************************************
-   Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
-******************************************************************************/
-
-using NumSharp;
-using System;
-using System.Diagnostics;
-using Tensorflow;
-using Tensorflow.Hub;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples
-{
-    /// <summary>
-    /// Implement K-Means algorithm with TensorFlow.NET, and apply it to classify
-    /// handwritten digit images.
-    /// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/kmeans.py
-    /// </summary>
-    public class KMeansClustering : IExample
-    {
-        public bool Enabled { get; set; } = false;
-        public string Name => "K-means Clustering";
-        public bool IsImportingGraph { get; set; } = true;
-
-        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<MnistDataSet> mnist;
-        NDArray full_data_x;
-        int num_steps = 20; // Total steps to train
-        int k = 25; // The number of clusters
-        int num_classes = 10; // The 10 digits
-        int num_features = 784; // Each image is 28x28 pixels
-
-        float accuray_test = 0f;
-
-        public bool Run()
-        {
-            PrepareData();
-            var graph = ImportGraph();
-            using (var sess = tf.Session(graph))
-            {
-                Train(sess);
-            }
-
-            return accuray_test > 0.70;
-        }
-
-        public void PrepareData()
-        {
-            var loader = new MnistModelLoader();
-
-            var setting = new ModelLoadSetting
-            {
-                TrainDir = ".resources/mnist",
-                OneHot = true,
-                TrainSize = train_size,
-                ValidationSize = validation_size,
-                TestSize = test_size,
-                ShowProgressInConsole = true
-            };
-
-            mnist = loader.LoadAsync(setting).Result;
-
-            full_data_x = mnist.Train.Data;
-
-            // download graph meta data
-            string url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/graph/kmeans.meta";
-            loader.DownloadAsync(url, ".resources/graph", "kmeans.meta").Wait();
-        }
-
-        public Graph ImportGraph()
-        {
-            var graph = tf.Graph().as_default();
-
-            tf.train.import_meta_graph(".resources/graph/kmeans.meta");
-
-            return graph;
-        }
-
-        public Graph BuildGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Train(Session sess)
-        {
-            var graph = tf.Graph();
-
-            // Input images
-            Tensor X = graph.get_operation_by_name("Placeholder"); // tf.placeholder(tf.float32, shape: new TensorShape(-1, num_features));
-            //  Labels (for assigning a label to a centroid and testing)
-            Tensor Y = graph.get_operation_by_name("Placeholder_1"); // tf.placeholder(tf.float32, shape: new TensorShape(-1, num_classes));
-
-            // K-Means Parameters
-            //var kmeans = new KMeans(X, k, distance_metric: KMeans.COSINE_DISTANCE, use_mini_batch: true);
-
-            // Build KMeans graph
-            //var training_graph = kmeans.training_graph();
-
-            var init_vars = tf.global_variables_initializer();
-            Tensor init_op = graph.get_operation_by_name("cond/Merge");
-            var train_op = graph.get_operation_by_name("group_deps");
-            Tensor avg_distance = graph.get_operation_by_name("Mean");
-            Tensor cluster_idx = graph.get_operation_by_name("Squeeze_1");
-            NDArray result = null;
-
-            sess.run(init_vars, new FeedItem(X, full_data_x));
-            sess.run(init_op, new FeedItem(X, full_data_x));
-
-            // Training
-            var sw = new Stopwatch();
-
-            foreach (var i in range(1, num_steps + 1))
-            {
-                sw.Restart();
-                result = sess.run(new ITensorOrOperation[] { train_op, avg_distance, cluster_idx }, new FeedItem(X, full_data_x));
-                sw.Stop();
-
-                if (i % 4 == 0 || i == 1)
-                    print($"Step {i}, Avg Distance: {result[1]} Elapse: {sw.ElapsedMilliseconds}ms");
-            }
-
-            var idx = result[2].Data<int>();
-
-            // Assign a label to each centroid
-            // Count total number of labels per centroid, using the label of each training
-            // sample to their closest centroid (given by 'idx')
-            var counts = np.zeros((k, num_classes), np.float32);
-
-            sw.Start();
-            foreach (var i in range(idx.Count))
-            {
-                var x = mnist.Train.Labels[i];
-                counts[idx[i]] += x;
-            }
-
-            sw.Stop();
-            print($"Assign a label to each centroid took {sw.ElapsedMilliseconds}ms");
-
-            // Assign the most frequent label to the centroid
-            var labels_map_array = np.argmax(counts, 1);
-            var labels_map = tf.convert_to_tensor(labels_map_array);
-
-            // Evaluation ops
-            // Lookup: centroid_id -> label
-            var cluster_label = tf.nn.embedding_lookup(labels_map, cluster_idx);
-
-            // Compute accuracy
-            var correct_prediction = tf.equal(cluster_label, tf.cast(tf.argmax(Y, 1), tf.int32));
-            var cast = tf.cast(correct_prediction, tf.float32);
-            var accuracy_op = tf.reduce_mean(cast);
-
-            // Test Model
-            var (test_x, test_y) = (mnist.Test.Data, mnist.Test.Labels);
-            result = sess.run(accuracy_op, new FeedItem(X, test_x), new FeedItem(Y, test_y));
-            accuray_test = result;
-            print($"Test Accuracy: {accuray_test}");
-        }
-
-        public void Predict(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Test(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-    }
-}

test/TensorFlowNET.Examples/BasicModels/LinearRegression.cs

@@ -1,145 +0,0 @@
-/*****************************************************************************
-   Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
-******************************************************************************/
-
-using NumSharp;
-using System;
-using Tensorflow;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples
-{
-    /// <summary>
-    /// A linear regression learning algorithm example using TensorFlow library.
-    /// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/linear_regression.py
-    /// </summary>
-    public class LinearRegression : IExample
-    {
-        public bool Enabled { get; set; } = true;
-        public string Name => "Linear Regression";
-        public bool IsImportingGraph { get; set; } = false;
-
-        public int training_epochs = 1000;
-
-        // Parameters
-        float learning_rate = 0.01f;
-        int display_step = 50;
-
-        NumPyRandom rng = np.random;
-        NDArray train_X, train_Y;
-        int n_samples;
-
-        public bool Run()
-        {
-            // Training Data
-            PrepareData();
-
-            // tf Graph Input
-            var X = tf.placeholder(tf.float32);
-            var Y = tf.placeholder(tf.float32);
-
-            // Set model weights 
-            // We can set a fixed init value in order to debug
-            // var rnd1 = rng.randn<float>();
-            // var rnd2 = rng.randn<float>();
-            var W = tf.Variable(-0.06f, name: "weight");
-            var b = tf.Variable(-0.73f, name: "bias");
-
-            // Construct a linear model
-            var pred = tf.add(tf.multiply(X, W), b);
-
-            // Mean squared error
-            var cost = tf.reduce_sum(tf.pow(pred - Y, 2.0f)) / (2.0f * n_samples);
-
-            // Gradient descent
-            // Note, minimize() knows to modify W and b because Variable objects are trainable=True by default
-            var optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost);
-
-            // Initialize the variables (i.e. assign their default value)
-            var init = tf.global_variables_initializer();
-
-            // Start training
-            using (var sess = tf.Session())
-            {
-                // Run the initializer
-                sess.run(init);
-
-                // Fit all training data
-                for (int epoch = 0; epoch < training_epochs; epoch++)
-                {
-                    foreach (var (x, y) in zip<float>(train_X, train_Y))
-                        sess.run(optimizer, (X, x), (Y, y));
-
-                    // Display logs per epoch step
-                    if ((epoch + 1) % display_step == 0)
-                    {
-                        var c = sess.run(cost, (X, train_X), (Y, train_Y));
-                        Console.WriteLine($"Epoch: {epoch + 1} cost={c} " + $"W={sess.run(W)} b={sess.run(b)}");
-                    }
-                }
-
-                Console.WriteLine("Optimization Finished!");
-                var training_cost = sess.run(cost, (X, train_X), (Y, train_Y));
-                Console.WriteLine($"Training cost={training_cost} W={sess.run(W)} b={sess.run(b)}");
-
-                // Testing example
-                var test_X = np.array(6.83f, 4.668f, 8.9f, 7.91f, 5.7f, 8.7f, 3.1f, 2.1f);
-                var test_Y = np.array(1.84f, 2.273f, 3.2f, 2.831f, 2.92f, 3.24f, 1.35f, 1.03f);
-                Console.WriteLine("Testing... (Mean square loss Comparison)");
-                var testing_cost = sess.run(tf.reduce_sum(tf.pow(pred - Y, 2.0f)) / (2.0f * test_X.shape[0]),
-                    (X, test_X), (Y, test_Y));
-                Console.WriteLine($"Testing cost={testing_cost}");
-                var diff = Math.Abs((float)training_cost - (float)testing_cost);
-                Console.WriteLine($"Absolute mean square loss difference: {diff}");
-
-                return diff < 0.01;
-            }
-        }
-
-        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];
-        }
-
-        public Graph ImportGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public Graph BuildGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Train(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Predict(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Test(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-    }
-}

test/TensorFlowNET.Examples/BasicModels/LogisticRegression.cs

@@ -1,190 +0,0 @@
-/*****************************************************************************
-   Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
-******************************************************************************/
-
-using NumSharp;
-using System;
-using System.Diagnostics;
-using System.IO;
-using Tensorflow;
-using Tensorflow.Hub;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples
-{
-    /// <summary>
-    /// A logistic regression learning algorithm example using TensorFlow library.
-    /// This example is using the MNIST database of handwritten digits
-    /// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/logistic_regression.py
-    /// </summary>
-    public class LogisticRegression : IExample
-    {
-        public bool Enabled { get; set; } = true;
-        public string Name => "Logistic Regression";
-        public bool IsImportingGraph { get; set; } = false;
-
-
-        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;
-        private int display_step = 1;
-
-        Datasets<MnistDataSet> mnist;
-
-        public bool Run()
-        {
-            PrepareData();
-
-            // tf Graph Input
-            var x = tf.placeholder(tf.float32, new TensorShape(-1, 784)); // mnist data image of shape 28*28=784
-            var y = tf.placeholder(tf.float32, new TensorShape(-1, 10)); // 0-9 digits recognition => 10 classes
-
-            // Set model weights
-            var W = tf.Variable(tf.zeros(new Shape(784, 10)));
-            var b = tf.Variable(tf.zeros(new Shape(10)));
-
-            // Construct model
-            var pred = tf.nn.softmax(tf.matmul(x, W) + b); // Softmax
-
-            // Minimize error using cross entropy
-            var cost = tf.reduce_mean(-tf.reduce_sum(y * tf.log(pred), reduction_indices: 1));
-
-            // Gradient Descent
-            var optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost);
-
-            // Initialize the variables (i.e. assign their default value)
-            var init = tf.global_variables_initializer();
-
-            var sw = new Stopwatch();
-
-            using (var sess = tf.Session())
-            {
-                // Run the initializer
-                sess.run(init);
-
-                // Training cycle
-                foreach (var epoch in range(training_epochs))
-                {
-                    sw.Start();
-
-                    var avg_cost = 0.0f;
-                    var total_batch = mnist.Train.NumOfExamples / batch_size;
-                    // Loop over all batches
-                    foreach (var i in range(total_batch))
-                    {
-                        var (batch_xs, batch_ys) = mnist.Train.GetNextBatch(batch_size);
-                        // Run optimization op (backprop) and cost op (to get loss value)
-                        (_, float c) = sess.run((optimizer, cost), 
-                            (x, batch_xs), 
-                            (y, batch_ys));
-
-                        // Compute average loss
-                        avg_cost += c / total_batch;
-                    }
-
-                    sw.Stop();
-
-                    // Display logs per epoch step
-                    if ((epoch + 1) % display_step == 0)
-                        print($"Epoch: {(epoch + 1):D4} Cost: {avg_cost:G9} Elapse: {sw.ElapsedMilliseconds}ms");
-
-                    sw.Reset();
-                }
-
-                print("Optimization Finished!");
-                // SaveModel(sess);
-
-                // Test model
-                var correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1));
-                // Calculate accuracy
-                var accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32));
-                float acc = accuracy.eval(sess, (x, mnist.Test.Data), (y, mnist.Test.Labels));
-                print($"Accuracy: {acc:F4}");
-
-                return acc > 0.9;
-            }
-        }
-
-        public void PrepareData()
-        {
-            mnist = MnistModelLoader.LoadAsync(".resources/mnist", oneHot: true, trainSize: train_size, validationSize: validation_size, testSize: test_size, showProgressInConsole: true).Result;
-        }
-
-        public void SaveModel(Session sess)
-        {
-            var saver = tf.train.Saver();
-            var save_path = saver.save(sess, ".resources/logistic_regression/model.ckpt");
-            tf.train.write_graph(sess.graph, ".resources/logistic_regression", "model.pbtxt", as_text: true);
-
-            FreezeGraph.freeze_graph(input_graph: ".resources/logistic_regression/model.pbtxt",
-                              input_saver: "",
-                              input_binary: false,
-                              input_checkpoint: ".resources/logistic_regression/model.ckpt",
-                              output_node_names: "Softmax",
-                              restore_op_name: "save/restore_all",
-                              filename_tensor_name: "save/Const:0",
-                              output_graph: ".resources/logistic_regression/model.pb",
-                              clear_devices: true,
-                              initializer_nodes: "");
-        }
-
-        public void Predict(Session sess)
-        {
-            var graph = new Graph().as_default();
-            graph.Import(Path.Join(".resources/logistic_regression", "model.pb"));
-
-            // restoring the model
-            // var saver = tf.train.import_meta_graph("logistic_regression/tensorflowModel.ckpt.meta");
-            // saver.restore(sess, tf.train.latest_checkpoint('logistic_regression'));
-            var pred = graph.OperationByName("Softmax");
-            var output = pred.outputs[0];
-            var x = graph.OperationByName("Placeholder");
-            var input = x.outputs[0];
-
-            // predict
-            var (batch_xs, batch_ys) = mnist.Train.GetNextBatch(10);
-            var results = sess.run(output, new FeedItem(input, batch_xs[np.arange(1)]));
-
-            if (results[0].argmax() == (batch_ys[0] as NDArray).argmax())
-                print("predicted OK!");
-            else
-                throw new ValueError("predict error, should be 90% accuracy");
-        }
-
-        public Graph ImportGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public Graph BuildGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Train(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Test(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-    }
-}

test/TensorFlowNET.Examples/BasicModels/NaiveBayesClassifier.cs

@@ -1,221 +0,0 @@
-/*****************************************************************************
-   Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
-******************************************************************************/
-
-using System;
-using System.Collections.Generic;
-using Tensorflow;
-using NumSharp;
-using static Tensorflow.Binding;
-using System.IO;
-using TensorFlowNET.Examples.Utility;
-
-namespace TensorFlowNET.Examples
-{
-    /// <summary>
-    /// https://github.com/nicolov/naive_bayes_tensorflow
-    /// </summary>
-    public class NaiveBayesClassifier : IExample
-    {
-        public bool Enabled { get; set; } = true;
-        public string Name => "Naive Bayes Classifier";
-        public bool IsImportingGraph { get; set; } = false;
-
-        public NDArray X, y;
-        public Normal dist { get; set; }
-        public bool Run()
-        {
-            PrepareData();
-
-            fit(X, y);
-
-            // Create a regular grid and classify each point 
-            float x_min = X.amin(0).Data<float>()[0] - 0.5f;
-            float y_min = X.amin(0).Data<float>()[1] - 0.5f;
-            float x_max = X.amax(0).Data<float>()[1] + 0.5f;
-            float y_max = X.amax(0).Data<float>()[1] + 0.5f;
-
-            var (xx, yy) = np.meshgrid(np.linspace(x_min, x_max, 30), np.linspace(y_min, y_max, 30));
-            using (var sess = tf.Session())
-            {
-                //var samples = np.vstack<float>(xx.ravel(), yy.ravel());
-                //samples = np.transpose(samples);
-                var array = np.Load<double[,]>(Path.Join("nb", "nb_example.npy"));
-                var samples = np.array(array).astype(np.float32);
-                var Z = sess.run(predict(samples));
-            }
-
-            return true;
-        }
-
-        public void fit(NDArray X, NDArray y)
-        {
-            var unique_y = np.unique(y);
-
-            var dic = new Dictionary<int, List<List<float>>>();
-            // Init uy in dic
-            foreach (int uy in unique_y.Data<int>())
-            {
-                dic.Add(uy, new List<List<float>>());
-            }
-            // Separate training points by class 
-            // Shape : nb_classes * nb_samples * nb_features
-            int maxCount = 0;
-            for (int i = 0; i < y.size; i++)
-            {
-                var curClass = y[i];
-                var l = dic[curClass];
-                var pair = new List<float>();
-                pair.Add(X[i,0]);
-                pair.Add(X[i, 1]);
-                l.Add(pair);
-                if (l.Count > maxCount)
-                {
-                    maxCount = l.Count;
-                }
-                dic[curClass] = l;
-            }
-            float[,,] points = new float[dic.Count, maxCount, X.shape[1]];
-            foreach (KeyValuePair<int, List<List<float>>> kv in dic)
-            {
-                int j = (int) kv.Key;
-                for (int i = 0; i < maxCount; i++)
-                {
-                    for (int k = 0; k < X.shape[1]; k++)
-                    {
-                        points[j, i, k] = kv.Value[i][k];
-                    }
-                }
-
-            }
-            var points_by_class = np.array(points);
-            // estimate mean and variance for each class / feature
-            // shape : nb_classes * nb_features
-            var cons = tf.constant(points_by_class);
-            var tup = tf.nn.moments(cons, new int[]{1});
-            var mean = tup.Item1;
-            var variance = tup.Item2;
-
-            // Create a 3x2 univariate normal distribution with the 
-            // Known mean and variance           
-            var dist = tf.distributions.Normal(mean, tf.sqrt(variance));
-            this.dist = dist;
-        }
-
-        public Tensor predict(NDArray X)
-        {
-            if (dist == null)
-            {
-                throw new ArgumentNullException("cant not find the model (normal distribution)!");
-            }
-            int nb_classes = (int) dist.scale().shape[0];
-            int nb_features = (int)dist.scale().shape[1];
-
-            // Conditional probabilities log P(x|c) with shape
-            // (nb_samples, nb_classes)
-            var t1= ops.convert_to_tensor(X, TF_DataType.TF_FLOAT);
-            var t2 = ops.convert_to_tensor(new int[] { 1, nb_classes });
-            Tensor tile = tf.tile(t1, t2);
-            var t3 = ops.convert_to_tensor(new int[] { -1, nb_classes, nb_features });
-            Tensor r = tf.reshape(tile, t3);
-            var cond_probs = tf.reduce_sum(dist.log_prob(r), 2);
-            // uniform priors
-            float[] tem = new float[nb_classes];
-            for (int i = 0; i < tem.Length; i++)
-            {
-                tem[i] = 1.0f / nb_classes;
-            }
-            var priors = np.log(np.array<float>(tem));
-
-            // posterior log probability, log P(c) + log P(x|c)
-            var joint_likelihood = tf.add(ops.convert_to_tensor(priors, TF_DataType.TF_FLOAT), cond_probs);
-            // normalize to get (log)-probabilities
-
-            var norm_factor = tf.reduce_logsumexp(joint_likelihood, new int[] { 1 }, keepdims: true);
-            var log_prob = joint_likelihood - norm_factor;
-            // exp to get the actual probabilities
-            return tf.exp(log_prob);
-        }
-
-        public void PrepareData()
-        {
-            #region Training data
-            X = np.array(new float[,] {
-                {5.1f, 3.5f}, {4.9f, 3.0f}, {4.7f, 3.2f}, {4.6f, 3.1f}, {5.0f, 3.6f}, {5.4f, 3.9f},
-                {4.6f, 3.4f}, {5.0f, 3.4f}, {4.4f, 2.9f}, {4.9f, 3.1f}, {5.4f, 3.7f}, {4.8f, 3.4f},
-                {4.8f, 3.0f}, {4.3f, 3.0f}, {5.8f, 4.0f}, {5.7f, 4.4f}, {5.4f, 3.9f}, {5.1f, 3.5f},
-                {5.7f, 3.8f}, {5.1f, 3.8f}, {5.4f, 3.4f}, {5.1f, 3.7f}, {5.1f, 3.3f}, {4.8f, 3.4f},
-                {5.0f, 3.0f}, {5.0f, 3.4f}, {5.2f, 3.5f}, {5.2f, 3.4f}, {4.7f, 3.2f}, {4.8f, 3.1f},
-                {5.4f, 3.4f}, {5.2f, 4.1f}, {5.5f, 4.2f}, {4.9f, 3.1f}, {5.0f, 3.2f}, {5.5f, 3.5f},
-                {4.9f, 3.6f}, {4.4f, 3.0f}, {5.1f, 3.4f}, {5.0f, 3.5f}, {4.5f, 2.3f}, {4.4f, 3.2f},
-                {5.0f, 3.5f}, {5.1f, 3.8f}, {4.8f, 3.0f}, {5.1f, 3.8f}, {4.6f, 3.2f}, {5.3f, 3.7f},
-                {5.0f, 3.3f}, {7.0f, 3.2f}, {6.4f, 3.2f}, {6.9f, 3.1f}, {5.5f, 2.3f}, {6.5f, 2.8f},
-                {5.7f, 2.8f}, {6.3f, 3.3f}, {4.9f, 2.4f}, {6.6f, 2.9f}, {5.2f, 2.7f}, {5.0f, 2.0f},
-                {5.9f, 3.0f}, {6.0f, 2.2f}, {6.1f, 2.9f}, {5.6f, 2.9f}, {6.7f, 3.1f}, {5.6f, 3.0f},
-                {5.8f, 2.7f}, {6.2f, 2.2f}, {5.6f, 2.5f}, {5.9f, 3.0f}, {6.1f, 2.8f}, {6.3f, 2.5f},
-                {6.1f, 2.8f}, {6.4f, 2.9f}, {6.6f, 3.0f}, {6.8f, 2.8f}, {6.7f, 3.0f}, {6.0f, 2.9f},
-                {5.7f, 2.6f}, {5.5f, 2.4f}, {5.5f, 2.4f}, {5.8f, 2.7f}, {6.0f, 2.7f}, {5.4f, 3.0f},
-                {6.0f, 3.4f}, {6.7f, 3.1f}, {6.3f, 2.3f}, {5.6f, 3.0f}, {5.5f, 2.5f}, {5.5f, 2.6f},
-                {6.1f, 3.0f}, {5.8f, 2.6f}, {5.0f, 2.3f}, {5.6f, 2.7f}, {5.7f, 3.0f}, {5.7f, 2.9f},
-                {6.2f, 2.9f}, {5.1f, 2.5f}, {5.7f, 2.8f}, {6.3f, 3.3f}, {5.8f, 2.7f}, {7.1f, 3.0f},
-                {6.3f, 2.9f}, {6.5f, 3.0f}, {7.6f, 3.0f}, {4.9f, 2.5f}, {7.3f, 2.9f}, {6.7f, 2.5f},
-                {7.2f, 3.6f}, {6.5f, 3.2f}, {6.4f, 2.7f}, {6.8f, 3.0f}, {5.7f, 2.5f}, {5.8f, 2.8f},
-                {6.4f, 3.2f}, {6.5f, 3.0f}, {7.7f, 3.8f}, {7.7f, 2.6f}, {6.0f, 2.2f}, {6.9f, 3.2f},
-                {5.6f, 2.8f}, {7.7f, 2.8f}, {6.3f, 2.7f}, {6.7f, 3.3f}, {7.2f, 3.2f}, {6.2f, 2.8f},
-                {6.1f, 3.0f}, {6.4f, 2.8f}, {7.2f, 3.0f}, {7.4f, 2.8f}, {7.9f, 3.8f}, {6.4f, 2.8f},
-                {6.3f, 2.8f}, {6.1f, 2.6f}, {7.7f, 3.0f}, {6.3f, 3.4f}, {6.4f, 3.1f}, {6.0f, 3.0f},
-                {6.9f, 3.1f}, {6.7f, 3.1f}, {6.9f, 3.1f}, {5.8f, 2.7f}, {6.8f, 3.2f}, {6.7f, 3.3f},
-                {6.7f, 3.0f}, {6.3f, 2.5f}, {6.5f, 3.0f}, {6.2f, 3.4f}, {5.9f, 3.0f}, {5.8f, 3.0f}});
-
-            y = np.array(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
-                        0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
-                        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
-                        1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-                        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
-                        2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2);
-
-
-            string url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/data/nb_example.npy";
-            Web.Download(url, "nb", "nb_example.npy");
-            #endregion
-        }
-
-        public Graph ImportGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public Graph BuildGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Train(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Predict(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Test(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-    }
-}

test/TensorFlowNET.Examples/BasicModels/NearestNeighbor.cs

@@ -1,118 +0,0 @@
-/*****************************************************************************
-   Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
-******************************************************************************/
-
-using NumSharp;
-using System;
-using Tensorflow;
-using Tensorflow.Hub;
-using static Tensorflow.Binding;
-
-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 : IExample
-    {
-        public bool Enabled { get; set; } = true;
-        public string Name => "Nearest Neighbor";
-        Datasets<MnistDataSet> mnist;
-        NDArray Xtr, Ytr, Xte, Yte;
-        public int? TrainSize = null;
-        public int ValidationSize = 5000;
-        public int? TestSize = null;
-        public bool IsImportingGraph { get; set; } = false;
-
-
-        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();
-            using (var sess = tf.Session())
-            {
-                // Run the initializer
-                sess.run(init);
-
-                PrepareData();
-
-                foreach(int i in range(Xte.shape[0]))
-                {
-                    // Get nearest neighbor
-                    long nn_index = sess.run(pred, (xtr, Xtr), (xte, Xte[i]));
-                    // Get nearest neighbor class label and compare it to its true label
-                    int index = (int)nn_index;
-
-                    if (i % 10 == 0 || i == 0)
-                        print($"Test {i} Prediction: {np.argmax(Ytr[index])} True Class: {np.argmax(Yte[i])}");
-
-                    // Calculate accuracy
-                    if (np.argmax(Ytr[index]) == np.argmax(Yte[i]))
-                        accuracy += 1f/ Xte.shape[0];
-                }
-
-                print($"Accuracy: {accuracy}");
-            }
-
-            return accuracy > 0.8;
-        }
-
-        public void PrepareData()
-        {
-            mnist = MnistModelLoader.LoadAsync(".resources/mnist", oneHot: true, trainSize: TrainSize, validationSize: ValidationSize, testSize: TestSize, showProgressInConsole: true).Result;
-            // In this example, we limit mnist data
-            (Xtr, Ytr) = mnist.Train.GetNextBatch(TrainSize == null ? 5000 : TrainSize.Value / 100); // 5000 for training (nn candidates)
-            (Xte, Yte) = mnist.Test.GetNextBatch(TestSize == null ? 200 : TestSize.Value / 100); // 200 for testing
-        }
-
-        public Graph ImportGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public Graph BuildGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Train(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Predict(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Test(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-    }
-}

test/TensorFlowNET.Examples/BasicOperations.cs

@@ -1,187 +0,0 @@
-using NumSharp;
-using System;
-using Tensorflow;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples
-{
-    /// <summary>
-    /// Basic Operations example using TensorFlow library.
-    /// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/basic_operations.py
-    /// </summary>
-    public class BasicOperations : IExample
-    {
-        public bool Enabled { get; set; } = true;
-        public string Name => "Basic Operations";
-        public bool IsImportingGraph { get; set; } = false;
-
-        private Session sess;
-
-        public bool Run()
-        {
-            // Basic constant operations
-            // The value returned by the constructor represents the output
-            // of the Constant op.
-            var a = tf.constant(2);
-            var b = tf.constant(3);
-            
-            // Launch the default graph.
-            using (sess = tf.Session())
-            {
-                Console.WriteLine("a=2, b=3");
-                Console.WriteLine($"Addition with constants: {sess.run(a + b)}");
-                Console.WriteLine($"Multiplication with constants: {sess.run(a * b)}");
-            }
-
-            // Basic Operations with variable as graph input
-            // The value returned by the constructor represents the output
-            // of the Variable op. (define as input when running session)
-            // tf Graph input
-            a = tf.placeholder(tf.int16);
-            b = tf.placeholder(tf.int16);
-
-            // Define some operations
-            var add = tf.add(a, b);
-            var mul = tf.multiply(a, b);
-
-            // Launch the default graph.
-            using(sess = tf.Session())
-            {
-                var feed_dict = new FeedItem[]
-                {
-                    new FeedItem(a, (short)2),
-                    new FeedItem(b, (short)3)
-                };
-                // Run every operation with variable input
-                Console.WriteLine($"Addition with variables: {sess.run(add, feed_dict)}");
-                Console.WriteLine($"Multiplication with variables: {sess.run(mul, feed_dict)}");
-            }
-
-            // ----------------
-            // More in details:
-            // Matrix Multiplication from TensorFlow official tutorial
-
-            // Create a Constant op that produces a 1x2 matrix.  The op is
-            // added as a node to the default graph.
-            //
-            // The value returned by the constructor represents the output
-            // of the Constant op.
-            var nd1 = np.array(3, 3).reshape(1, 2);
-            var matrix1 = tf.constant(nd1);
-
-            // Create another Constant that produces a 2x1 matrix.
-            var nd2 = np.array(2, 2).reshape(2, 1);
-            var matrix2 = tf.constant(nd2);
-
-            // Create a Matmul op that takes 'matrix1' and 'matrix2' as inputs.
-            // The returned value, 'product', represents the result of the matrix
-            // multiplication.
-            var product = tf.matmul(matrix1, matrix2);
-
-            // To run the matmul op we call the session 'run()' method, passing 'product'
-            // which represents the output of the matmul op.  This indicates to the call
-            // that we want to get the output of the matmul op back.
-            //
-            // All inputs needed by the op are run automatically by the session.  They
-            // typically are run in parallel.
-            //
-            // The call 'run(product)' thus causes the execution of threes ops in the
-            // graph: the two constants and matmul.
-            //
-            // The output of the op is returned in 'result' as a numpy `ndarray` object.
-            using (sess = tf.Session())
-            {
-                var result = sess.run(product);
-                Console.WriteLine(result.ToString()); // ==> [[ 12.]]
-            };
-
-            // `BatchMatMul` is actually embedded into the `MatMul` operation on the tf.dll side. Every time we ask
-            // for a multiplication between matrices with rank > 2, the first rank - 2 dimensions are checked to be consistent
-            // across the two matrices and a common matrix multiplication is done on the residual 2 dimensions.
-            //
-            // np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9]).reshape(3, 3, 3)
-            // array([[[1, 2, 3],
-            //     [4, 5, 6],
-            //     [7, 8, 9]],
-            //
-            //     [[1, 2, 3],
-            //     [4, 5, 6],
-            //     [7, 8, 9]],
-            //
-            //     [[1, 2, 3],
-            //     [4, 5, 6],
-            //     [7, 8, 9]]])
-            var firstTensor = tf.convert_to_tensor(
-                np.reshape(
-                    np.array<float>(1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9),
-                    3, 3, 3));
-            //
-            // np.array([0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0]).reshape(3,3,2)
-            // array([[[0, 1],
-            //     [0, 1],
-            //     [0, 1]],
-            //
-            //     [[0, 1],
-            //     [0, 0],
-            //     [1, 0]],
-            //
-            //     [[1, 0],
-            //     [1, 0],
-            //     [1, 0]]])
-            var secondTensor = tf.convert_to_tensor(
-                np.reshape(
-                    np.array<float>(0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0),
-                    3, 3, 2));
-            var batchMul = tf.batch_matmul(firstTensor, secondTensor);
-            var checkTensor = np.array<float>(0, 6, 0, 15, 0, 24, 3, 1, 6, 4, 9, 7, 6, 0, 15, 0, 24, 0);
-            using (var sess = tf.Session())
-            {
-                var result = sess.run(batchMul);
-                Console.WriteLine(result.ToString());
-                //
-                // ==> array([[[0, 6],
-                //         [0, 15],
-                //         [0, 24]],
-                //
-                //         [[ 3,  1],
-                //         [ 6,  4],
-                //         [ 9,  7]],
-                //
-                //         [[ 6,  0],
-                //         [15,  0],
-                //         [24,  0]]])
-                return np.reshape(result, 18)
-                    .array_equal(checkTensor);
-            }
-        }
-
-        public void PrepareData()
-        {
-        }
-
-        public Graph ImportGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public Graph BuildGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Train(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Predict(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Test(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-    }
-}

test/TensorFlowNET.Examples/HelloWorld.cs

@@ -1,66 +0,0 @@
-using System;
-using Tensorflow;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples
-{
-    /// <summary>
-    /// Simple hello world using TensorFlow
-    /// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/1_Introduction/helloworld.py
-    /// </summary>
-    public class HelloWorld : IExample
-    {
-        public bool Enabled { get; set; } = true;
-        public string Name => "Hello World";
-        public bool IsImportingGraph { get; set; } = false;
-
-        public bool Run()
-        {
-            /* Create a Constant op
-               The op is added as a node to the default graph.
-            
-               The value returned by the constructor represents the output
-               of the Constant op. */
-            var str = "Hello, TensorFlow.NET!";
-            var hello = tf.constant(str);
-
-            // Start tf session
-            using (var sess = tf.Session())
-            {
-                // Run the op
-                var result = sess.run(hello);
-                Console.WriteLine(result.ToString());
-                return result.ToString().Equals(str);
-            }
-        }
-
-        public void PrepareData()
-        {
-        }
-
-        public Graph ImportGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public Graph BuildGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Train(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Predict(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Test(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-    }
-}

test/TensorFlowNET.Examples/IExample.cs

@@ -1,59 +0,0 @@
-/*****************************************************************************
-   Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
-******************************************************************************/
-
-using Tensorflow;
-
-namespace TensorFlowNET.Examples
-{
-    /// <summary>
-    /// Interface of Example project
-    /// All example should implement IExample so the entry program will find it.
-    /// </summary>
-    public interface IExample
-    {
-        /// <summary>
-        /// True to run example
-        /// </summary>
-        bool Enabled { get; set; }
-
-        /// <summary>
-        /// Set true to import the computation graph instead of building it.
-        /// </summary>
-        bool IsImportingGraph { get; set; }
-
-        string Name { get; }
-
-        bool Run();
-
-        /// <summary>
-        /// Build dataflow graph, train and predict
-        /// </summary>
-        /// <returns></returns>
-        void Train(Session sess);
-        void Test(Session sess);
-
-        void Predict(Session sess);
-
-        Graph ImportGraph();
-
-        Graph BuildGraph();
-
-        /// <summary>
-        /// Prepare dataset
-        /// </summary>
-        void PrepareData();
-    }
-}

test/TensorFlowNET.Examples/ImageProcessing/CIFAR10-CNN.cs

@@ -1,73 +0,0 @@
-/*****************************************************************************
-   Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
-******************************************************************************/
-
-using System;
-using System.Collections.Generic;
-using System.Text;
-using Tensorflow;
-using TensorFlowDatasets;
-
-namespace TensorFlowNET.Examples
-{
-    /// <summary>
-    /// https://www.tensorflow.org/tutorials/images/deep_cnn
-    /// </summary>
-    public class CIFAR10_CNN : IExample
-    {
-        public bool Enabled { get; set; } = true;
-        public bool IsImportingGraph { get; set; } = false;
-
-        public string Name => "CIFAR-10 CNN";
-
-        public bool Run()
-        {
-            PrepareData();
-
-            return true;
-        }
-
-        public Graph BuildGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public Graph ImportGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Predict(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void PrepareData()
-        {
-            var tfds = new DatasetBuilder();
-            tfds.download_and_prepare();
-        }
-
-        public void Test(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Train(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-    }
-}

test/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionCNN.cs

@@ -1,338 +0,0 @@
-/*****************************************************************************
-   Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
-******************************************************************************/
-
-using NumSharp;
-using System;
-using System.Diagnostics;
-using Tensorflow;
-using Tensorflow.Hub;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples
-{
-    /// <summary>
-    /// Convolutional Neural Network classifier for Hand Written Digits
-    /// CNN architecture with two convolutional layers, followed by two fully-connected layers at the end.
-    /// Use Stochastic Gradient Descent (SGD) optimizer. 
-    /// http://www.easy-tf.com/tf-tutorials/convolutional-neural-nets-cnns/cnn1
-    /// </summary>
-    public class DigitRecognitionCNN : IExample
-    {
-        public bool Enabled { get; set; } = true;
-        public bool IsImportingGraph { get; set; } = false;
-
-        public string Name => "MNIST CNN";
-
-        string logs_path = "logs";
-
-        const int img_h = 28, img_w = 28; // MNIST images are 28x28
-        int n_classes = 10; // Number of classes, one class per digit
-        int n_channels = 1;
-
-        // Hyper-parameters
-        int epochs = 5; // accuracy > 98%
-        int batch_size = 100;
-        float learning_rate = 0.001f;
-        Datasets<MnistDataSet> mnist;
-
-        // Network configuration
-        // 1st Convolutional Layer
-        int filter_size1 = 5;  // Convolution filters are 5 x 5 pixels.
-        int num_filters1 = 16; //  There are 16 of these filters.
-        int stride1 = 1;  // The stride of the sliding window
-
-        // 2nd Convolutional Layer
-        int filter_size2 = 5; // Convolution filters are 5 x 5 pixels.
-        int num_filters2 = 32;// There are 32 of these filters.
-        int stride2 = 1;  // The stride of the sliding window
-
-        // Fully-connected layer.
-        int h1 = 128; // Number of neurons in fully-connected layer.
-
-        Tensor x, y;
-        Tensor loss, accuracy, cls_prediction;
-        Operation optimizer;
-
-        int display_freq = 100;
-        float accuracy_test = 0f;
-        float loss_test = 1f;
-
-        NDArray x_train, y_train;
-        NDArray x_valid, y_valid;
-        NDArray x_test, y_test;
-
-        public bool Run()
-        {
-            PrepareData();
-            BuildGraph();
-
-            using (var sess = tf.Session())
-            {
-                Train(sess);
-                Test(sess);
-            }
-
-            return loss_test < 0.05 && accuracy_test > 0.98;
-        }
-
-        public Graph BuildGraph()
-        {
-            var graph = new Graph().as_default();
-
-            tf_with(tf.name_scope("Input"), delegate
-            {
-                // Placeholders for inputs (x) and outputs(y)
-                x = tf.placeholder(tf.float32, shape: (-1, img_h, img_w, n_channels), name: "X");
-                y = tf.placeholder(tf.float32, shape: (-1, n_classes), name: "Y");
-            });
-
-            var conv1 = conv_layer(x, filter_size1, num_filters1, stride1, name: "conv1");
-            var pool1 = max_pool(conv1, ksize: 2, stride: 2, name: "pool1");
-            var conv2 = conv_layer(pool1, filter_size2, num_filters2, stride2, name: "conv2");
-            var pool2 = max_pool(conv2, ksize: 2, stride: 2, name: "pool2");
-            var layer_flat = flatten_layer(pool2);
-            var fc1 = fc_layer(layer_flat, h1, "FC1", use_relu: true);
-            var output_logits = fc_layer(fc1, n_classes, "OUT", use_relu: false);
-
-            tf_with(tf.variable_scope("Train"), delegate
-            {
-                tf_with(tf.variable_scope("Loss"), delegate
-                {
-                    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels: y, logits: output_logits), name: "loss");
-                });
-
-                tf_with(tf.variable_scope("Optimizer"), delegate
-                {
-                    optimizer = tf.train.AdamOptimizer(learning_rate: learning_rate, name: "Adam-op").minimize(loss);
-                });
-
-                tf_with(tf.variable_scope("Accuracy"), delegate
-                {
-                    var correct_prediction = tf.equal(tf.argmax(output_logits, 1), tf.argmax(y, 1), name: "correct_pred");
-                    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name: "accuracy");
-                });
-
-                tf_with(tf.variable_scope("Prediction"), delegate
-                {
-                    cls_prediction = tf.argmax(output_logits, axis: 1, name: "predictions");
-                });
-            });
-
-            return graph;
-        }
-
-        public void Train(Session sess)
-        {
-            // Number of training iterations in each epoch
-            var num_tr_iter = y_train.shape[0] / batch_size;
-
-            var init = tf.global_variables_initializer();
-            sess.run(init);
-
-            float loss_val = 100.0f;
-            float accuracy_val = 0f;
-
-            var sw = new Stopwatch();
-            sw.Start();
-            foreach (var epoch in range(epochs))
-            {
-                print($"Training epoch: {epoch + 1}");
-                // Randomly shuffle the training data at the beginning of each epoch 
-                (x_train, y_train) = mnist.Randomize(x_train, y_train);
-
-                foreach (var iteration in range(num_tr_iter))
-                {
-                    var start = iteration * batch_size;
-                    var end = (iteration + 1) * batch_size;
-                    var (x_batch, y_batch) = mnist.GetNextBatch(x_train, y_train, start, end);
-
-                    // Run optimization op (backprop)
-                    sess.run(optimizer, (x, x_batch), (y, y_batch));
-
-                    if (iteration % display_freq == 0)
-                    {
-                        // Calculate and display the batch loss and accuracy
-                        var result = sess.run(new[] { loss, accuracy }, new FeedItem(x, x_batch), new FeedItem(y, y_batch));
-                        loss_val = result[0];
-                        accuracy_val = result[1];
-                        print($"iter {iteration.ToString("000")}: Loss={loss_val.ToString("0.0000")}, Training Accuracy={accuracy_val.ToString("P")} {sw.ElapsedMilliseconds}ms");
-                        sw.Restart();
-                    }
-                }
-
-                // Run validation after every epoch
-                (loss_val, accuracy_val) = sess.run((loss, accuracy), (x, x_valid), (y, y_valid));
-                print("---------------------------------------------------------");
-                print($"Epoch: {epoch + 1}, validation loss: {loss_val.ToString("0.0000")}, validation accuracy: {accuracy_val.ToString("P")}");
-                print("---------------------------------------------------------");
-            }
-        }
-
-        public void Test(Session sess)
-        {
-            (loss_test, accuracy_test) = sess.run((loss, accuracy), (x, x_test), (y, y_test));
-            print("---------------------------------------------------------");
-            print($"Test loss: {loss_test.ToString("0.0000")}, test accuracy: {accuracy_test.ToString("P")}");
-            print("---------------------------------------------------------");
-        }
-
-        /// <summary>
-        /// Create a 2D convolution layer
-        /// </summary>
-        /// <param name="x">input from previous layer</param>
-        /// <param name="filter_size">size of each filter</param>
-        /// <param name="num_filters">number of filters(or output feature maps)</param>
-        /// <param name="stride">filter stride</param>
-        /// <param name="name">layer name</param>
-        /// <returns>The output array</returns>
-        private Tensor conv_layer(Tensor x, int filter_size, int num_filters, int stride, string name)
-        {
-            return tf_with(tf.variable_scope(name), delegate {
-
-                var num_in_channel = x.shape[x.NDims - 1];
-                var shape = new[] { filter_size, filter_size, num_in_channel, num_filters };
-                var W = weight_variable("W", shape);
-                // var tf.summary.histogram("weight", W);
-                var b = bias_variable("b", new[] { num_filters });
-                // tf.summary.histogram("bias", b);
-                var layer = tf.nn.conv2d(x, W,
-                                     strides: new[] { 1, stride, stride, 1 },
-                                     padding: "SAME");
-                layer += b;
-                return tf.nn.relu(layer);
-            });
-        }
-
-        /// <summary>
-        /// Create a max pooling layer
-        /// </summary>
-        /// <param name="x">input to max-pooling layer</param>
-        /// <param name="ksize">size of the max-pooling filter</param>
-        /// <param name="stride">stride of the max-pooling filter</param>
-        /// <param name="name">layer name</param>
-        /// <returns>The output array</returns>
-        private Tensor max_pool(Tensor x, int ksize, int stride, string name)
-        {
-            return tf.nn.max_pool(x,
-                ksize: new[] { 1, ksize, ksize, 1 },
-                strides: new[] { 1, stride, stride, 1 },
-                padding: "SAME",
-                name: name);
-        }
-
-        /// <summary>
-        /// Flattens the output of the convolutional layer to be fed into fully-connected layer
-        /// </summary>
-        /// <param name="layer">input array</param>
-        /// <returns>flattened array</returns>
-        private Tensor flatten_layer(Tensor layer)
-        {
-            return tf_with(tf.variable_scope("Flatten_layer"), delegate
-            {
-                var layer_shape = layer.TensorShape;
-                var num_features = layer_shape[new Slice(1, 4)].size;
-                var layer_flat = tf.reshape(layer, new[] { -1, num_features });
-
-                return layer_flat;
-            });
-        }
-
-        /// <summary>
-        /// Create a weight variable with appropriate initialization
-        /// </summary>
-        /// <param name="name"></param>
-        /// <param name="shape"></param>
-        /// <returns></returns>
-        private RefVariable weight_variable(string name, int[] shape)
-        {
-            var initer = tf.truncated_normal_initializer(stddev: 0.01f);
-            return tf.get_variable(name,
-                                   dtype: tf.float32,
-                                   shape: shape,
-                                   initializer: initer);
-        }
-
-        /// <summary>
-        /// Create a bias variable with appropriate initialization
-        /// </summary>
-        /// <param name="name"></param>
-        /// <param name="shape"></param>
-        /// <returns></returns>
-        private RefVariable bias_variable(string name, int[] shape)
-        {
-            var initial = tf.constant(0f, shape: shape, dtype: tf.float32);
-            return tf.get_variable(name,
-                           dtype: tf.float32,
-                           initializer: initial);
-        }
-
-        /// <summary>
-        /// Create a fully-connected layer
-        /// </summary>
-        /// <param name="x">input from previous layer</param>
-        /// <param name="num_units">number of hidden units in the fully-connected layer</param>
-        /// <param name="name">layer name</param>
-        /// <param name="use_relu">boolean to add ReLU non-linearity (or not)</param>
-        /// <returns>The output array</returns>
-        private Tensor fc_layer(Tensor x, int num_units, string name, bool use_relu = true)
-        {
-            return tf_with(tf.variable_scope(name), delegate
-            {
-                var in_dim = x.shape[1];
-
-                var W = weight_variable("W_" + name, shape: new[] { in_dim, num_units });
-                var b = bias_variable("b_" + name, new[] { num_units });
-
-                var layer = tf.matmul(x, W) + b;
-                if (use_relu)
-                    layer = tf.nn.relu(layer);
-
-                return layer;
-            });
-        } 
-            
-        public void PrepareData()
-        {
-            mnist = MnistModelLoader.LoadAsync(".resources/mnist", oneHot: true, showProgressInConsole: true).Result;
-            (x_train, y_train) = Reformat(mnist.Train.Data, mnist.Train.Labels);
-            (x_valid, y_valid) = Reformat(mnist.Validation.Data, mnist.Validation.Labels);
-            (x_test, y_test) = Reformat(mnist.Test.Data, mnist.Test.Labels);
-
-            print("Size of:");
-            print($"- Training-set:\t\t{len(mnist.Train.Data)}");
-            print($"- Validation-set:\t{len(mnist.Validation.Data)}");
-        }
-
-        /// <summary>
-        /// Reformats the data to the format acceptable for convolutional layers
-        /// </summary>
-        /// <param name="x"></param>
-        /// <param name="y"></param>
-        /// <returns></returns>
-        private (NDArray, NDArray) Reformat(NDArray x, NDArray y)
-        {
-            var (img_size, num_ch, num_class) = (np.sqrt(x.shape[1]).astype(np.int32), 1, len(np.unique(np.argmax(y, 1))));
-            var dataset = x.reshape(x.shape[0], img_size, img_size, num_ch).astype(np.float32);
-            //y[0] = np.arange(num_class) == y[0];
-            //var labels = (np.arange(num_class) == y.reshape(y.shape[0], 1, y.shape[1])).astype(np.float32);
-            return (dataset, y);
-        }
-
-        public Graph ImportGraph() => throw new NotImplementedException();
-
-        public void Predict(Session sess) => throw new NotImplementedException();
-    }
-}

test/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionNN.cs

@@ -1,182 +0,0 @@
-/*****************************************************************************
-   Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
-******************************************************************************/
-
-using NumSharp;
-using System;
-using System.Diagnostics;
-using Tensorflow;
-using Tensorflow.Hub;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples
-{
-    /// <summary>
-    /// Neural Network classifier for Hand Written Digits
-    /// Sample Neural Network architecture with two layers implemented for classifying MNIST digits.
-    /// Use Stochastic Gradient Descent (SGD) optimizer. 
-    /// http://www.easy-tf.com/tf-tutorials/neural-networks
-    /// </summary>
-    public class DigitRecognitionNN : IExample
-    {
-        public bool Enabled { get; set; } = true;
-        public bool IsImportingGraph { get; set; } = false;
-
-        public string Name => "Digits Recognition Neural Network";
-
-        const int img_h = 28;
-        const int img_w = 28;
-        int img_size_flat = img_h * img_w; // 784, the total number of pixels
-        int n_classes = 10; // Number of classes, one class per digit
-        // Hyper-parameters
-        int epochs = 10;
-        int batch_size = 100;
-        float learning_rate = 0.001f;
-        int h1 = 200; // number of nodes in the 1st hidden layer
-        Datasets<MnistDataSet> mnist;
-
-        Tensor x, y;
-        Tensor loss, accuracy;
-        Operation optimizer;
-
-        int display_freq = 100;
-        float accuracy_test = 0f;
-        float loss_test = 1f;
-
-        public bool Run()
-        {
-            PrepareData();
-            BuildGraph();
-
-            using (var sess = tf.Session())
-            {
-                Train(sess);
-                Test(sess);
-            };
-
-            return loss_test < 0.09 && accuracy_test > 0.95;
-        }
-
-        public Graph BuildGraph()
-        {
-            var graph = new Graph().as_default();
-
-            // Placeholders for inputs (x) and outputs(y)
-            x = tf.placeholder(tf.float32, shape: (-1, img_size_flat), name: "X");
-            y = tf.placeholder(tf.float32, shape: (-1, n_classes), name: "Y");
-
-            // Create a fully-connected layer with h1 nodes as hidden layer
-            var fc1 = fc_layer(x, h1, "FC1", use_relu: true);
-            // Create a fully-connected layer with n_classes nodes as output layer
-            var output_logits = fc_layer(fc1, n_classes, "OUT", use_relu: false);
-            // Define the loss function, optimizer, and accuracy
-            var logits = tf.nn.softmax_cross_entropy_with_logits(labels: y, logits: output_logits);
-            loss = tf.reduce_mean(logits, name: "loss");
-            optimizer = tf.train.AdamOptimizer(learning_rate: learning_rate, name: "Adam-op").minimize(loss);
-            var correct_prediction = tf.equal(tf.argmax(output_logits, 1), tf.argmax(y, 1), name: "correct_pred");
-            accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name: "accuracy");
-
-            // Network predictions
-            var cls_prediction = tf.argmax(output_logits, axis: 1, name: "predictions");
-
-            return graph;
-        }
-
-        private Tensor fc_layer(Tensor x, int num_units, string name, bool use_relu = true)
-        {
-            var in_dim = x.shape[1];
-
-            var initer = tf.truncated_normal_initializer(stddev: 0.01f);
-            var W = tf.get_variable("W_" + name,
-                        dtype: tf.float32,
-                        shape: (in_dim, num_units),
-                        initializer: initer);
-
-            var initial = tf.constant(0f, num_units);
-            var b = tf.get_variable("b_" + name,
-                        dtype: tf.float32,
-                        initializer: initial);
-
-            var layer = tf.matmul(x, W) + b;
-            if (use_relu)
-                layer = tf.nn.relu(layer);
-
-            return layer;
-        } 
-
-        public Graph ImportGraph() => throw new NotImplementedException();
-
-        public void Predict(Session sess) => throw new NotImplementedException();
-            
-        public void PrepareData()
-        {
-            mnist = MnistModelLoader.LoadAsync(".resources/mnist", oneHot: true, showProgressInConsole: true).Result;
-        }
-
-        public void Train(Session sess)
-        {
-            // Number of training iterations in each epoch
-            var num_tr_iter = mnist.Train.Labels.shape[0] / batch_size;
-
-            var init = tf.global_variables_initializer();
-            sess.run(init);
-
-            float loss_val = 100.0f;
-            float accuracy_val = 0f;
-
-            var sw = new Stopwatch();
-            sw.Start();
-
-            foreach (var epoch in range(epochs))
-            {
-                print($"Training epoch: {epoch + 1}");
-                // Randomly shuffle the training data at the beginning of each epoch 
-                var (x_train, y_train) = mnist.Randomize(mnist.Train.Data, mnist.Train.Labels);
-
-                foreach (var iteration in range(num_tr_iter))
-                {
-                    var start = iteration * batch_size;
-                    var end = (iteration + 1) * batch_size;
-                    var (x_batch, y_batch) = mnist.GetNextBatch(x_train, y_train, start, end);
-
-                    // Run optimization op (backprop)
-                    sess.run(optimizer, (x, x_batch), (y, y_batch));
-
-                    if (iteration % display_freq == 0)
-                    {
-                        // Calculate and display the batch loss and accuracy
-                        (loss_val, accuracy_val) = sess.run((loss, accuracy), (x, x_batch), (y, y_batch));
-                        print($"iter {iteration.ToString("000")}: Loss={loss_val.ToString("0.0000")}, Training Accuracy={accuracy_val.ToString("P")} {sw.ElapsedMilliseconds}ms");
-                        sw.Restart();
-                    }
-                }
-
-                // Run validation after every epoch
-                (loss_val, accuracy_val) = sess.run((loss, accuracy), (x, mnist.Validation.Data), (y, mnist.Validation.Labels));
-                print("---------------------------------------------------------");
-                print($"Epoch: {epoch + 1}, validation loss: {loss_val.ToString("0.0000")}, validation accuracy: {accuracy_val.ToString("P")}");
-                print("---------------------------------------------------------");
-            }
-        }
-
-        public void Test(Session sess)
-        {
-            (loss_test, accuracy_test) = sess.run((loss, accuracy), (x, mnist.Test.Data), (y, mnist.Test.Labels));
-            print("---------------------------------------------------------");
-            print($"Test loss: {loss_test.ToString("0.0000")}, test accuracy: {accuracy_test.ToString("P")}");
-            print("---------------------------------------------------------");
-        }
-    }
-}

test/TensorFlowNET.Examples/ImageProcessing/DigitRecognitionRNN.cs

@@ -1,161 +0,0 @@
-/*****************************************************************************
-   Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
-******************************************************************************/
-
-using NumSharp;
-using System;
-using Tensorflow;
-using Tensorflow.Hub;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples
-{
-    /// <summary>
-    /// Recurrent Neural Network for handwritten digits MNIST.
-    /// https://medium.com/machine-learning-algorithms/mnist-using-recurrent-neural-network-2d070a5915a2
-    /// </summary>
-    public class DigitRecognitionRNN : IExample
-    {
-        public bool Enabled { get; set; } = false;
-        public bool IsImportingGraph { get; set; } = false;
-
-        public string Name => "MNIST RNN";
-
-        string logs_path = "logs";
-
-        // Hyper-parameters
-        int n_neurons = 128;
-        float learning_rate = 0.001f;
-        int batch_size = 128;
-        int epochs = 10;
-
-        int n_steps = 28;
-        int n_inputs = 28;
-        int n_outputs = 10;
-
-        Datasets<MnistDataSet> mnist;
-
-        Tensor x, y;
-        Tensor loss, accuracy, cls_prediction;
-        Operation optimizer;
-
-        int display_freq = 100;
-        float accuracy_test = 0f;
-        float loss_test = 1f;
-
-        NDArray x_train, y_train;
-        NDArray x_valid, y_valid;
-        NDArray x_test, y_test;
-
-        public bool Run()
-        {
-            PrepareData();
-            BuildGraph();
-
-            using (var sess = tf.Session())
-            {
-                Train(sess);
-                Test(sess);
-            }
-
-            return loss_test < 0.09 && accuracy_test > 0.95;
-        }
-
-        public Graph BuildGraph()
-        {
-            var graph = new Graph().as_default();
-
-            var X = tf.placeholder(tf.float32, new[] { -1, n_steps, n_inputs });
-            var y = tf.placeholder(tf.int32, new[] { -1 });
-            var cell = tf.nn.rnn_cell.BasicRNNCell(num_units: n_neurons);
-            var (output, state) = tf.nn.dynamic_rnn(cell, X, dtype: tf.float32);
-
-            return graph;
-        }
-
-        public void Train(Session sess)
-        {
-            // Number of training iterations in each epoch
-            var num_tr_iter = y_train.shape[0] / batch_size;
-
-            var init = tf.global_variables_initializer();
-            sess.run(init);
-
-            float loss_val = 100.0f; 
-            float accuracy_val = 0f;
-
-            foreach (var epoch in range(epochs))
-            {
-                print($"Training epoch: {epoch + 1}");
-                // Randomly shuffle the training data at the beginning of each epoch 
-                (x_train, y_train) = mnist.Randomize(x_train, y_train);
-
-                foreach (var iteration in range(num_tr_iter))
-                {
-                    var start = iteration * batch_size;
-                    var end = (iteration + 1) * batch_size;
-                    var (x_batch, y_batch) = mnist.GetNextBatch(x_train, y_train, start, end);
-
-                    // Run optimization op (backprop)
-                    sess.run(optimizer, new FeedItem(x, x_batch), new FeedItem(y, y_batch));
-
-                    if (iteration % display_freq == 0)
-                    {
-                        // Calculate and display the batch loss and accuracy
-                        var result = sess.run(new[] { loss, accuracy }, new FeedItem(x, x_batch), new FeedItem(y, y_batch));
-                        loss_val = result[0];
-                        accuracy_val = result[1];
-                        print($"iter {iteration.ToString("000")}: Loss={loss_val.ToString("0.0000")}, Training Accuracy={accuracy_val.ToString("P")}");
-                    }
-                }
-
-                // Run validation after every epoch
-                var results1 = sess.run(new[] { loss, accuracy }, new FeedItem(x, x_valid), new FeedItem(y, y_valid));
-                loss_val = results1[0];
-                accuracy_val = results1[1];
-                print("---------------------------------------------------------");
-                print($"Epoch: {epoch + 1}, validation loss: {loss_val.ToString("0.0000")}, validation accuracy: {accuracy_val.ToString("P")}");
-                print("---------------------------------------------------------");
-            }
-        }
-
-        public void Test(Session sess)
-        {
-            var result = sess.run(new[] { loss, accuracy }, new FeedItem(x, x_test), new FeedItem(y, y_test));
-            loss_test = result[0];
-            accuracy_test = result[1];
-            print("---------------------------------------------------------");
-            print($"Test loss: {loss_test.ToString("0.0000")}, test accuracy: {accuracy_test.ToString("P")}");
-            print("---------------------------------------------------------");
-        }
-
-        public void PrepareData()
-        {
-            mnist = MnistModelLoader.LoadAsync(".resources/mnist", oneHot: true, showProgressInConsole: true).Result;
-            (x_train, y_train) = (mnist.Train.Data, mnist.Train.Labels);
-            (x_valid, y_valid) = (mnist.Validation.Data, mnist.Validation.Labels);
-            (x_test, y_test) = (mnist.Test.Data, mnist.Test.Labels);
-
-            print("Size of:");
-            print($"- Training-set:\t\t{len(mnist.Train.Data)}");
-            print($"- Validation-set:\t{len(mnist.Validation.Data)}");
-            print($"- Test-set:\t\t{len(mnist.Test.Data)}");
-        }
-
-        public Graph ImportGraph() => throw new NotImplementedException();
-
-        public void Predict(Session sess) => throw new NotImplementedException();
-    }
-}

test/TensorFlowNET.Examples/ImageProcessing/ImageBackgroundRemoval.cs

@@ -1,84 +0,0 @@
-using System;
-using System.IO;
-using Tensorflow;
-using TensorFlowNET.Examples.Utility;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples
-{
-    /// <summary>
-    /// This example removes the background from an input image.
-    /// 
-    /// https://github.com/susheelsk/image-background-removal
-    /// </summary>
-    public class ImageBackgroundRemoval : IExample
-    {
-        public bool Enabled { get; set; } = false;
-        public bool IsImportingGraph { get; set; } = true;
-
-        public string Name => "Image Background Removal";
-
-        string dataDir = "deeplabv3";
-        string modelDir = "deeplabv3_mnv2_pascal_train_aug";
-        string modelName = "frozen_inference_graph.pb";
-
-        public bool Run()
-        {
-            PrepareData();
-
-            // import GraphDef from pb file
-            var graph = new Graph().as_default();
-            graph.Import(Path.Join(dataDir, modelDir, modelName));
-
-            Tensor output = graph.OperationByName("SemanticPredictions");
-
-            using (var sess = tf.Session(graph))
-            {
-                // Runs inference on a single image.
-                sess.run(output, new FeedItem(output, "[np.asarray(resized_image)]"));
-            }
-
-            return false;
-        }
-
-        public void PrepareData()
-        {
-            // get mobile_net_model file
-            string fileName = "deeplabv3_mnv2_pascal_train_aug_2018_01_29.tar.gz";
-            string url = $"http://download.tensorflow.org/models/{fileName}";
-            Web.Download(url, dataDir, fileName);
-            Compress.ExtractTGZ(Path.Join(dataDir, fileName), dataDir);
-
-            // xception_model, better accuracy
-            /*fileName = "deeplabv3_pascal_train_aug_2018_01_04.tar.gz";
-            url = $"http://download.tensorflow.org/models/{fileName}";
-            Web.Download(url, modelDir, fileName);
-            Compress.ExtractTGZ(Path.Join(modelDir, fileName), modelDir);*/
-        }
-
-        public Graph ImportGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public Graph BuildGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Train(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Predict(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Test(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-    }
-}

test/TensorFlowNET.Examples/ImageProcessing/ImageRecognitionInception.cs

@@ -1,140 +0,0 @@
-using NumSharp;
-using System;
-using System.Collections.Generic;
-using System.Diagnostics;
-using System.IO;
-using Console = Colorful.Console;
-using Tensorflow;
-using System.Drawing;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples
-{
-    /// <summary>
-    /// Inception v3 is a widely-used image recognition model 
-    /// that has been shown to attain greater than 78.1% accuracy on the ImageNet dataset. 
-    /// The model is the culmination of many ideas developed by multiple researchers over the years.
-    /// </summary>
-    public class ImageRecognitionInception : IExample
-    {
-        public bool Enabled { get; set; } = true;
-        public string Name => "Image Recognition Inception";
-        public bool IsImportingGraph { get; set; } = false;
-
-
-        string dir = "ImageRecognitionInception";
-        string pbFile = "tensorflow_inception_graph.pb";
-        string labelFile = "imagenet_comp_graph_label_strings.txt";
-        List<NDArray> file_ndarrays = new List<NDArray>();
-
-        public bool Run()
-        {
-            PrepareData();
-           
-            var graph = new Graph();
-            //import GraphDef from pb file
-            graph.Import(Path.Join(dir, pbFile));
-
-            var input_name = "input";
-            var output_name = "output";
-
-            var input_operation = graph.OperationByName(input_name);
-            var output_operation = graph.OperationByName(output_name);
-
-            var labels = File.ReadAllLines(Path.Join(dir, labelFile));
-            var result_labels = new List<string>();
-            var sw = new Stopwatch();
-
-            using (var sess = tf.Session(graph))
-            {
-                foreach (var nd in file_ndarrays)
-                {
-                    sw.Restart();
-
-                    var results = sess.run(output_operation.outputs[0], (input_operation.outputs[0], nd));
-                    results = np.squeeze(results);
-                    int idx = np.argmax(results);
-
-                    Console.WriteLine($"{labels[idx]} {results[idx]} in {sw.ElapsedMilliseconds}ms", Color.Tan);
-                    result_labels.Add(labels[idx]);
-                }
-            }
-            
-            return result_labels.Contains("military uniform");
-        }
-
-        private NDArray ReadTensorFromImageFile(string file_name,
-                                int input_height = 224,
-                                int input_width = 224,
-                                int input_mean = 117,
-                                int input_std = 1)
-        {
-            var graph = tf.Graph().as_default();
-
-            var file_reader = tf.read_file(file_name, "file_reader");
-            var decodeJpeg = tf.image.decode_jpeg(file_reader, channels: 3, name: "DecodeJpeg");
-            var cast = tf.cast(decodeJpeg, tf.float32);
-            var dims_expander = tf.expand_dims(cast, 0);
-            var resize = tf.constant(new int[] { input_height, input_width });
-            var bilinear = tf.image.resize_bilinear(dims_expander, resize);
-            var sub = tf.subtract(bilinear, new float[] { input_mean });
-            var normalized = tf.divide(sub, new float[] { input_std });
-
-            using (var sess = tf.Session(graph))
-                return sess.run(normalized);
-        }
-
-        public void PrepareData()
-        {
-            Directory.CreateDirectory(dir);
-
-            // get model file
-            string url = "https://storage.googleapis.com/download.tensorflow.org/models/inception5h.zip";
-
-            Utility.Web.Download(url, dir, "inception5h.zip");
-
-            Utility.Compress.UnZip(Path.Join(dir, "inception5h.zip"), dir);
-
-            // download sample picture
-            Directory.CreateDirectory(Path.Join(dir, "img"));
-            url = $"https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/examples/label_image/data/grace_hopper.jpg";
-            Utility.Web.Download(url, Path.Join(dir, "img"), "grace_hopper.jpg");
-
-            url = $"https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/data/shasta-daisy.jpg";
-            Utility.Web.Download(url, Path.Join(dir, "img"), "shasta-daisy.jpg");
-
-            // load image file
-            var files = Directory.GetFiles(Path.Join(dir, "img"));
-            for (int i = 0; i < files.Length; i++)
-            {
-                var nd = ReadTensorFromImageFile(files[i]);
-                file_ndarrays.Add(nd);
-            }
-        }
-
-        public Graph ImportGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public Graph BuildGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Train(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Predict(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Test(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-    }
-}

test/TensorFlowNET.Examples/ImageProcessing/InceptionArchGoogLeNet.cs

@@ -1,133 +0,0 @@
-using NumSharp;
-using System;
-using System.IO;
-using System.Linq;
-using Tensorflow;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples
-{
-    /// <summary>
-    /// Inception Architecture for Computer Vision
-    /// Port from tensorflow\examples\label_image\label_image.py
-    /// </summary>
-    public class InceptionArchGoogLeNet : IExample
-    {
-        public bool Enabled { get; set; } = false;
-        public string Name => "Inception Arch GoogLeNet";
-        public bool IsImportingGraph { get; set; } = false;
-
-
-        string dir = "label_image_data";
-        string pbFile = "inception_v3_2016_08_28_frozen.pb";
-        string labelFile = "imagenet_slim_labels.txt";
-        string picFile = "grace_hopper.jpg";
-        int input_height = 299;
-        int input_width = 299;
-        int input_mean = 0;
-        int input_std = 255;
-        string input_name = "import/input";
-        string output_name = "import/InceptionV3/Predictions/Reshape_1";
-
-        public bool Run()
-        {
-            PrepareData();
-
-            var labels = File.ReadAllLines(Path.Join(dir, labelFile));
-            
-            var nd = ReadTensorFromImageFile(Path.Join(dir, picFile),
-                input_height: input_height,
-                input_width: input_width,
-                input_mean: input_mean,
-                input_std: input_std);
-
-            var graph = new Graph();
-            graph.Import(Path.Join(dir, pbFile));
-            var input_operation = graph.get_operation_by_name(input_name);
-            var output_operation = graph.get_operation_by_name(output_name);
-
-            NDArray results;
-            using (var sess = tf.Session(graph))
-            {
-                results = sess.run(output_operation.outputs[0],
-                    new FeedItem(input_operation.outputs[0], nd));
-            }
-
-            results = np.squeeze(results);
-
-            var argsort = results.argsort<float>();
-            var top_k = argsort.Data<float>()
-                .Skip(results.size - 5)
-                .Reverse()
-                .ToArray();
-
-            foreach (float idx in top_k)
-                Console.WriteLine($"{picFile}: {idx} {labels[(int)idx]}, {results[(int)idx]}");
-
-            return true;
-        }
-
-        private NDArray ReadTensorFromImageFile(string file_name,
-                                int input_height = 299,
-                                int input_width = 299,
-                                int input_mean = 0,
-                                int input_std = 255)
-        {
-            var graph = tf.Graph().as_default();
-
-            var file_reader = tf.read_file(file_name, "file_reader");
-            var image_reader = tf.image.decode_jpeg(file_reader, channels: 3, name: "jpeg_reader");
-            var caster = tf.cast(image_reader, tf.float32);
-            var dims_expander = tf.expand_dims(caster, 0);
-            var resize = tf.constant(new int[] { input_height, input_width });
-            var bilinear = tf.image.resize_bilinear(dims_expander, resize);
-            var sub = tf.subtract(bilinear, new float[] { input_mean });
-            var normalized = tf.divide(sub, new float[] { input_std });
-
-            using (var sess = tf.Session(graph))
-                return sess.run(normalized);
-        }
-
-        public void PrepareData()
-        {
-            Directory.CreateDirectory(dir);
-
-            // get model file
-            string url = "https://storage.googleapis.com/download.tensorflow.org/models/inception_v3_2016_08_28_frozen.pb.tar.gz";
-            
-            Utility.Web.Download(url, dir, $"{pbFile}.tar.gz");
-
-            Utility.Compress.ExtractTGZ(Path.Join(dir, $"{pbFile}.tar.gz"), dir);
-
-            // download sample picture
-            string pic = "grace_hopper.jpg";
-            url = $"https://raw.githubusercontent.com/tensorflow/tensorflow/master/tensorflow/examples/label_image/data/{pic}";
-            Utility.Web.Download(url, dir, pic);
-        }
-
-        public Graph ImportGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public Graph BuildGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Train(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Predict(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Test(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-    }
-}

test/TensorFlowNET.Examples/ImageProcessing/ObjectDetection.cs

@@ -1,175 +0,0 @@
-/*****************************************************************************
-   Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
-******************************************************************************/
-
-using NumSharp;
-using System;
-using System.IO;
-using Tensorflow;
-using TensorFlowNET.Examples.Utility;
-using System.Drawing;
-using System.Drawing.Drawing2D;
-using System.Linq;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples
-{
-    public class ObjectDetection : IExample
-    {
-        public bool Enabled { get; set; } = true;
-        public string Name => "Object Detection";
-        public bool IsImportingGraph { get; set; } = true;
-
-        public float MIN_SCORE = 0.5f;
-
-        string modelDir = "ssd_mobilenet_v1_coco_2018_01_28";
-        string imageDir = "images";
-        string pbFile = "frozen_inference_graph.pb";
-        string labelFile = "mscoco_label_map.pbtxt";
-        string picFile = "input.jpg";
-
-        NDArray imgArr;
-
-        public bool Run()
-        {
-            PrepareData();
-
-            // read in the input image
-            imgArr = ReadTensorFromImageFile(Path.Join(imageDir, "input.jpg"));
-
-            var graph = IsImportingGraph ? ImportGraph() : BuildGraph();
-
-            using (var sess = tf.Session(graph))
-                Predict(sess);
-
-            return true;
-        }
-
-        public Graph ImportGraph()
-        {
-            var graph = new Graph().as_default();
-            graph.Import(Path.Join(modelDir, pbFile));
-
-            return graph;
-        }
-
-        public void Predict(Session sess)
-        {
-            var graph = tf.get_default_graph();
-
-            Tensor tensorNum = graph.OperationByName("num_detections");
-            Tensor tensorBoxes = graph.OperationByName("detection_boxes");
-            Tensor tensorScores = graph.OperationByName("detection_scores");
-            Tensor tensorClasses = graph.OperationByName("detection_classes");
-            Tensor imgTensor = graph.OperationByName("image_tensor");
-            Tensor[] outTensorArr = new Tensor[] { tensorNum, tensorBoxes, tensorScores, tensorClasses };
-
-            var results = sess.run(outTensorArr, new FeedItem(imgTensor, imgArr));
-
-            buildOutputImage(results);
-        }
-
-        public void PrepareData()
-        {
-            // get model file
-            string url = "http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v1_coco_2018_01_28.tar.gz";
-            Web.Download(url, modelDir, "ssd_mobilenet_v1_coco.tar.gz");
-
-            Compress.ExtractTGZ(Path.Join(modelDir, "ssd_mobilenet_v1_coco.tar.gz"), "./");
-
-            // download sample picture
-            url = $"https://github.com/tensorflow/models/raw/master/research/object_detection/test_images/image2.jpg";
-            Web.Download(url, imageDir, "input.jpg");
-
-            // download the pbtxt file
-            url = $"https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/data/mscoco_label_map.pbtxt";
-            Web.Download(url, modelDir, "mscoco_label_map.pbtxt");
-        }
-
-        private NDArray ReadTensorFromImageFile(string file_name)
-        {
-            var graph = tf.Graph().as_default();
-
-            var file_reader = tf.read_file(file_name, "file_reader");
-            var decodeJpeg = tf.image.decode_jpeg(file_reader, channels: 3, name: "DecodeJpeg");
-            var casted = tf.cast(decodeJpeg, TF_DataType.TF_UINT8);
-            var dims_expander = tf.expand_dims(casted, 0);
-
-            using (var sess = tf.Session(graph))
-                return sess.run(dims_expander);
-        }
-
-        private void buildOutputImage(NDArray[] resultArr)
-        {
-            // get pbtxt items
-            PbtxtItems pbTxtItems = PbtxtParser.ParsePbtxtFile(Path.Join(modelDir, "mscoco_label_map.pbtxt"));
-
-            // get bitmap
-            Bitmap bitmap = new Bitmap(Path.Join(imageDir, "input.jpg"));
-
-            var scores = resultArr[2].AsIterator<float>();
-            var boxes = resultArr[1].GetData<float>();
-            var id = np.squeeze(resultArr[3]).GetData<float>();
-            for (int i=0; i< scores.size; i++)
-            {
-                float score = scores.MoveNext();
-                if (score > MIN_SCORE)
-                {
-                    float top = boxes[i * 4] * bitmap.Height;
-                    float left = boxes[i * 4 + 1] * bitmap.Width;
-                    float bottom = boxes[i * 4 + 2] * bitmap.Height;
-                    float right = boxes[i * 4 + 3] * bitmap.Width;
-
-                    Rectangle rect = new Rectangle()
-                    {
-                        X = (int)left,
-                        Y = (int)top,
-                        Width = (int)(right - left),
-                        Height = (int)(bottom - top)
-                    };
-
-                    string name = pbTxtItems.items.Where(w => w.id == id[i]).Select(s=>s.display_name).FirstOrDefault();
-
-                    drawObjectOnBitmap(bitmap, rect, score, name);
-                }
-            }
-
-            string path = Path.Join(imageDir, "output.jpg");
-            bitmap.Save(path);
-            Console.WriteLine($"Processed image is saved as {path}");
-        }
-
-        private void drawObjectOnBitmap(Bitmap bmp, Rectangle rect, float score, string name)
-        {
-            using (Graphics graphic = Graphics.FromImage(bmp))
-            {
-                graphic.SmoothingMode = SmoothingMode.AntiAlias;
-                
-                using (Pen pen = new Pen(Color.Red, 2))
-                {
-                    graphic.DrawRectangle(pen, rect);
-
-                    Point p = new Point(rect.Right + 5, rect.Top + 5);
-                    string text = string.Format("{0}:{1}%", name, (int)(score * 100));
-                    graphic.DrawString(text, new Font("Verdana", 8), Brushes.Red, p);
-                }
-            }
-        }
-
-        public Graph BuildGraph() => throw new NotImplementedException();
-        public void Train(Session sess) => throw new NotImplementedException();
-        public void Test(Session sess) => throw new NotImplementedException();
-    }
-}

test/TensorFlowNET.Examples/ImageProcessing/ObjectDetection/Main.cs

@@ -1,88 +0,0 @@
-using System;
-using System.Collections.Generic;
-using System.IO;
-using System.Text;
-using Tensorflow;
-using Tensorflow.Contrib.Train;
-using Tensorflow.Estimators;
-using Tensorflow.Models.ObjectDetection;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples.ImageProcessing.ObjectDetection
-{
-    public class Main : IExample
-    {
-        public bool Enabled { get; set; } = true;
-        public bool IsImportingGraph { get; set; } = true;
-
-        public string Name => "Object Detection API";
-
-        ModelLib model_lib = new ModelLib();
-
-        string model_dir = "D:/Projects/PythonLab/tf-models/research/object_detection/models/model";
-        string pipeline_config_path = "ObjectDetection/Models/faster_rcnn_resnet101_voc07.config";
-        int num_train_steps = 50;
-        int sample_1_of_n_eval_examples = 1;
-        int sample_1_of_n_eval_on_train_examples = 5;
-
-        public bool Run()
-        {
-            var config = tf.estimator.RunConfig(model_dir: model_dir);
-
-            var train_and_eval_dict = model_lib.create_estimator_and_inputs(run_config: config,
-                hparams: new HParams(true),
-                pipeline_config_path: pipeline_config_path,
-                train_steps: num_train_steps,
-                sample_1_of_n_eval_examples: sample_1_of_n_eval_examples,
-                sample_1_of_n_eval_on_train_examples: sample_1_of_n_eval_on_train_examples);
-
-            var estimator = train_and_eval_dict.estimator;
-            var train_input_fn = train_and_eval_dict.train_input_fn;
-            var eval_input_fns = train_and_eval_dict.eval_input_fns;
-            var eval_on_train_input_fn = train_and_eval_dict.eval_on_train_input_fn;
-            var predict_input_fn = train_and_eval_dict.predict_input_fn;
-            var train_steps = train_and_eval_dict.train_steps;
-
-            var (train_spec, eval_specs) = model_lib.create_train_and_eval_specs(train_input_fn,
-                eval_input_fns,
-                eval_on_train_input_fn,
-                predict_input_fn,
-                train_steps,
-                eval_on_train_data: false);
-
-            // Currently only a single Eval Spec is allowed.
-            tf.estimator.train_and_evaluate(estimator, train_spec, eval_specs[0]);
-
-            return true;
-        }
-
-        public Graph BuildGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public Graph ImportGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Predict(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void PrepareData()
-        {
-        }
-
-        public void Train(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        void IExample.Test(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-    }
-}

test/TensorFlowNET.Examples/ImageProcessing/RetrainImageClassifier.cs

@@ -1,791 +0,0 @@
-/*****************************************************************************
-   Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
-******************************************************************************/
-
-using Google.Protobuf;
-using NumSharp;
-using System;
-using System.Collections.Generic;
-using System.Diagnostics;
-using System.IO;
-using System.Linq;
-using System.Threading.Tasks;
-using Tensorflow;
-using TensorFlowNET.Examples.Utility;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples
-{
-    /// <summary>
-    /// In this tutorial, we will reuse the feature extraction capabilities from powerful image classifiers trained on ImageNet 
-    /// and simply train a new classification layer on top. Transfer learning is a technique that shortcuts much of this 
-    /// by taking a piece of a model that has already been trained on a related task and reusing it in a new model.
-    /// 
-    /// https://www.tensorflow.org/hub/tutorials/image_retraining
-    /// </summary>
-    public class RetrainImageClassifier : IExample
-    {
-        public int Priority => 16;
-
-        public bool Enabled { get; set; } = true;
-        public bool IsImportingGraph { get; set; } = true;
-
-        public string Name => "Retrain Image Classifier";
-
-        const string data_dir = "retrain_images";
-        string summaries_dir = Path.Join(data_dir, "retrain_logs");
-        string image_dir = Path.Join(data_dir, "flower_photos");
-        string bottleneck_dir = Path.Join(data_dir, "bottleneck");
-        string output_graph = Path.Join(data_dir, "output_graph.pb");
-        string output_labels = Path.Join(data_dir, "output_labels.txt");
-        // The location where variable checkpoints will be stored.
-        string CHECKPOINT_NAME = Path.Join(data_dir, "_retrain_checkpoint");
-        string tfhub_module = "https://tfhub.dev/google/imagenet/inception_v3/feature_vector/3";
-        string input_tensor_name = "Placeholder";
-        string final_tensor_name = "Score";
-        float testing_percentage = 0.1f;
-        float validation_percentage = 0.1f;
-        float learning_rate = 0.01f;
-        Tensor resized_image_tensor;
-        Dictionary<string, Dictionary<string, string[]>> image_lists;
-        int how_many_training_steps = 100;
-        int eval_step_interval = 10;
-        int train_batch_size = 100;
-        int test_batch_size = -1;
-        int validation_batch_size = 100;
-        int intermediate_store_frequency = 0;
-        int class_count = 0;
-        const int MAX_NUM_IMAGES_PER_CLASS = 134217727;
-        Operation train_step;
-        Tensor final_tensor;
-        Tensor bottleneck_input;
-        Tensor cross_entropy;
-        Tensor ground_truth_input;
-        Tensor bottleneck_tensor;
-        bool wants_quantization;
-        float test_accuracy;
-        NDArray predictions;
-
-        public bool Run()
-        {
-            PrepareData();
-
-            #region For debug purpose
-
-            // predict images
-            // Predict(null);
-
-            // load saved pb and test new images.
-            // Test(null); 
-
-            #endregion
-
-            var graph = IsImportingGraph ? ImportGraph() : BuildGraph();
-
-            using (var sess = tf.Session(graph))
-            {
-                Train(sess);
-            }
-
-            return test_accuracy > 0.75f;
-        }
-
-        /// <summary>
-        /// Runs a final evaluation on an eval graph using the test data set.
-        /// </summary>
-        /// <param name="train_session"></param>
-        /// <param name="module_spec"></param>
-        /// <param name="class_count"></param>
-        /// <param name="image_lists"></param>
-        /// <param name="jpeg_data_tensor"></param>
-        /// <param name="decoded_image_tensor"></param>
-        /// <param name="resized_image_tensor"></param>
-        /// <param name="bottleneck_tensor"></param>
-        private (float, NDArray) run_final_eval(Session train_session, object module_spec, int class_count,
-            Dictionary<string, Dictionary<string, string[]>> image_lists,
-            Tensor jpeg_data_tensor, Tensor decoded_image_tensor,
-            Tensor resized_image_tensor, Tensor bottleneck_tensor)
-        {
-            var (test_bottlenecks, test_ground_truth, test_filenames) = get_random_cached_bottlenecks(train_session, image_lists,
-                                    test_batch_size, "testing", bottleneck_dir, image_dir, jpeg_data_tensor,
-                                    decoded_image_tensor, resized_image_tensor, bottleneck_tensor, tfhub_module);
-
-            var (eval_session, _, bottleneck_input, ground_truth_input, evaluation_step,
-                prediction) = build_eval_session(class_count);
-
-            (float accuracy, NDArray prediction1) = eval_session.run((evaluation_step, prediction),
-                  (bottleneck_input, test_bottlenecks),
-                  (ground_truth_input, test_ground_truth));
-
-            print($"final test accuracy: {(accuracy * 100).ToString("G4")}% (N={len(test_bottlenecks)})");
-
-            return (accuracy, prediction1);
-        }
-
-        private (Session, Tensor, Tensor, Tensor, Tensor, Tensor)
-            build_eval_session(int class_count)
-        {
-            // If quantized, we need to create the correct eval graph for exporting.
-            var (eval_graph, bottleneck_tensor, resized_input_tensor, wants_quantization) = create_module_graph();
-            var eval_sess = tf.Session(graph: eval_graph);
-            Tensor evaluation_step = null;
-            Tensor prediction = null;
-
-            var graph = eval_graph.as_default();
-            // Add the new layer for exporting.
-            var (_, _, bottleneck_input, ground_truth_input, final_tensor) =
-                add_final_retrain_ops(class_count, final_tensor_name, bottleneck_tensor,
-                    wants_quantization, is_training: false);
-
-            // Now we need to restore the values from the training graph to the eval
-            // graph.
-            tf.train.Saver().restore(eval_sess, CHECKPOINT_NAME);
-
-            (evaluation_step, prediction) = add_evaluation_step(final_tensor,
-                                                    ground_truth_input);
-
-            return (eval_sess, resized_input_tensor, bottleneck_input, ground_truth_input,
-                evaluation_step, prediction);
-        }
-
-        /// <summary>
-        /// Adds a new softmax and fully-connected layer for training and eval.
-        /// 
-        /// We need to retrain the top layer to identify our new classes, so this function
-        /// adds the right operations to the graph, along with some variables to hold the
-        /// weights, and then sets up all the gradients for the backward pass.
-        /// 
-        /// The set up for the softmax and fully-connected layers is based on:
-        /// https://www.tensorflow.org/tutorials/mnist/beginners/index.html
-        /// </summary>
-        /// <param name="class_count"></param>
-        /// <param name="final_tensor_name"></param>
-        /// <param name="bottleneck_tensor"></param>
-        /// <param name="quantize_layer"></param>
-        /// <param name="is_training"></param>
-        /// <returns></returns>
-        private (Operation, Tensor, Tensor, Tensor, Tensor) add_final_retrain_ops(int class_count, string final_tensor_name,
-            Tensor bottleneck_tensor, bool quantize_layer, bool is_training)
-        {
-            var (batch_size, bottleneck_tensor_size) = (bottleneck_tensor.TensorShape.dims[0], bottleneck_tensor.TensorShape.dims[1]);
-            tf_with(tf.name_scope("input"), scope =>
-            {
-                bottleneck_input = tf.placeholder_with_default(
-                    bottleneck_tensor,
-                    shape: bottleneck_tensor.TensorShape.dims,
-                    name: "BottleneckInputPlaceholder");
-
-                ground_truth_input = tf.placeholder(tf.int64, new TensorShape(batch_size), name: "GroundTruthInput");
-            });
-
-            // Organizing the following ops so they are easier to see in TensorBoard.
-            string layer_name = "final_retrain_ops";
-            Tensor logits = null;
-            tf_with(tf.name_scope(layer_name), scope =>
-            {
-                RefVariable layer_weights = null;
-                tf_with(tf.name_scope("weights"), delegate
-                {
-                    var initial_value = tf.truncated_normal(new int[] { bottleneck_tensor_size, class_count }, stddev: 0.001f);
-                    layer_weights = tf.Variable(initial_value, name: "final_weights");
-                    variable_summaries(layer_weights);
-                });
-
-                RefVariable layer_biases = null;
-                tf_with(tf.name_scope("biases"), delegate
-                {
-                    layer_biases = tf.Variable(tf.zeros(new TensorShape(class_count)), name: "final_biases");
-                    variable_summaries(layer_biases);
-                });
-
-                tf_with(tf.name_scope("Wx_plus_b"), delegate
-                {
-                    logits = tf.matmul(bottleneck_input, layer_weights) + layer_biases;
-                    tf.summary.histogram("pre_activations", logits);
-                });
-            });
-
-            final_tensor = tf.nn.softmax(logits, name: final_tensor_name);
-
-            // The tf.contrib.quantize functions rewrite the graph in place for
-            // quantization. The imported model graph has already been rewritten, so upon
-            // calling these rewrites, only the newly added final layer will be
-            // transformed.
-            if (quantize_layer)
-            {
-                throw new NotImplementedException("quantize_layer");
-                /*if (is_training)
-                    tf.contrib.quantize.create_training_graph();
-                else
-                    tf.contrib.quantize.create_eval_graph();*/
-            }
-
-            tf.summary.histogram("activations", final_tensor);
-
-            // If this is an eval graph, we don't need to add loss ops or an optimizer.
-            if (!is_training)
-                return (null, null, bottleneck_input, ground_truth_input, final_tensor);
-
-            Tensor cross_entropy_mean = null;
-            tf_with(tf.name_scope("cross_entropy"), delegate
-            {
-                cross_entropy_mean = tf.losses.sparse_softmax_cross_entropy(
-                    labels: ground_truth_input, logits: logits);
-            });
-
-            tf.summary.scalar("cross_entropy", cross_entropy_mean);
-
-            tf_with(tf.name_scope("train"), delegate
-            {
-                var optimizer = tf.train.GradientDescentOptimizer(learning_rate);
-                train_step = optimizer.minimize(cross_entropy_mean);
-            });
-
-            return (train_step, cross_entropy_mean, bottleneck_input, ground_truth_input,
-                final_tensor);
-        }
-
-        private void variable_summaries(RefVariable var)
-        {
-            tf_with(tf.name_scope("summaries"), delegate
-            {
-                var mean = tf.reduce_mean(var);
-                tf.summary.scalar("mean", mean);
-                Tensor stddev = null;
-                tf_with(tf.name_scope("stddev"), delegate
-                {
-                    stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)));
-                });
-                tf.summary.scalar("stddev", stddev);
-                tf.summary.scalar("max", tf.reduce_max(var));
-                tf.summary.scalar("min", tf.reduce_min(var));
-                tf.summary.histogram("histogram", var);
-            });
-        }
-
-        private (Graph, Tensor, Tensor, bool) create_module_graph()
-        {
-            var (height, width) = (299, 299);
-            var graph = tf.Graph().as_default();
-            tf.train.import_meta_graph("graph/InceptionV3.meta");
-            Tensor resized_input_tensor = graph.OperationByName(input_tensor_name); //tf.placeholder(tf.float32, new TensorShape(-1, height, width, 3));
-                                                                                // var m = hub.Module(module_spec);
-            Tensor bottleneck_tensor = graph.OperationByName("module_apply_default/hub_output/feature_vector/SpatialSqueeze");// m(resized_input_tensor);
-            var wants_quantization = false;
-            return (graph, bottleneck_tensor, resized_input_tensor, wants_quantization);
-        }
-
-        private (NDArray, long[], string[]) get_random_cached_bottlenecks(Session sess, Dictionary<string, Dictionary<string, string[]>> image_lists,
-            int how_many, string category, string bottleneck_dir, string image_dir,
-            Tensor jpeg_data_tensor, Tensor decoded_image_tensor, Tensor resized_input_tensor,
-            Tensor bottleneck_tensor, string module_name)
-        {
-            var bottlenecks = new List<float[]>();
-            var ground_truths = new List<long>();
-            var filenames = new List<string>();
-            class_count = image_lists.Keys.Count;
-            if (how_many >= 0)
-            {
-                // Retrieve a random sample of bottlenecks.
-                foreach (var unused_i in range(how_many))
-                {
-                    int label_index = new Random().Next(class_count);
-                    string label_name = image_lists.Keys.ToArray()[label_index];
-                    int image_index = new Random().Next(MAX_NUM_IMAGES_PER_CLASS);
-                    string image_name = get_image_path(image_lists, label_name, image_index,
-                                      image_dir, category);
-                    var bottleneck = get_or_create_bottleneck(
-                      sess, image_lists, label_name, image_index, image_dir, category,
-                      bottleneck_dir, jpeg_data_tensor, decoded_image_tensor,
-                      resized_input_tensor, bottleneck_tensor, module_name);
-                    bottlenecks.Add(bottleneck);
-                    ground_truths.Add(label_index);
-                    filenames.Add(image_name);
-                }
-            }
-            else
-            {
-                // Retrieve all bottlenecks.
-                foreach (var (label_index, label_name) in enumerate(image_lists.Keys.ToArray()))
-                {
-                    foreach (var (image_index, image_name) in enumerate(image_lists[label_name][category]))
-                    {
-                        var bottleneck = get_or_create_bottleneck(
-                            sess, image_lists, label_name, image_index, image_dir, category,
-                            bottleneck_dir, jpeg_data_tensor, decoded_image_tensor,
-                            resized_input_tensor, bottleneck_tensor, module_name);
-
-                        bottlenecks.Add(bottleneck);
-                        ground_truths.Add(label_index);
-                        filenames.Add(image_name);
-                    }
-                }
-            }
-
-            return (bottlenecks.ToArray(), ground_truths.ToArray(), filenames.ToArray());
-        }
-
-        /// <summary>
-        /// Inserts the operations we need to evaluate the accuracy of our results.
-        /// </summary>
-        /// <param name="result_tensor"></param>
-        /// <param name="ground_truth_tensor"></param>
-        /// <returns></returns>
-        private (Tensor, Tensor) add_evaluation_step(Tensor result_tensor, Tensor ground_truth_tensor)
-        {
-            Tensor evaluation_step = null, correct_prediction = null, prediction = null;
-
-            tf_with(tf.name_scope("accuracy"), scope =>
-            {
-                tf_with(tf.name_scope("correct_prediction"), delegate
-                {
-                    prediction = tf.argmax(result_tensor, 1);
-                    correct_prediction = tf.equal(prediction, ground_truth_tensor);
-                });
-
-                tf_with(tf.name_scope("accuracy"), delegate
-                {
-                    evaluation_step = tf.reduce_mean(tf.cast(correct_prediction, tf.float32));
-                });
-            });
-
-            tf.summary.scalar("accuracy", evaluation_step);
-            return (evaluation_step, prediction);
-        }
-
-        /// <summary>
-        /// Ensures all the training, testing, and validation bottlenecks are cached.
-        /// </summary>
-        /// <param name="sess"></param>
-        /// <param name="image_lists"></param>
-        /// <param name="image_dir"></param>
-        /// <param name="bottleneck_dir"></param>
-        /// <param name="jpeg_data_tensor"></param>
-        /// <param name="decoded_image_tensor"></param>
-        /// <param name="resized_image_tensor"></param>
-        /// <param name="bottleneck_tensor"></param>
-        /// <param name="tfhub_module"></param>
-        private void cache_bottlenecks(Session sess, Dictionary<string, Dictionary<string, string[]>> image_lists,
-            string image_dir, string bottleneck_dir, Tensor jpeg_data_tensor, Tensor decoded_image_tensor,
-            Tensor resized_input_tensor, Tensor bottleneck_tensor, string module_name)
-        {
-            int how_many_bottlenecks = 0;
-            var kvs = image_lists.ToArray();
-            var categories = new string[] {"training", "testing", "validation"};
-            Parallel.For(0, kvs.Length, i =>
-            {
-                var (label_name, label_lists) = kvs[i];
-
-                Parallel.For(0, categories.Length, j =>
-                {
-                    var category = categories[j];
-                    var category_list = label_lists[category];
-                    foreach (var (index, unused_base_name) in enumerate(category_list))
-                    {
-                        get_or_create_bottleneck(sess, image_lists, label_name, index, image_dir, category,
-                            bottleneck_dir, jpeg_data_tensor, decoded_image_tensor,
-                            resized_input_tensor, bottleneck_tensor, module_name);
-                        how_many_bottlenecks++;
-                        if (how_many_bottlenecks % 300 == 0)
-                            print($"{how_many_bottlenecks} bottleneck files created.");
-                    }
-                });
-            });
-        }
-
-        private float[] get_or_create_bottleneck(Session sess, Dictionary<string, Dictionary<string, string[]>> image_lists,
-            string label_name, int index, string image_dir, string category, string bottleneck_dir,
-            Tensor jpeg_data_tensor, Tensor decoded_image_tensor, Tensor resized_input_tensor,
-            Tensor bottleneck_tensor, string module_name)
-        {
-            var label_lists = image_lists[label_name];
-            var sub_dir_path = Path.Join(bottleneck_dir, label_name);
-            Directory.CreateDirectory(sub_dir_path);
-            string bottleneck_path = get_bottleneck_path(image_lists, label_name, index,
-                                        bottleneck_dir, category, module_name);
-
-            if (!File.Exists(bottleneck_path))
-                create_bottleneck_file(bottleneck_path, image_lists, label_name, index,
-                                       image_dir, category, sess, jpeg_data_tensor,
-                                       decoded_image_tensor, resized_input_tensor,
-                                       bottleneck_tensor);
-            var bottleneck_string = File.ReadAllText(bottleneck_path);
-            var bottleneck_values = Array.ConvertAll(bottleneck_string.Split(','), x => float.Parse(x));
-            return bottleneck_values;
-        }
-
-        private void create_bottleneck_file(string bottleneck_path, Dictionary<string, Dictionary<string, string[]>> image_lists,
-            string label_name, int index, string image_dir, string category, Session sess,
-            Tensor jpeg_data_tensor, Tensor decoded_image_tensor, Tensor resized_input_tensor, Tensor bottleneck_tensor)
-        {
-            // Create a single bottleneck file.
-            print("Creating bottleneck at " + bottleneck_path);
-            var image_path = get_image_path(image_lists, label_name, index, image_dir, category);
-            if (!File.Exists(image_path))
-                print($"File does not exist {image_path}");
-
-            var image_data = File.ReadAllBytes(image_path);
-            var bottleneck_values = run_bottleneck_on_image(
-                sess, image_data, jpeg_data_tensor, decoded_image_tensor,
-                resized_input_tensor, bottleneck_tensor);
-            var values = bottleneck_values.Data<float>();
-            var bottleneck_string = string.Join(",", values);
-            File.WriteAllText(bottleneck_path, bottleneck_string);
-        }
-
-        /// <summary>
-        /// Runs inference on an image to extract the 'bottleneck' summary layer.
-        /// </summary>
-        /// <param name="sess">Current active TensorFlow Session.</param>
-        /// <param name="image_data">Data of raw JPEG data.</param>
-        /// <param name="image_data_tensor">Input data layer in the graph.</param>
-        /// <param name="decoded_image_tensor">Output of initial image resizing and preprocessing.</param>
-        /// <param name="resized_input_tensor">The input node of the recognition graph.</param>
-        /// <param name="bottleneck_tensor">Layer before the final softmax.</param>
-        /// <returns></returns>
-        private NDArray run_bottleneck_on_image(Session sess, byte[] image_data, Tensor image_data_tensor,
-                            Tensor decoded_image_tensor, Tensor resized_input_tensor, Tensor bottleneck_tensor)
-        {
-            // First decode the JPEG image, resize it, and rescale the pixel values.
-            var resized_input_values = sess.run(decoded_image_tensor, new FeedItem(image_data_tensor, new Tensor(image_data, TF_DataType.TF_STRING)));
-            // Then run it through the recognition network.
-            var bottleneck_values = sess.run(bottleneck_tensor, new FeedItem(resized_input_tensor, resized_input_values))[0];
-            bottleneck_values = np.squeeze(bottleneck_values);
-            return bottleneck_values;
-        }
-
-        private string get_bottleneck_path(Dictionary<string, Dictionary<string, string[]>> image_lists, string label_name, int index,
-            string bottleneck_dir, string category, string module_name)
-        {
-            module_name = (module_name.Replace("://", "~")  // URL scheme.
-                 .Replace('/', '~')  // URL and Unix paths.
-                 .Replace(':', '~').Replace('\\', '~'));  // Windows paths.
-            return get_image_path(image_lists, label_name, index, bottleneck_dir,
-                                  category) + "_" + module_name + ".txt";
-        }
-
-        private string get_image_path(Dictionary<string, Dictionary<string, string[]>> image_lists, string label_name,
-            int index, string image_dir, string category)
-        {
-            if (!image_lists.ContainsKey(label_name))
-                print($"Label does not exist {label_name}");
-
-            var label_lists = image_lists[label_name];
-            if (!label_lists.ContainsKey(category))
-                print($"Category does not exist {category}");
-            var category_list = label_lists[category];
-            if (category_list.Length == 0)
-                print($"Label {label_name} has no images in the category {category}.");
-
-            var mod_index = index % len(category_list);
-            var base_name = category_list[mod_index].Split(Path.DirectorySeparatorChar).Last();
-            var sub_dir = label_name;
-            var full_path = Path.Join(image_dir, sub_dir, base_name);
-            return full_path;
-        }
-
-        /// <summary>
-        /// Saves an graph to file, creating a valid quantized one if necessary.
-        /// </summary>
-        /// <param name="graph_file_name"></param>
-        /// <param name="class_count"></param>
-        private void save_graph_to_file(string graph_file_name, int class_count)
-        {
-            var (sess, _, _, _, _, _) = build_eval_session(class_count);
-            var graph = sess.graph;
-            var output_graph_def = tf.graph_util.convert_variables_to_constants(
-                sess, graph.as_graph_def(), new string[] { final_tensor_name });
-            File.WriteAllBytes(graph_file_name, output_graph_def.ToByteArray());
-        }
-
-        public void PrepareData()
-        {
-            // get a set of images to teach the network about the new classes
-            string fileName = "flower_photos.tgz";
-            string url = $"http://download.tensorflow.org/example_images/{fileName}";
-            Web.Download(url, data_dir, fileName);
-            Compress.ExtractTGZ(Path.Join(data_dir, fileName), data_dir);
-
-            // download graph meta data
-            url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/graph/InceptionV3.meta";
-            Web.Download(url, "graph", "InceptionV3.meta");
-
-            // download variables.data checkpoint file.
-            url = "https://github.com/SciSharp/TensorFlow.NET/raw/master/data/tfhub_modules.zip";
-            Web.Download(url, data_dir, "tfhub_modules.zip");
-            Compress.UnZip(Path.Join(data_dir, "tfhub_modules.zip"), "tfhub_modules");
-
-            // Prepare necessary directories that can be used during training
-            Directory.CreateDirectory(summaries_dir);
-            Directory.CreateDirectory(bottleneck_dir);
-
-            // Look at the folder structure, and create lists of all the images.
-            image_lists = create_image_lists();
-            class_count = len(image_lists);
-            if (class_count == 0)
-                print($"No valid folders of images found at {image_dir}");
-            if (class_count == 1)
-                print("Only one valid folder of images found at " +
-                     image_dir +
-                     " - multiple classes are needed for classification.");
-        }
-
-        private (Tensor, Tensor) add_jpeg_decoding()
-        {
-            // height, width, depth
-            var input_dim = (299, 299, 3);
-            var jpeg_data = tf.placeholder(tf.@string, name: "DecodeJPGInput");
-            var decoded_image = tf.image.decode_jpeg(jpeg_data, channels: input_dim.Item3);
-            // Convert from full range of uint8 to range [0,1] of float32.
-            var decoded_image_as_float = tf.image.convert_image_dtype(decoded_image, tf.float32);
-            var decoded_image_4d = tf.expand_dims(decoded_image_as_float, 0);
-            var resize_shape = tf.stack(new int[] { input_dim.Item1, input_dim.Item2 });
-            var resize_shape_as_int = tf.cast(resize_shape, dtype: tf.int32);
-            var resized_image = tf.image.resize_bilinear(decoded_image_4d, resize_shape_as_int);
-            return (jpeg_data, resized_image);
-        }
-
-        /// <summary>
-        /// Builds a list of training images from the file system.
-        /// </summary>
-        private Dictionary<string, Dictionary<string, string[]>> create_image_lists()
-        {
-            var sub_dirs = tf.gfile.Walk(image_dir)
-                .Select(x => x.Item1)
-                .OrderBy(x => x)
-                .ToArray();
-
-            var result = new Dictionary<string, Dictionary<string, string[]>>();
-
-            foreach (var sub_dir in sub_dirs)
-            {
-                var dir_name = sub_dir.Split(Path.DirectorySeparatorChar).Last();
-                print($"Looking for images in '{dir_name}'");
-                var file_list = Directory.GetFiles(sub_dir);
-                if (len(file_list) < 20)
-                    print($"WARNING: Folder has less than 20 images, which may cause issues.");
-
-                var label_name = dir_name.ToLower();
-                result[label_name] = new Dictionary<string, string[]>();
-                int testing_count = (int)Math.Floor(file_list.Length * testing_percentage);
-                int validation_count = (int)Math.Floor(file_list.Length * validation_percentage);
-                result[label_name]["testing"] = file_list.Take(testing_count).ToArray();
-                result[label_name]["validation"] = file_list.Skip(testing_count).Take(validation_count).ToArray();
-                result[label_name]["training"] = file_list.Skip(testing_count + validation_count).ToArray();
-            }
-
-            return result;
-        }
-
-        public Graph ImportGraph()
-        {
-            Graph graph;
-
-            // Set up the pre-trained graph.
-            (graph, bottleneck_tensor, resized_image_tensor, wants_quantization) =
-                create_module_graph();
-
-            // Add the new layer that we'll be training.
-            (train_step, cross_entropy, bottleneck_input,
-             ground_truth_input, final_tensor) = add_final_retrain_ops(
-                 class_count, final_tensor_name, bottleneck_tensor,
-                 wants_quantization, is_training: true);
-
-            return graph;
-        }
-
-        public Graph BuildGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Train(Session sess)
-        {
-            var sw = new Stopwatch();
-
-            // Initialize all weights: for the module to their pretrained values,
-            // and for the newly added retraining layer to random initial values.
-            var init = tf.global_variables_initializer();
-            sess.run(init);
-
-            var (jpeg_data_tensor, decoded_image_tensor) = add_jpeg_decoding();
-
-            // We'll make sure we've calculated the 'bottleneck' image summaries and
-            // cached them on disk.
-            cache_bottlenecks(sess, image_lists, image_dir,
-                    bottleneck_dir, jpeg_data_tensor,
-                    decoded_image_tensor, resized_image_tensor,
-                    bottleneck_tensor, tfhub_module);
-
-            // Create the operations we need to evaluate the accuracy of our new layer.
-            var (evaluation_step, _) = add_evaluation_step(final_tensor, ground_truth_input);
-
-            // Merge all the summaries and write them out to the summaries_dir
-            var merged = tf.summary.merge_all();
-            var train_writer = tf.summary.FileWriter(summaries_dir + "/train", sess.graph);
-            var validation_writer = tf.summary.FileWriter(summaries_dir + "/validation", sess.graph);
-
-            // Create a train saver that is used to restore values into an eval graph
-            // when exporting models.
-            var train_saver = tf.train.Saver();
-            train_saver.save(sess, CHECKPOINT_NAME);
-
-            sw.Restart();
-
-            for (int i = 0; i < how_many_training_steps; i++)
-            {
-                var (train_bottlenecks, train_ground_truth, _) = get_random_cached_bottlenecks(
-                     sess, image_lists, train_batch_size, "training",
-                     bottleneck_dir, image_dir, jpeg_data_tensor,
-                     decoded_image_tensor, resized_image_tensor, bottleneck_tensor,
-                     tfhub_module);
-
-                // Feed the bottlenecks and ground truth into the graph, and run a training
-                // step. Capture training summaries for TensorBoard with the `merged` op.
-                var results = sess.run(
-                      new ITensorOrOperation[] { merged, train_step },
-                      new FeedItem(bottleneck_input, train_bottlenecks),
-                      new FeedItem(ground_truth_input, train_ground_truth));
-                var train_summary = results[0];
-
-                // TODO
-                // train_writer.add_summary(train_summary, i);
-
-                // Every so often, print out how well the graph is training.
-                bool is_last_step = (i + 1 == how_many_training_steps);
-                if ((i % eval_step_interval) == 0 || is_last_step)
-                {
-                    (float train_accuracy, float cross_entropy_value) = sess.run((evaluation_step, cross_entropy),
-                        (bottleneck_input, train_bottlenecks),
-                        (ground_truth_input, train_ground_truth));
-                    print($"{DateTime.Now}: Step {i + 1}: Train accuracy = {train_accuracy * 100}%,  Cross entropy = {cross_entropy_value.ToString("G4")}");
-
-                    var (validation_bottlenecks, validation_ground_truth, _) = get_random_cached_bottlenecks(
-                        sess, image_lists, validation_batch_size, "validation",
-                        bottleneck_dir, image_dir, jpeg_data_tensor,
-                        decoded_image_tensor, resized_image_tensor, bottleneck_tensor,
-                        tfhub_module);
-
-                    // Run a validation step and capture training summaries for TensorBoard
-                    // with the `merged` op.
-                    (_, float validation_accuracy) = sess.run((merged, evaluation_step),
-                        (bottleneck_input, validation_bottlenecks),
-                        (ground_truth_input, validation_ground_truth));
-
-                    // validation_writer.add_summary(validation_summary, i);
-                    print($"{DateTime.Now}: Step {i + 1}: Validation accuracy = {validation_accuracy * 100}% (N={len(validation_bottlenecks)}) {sw.ElapsedMilliseconds}ms");
-                    sw.Restart();
-                }
-
-                // Store intermediate results
-                int intermediate_frequency = intermediate_store_frequency;
-                if (intermediate_frequency > 0 && i % intermediate_frequency == 0 && i > 0)
-                {
-
-                }
-            }
-
-            // After training is complete, force one last save of the train checkpoint.
-            train_saver.save(sess, CHECKPOINT_NAME);
-
-            // We've completed all our training, so run a final test evaluation on
-            // some new images we haven't used before.
-            (test_accuracy, predictions) = run_final_eval(sess, null, class_count, image_lists,
-                           jpeg_data_tensor, decoded_image_tensor, resized_image_tensor,
-                           bottleneck_tensor);
-
-            // Write out the trained graph and labels with the weights stored as
-            // constants.
-            print($"Save final result to : {output_graph}");
-            save_graph_to_file(output_graph, class_count);
-            File.WriteAllText(output_labels, string.Join("\n", image_lists.Keys));
-        }
-
-        /// <summary>
-        /// Prediction
-        /// labels mapping, it's from output_lables.txt
-        /// 0 - daisy
-        /// 1 - dandelion
-        /// 2 - roses
-        /// 3 - sunflowers
-        /// 4 - tulips
-        /// </summary>
-        /// <param name="sess_"></param>
-        public void Predict(Session sess_)
-        {
-            if (!File.Exists(output_graph))
-                return;
-
-            var labels = File.ReadAllLines(output_labels);
-
-            // predict image
-            var img_path = Path.Join(image_dir, "daisy", "5547758_eea9edfd54_n.jpg");
-            var fileBytes = ReadTensorFromImageFile(img_path);
-
-            // import graph and variables
-            var graph = new Graph();
-            graph.Import(output_graph, "");
-
-            Tensor input = graph.OperationByName(input_tensor_name);
-            Tensor output = graph.OperationByName(final_tensor_name);
-
-            using (var sess = tf.Session(graph))
-            {
-                var result = sess.run(output, (input, fileBytes));
-                var prob = np.squeeze(result);
-                var idx = np.argmax(prob);
-                print($"Prediction result: [{labels[idx]} {prob[idx]}] for {img_path}.");
-            }
-        }
-
-        private NDArray ReadTensorFromImageFile(string file_name,
-                        int input_height = 299,
-                        int input_width = 299,
-                        int input_mean = 0,
-                        int input_std = 255)
-        {
-            var graph = tf.Graph().as_default();
-
-            var file_reader = tf.read_file(file_name, "file_reader");
-            var image_reader = tf.image.decode_jpeg(file_reader, channels: 3, name: "jpeg_reader");
-            var caster = tf.cast(image_reader, tf.float32);
-            var dims_expander = tf.expand_dims(caster, 0);
-            var resize = tf.constant(new int[] { input_height, input_width });
-            var bilinear = tf.image.resize_bilinear(dims_expander, resize);
-            var sub = tf.subtract(bilinear, new float[] { input_mean });
-            var normalized = tf.divide(sub, new float[] { input_std });
-
-            using (var sess = tf.Session(graph))
-                return sess.run(normalized);
-        }
-
-        public void Test(Session sess_)
-        {
-            if (!File.Exists(output_graph))
-                return;
-
-            var graph = new Graph();
-            graph.Import(output_graph);
-            var (jpeg_data_tensor, decoded_image_tensor) = add_jpeg_decoding();
-
-            tf_with(tf.Session(graph), sess =>
-            {
-                (test_accuracy, predictions) = run_final_eval(sess, null, class_count, image_lists,
-                               jpeg_data_tensor, decoded_image_tensor, resized_image_tensor,
-                               bottleneck_tensor);
-            });
-        }
-    }
-}

test/TensorFlowNET.Examples/ImageProcessing/YOLO/Dataset.cs

@@ -1,54 +0,0 @@
-using NumSharp;
-using System;
-using System.Collections.Generic;
-using System.IO;
-using System.Text;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples.ImageProcessing.YOLO
-{
-    public class Dataset
-    {
-        string annot_path;
-        int[] input_sizes;
-        int batch_size;
-        bool data_aug;
-        int[] train_input_sizes;
-        NDArray strides;
-        NDArray anchors;
-        Dictionary<int, string> classes;
-        int num_classes;
-        int anchor_per_scale;
-        int max_bbox_per_scale;
-        string[] annotations;
-        int num_samples;
-        int batch_count;
-
-        public int Length = 0;
-
-        public Dataset(string dataset_type, Config cfg)
-        {
-            annot_path = dataset_type == "train" ? cfg.TRAIN.ANNOT_PATH : cfg.TEST.ANNOT_PATH;
-            input_sizes = dataset_type == "train" ? cfg.TRAIN.INPUT_SIZE : cfg.TEST.INPUT_SIZE;
-            batch_size = dataset_type == "train" ? cfg.TRAIN.BATCH_SIZE : cfg.TEST.BATCH_SIZE;
-            data_aug = dataset_type == "train" ? cfg.TRAIN.DATA_AUG : cfg.TEST.DATA_AUG;
-            train_input_sizes = cfg.TRAIN.INPUT_SIZE;
-            strides = np.array(cfg.YOLO.STRIDES);
-
-            classes = Utils.read_class_names(cfg.YOLO.CLASSES);
-            num_classes = classes.Count;
-            anchors = np.array(Utils.get_anchors(cfg.YOLO.ANCHORS));
-            anchor_per_scale = cfg.YOLO.ANCHOR_PER_SCALE;
-            max_bbox_per_scale = 150;
-
-            annotations = load_annotations();
-            num_samples = len(annotations);
-            batch_count = 0;
-        }
-
-        string[] load_annotations()
-        {
-            return File.ReadAllLines(annot_path);
-        }
-    }
-}

test/TensorFlowNET.Examples/ImageProcessing/YOLO/Main.cs

@@ -1,243 +0,0 @@
-using System;
-using System.Collections.Generic;
-using System.IO;
-using System.Linq;
-using System.Text;
-using Tensorflow;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples.ImageProcessing.YOLO
-{
-    /// <summary>
-    /// Implementation of YOLO v3 object detector in Tensorflow
-    /// https://github.com/YunYang1994/tensorflow-yolov3
-    /// </summary>
-    public class Main : IExample
-    {
-        public bool Enabled { get; set; } = true;
-        public bool IsImportingGraph { get; set; } = false;
-        public string Name => "YOLOv3";
-
-        #region args
-        Dictionary<int, string> classes;
-        int num_classes;
-        float learn_rate_init;
-        float learn_rate_end;
-        int first_stage_epochs;
-        int second_stage_epochs;
-        int warmup_periods;
-        string time;
-        float moving_ave_decay;
-        int max_bbox_per_scale;
-        int steps_per_period;
-
-        Dataset trainset, testset;
-
-        Config cfg;
-
-        Tensor input_data;
-        Tensor label_sbbox;
-        Tensor label_mbbox;
-        Tensor label_lbbox;
-        Tensor true_sbboxes;
-        Tensor true_mbboxes;
-        Tensor true_lbboxes;
-        Tensor trainable;
-
-        Session sess;
-        YOLOv3 model;
-        VariableV1[] net_var;
-        Tensor giou_loss, conf_loss, prob_loss;
-        RefVariable global_step;
-        Tensor learn_rate;
-        Tensor loss;
-        List<RefVariable> first_stage_trainable_var_list;
-        Operation train_op_with_frozen_variables;
-        Operation train_op_with_all_variables;
-        Saver loader;
-        Saver saver;
-        #endregion
-
-        public bool Run()
-        {
-            PrepareData();
-
-            var graph = IsImportingGraph ? ImportGraph() : BuildGraph();
-
-            var options = new SessionOptions();
-            options.SetConfig(new ConfigProto { AllowSoftPlacement = true });
-            using (var sess = tf.Session(graph, opts: options))
-            {
-                Train(sess);
-            }
-
-            return true;
-        }
-
-        public void Train(Session sess)
-        {
-            sess.run(tf.global_variables_initializer());
-            print($"=> Restoring weights from: {cfg.TRAIN.INITIAL_WEIGHT} ... ");
-            loader.restore(sess, cfg.TRAIN.INITIAL_WEIGHT);
-        }
-
-        public void Test(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public Graph BuildGraph()
-        {
-            var graph = new Graph().as_default();
-
-            tf_with(tf.name_scope("define_input"), scope =>
-            {
-                input_data = tf.placeholder(dtype: tf.float32, name: "input_data");
-                label_sbbox = tf.placeholder(dtype: tf.float32, name: "label_sbbox");
-                label_mbbox = tf.placeholder(dtype: tf.float32, name: "label_mbbox");
-                label_lbbox = tf.placeholder(dtype: tf.float32, name: "label_lbbox");
-                true_sbboxes = tf.placeholder(dtype: tf.float32, name: "sbboxes");
-                true_mbboxes = tf.placeholder(dtype: tf.float32, name: "mbboxes");
-                true_lbboxes = tf.placeholder(dtype: tf.float32, name: "lbboxes");
-                trainable = tf.placeholder(dtype: tf.@bool, name: "training");
-            });
-
-            tf_with(tf.name_scope("define_loss"), scope =>
-            {
-                model = new YOLOv3(cfg, input_data, trainable);
-                net_var = tf.global_variables();
-                (giou_loss, conf_loss, prob_loss) = model.compute_loss(
-                                                    label_sbbox, label_mbbox, label_lbbox,
-                                                    true_sbboxes, true_mbboxes, true_lbboxes);
-                loss = giou_loss + conf_loss + prob_loss;
-            });
-
-            Tensor global_step_update = null;
-            tf_with(tf.name_scope("learn_rate"), scope =>
-            {
-                global_step = tf.Variable(1.0, dtype: tf.float64, trainable: false, name: "global_step");
-                var warmup_steps = tf.constant(warmup_periods * steps_per_period,
-                                        dtype: tf.float64, name: "warmup_steps");
-                var train_steps = tf.constant((first_stage_epochs + second_stage_epochs) * steps_per_period,
-                                        dtype: tf.float64, name: "train_steps");
-
-                learn_rate = tf.cond(
-                    pred: global_step < warmup_steps,
-                    true_fn: delegate
-                    {
-                        return global_step / warmup_steps * learn_rate_init;
-                    },
-                    false_fn: delegate
-                    {
-                        return learn_rate_end + 0.5 * (learn_rate_init - learn_rate_end) *
-                            (1 + tf.cos(
-                                (global_step - warmup_steps) / (train_steps - warmup_steps) * Math.PI));
-                    }
-                );
-
-                global_step_update = tf.assign_add(global_step, 1.0f);
-            });
-
-            Operation moving_ave = null;
-            tf_with(tf.name_scope("define_weight_decay"), scope =>
-            {
-                var emv = tf.train.ExponentialMovingAverage(moving_ave_decay);
-                var vars = tf.trainable_variables().Select(x => (RefVariable)x).ToArray();
-                moving_ave = emv.apply(vars);
-            });
-
-            tf_with(tf.name_scope("define_first_stage_train"), scope =>
-            {
-                first_stage_trainable_var_list = new List<RefVariable>();
-                foreach (var var in tf.trainable_variables())
-                {
-                    var var_name = var.op.name;
-                    var var_name_mess = var_name.Split('/');
-                    if (new[] { "conv_sbbox", "conv_mbbox", "conv_lbbox" }.Contains(var_name_mess[0]))
-                        first_stage_trainable_var_list.Add(var as RefVariable);
-                }
-
-                var adam = tf.train.AdamOptimizer(learn_rate);
-                var first_stage_optimizer = adam.minimize(loss, var_list: first_stage_trainable_var_list);
-                tf_with(tf.control_dependencies(tf.get_collection<Operation>(tf.GraphKeys.UPDATE_OPS).ToArray()), delegate
-                {
-                    tf_with(tf.control_dependencies(new ITensorOrOperation[] { first_stage_optimizer, global_step_update }), delegate
-                    {
-                        tf_with(tf.control_dependencies(new[] { moving_ave }), delegate
-                        {
-                            train_op_with_frozen_variables = tf.no_op();
-                        });
-                    });
-                });
-            });
-
-            tf_with(tf.name_scope("define_second_stage_train"), delegate
-            {
-                var second_stage_trainable_var_list = tf.trainable_variables().Select(x => x as RefVariable).ToList();
-                var adam = tf.train.AdamOptimizer(learn_rate);
-                var second_stage_optimizer = adam.minimize(loss, var_list: second_stage_trainable_var_list);
-                tf_with(tf.control_dependencies(tf.get_collection<Operation>(tf.GraphKeys.UPDATE_OPS).ToArray()), delegate
-                {
-                    tf_with(tf.control_dependencies(new ITensorOrOperation[] { second_stage_optimizer, global_step_update }), delegate
-                    {
-                        tf_with(tf.control_dependencies(new[] { moving_ave }), delegate
-                        {
-                            train_op_with_all_variables = tf.no_op();
-                        });
-                    });
-                });
-            });
-
-            tf_with(tf.name_scope("loader_and_saver"), delegate
-            {
-                loader = tf.train.Saver(net_var);
-                saver = tf.train.Saver(tf.global_variables(), max_to_keep: 10);
-            });
-
-            tf_with(tf.name_scope("summary"), delegate
-            {
-                tf.summary.scalar("learn_rate", learn_rate);
-                tf.summary.scalar("giou_loss", giou_loss);
-                tf.summary.scalar("conf_loss", conf_loss);
-                tf.summary.scalar("prob_loss", prob_loss);
-                tf.summary.scalar("total_loss", loss);
-            });
-
-            return graph;
-        }
-
-        public Graph ImportGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Predict(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void PrepareData()
-        {
-            cfg = new Config(Name);
-
-            string dataDir = Path.Combine(Name, "data");
-            Directory.CreateDirectory(dataDir);
-
-            classes = Utils.read_class_names(cfg.YOLO.CLASSES);
-            num_classes = classes.Count;
-
-            learn_rate_init = cfg.TRAIN.LEARN_RATE_INIT;
-            learn_rate_end = cfg.TRAIN.LEARN_RATE_END;
-            first_stage_epochs = cfg.TRAIN.FISRT_STAGE_EPOCHS;
-            second_stage_epochs = cfg.TRAIN.SECOND_STAGE_EPOCHS;
-            warmup_periods = cfg.TRAIN.WARMUP_EPOCHS;
-            DateTime now = DateTime.Now;
-            time = $"{now.Year}-{now.Month}-{now.Day}-{now.Hour}-{now.Minute}-{now.Minute}";
-            moving_ave_decay = cfg.YOLO.MOVING_AVE_DECAY;
-            max_bbox_per_scale = 150;
-            trainset = new Dataset("train", cfg);
-            testset = new Dataset("test", cfg);
-            steps_per_period = trainset.Length;
-        }
-    }
-}

test/TensorFlowNET.Examples/ImageProcessing/YOLO/Utils.cs

@@ -1,27 +0,0 @@
-using NumSharp;
-using System;
-using System.Collections.Generic;
-using System.IO;
-using System.Linq;
-using System.Text;
-
-namespace TensorFlowNET.Examples.ImageProcessing.YOLO
-{
-    class Utils
-    {
-        public static Dictionary<int, string> read_class_names(string file)
-        {
-            var classes = new Dictionary<int, string>();
-            foreach (var line in File.ReadAllLines(file))
-                classes[classes.Count] = line;
-            return classes;
-        }
-
-        public static NDArray get_anchors(string file)
-        {
-            return np.array(File.ReadAllText(file).Split(',')
-                .Select(x => float.Parse(x))
-                .ToArray()).reshape(3, 3, 2);
-        }
-    }
-}

test/TensorFlowNET.Examples/ImageProcessing/YOLO/YOLOv3.cs

@@ -1,291 +0,0 @@
-using NumSharp;
-using System;
-using System.Collections.Generic;
-using System.Text;
-using Tensorflow;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples.ImageProcessing.YOLO
-{
-    public class YOLOv3
-    {
-        Config cfg;
-        Tensor trainable;
-        Tensor input_data;
-        Dictionary<int, string> classes;
-        int num_class;
-        NDArray strides;
-        NDArray anchors;
-        int anchor_per_scale;
-        float iou_loss_thresh;
-        string upsample_method;
-        Tensor conv_lbbox;
-        Tensor conv_mbbox;
-        Tensor conv_sbbox;
-        Tensor pred_sbbox;
-        Tensor pred_mbbox;
-        Tensor pred_lbbox;
-
-        public YOLOv3(Config cfg_, Tensor input_data_, Tensor trainable_)
-        {
-            cfg = cfg_;
-            input_data = input_data_;
-            trainable = trainable_;
-            classes = Utils.read_class_names(cfg.YOLO.CLASSES);
-            num_class = len(classes);
-            strides = np.array(cfg.YOLO.STRIDES);
-            anchors = Utils.get_anchors(cfg.YOLO.ANCHORS);
-            anchor_per_scale = cfg.YOLO.ANCHOR_PER_SCALE;
-            iou_loss_thresh = cfg.YOLO.IOU_LOSS_THRESH;
-            upsample_method = cfg.YOLO.UPSAMPLE_METHOD;
-
-            (conv_lbbox, conv_mbbox, conv_sbbox) = __build_nework(input_data);
-
-            tf_with(tf.variable_scope("pred_sbbox"), scope =>
-            {
-                pred_sbbox = decode(conv_sbbox, anchors[0], strides[0]);
-            });
-
-            tf_with(tf.variable_scope("pred_mbbox"), scope =>
-            {
-                pred_mbbox = decode(conv_mbbox, anchors[1], strides[1]);
-            });
-
-            tf_with(tf.variable_scope("pred_lbbox"), scope =>
-            {
-                pred_lbbox = decode(conv_lbbox, anchors[2], strides[2]);
-            });
-        }
-
-        private (Tensor, Tensor, Tensor) __build_nework(Tensor input_data)
-        {
-            Tensor route_1, route_2;
-            (route_1, route_2, input_data) = backbone.darknet53(input_data, trainable);
-            input_data = common.convolutional(input_data, new[] { 1, 1, 1024, 512 }, trainable, "conv52");
-            input_data = common.convolutional(input_data, new[] { 3, 3, 512, 1024 }, trainable, "conv53");
-            input_data = common.convolutional(input_data, new[] { 1, 1, 1024, 512 }, trainable, "conv54");
-            input_data = common.convolutional(input_data, new[] { 3, 3, 512, 1024 }, trainable, "conv55");
-            input_data = common.convolutional(input_data, new[] { 1, 1, 1024, 512 }, trainable, "conv56");
-
-            var conv_lobj_branch = common.convolutional(input_data, new[] { 3, 3, 512, 1024 }, trainable, name: "conv_lobj_branch");
-            var conv_lbbox = common.convolutional(conv_lobj_branch, new[] { 1, 1, 1024, 3 * (num_class + 5) },
-                                          trainable: trainable, name: "conv_lbbox", activate: false, bn: false);
-
-            input_data = common.convolutional(input_data, new[] { 1, 1, 512, 256 }, trainable, "conv57");
-            input_data = common.upsample(input_data, name: "upsample0", method: upsample_method);
-
-            tf_with(tf.variable_scope("route_1"), delegate
-            {
-                input_data = tf.concat(new[] { input_data, route_2 }, axis: -1);
-            });
-
-            input_data = common.convolutional(input_data, new[] { 1, 1, 768, 256 }, trainable, "conv58");
-            input_data = common.convolutional(input_data, new[] { 3, 3, 256, 512 }, trainable, "conv59");
-            input_data = common.convolutional(input_data, new[] { 1, 1, 512, 256 }, trainable, "conv60");
-            input_data = common.convolutional(input_data, new[] { 3, 3, 256, 512 }, trainable, "conv61");
-            input_data = common.convolutional(input_data, new[] { 1, 1, 512, 256 }, trainable, "conv62");
-
-            var conv_mobj_branch = common.convolutional(input_data, new[] { 3, 3, 256, 512 }, trainable, name: "conv_mobj_branch");
-            conv_mbbox = common.convolutional(conv_mobj_branch, new[] { 1, 1, 512, 3 * (num_class + 5) },
-                                          trainable: trainable, name: "conv_mbbox", activate: false, bn: false);
-
-            input_data = common.convolutional(input_data, new[] { 1, 1, 256, 128 }, trainable, "conv63");
-            input_data = common.upsample(input_data, name: "upsample1", method: upsample_method);
-
-            tf_with(tf.variable_scope("route_2"), delegate
-            {
-                input_data = tf.concat(new[] { input_data, route_1 }, axis: -1);
-            });
-
-            input_data = common.convolutional(input_data, new[] { 1, 1, 384, 128 }, trainable, "conv64");
-            input_data = common.convolutional(input_data, new[] { 3, 3, 128, 256 }, trainable, "conv65");
-            input_data = common.convolutional(input_data, new[] { 1, 1, 256, 128 }, trainable, "conv66");
-            input_data = common.convolutional(input_data, new[] { 3, 3, 128, 256 }, trainable, "conv67");
-            input_data = common.convolutional(input_data, new[] { 1, 1, 256, 128 }, trainable, "conv68");
-
-            var conv_sobj_branch = common.convolutional(input_data, new[] { 3, 3, 128, 256 }, trainable, name: "conv_sobj_branch");
-            conv_sbbox = common.convolutional(conv_sobj_branch, new[] { 1, 1, 256, 3 * (num_class + 5) },
-                                          trainable: trainable, name: "conv_sbbox", activate: false, bn: false);
-            
-            return (conv_lbbox, conv_mbbox, conv_sbbox);
-        }
-
-        private Tensor decode(Tensor conv_output, NDArray anchors, int stride)
-        {
-            var conv_shape = tf.shape(conv_output);
-            var batch_size = conv_shape[0];
-            var output_size = conv_shape[1];
-            anchor_per_scale = len(anchors);
-
-            conv_output = tf.reshape(conv_output, new object[] { batch_size, output_size, output_size, anchor_per_scale, 5 + num_class });
-
-            var conv_raw_dxdy = conv_output[":", ":", ":", ":", "0:2"];
-            var conv_raw_dwdh = conv_output[":", ":", ":", ":", "2:4"];
-            var conv_raw_conf = conv_output[":", ":", ":", ":", "4:5"];
-            var conv_raw_prob = conv_output[":", ":", ":", ":", "5:"];
-
-            var y = tf.tile(tf.range(output_size, dtype: tf.int32)[":", tf.newaxis], new object[] { 1, output_size });
-            var x = tf.tile(tf.range(output_size, dtype: tf.int32)[tf.newaxis, ":"], new object[] { output_size, 1 });
-
-            var xy_grid = tf.concat(new[] { x[":", ":", tf.newaxis], y[":", ":", tf.newaxis] }, axis: -1);
-            xy_grid = tf.tile(xy_grid[tf.newaxis, ":", ":", tf.newaxis, ":"], new object[] { batch_size, 1, 1, anchor_per_scale, 1 });
-            xy_grid = tf.cast(xy_grid, tf.float32);
-
-            var pred_xy = (tf.sigmoid(conv_raw_dxdy) + xy_grid) * stride;
-            var pred_wh = (tf.exp(conv_raw_dwdh) * anchors) * stride;
-            var pred_xywh = tf.concat(new[] { pred_xy, pred_wh }, axis: -1);
-
-            var pred_conf = tf.sigmoid(conv_raw_conf);
-            var pred_prob = tf.sigmoid(conv_raw_prob);
-
-            return tf.concat(new[] { pred_xywh, pred_conf, pred_prob }, axis: -1);
-        }
-
-        public (Tensor, Tensor, Tensor) compute_loss(Tensor label_sbbox, Tensor label_mbbox, Tensor label_lbbox,
-            Tensor true_sbbox, Tensor true_mbbox, Tensor true_lbbox)
-        {
-            Tensor giou_loss = null, conf_loss = null, prob_loss = null;
-            (Tensor, Tensor, Tensor) loss_sbbox = (null, null, null);
-            (Tensor, Tensor, Tensor) loss_mbbox = (null, null, null);
-            (Tensor, Tensor, Tensor) loss_lbbox = (null, null, null);
-
-            tf_with(tf.name_scope("smaller_box_loss"), delegate
-            {
-                loss_sbbox = loss_layer(conv_sbbox, pred_sbbox, label_sbbox, true_sbbox,
-                                         anchors: anchors[0], stride: strides[0]);
-            });
-
-            tf_with(tf.name_scope("medium_box_loss"), delegate
-            {
-                loss_mbbox = loss_layer(conv_mbbox, pred_mbbox, label_mbbox, true_mbbox,
-                                         anchors: anchors[1], stride: strides[1]);
-            });
-
-            tf_with(tf.name_scope("bigger_box_loss"), delegate
-            {
-                loss_lbbox = loss_layer(conv_lbbox, pred_lbbox, label_lbbox, true_lbbox,
-                                         anchors: anchors[2], stride: strides[2]);
-            });
-
-            tf_with(tf.name_scope("giou_loss"), delegate
-            {
-                giou_loss = loss_sbbox.Item1 + loss_mbbox.Item1 + loss_lbbox.Item1;
-            });
-
-            tf_with(tf.name_scope("conf_loss"), delegate
-            {
-                conf_loss = loss_sbbox.Item2 + loss_mbbox.Item2 + loss_lbbox.Item2;
-            });
-
-            tf_with(tf.name_scope("prob_loss"), delegate
-            {
-                prob_loss = loss_sbbox.Item3 + loss_mbbox.Item3 + loss_lbbox.Item3;
-            });
-
-            return (giou_loss, conf_loss, prob_loss);
-        }
-
-        public (Tensor, Tensor, Tensor) loss_layer(Tensor conv, Tensor pred, Tensor label, Tensor bboxes, NDArray anchors, int stride)
-        {
-            var conv_shape = tf.shape(conv);
-            var batch_size = conv_shape[0];
-            var output_size = conv_shape[1];
-            var input_size = stride * output_size;
-            conv = tf.reshape(conv, new object[] {batch_size, output_size, output_size,
-                                 anchor_per_scale, 5 + num_class });
-            var conv_raw_conf = conv[":", ":", ":", ":", "4:5"];
-            var conv_raw_prob = conv[":", ":", ":", ":", "5:"];
-
-            var pred_xywh = pred[":", ":", ":", ":", "0:4"];
-            var pred_conf = pred[":", ":", ":", ":", "4:5"];
-
-            var label_xywh = label[":", ":", ":", ":", "0:4"];
-            var respond_bbox = label[":", ":", ":", ":", "4:5"];
-            var label_prob = label[":", ":", ":", ":", "5:"];
-
-            var giou = tf.expand_dims(bbox_giou(pred_xywh, label_xywh), axis: -1);
-            input_size = tf.cast(input_size, tf.float32);
-
-            var bbox_loss_scale = 2.0 - 1.0 * label_xywh[":", ":", ":", ":", "2:3"] * label_xywh[":", ":", ":", ":", "3:4"] / (tf.sqrt(input_size));
-            var giou_loss = respond_bbox * bbox_loss_scale * (1 - giou);
-
-            var iou = bbox_iou(pred_xywh[":", ":", ":", ":", tf.newaxis, ":"], bboxes[":", tf.newaxis, tf.newaxis, tf.newaxis, ":", ":"]);
-            var max_iou = tf.expand_dims(tf.reduce_max(iou, axis: new[] { -1 }), axis: -1);
-
-            var respond_bgd = (1.0 - respond_bbox) * tf.cast(max_iou < iou_loss_thresh, tf.float32);
-
-            var conf_focal = focal(respond_bbox, pred_conf);
-
-            var conf_loss = conf_focal * (
-                respond_bbox * tf.nn.sigmoid_cross_entropy_with_logits(labels: respond_bbox, logits: conv_raw_conf) +
-                respond_bgd * tf.nn.sigmoid_cross_entropy_with_logits(labels: respond_bbox, logits: conv_raw_conf));
-
-            var prob_loss = respond_bbox * tf.nn.sigmoid_cross_entropy_with_logits(labels: label_prob, logits: conv_raw_prob);
-
-            giou_loss = tf.reduce_mean(tf.reduce_sum(giou_loss, axis: new[] { 1, 2, 3, 4 }));
-            conf_loss = tf.reduce_mean(tf.reduce_sum(conf_loss, axis: new[] { 1, 2, 3, 4 }));
-            prob_loss = tf.reduce_mean(tf.reduce_sum(prob_loss, axis: new[] { 1, 2, 3, 4 }));
-
-            return (giou_loss, conf_loss, prob_loss);
-        }
-
-        public Tensor focal(Tensor target, Tensor actual, int alpha = 1, int gamma = 2)
-        {
-            var focal_loss = alpha * tf.pow(tf.abs(target - actual), gamma);
-            return focal_loss;
-        }
-
-        public Tensor bbox_giou(Tensor boxes1, Tensor boxes2)
-        {
-            boxes1 = tf.concat(new[] { boxes1["...", ":2"] - boxes1["...", "2:"] * 0.5,
-                            boxes1["...", ":2"] + boxes1["...", "2:"] * 0.5}, axis: -1);
-            boxes2 = tf.concat(new[] { boxes2["...", ":2"] - boxes2["...", "2:"] * 0.5,
-                            boxes2["...", ":2"] + boxes2["...", "2:"] * 0.5}, axis: -1);
-
-            boxes1 = tf.concat(new[] { tf.minimum(boxes1["...", ":2"], boxes1["...", "2:"]),
-                            tf.maximum(boxes1["...", ":2"], boxes1["...", "2:"])}, axis: -1);
-            boxes2 = tf.concat(new[] { tf.minimum(boxes2["...", ":2"], boxes2["...", "2:"]),
-                            tf.maximum(boxes2["...", ":2"], boxes2["...", "2:"])}, axis: -1);
-
-            var boxes1_area = (boxes1["...", "2"] - boxes1["...", "0"]) * (boxes1["...", "3"] - boxes1["...", "1"]);
-            var boxes2_area = (boxes2["...", "2"] - boxes2["...", "0"]) * (boxes2["...", "3"] - boxes2["...", "1"]);
-
-            var left_up = tf.maximum(boxes1["...", ":2"], boxes2["...", ":2"]);
-            var right_down = tf.minimum(boxes1["...", "2:"], boxes2["...", "2:"]);
-
-            var inter_section = tf.maximum(right_down - left_up, 0.0f);
-            var inter_area = inter_section["...", "0"] * inter_section["...", "1"];
-            var union_area = boxes1_area + boxes2_area - inter_area;
-            var iou = inter_area / union_area;
-
-            var enclose_left_up = tf.minimum(boxes1["...", ":2"], boxes2["...", ":2"]);
-            var enclose_right_down = tf.maximum(boxes1["...", "2:"], boxes2["...", "2:"]);
-            var enclose = tf.maximum(enclose_right_down - enclose_left_up, 0.0);
-            var enclose_area = enclose["...", "0"] * enclose["...", "1"];
-            var giou = iou - 1.0 * (enclose_area - union_area) / enclose_area;
-
-            return giou;
-        }
-
-        public Tensor bbox_iou(Tensor boxes1, Tensor boxes2)
-        {
-            var boxes1_area = boxes1["...", "2"] * boxes1["...", "3"];
-            var boxes2_area = boxes2["...", "2"] * boxes2["...", "3"];
-
-            boxes1 = tf.concat(new[] { boxes1["...", ":2"] - boxes1["...", "2:"] * 0.5,
-                            boxes1["...", ":2"] + boxes1["...", "2:"] * 0.5}, axis: -1);
-            boxes2 = tf.concat(new[] { boxes2["...", ":2"] - boxes2["...", "2:"] * 0.5,
-                            boxes2["...", ":2"] + boxes2["...", "2:"] * 0.5}, axis: -1);
-
-            var left_up = tf.maximum(boxes1["...", ":2"], boxes2["...", ":2"]);
-            var right_down = tf.minimum(boxes1["...", "2:"], boxes2["...", "2:"]);
-
-            var inter_section = tf.maximum(right_down - left_up, 0.0);
-            var inter_area = inter_section["...", "0"] * inter_section["...", "1"];
-            var union_area = boxes1_area + boxes2_area - inter_area;
-            var iou = 1.0 * inter_area / union_area;
-
-            return iou;
-        }
-    }
-}

test/TensorFlowNET.Examples/ImageProcessing/YOLO/backbone.cs

@@ -1,51 +0,0 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
-using Tensorflow;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples.ImageProcessing.YOLO
-{
-    class backbone
-    {
-        public static (Tensor, Tensor, Tensor) darknet53(Tensor input_data, Tensor trainable)
-        {
-            return tf_with(tf.variable_scope("darknet"), scope =>
-            {
-                input_data = common.convolutional(input_data, filters_shape: new int[] { 3, 3, 3, 32 }, trainable: trainable, name: "conv0");
-                input_data = common.convolutional(input_data, filters_shape: new int[] { 3, 3, 32, 64 }, trainable: trainable, name: "conv1", downsample: true);
-
-                foreach (var i in range(1))
-                    input_data = common.residual_block(input_data, 64, 32, 64, trainable: trainable, name: $"residual{i + 0}");
-
-                input_data = common.convolutional(input_data, filters_shape: new[] { 3, 3, 64, 128 },
-                                          trainable: trainable, name: "conv4", downsample: true);
-
-                foreach (var i in range(2))
-                    input_data = common.residual_block(input_data, 128, 64, 128, trainable: trainable, name: $"residual{i + 1}");
-
-                input_data = common.convolutional(input_data, filters_shape: new[] { 3, 3, 128, 256 },
-                                          trainable: trainable, name: "conv9", downsample: true);
-
-                foreach (var i in range(8))
-                    input_data = common.residual_block(input_data, 256, 128, 256, trainable: trainable, name: $"residual{i + 3}");
-
-                var route_1 = input_data;
-                input_data = common.convolutional(input_data, filters_shape: new int[] { 3, 3, 256, 512 },
-                                          trainable: trainable, name: "conv26", downsample: true);
-
-                foreach (var i in range(8))
-                    input_data = common.residual_block(input_data, 512, 256, 512, trainable: trainable, name: $"residual{i + 11}");
-
-                var route_2 = input_data;
-                input_data = common.convolutional(input_data, filters_shape: new[] { 3, 3, 512, 1024 },
-                                          trainable: trainable, name: "conv43", downsample: true);
-
-                foreach (var i in range(4))
-                    input_data = common.residual_block(input_data, 1024, 512, 1024, trainable: trainable, name: $"residual{i + 19}");
-
-                return (route_1, route_2, input_data);
-            });
-        }
-    }
-}

test/TensorFlowNET.Examples/ImageProcessing/YOLO/common.cs

@@ -1,100 +0,0 @@
-using System;
-using System.Collections.Generic;
-using System.Diagnostics;
-using System.Linq;
-using System.Text;
-using Tensorflow;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples.ImageProcessing.YOLO
-{
-    class common
-    {
-        public static Tensor convolutional(Tensor input_data, int[] filters_shape, Tensor trainable,
-            string name, bool downsample = false, bool activate = true, 
-            bool bn = true)
-        {
-            return tf_with(tf.variable_scope(name), scope =>
-            {
-                int[] strides;
-                string padding;
-
-                if (downsample)
-                {
-                    (int pad_h, int pad_w) = ((int)Math.Floor((filters_shape[0] - 2) / 2.0f) + 1, (int)Math.Floor((filters_shape[1] - 2) / 2.0f) + 1);
-                    var paddings = tf.constant(new int[,] { { 0, 0 }, { pad_h, pad_h }, { pad_w, pad_w }, { 0, 0 } });
-                    input_data = tf.pad(input_data, paddings, "CONSTANT");
-                    strides = new[] { 1, 2, 2, 1 };
-                    padding = "VALID";
-                }
-                else
-                {
-                    strides = new int[] { 1, 1, 1, 1 };
-                    padding = "SAME";
-                }
-
-                var weight = tf.get_variable(name: "weight", dtype: tf.float32, trainable: true,
-                    shape: filters_shape, initializer: tf.random_normal_initializer(stddev: 0.01f));
-
-                var conv = tf.nn.conv2d(input: input_data, filter: weight, strides: strides, padding: padding);
-
-                if (bn)
-                {
-                    conv = tf.layers.batch_normalization(conv, beta_initializer: tf.zeros_initializer,
-                                                 gamma_initializer: tf.ones_initializer,
-                                                 moving_mean_initializer: tf.zeros_initializer,
-                                                 moving_variance_initializer: tf.ones_initializer, training: trainable);
-                }
-                else
-                {
-                    var bias = tf.get_variable(name: "bias", shape: filters_shape.Last(), trainable: true,
-                       dtype: tf.float32, initializer: tf.constant_initializer(0.0f));
-                    conv = tf.nn.bias_add(conv, bias);
-                }
-
-                if (activate)
-                    conv = tf.nn.leaky_relu(conv, alpha: 0.1f);
-
-                return conv;
-            });
-        }
-
-        public static Tensor upsample(Tensor input_data, string name, string method = "deconv")
-        {
-            Debug.Assert(new[] { "resize", "deconv" }.Contains(method));
-            Tensor output = null;
-            if (method == "resize")
-            {
-                tf_with(tf.variable_scope(name), delegate
-                {
-                    var input_shape = tf.shape(input_data);
-                    output = tf.image.resize_nearest_neighbor(input_data, new Tensor[] { input_shape[1] * 2, input_shape[2] * 2 });
-                });
-            }
-            else if(method == "deconv")
-            {
-                throw new NotImplementedException("upsample.deconv");
-            }
-
-            return output;
-        }
-
-        public static Tensor residual_block(Tensor input_data, int input_channel, int filter_num1, 
-            int filter_num2, Tensor trainable, string name)
-        {
-            var short_cut = input_data;
-
-            return tf_with(tf.variable_scope(name), scope =>
-            {
-                input_data = convolutional(input_data, filters_shape: new int[] { 1, 1, input_channel, filter_num1 },
-                                   trainable: trainable, name: "conv1");
-                input_data = convolutional(input_data, filters_shape: new int[] { 3, 3, filter_num1, filter_num2 },
-                                   trainable: trainable, name: "conv2");
-
-                var residual_output = input_data + short_cut;
-
-                return residual_output;
-            });
-        }
-    }
-}

test/TensorFlowNET.Examples/ImageProcessing/YOLO/config.cs

@@ -1,94 +0,0 @@
-using System;
-using System.Collections.Generic;
-using System.IO;
-using System.Text;
-
-namespace TensorFlowNET.Examples.ImageProcessing.YOLO
-{
-    public class Config
-    {
-        public YoloConfig YOLO;
-        public TrainConfig TRAIN;
-        public TestConfig TEST;
-
-        public Config(string root)
-        {
-            YOLO = new YoloConfig(root);
-            TRAIN = new TrainConfig(root);
-            TEST = new TestConfig(root);
-        }
-
-        public class YoloConfig
-        {
-            string _root;
-
-            public string CLASSES;
-            public string ANCHORS;
-            public float MOVING_AVE_DECAY = 0.9995f;
-            public int[] STRIDES = new int[] { 8, 16, 32 };
-            public int ANCHOR_PER_SCALE = 3;
-            public float IOU_LOSS_THRESH = 0.5f;
-            public string UPSAMPLE_METHOD = "resize";
-            public string ORIGINAL_WEIGHT;
-            public string DEMO_WEIGHT;
-
-            public YoloConfig(string root)
-            {
-                _root = root;
-                CLASSES = Path.Combine(_root, "data", "classes", "coco.names");
-                ANCHORS = Path.Combine(_root, "data", "anchors", "basline_anchors.txt");
-                ORIGINAL_WEIGHT = Path.Combine(_root, "checkpoint", "yolov3_coco.ckpt");
-                DEMO_WEIGHT = Path.Combine(_root, "checkpoint", "yolov3_coco_demo.ckpt");
-            }
-        }
-
-        public class TrainConfig
-        {
-            string _root;
-
-            public int BATCH_SIZE = 6;
-            public int[] INPUT_SIZE = new int[] { 320, 352, 384, 416, 448, 480, 512, 544, 576, 608 };
-            public bool DATA_AUG = true;
-            public float LEARN_RATE_INIT = 1e-4f;
-            public float LEARN_RATE_END = 1e-6f;
-            public int WARMUP_EPOCHS = 2;
-            public int FISRT_STAGE_EPOCHS = 20;
-            public int SECOND_STAGE_EPOCHS = 30;
-            public string INITIAL_WEIGHT;
-            public string ANNOT_PATH;
-
-            public TrainConfig(string root)
-            {
-                _root = root;
-                INITIAL_WEIGHT = Path.Combine(_root, "checkpoint", "yolov3_coco_demo.ckpt");
-                ANNOT_PATH = Path.Combine(_root, "data", "dataset", "voc_train.txt");
-            }
-        }
-
-        public class TestConfig
-        {
-            string _root;
-
-            public int BATCH_SIZE = 2;
-            public int[] INPUT_SIZE = new int[] { 544 };
-            public bool DATA_AUG = false;
-            public bool WRITE_IMAGE = true;
-            public string WRITE_IMAGE_PATH;
-            public string WEIGHT_FILE;
-            public bool WRITE_IMAGE_SHOW_LABEL = true;
-            public bool SHOW_LABEL = true;
-            public int SECOND_STAGE_EPOCHS = 30;
-            public float SCORE_THRESHOLD = 0.3f;
-            public float IOU_THRESHOLD = 0.45f;
-            public string ANNOT_PATH;
-
-            public TestConfig(string root)
-            {
-                _root = root;
-                ANNOT_PATH = Path.Combine(_root, "data", "dataset", "voc_test.txt");
-                WRITE_IMAGE_PATH = Path.Combine(_root, "data", "detection");
-                WEIGHT_FILE = Path.Combine(_root, "checkpoint", "yolov3_test_loss=9.2099.ckpt-5");
-            }
-        }
-    }
-}

test/TensorFlowNET.Examples/Keras.cs

@@ -1,99 +0,0 @@
-using System;
-using System.Collections.Generic;
-using Tensorflow;
-using Keras.Layers;
-using NumSharp;
-using Keras;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples
-{
-    public class Keras : IExample
-    {
-        public bool Enabled { get; set; } = true;
-        public bool IsImportingGraph { get; set; } = false;
-
-        public string Name => "Keras";
-
-        public bool Run()
-        {
-            Console.WriteLine("================================== Keras ==================================");
-
-            #region data
-            var batch_size = 1000;
-            var (X, Y) = XOR(batch_size);
-            //var (X, Y, batch_size) = (np.array(new float[,]{{1, 0 },{1, 1 },{0, 0 },{0, 1 }}), np.array(new int[] { 0, 1, 1, 0 }), 4);
-            #endregion
-
-            #region features
-            var (features, labels) = (new Tensor(X), new Tensor(Y));
-            var num_steps = 10000;
-            #endregion
-
-            #region model
-            var m = new Model();
-
-            //m.Add(new Dense(8, name: "Hidden", activation: tf.nn.relu())).Add(new Dense(1, name:"Output"));
-
-            m.Add(
-                new ILayer[] {
-                    new Dense(8, name: "Hidden_1", activation: tf.nn.relu()),
-                    new Dense(1, name: "Output")
-                });
-
-            m.train(num_steps, (X, Y));
-            #endregion
-
-            return true;
-        }
-
-        static (NDArray, NDArray) XOR(int samples)
-        {
-            var X = new List<float[]>();
-            var Y = new List<float>();
-            var r = new Random();
-            for (int i = 0; i < samples; i++)
-            {
-                var x1 = (float)r.Next(0, 2);
-                var x2 = (float)r.Next(0, 2);
-                var y = 0.0f;
-                if (x1 == x2)
-                    y = 1.0f;
-                X.Add(new float[] { x1, x2 });
-                Y.Add(y);
-            }
-            
-            return (np.array(X.ToArray()), np.array(Y.ToArray()));
-        }
-
-        public Graph BuildGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public Graph ImportGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Predict(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void PrepareData()
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Test(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Train(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-    }
-}

test/TensorFlowNET.Examples/NeuralNetworks/FullyConnected.cs

@@ -1,153 +0,0 @@
-/*****************************************************************************
-   Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
-******************************************************************************/
-
-using NumSharp;
-using System;
-using System.Collections.Generic;
-using System.Diagnostics;
-using System.Text;
-using Tensorflow;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples
-{
-    /// <summary>
-    /// How to optimise your input pipeline with queues and multi-threading
-    /// https://blog.metaflow.fr/tensorflow-how-to-optimise-your-input-pipeline-with-queues-and-multi-threading-e7c3874157e0
-    /// </summary>
-    public class FullyConnected : IExample
-    {
-        public bool Enabled { get; set; } = true;
-        public bool IsImportingGraph { get; set; }
-
-        public string Name => "Fully Connected Neural Network";
-
-        Tensor input = null;
-        Tensor x_inputs_data = null;
-        Tensor y_inputs_data = null;
-        Tensor accuracy = null;
-        Tensor y_true = null;
-        Tensor loss_op = null;
-        Operation train_op = null;
-
-        public Graph BuildGraph()
-        {
-            var g = tf.get_default_graph();
-            
-            Tensor z = null;
-            
-            tf_with(tf.variable_scope("placeholder"), delegate
-            {
-                input = tf.placeholder(tf.float32, shape: (-1, 1024));
-                y_true = tf.placeholder(tf.int32, shape: (-1, 1));
-            });
-
-            tf_with(tf.variable_scope("FullyConnected"), delegate
-            {
-                var w = tf.get_variable("w", shape: (1024, 1024), initializer: tf.random_normal_initializer(stddev: 0.1f));
-                var b = tf.get_variable("b", shape: 1024, initializer: tf.constant_initializer(0.1));
-                z = tf.matmul(input, w) + b;
-                var y = tf.nn.relu(z);
-
-                var w2 = tf.get_variable("w2", shape: (1024, 1), initializer: tf.random_normal_initializer(stddev: 0.1f));
-                var b2 = tf.get_variable("b2", shape: 1, initializer: tf.constant_initializer(0.1));
-                z = tf.matmul(y, w2) + b2;
-            });
-
-            tf_with(tf.variable_scope("Loss"), delegate
-            {
-                var losses = tf.nn.sigmoid_cross_entropy_with_logits(tf.cast(y_true, tf.float32), z);
-                loss_op = tf.reduce_mean(losses);
-            });
-
-            tf_with(tf.variable_scope("Accuracy"), delegate
-            {
-                var y_pred = tf.cast(z > 0, tf.int32);
-                accuracy = tf.reduce_mean(tf.cast(tf.equal(y_pred, y_true), tf.float32));
-                // accuracy = tf.Print(accuracy, data =[accuracy], message = "accuracy:")
-            });
-
-            // We add the training operation, ...
-            var adam = tf.train.AdamOptimizer(0.01f);
-            train_op = adam.minimize(loss_op, name: "train_op");
-
-            return g;
-        }
-
-        public Graph ImportGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Predict(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void PrepareData()
-        {
-            // batches of 128 samples, each containing 1024 data points
-            x_inputs_data = tf.random_normal(new[] { 128, 1024 }, mean: 0, stddev: 1);
-            // We will try to predict this law:
-            // predict 1 if the sum of the elements is positive and 0 otherwise
-            y_inputs_data = tf.cast(tf.reduce_sum(x_inputs_data, axis: 1, keepdims: true) > 0, tf.int32);
-        }
-
-        public bool Run()
-        {
-            PrepareData();
-            var g = BuildGraph();
-            using (var sess = tf.Session())
-                Train(sess);
-            return true;
-        }
-
-        public void Test(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Train(Session sess)
-        {
-            var sw = new Stopwatch();
-            sw.Start();
-            // init variables
-            sess.run(tf.global_variables_initializer());
-
-            // check the accuracy before training
-            var (x_input, y_input) = sess.run((x_inputs_data, y_inputs_data));
-            sess.run(accuracy, (input, x_input), (y_true, y_input));
-
-            // training
-            foreach (var i in range(5000))
-            {
-                // by sampling some input data (fetching)
-                (x_input, y_input) = sess.run((x_inputs_data, y_inputs_data));
-                var (_, loss) = sess.run((train_op, loss_op), (input, x_input), (y_true, y_input));
-
-                // We regularly check the loss
-                if (i % 500 == 0)
-                    print($"iter:{i} - loss:{loss}");
-            }
-
-            // Finally, we check our final accuracy
-            (x_input, y_input) = sess.run((x_inputs_data, y_inputs_data));
-            sess.run(accuracy, (input, x_input), (y_true, y_input));
-
-            print($"Time taken: {sw.Elapsed.TotalSeconds}s");
-        }
-    }
-}

test/TensorFlowNET.Examples/NeuralNetworks/FullyConnectedInQueue.cs

@@ -1,129 +0,0 @@
-/*****************************************************************************
-   Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
-******************************************************************************/
-
-using NumSharp;
-using System;
-using System.Collections.Generic;
-using System.Diagnostics;
-using System.Text;
-using Tensorflow;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples
-{
-    /// <summary>
-    /// How to optimise your input pipeline with queues and multi-threading
-    /// https://blog.metaflow.fr/tensorflow-how-to-optimise-your-input-pipeline-with-queues-and-multi-threading-e7c3874157e0
-    /// </summary>
-    public class FullyConnectedInQueue : IExample
-    {
-        public bool Enabled { get; set; } = false;
-        public bool IsImportingGraph { get; set; }
-
-        public string Name => "Fully Connected Neural Network In Queue";
-
-        Tensor input = null;
-        Tensor x_inputs_data = null;
-        Tensor y_inputs_data = null;
-        Tensor accuracy = null;
-        Tensor y_true = null;
-        Tensor loss_op = null;
-        Operation train_op = null;
-
-        public Graph BuildGraph()
-        {
-            var g = tf.get_default_graph();
-
-            Tensor z = null;
-
-            // We build our small model: a basic two layers neural net with ReLU
-            tf_with(tf.variable_scope("queue"), delegate
-            {
-                // enqueue 5 batches
-                var q = tf.FIFOQueue(capacity: 5, dtype: tf.float32);
-                // We use the "enqueue" operation so 1 element of the queue is the full batch
-                var enqueue_op = q.enqueue(x_inputs_data);
-                var numberOfThreads = 1;
-                // var qr = tf.train.QueueRunner(q, [enqueue_op] * numberOfThreads);
-            });
-
-            return g;
-        }
-
-        public Graph ImportGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Predict(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void PrepareData()
-        {
-            // batches of 128 samples, each containing 1024 data points
-            x_inputs_data = tf.random_normal(new[] { 128, 1024 }, mean: 0, stddev: 1);
-            // We will try to predict this law:
-            // predict 1 if the sum of the elements is positive and 0 otherwise
-            y_inputs_data = tf.cast(tf.reduce_sum(x_inputs_data, axis: 1, keepdims: true) > 0, tf.int32);
-        }
-
-        public bool Run()
-        {
-            PrepareData();
-            var g = BuildGraph();
-            using (var sess = tf.Session())
-                Train(sess);
-            return true;
-        }
-
-        public void Test(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Train(Session sess)
-        {
-            var sw = new Stopwatch();
-            sw.Start();
-            // init variables
-            sess.run(tf.global_variables_initializer());
-
-            // check the accuracy before training
-            var (x_input, y_input) = sess.run((x_inputs_data, y_inputs_data));
-            sess.run(accuracy, (input, x_input), (y_true, y_input));
-
-            // training
-            foreach (var i in range(5000))
-            {
-                // by sampling some input data (fetching)
-                (x_input, y_input) = sess.run((x_inputs_data, y_inputs_data));
-                var (_, loss) = sess.run((train_op, loss_op), (input, x_input), (y_true, y_input));
-
-                // We regularly check the loss
-                if (i % 500 == 0)
-                    print($"iter:{i} - loss:{loss}");
-            }
-
-            // Finally, we check our final accuracy
-            (x_input, y_input) = sess.run((x_inputs_data, y_inputs_data));
-            sess.run(accuracy, (input, x_input), (y_true, y_input));
-
-            print($"Time taken: {sw.Elapsed.TotalSeconds}s");
-        }
-    }
-}

test/TensorFlowNET.Examples/NeuralNetworks/NeuralNetXor.cs

@@ -1,190 +0,0 @@
-/*****************************************************************************
-   Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
-******************************************************************************/
-
-using System;
-using NumSharp;
-using Tensorflow;
-using TensorFlowNET.Examples.Utility;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples
-{
-    /// <summary>
-    /// Simple vanilla neural net solving the famous XOR problem
-    /// https://github.com/amygdala/tensorflow-workshop/blob/master/workshop_sections/getting_started/xor/README.md
-    /// </summary>
-    public class NeuralNetXor : IExample
-    {
-        public bool Enabled { get; set; } = true;
-        public string Name => "NN XOR";
-        public bool IsImportingGraph { get; set; } = false;
-
-        public int num_steps = 10000;
-
-        private NDArray data;
-
-        private (Operation, Tensor, Tensor) make_graph(Tensor features,Tensor labels, int num_hidden = 8)
-        {
-            var stddev = 1 / Math.Sqrt(2);
-            var hidden_weights = tf.Variable(tf.truncated_normal(new int []{2, num_hidden}, seed:1, stddev: (float) stddev ));
-
-            // Shape [4, num_hidden]
-            var hidden_activations = tf.nn.relu(tf.matmul(features, hidden_weights));
-
-            var output_weights = tf.Variable(tf.truncated_normal(
-                new[] {num_hidden, 1},
-                seed: 17,
-                stddev: (float) (1 / Math.Sqrt(num_hidden))
-            ));
-
-            // Shape [4, 1]
-            var logits = tf.matmul(hidden_activations, output_weights);
-
-            // Shape [4]
-            var predictions = tf.tanh(tf.squeeze(logits));
-            var loss = tf.reduce_mean(tf.square(predictions - tf.cast(labels, tf.float32)), name:"loss");
-
-            var gs = tf.Variable(0, trainable: false, name: "global_step");
-            var train_op = tf.train.GradientDescentOptimizer(0.2f).minimize(loss, global_step: gs);
-
-            return (train_op, loss, gs);
-        }
-
-        public bool Run()
-        {
-            PrepareData();
-            float loss_value = 0;
-            if (IsImportingGraph)
-                loss_value = RunWithImportedGraph();
-            else
-                loss_value = RunWithBuiltGraph();
-
-            return loss_value < 0.0628;
-        }
-
-        private float RunWithImportedGraph()
-        {
-            var graph = tf.Graph().as_default();
-
-            tf.train.import_meta_graph("graph/xor.meta");
-
-            Tensor features = graph.get_operation_by_name("Placeholder");
-            Tensor labels = graph.get_operation_by_name("Placeholder_1");
-            Tensor loss = graph.get_operation_by_name("loss");
-            Tensor train_op = graph.get_operation_by_name("train_op");
-            Tensor global_step = graph.get_operation_by_name("global_step");
-
-            var init = tf.global_variables_initializer();
-            float loss_value = 0;
-            // Start tf session
-            using (var sess = tf.Session(graph))
-            {
-                sess.run(init);
-                var step = 0;
-
-                var y_ = np.array(new int[] { 1, 0, 0, 1 }, dtype: np.int32);
-                while (step < num_steps)
-                {
-                    // original python:
-                    //_, step, loss_value = sess.run(
-                    //          [train_op, gs, loss],
-                    //          feed_dict={features: xy, labels: y_}
-                    //      )
-                    (_, step, loss_value) = sess.run((train_op, global_step, loss), (features, data), (labels, y_));
-                    if (step == 1 || step % 1000 == 0)
-                        Console.WriteLine($"Step {step} loss: {loss_value}");
-                }
-                Console.WriteLine($"Final loss: {loss_value}");
-            }
-
-            return loss_value;
-        }
-
-        private float RunWithBuiltGraph()
-        {
-            var graph = tf.Graph().as_default();
-
-            var features = tf.placeholder(tf.float32, new TensorShape(4, 2));
-            var labels = tf.placeholder(tf.int32, new TensorShape(4));
-
-            var (train_op, loss, gs) = make_graph(features, labels);
-
-            var init = tf.global_variables_initializer();
-
-            float loss_value = 0;
-            // Start tf session
-            using (var sess = tf.Session(graph))
-            {
-                sess.run(init);
-                var step = 0;
-
-                var y_ = np.array(new int[] { 1, 0, 0, 1 }, dtype: np.int32);
-                while (step < num_steps)
-                {
-                    (_, step, loss_value) = sess.run((train_op, gs, loss), (features, data), (labels, y_));
-                    if (step == 1 || step % 1000 == 0)
-                        Console.WriteLine($"Step {step} loss: {loss_value}");
-                }
-                Console.WriteLine($"Final loss: {loss_value}");
-            }
-
-            return loss_value;
-        }
-
-        public void PrepareData()
-        {
-            data = new float[,]
-            {
-                {1, 0 },
-                {1, 1 },
-                {0, 0 },
-                {0, 1 }
-            };
-
-            if (IsImportingGraph)
-            {
-                // download graph meta data
-                string url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/graph/xor.meta";
-                Web.Download(url, "graph", "xor.meta");
-            }
-        }
-
-        public Graph ImportGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public Graph BuildGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Train(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Predict(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Test(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-    }
-}

test/TensorFlowNET.Examples/Program.cs

@@ -1,120 +0,0 @@
-/*****************************************************************************
-   Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
-******************************************************************************/
-
-using System;
-using System.Collections.Generic;
-using System.Diagnostics;
-using System.Drawing;
-using System.Linq;
-using System.Reflection;
-using Tensorflow;
-using Console = Colorful.Console;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples
-{
-    class Program
-    {
-        static void Main(string[] args)
-        {
-            int finished = 0;
-            var errors = new List<string>();
-            var success = new List<string>();
-
-            var parsedArgs = ParseArgs(args);
-
-            var examples = Assembly.GetEntryAssembly().GetTypes()
-                .Where(x => x.GetInterfaces().Contains(typeof(IExample)))
-                .Select(x => (IExample)Activator.CreateInstance(x))
-                .Where(x => x.Enabled)
-                .OrderBy(x => x.Name)
-                .ToArray();
-
-            if (parsedArgs.ContainsKey("ex"))
-                examples = examples.Where(x => x.Name == parsedArgs["ex"]).ToArray();
-
-            Console.WriteLine(Environment.OSVersion.ToString(), Color.Yellow);
-            Console.WriteLine($"TensorFlow Binary v{tf.VERSION}", Color.Yellow);
-            Console.WriteLine($"TensorFlow.NET v{Assembly.GetAssembly(typeof(TF_DataType)).GetName().Version}", Color.Yellow);
-
-            for (var i = 0; i < examples.Length; i++)
-                Console.WriteLine($"[{i}]: {examples[i].Name}");
-
-            var key = "0";
-            
-            if (examples.Length > 1)
-            {
-                Console.Write($"Choose one example to run, hit [Enter] to run all: ", Color.Yellow);
-                key = Console.ReadLine();
-            }
-
-            var sw = new Stopwatch();
-            for (var i = 0; i < examples.Length; i++)
-            {
-                if (i.ToString() != key && key != "") continue;
-
-                var example = examples[i];
-                Console.WriteLine($"{DateTime.UtcNow} Starting {example.Name}", Color.White);
-
-                try
-                {
-                    sw.Restart();
-                    bool isSuccess = example.Run();
-                    sw.Stop();
-
-                    if (isSuccess)
-                        success.Add($"Example: {example.Name} in {sw.Elapsed.TotalSeconds}s");
-                    else
-                        errors.Add($"Example: {example.Name} in {sw.Elapsed.TotalSeconds}s");
-                }
-                catch (Exception ex)
-                {
-                    errors.Add($"Example: {example.Name}");
-                    Console.WriteLine(ex);
-                }
-
-                finished++;
-                Console.WriteLine($"{DateTime.UtcNow} Completed {example.Name}", Color.White);
-            }
-
-            success.ForEach(x => Console.WriteLine($"{x} is OK!", Color.Green));
-            errors.ForEach(x => Console.WriteLine($"{x} is Failed!", Color.Red));
-
-            Console.WriteLine($"{finished} of {examples.Length} example(s) are completed.");
-            Console.ReadLine();
-        }
-
-        private static Dictionary<string, string> ParseArgs(string[] args)
-        {
-            var parsed = new Dictionary<string, string>();
-
-            for (int i = 0; i < args.Length; i++)
-            {
-                string key = args[i].Substring(1);
-                switch (key)
-                {
-                    case "ex":
-                        parsed.Add(key, args[++i]);
-                        break;
-                    default:
-                        break;
-                }
-            }
-
-            return parsed;
-        }
-    }
-}

test/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj

@@ -1,32 +0,0 @@
-<Project Sdk="Microsoft.NET.Sdk">
-
-  <PropertyGroup>
-    <OutputType>Exe</OutputType>
-    <TargetFramework>netcoreapp3.0</TargetFramework>
-    <GeneratePackageOnBuild>false</GeneratePackageOnBuild>
-  </PropertyGroup>
-
-  <PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|AnyCPU'">
-    <DefineConstants>DEBUG;TRACE</DefineConstants>
-  </PropertyGroup>
-
-  <ItemGroup>
-    <Compile Remove="Keras.cs" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <PackageReference Include="Colorful.Console" Version="1.2.9" />
-    <PackageReference Include="Newtonsoft.Json" Version="12.0.2" />
-    <PackageReference Include="SciSharp.TensorFlow.Redist" Version="1.14.0" />
-    <PackageReference Include="SharpZipLib" Version="1.1.0" />
-    <PackageReference Include="System.Drawing.Common" Version="4.5.1" />
-  </ItemGroup>
-
-  <ItemGroup>
-    <ProjectReference Include="..\..\src\TensorFlowDatasets\TensorFlowDatasets.csproj" />
-    <ProjectReference Include="..\..\src\TensorFlowNET.Core\TensorFlowNET.Core.csproj" />
-    <ProjectReference Include="..\..\src\TensorFlowNET.Models\TensorFlowNET.Models.csproj" />
-    <ProjectReference Include="..\..\src\TensorFlowText\TensorFlowText.csproj" />
-    <ProjectReference Include="..\..\src\TensorFlowHub\TensorFlowHub.csproj" />
-  </ItemGroup>
-</Project>

test/TensorFlowNET.Examples/TextProcessing/BinaryTextClassification.cs

@@ -1,164 +0,0 @@
-using System;
-using System.Collections.Generic;
-using System.IO;
-using Tensorflow;
-using Newtonsoft.Json;
-using System.Linq;
-using NumSharp;
-
-namespace TensorFlowNET.Examples
-{
-    /// <summary>
-    /// This example classifies movie reviews as positive or negative using the text of the review. 
-    /// This is a binary—or two-class—classification, an important and widely applicable kind of machine learning problem.
-    /// https://github.com/tensorflow/docs/blob/master/site/en/tutorials/keras/basic_text_classification.ipynb
-    /// </summary>
-    public class BinaryTextClassification : IExample
-    {
-        public bool Enabled { get; set; } = false;
-        public string Name => "Binary Text Classification";
-        public bool IsImportingGraph { get; set; } = true;
-
-        string dir = "binary_text_classification";
-        string dataFile = "imdb.zip";
-        NDArray train_data, train_labels, test_data, test_labels;
-
-        public bool Run()
-        {
-            PrepareData();
-
-            Console.WriteLine($"Training entries: {train_data.shape[0]}, labels: {train_labels.shape[0]}");
-
-            // A dictionary mapping words to an integer index
-            var word_index = GetWordIndex();
-            
-            /*train_data = keras.preprocessing.sequence.pad_sequences(train_data,
-                value: word_index["<PAD>"],
-                padding: "post",
-                maxlen: 256);
-
-            test_data = keras.preprocessing.sequence.pad_sequences(test_data,
-                value: word_index["<PAD>"],
-                padding: "post",
-                maxlen: 256);*/
-
-            // input shape is the vocabulary count used for the movie reviews (10,000 words)
-            int vocab_size = 10000;
-
-            var model = keras.Sequential();
-            var layer = keras.layers.Embedding(vocab_size, 16);
-            model.add(layer);
-
-            return false;
-        }
-
-        public void PrepareData()
-        {
-            Directory.CreateDirectory(dir);
-
-            // get model file
-            string url = $"https://github.com/SciSharp/TensorFlow.NET/raw/master/data/{dataFile}";
-
-            Utility.Web.Download(url, dir, "imdb.zip");
-            Utility.Compress.UnZip(Path.Join(dir, $"imdb.zip"), dir);
-
-            // prepare training dataset
-            var x_train = ReadData(Path.Join(dir, "x_train.txt"));
-            var labels_train = ReadData(Path.Join(dir, "y_train.txt"));
-            var indices_train = ReadData(Path.Join(dir, "indices_train.txt"));
-            x_train = x_train[indices_train];
-            labels_train = labels_train[indices_train];
-
-            var x_test = ReadData(Path.Join(dir, "x_test.txt"));
-            var labels_test = ReadData(Path.Join(dir, "y_test.txt"));
-            var indices_test = ReadData(Path.Join(dir, "indices_test.txt"));
-            x_test = x_test[indices_test];
-            labels_test = labels_test[indices_test];
-
-            // not completed
-            var xs = x_train.hstack(x_test);
-            var labels = labels_train.hstack(labels_test);
-
-            var idx = x_train.size;
-            var y_train = labels_train;
-            var y_test = labels_test;
-
-            // convert x_train
-            train_data = new NDArray(np.int32, (x_train.size, 256));
-            /*for (int i = 0; i < x_train.size; i++)
-                train_data[i] = x_train[i].Data<string>()[1].Split(',').Select(x => int.Parse(x)).ToArray();*/
-
-            test_data = new NDArray(np.int32, (x_test.size, 256));
-            /*for (int i = 0; i < x_test.size; i++)
-                test_data[i] = x_test[i].Data<string>()[1].Split(',').Select(x => int.Parse(x)).ToArray();*/
-
-            train_labels = y_train;
-            test_labels = y_test;
-        }
-
-        private NDArray ReadData(string file)
-        {
-            var lines = File.ReadAllLines(file);
-            var nd = new NDArray(lines[0].StartsWith("[") ? typeof(string) : np.int32, new Shape(lines.Length));
-
-            if (lines[0].StartsWith("["))
-            {
-                for (int i = 0; i < lines.Length; i++)
-                {
-                    /*var matches = Regex.Matches(lines[i], @"\d+\s*");
-                    var data = new int[matches.Count];
-                    for (int j = 0; j < data.Length; j++)
-                        data[j] = Convert.ToInt32(matches[j].Value);
-                    nd[i] = data.ToArray();*/
-                    nd[i] = lines[i].Substring(1, lines[i].Length - 2).Replace(" ", string.Empty);
-                }
-            }
-            else
-            {
-                for (int i = 0; i < lines.Length; i++)
-                    nd[i] = Convert.ToInt32(lines[i]);
-            }
-            return nd;
-        }
-
-        private Dictionary<string, int> GetWordIndex()
-        {
-            var result = new Dictionary<string, int>();
-            var json = File.ReadAllText(Path.Join(dir, "imdb_word_index.json"));
-            var dict = JsonConvert.DeserializeObject<Dictionary<string, int>>(json);
-
-            dict.Keys.Select(k => result[k] = dict[k] + 3).ToList();
-            result["<PAD>"] = 0;
-            result["<START>"] = 1;
-            result["<UNK>"] = 2; // unknown
-            result["<UNUSED>"] = 3;
-
-            return result;
-        }
-
-        public Graph ImportGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public Graph BuildGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Train(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Predict(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Test(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-    }
-}

test/TensorFlowNET.Examples/TextProcessing/CnnTextClassification.cs

@@ -1,272 +0,0 @@
-/*****************************************************************************
-   Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
-******************************************************************************/
-
-using System;
-using System.Collections.Generic;
-using System.Diagnostics;
-using System.IO;
-using System.Linq;
-using NumSharp;
-using Tensorflow;
-using Tensorflow.Sessions;
-using TensorFlowNET.Examples.Text;
-using TensorFlowNET.Examples.Utility;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples
-{
-    /// <summary>
-    /// https://github.com/dongjun-Lee/text-classification-models-tf
-    /// </summary>
-    public class CnnTextClassification : IExample
-    {
-        public bool Enabled { get; set; } = true;
-        public string Name => "CNN Text Classification";
-        public int? DataLimit = null;
-        public bool IsImportingGraph { get; set; } = false;
-
-        const string dataDir = "cnn_text";
-        string dataFileName = "dbpedia_csv.tar.gz";
-
-        string TRAIN_PATH = $"{dataDir}/dbpedia_csv/train.csv";
-        string TEST_PATH = $"{dataDir}/dbpedia_csv/test.csv";
-        
-        int NUM_CLASS = 14;
-        int BATCH_SIZE = 64;
-        int NUM_EPOCHS = 10;
-        int WORD_MAX_LEN = 100;
-        int CHAR_MAX_LEN = 1014;
-        
-        float loss_value = 0;
-        double max_accuracy = 0;
-
-        int alphabet_size = -1;
-        int vocabulary_size = -1;
-        NDArray train_x, valid_x, train_y, valid_y;
-
-        ITextModel textModel;
-        public string ModelName = "word_cnn"; // word_cnn | char_cnn | vd_cnn | word_rnn | att_rnn | rcnn
-
-        public bool Run()
-        {
-            PrepareData();
-            var graph = IsImportingGraph ? ImportGraph() : BuildGraph();
-
-            using (var sess = tf.Session(graph))
-                Train(sess);
-
-            return max_accuracy > 0.9;
-        }
-
-        // TODO: this originally is an SKLearn utility function. it randomizes train and test which we don't do here
-        private (NDArray, NDArray, NDArray, NDArray) train_test_split(NDArray x, NDArray y, float test_size = 0.3f)
-        {
-            Console.WriteLine("Splitting in Training and Testing data...");
-            int len = x.shape[0];
-            //int classes = y.Data<int>().Distinct().Count();
-            //int samples = len / classes;
-            int train_size = (int)Math.Round(len * (1 - test_size));
-            train_x = x[new Slice(stop: train_size), new Slice()];
-            valid_x = x[new Slice(start: train_size), new Slice()];
-            train_y = y[new Slice(stop: train_size)];
-            valid_y = y[new Slice(start: train_size)];
-            Console.WriteLine("\tDONE");
-
-            return (train_x, valid_x, train_y, valid_y);
-        }
-
-        private void FillWithShuffledLabels(int[][] x, int[] y, int[][] shuffled_x, int[] shuffled_y, Random random, Dictionary<int, HashSet<int>> labels)
-        {
-            int i = 0;
-            var label_keys = labels.Keys.ToArray();
-            while (i < shuffled_x.Length)
-            {
-                var key = label_keys[random.Next(label_keys.Length)];
-                var set = labels[key];
-                var index = set.First();
-                if (set.Count == 0)
-                {
-                    labels.Remove(key); // remove the set as it is empty
-                    label_keys = labels.Keys.ToArray();
-                }
-                shuffled_x[i] = x[index];
-                shuffled_y[i] = y[index];
-                i++;
-            }
-        }
-
-        private IEnumerable<(NDArray, NDArray, int)> batch_iter(NDArray inputs, NDArray outputs, int batch_size, int num_epochs)
-        {
-            var num_batches_per_epoch = (len(inputs) - 1) / batch_size + 1;
-            var total_batches = num_batches_per_epoch * num_epochs;
-            foreach (var epoch in range(num_epochs))
-            {
-                foreach (var batch_num in range(num_batches_per_epoch))
-                {
-                    var start_index = batch_num * batch_size;
-                    var end_index = Math.Min((batch_num + 1) * batch_size, len(inputs));
-                    if (end_index <= start_index)
-                        break;
-                    yield return (inputs[new Slice(start_index, end_index)], outputs[new Slice(start_index, end_index)], total_batches);
-                }
-            }
-        }
-
-        public void PrepareData()
-        {
-            // full dataset https://github.com/le-scientifique/torchDatasets/raw/master/dbpedia_csv.tar.gz
-            var url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/data/dbpedia_subset.zip";
-            Web.Download(url, dataDir, "dbpedia_subset.zip");
-            Compress.UnZip(Path.Combine(dataDir, "dbpedia_subset.zip"), Path.Combine(dataDir, "dbpedia_csv"));
-
-            Console.WriteLine("Building dataset...");
-            var (x, y) = (new int[0][], new int[0]);
-
-            if(ModelName == "char_cnn")
-            {
-                (x, y, alphabet_size) = DataHelpers.build_char_dataset(TRAIN_PATH, "char_cnn", CHAR_MAX_LEN);
-            }
-            else
-            {
-                var word_dict = DataHelpers.build_word_dict(TRAIN_PATH);
-                vocabulary_size = len(word_dict);
-                (x, y) = DataHelpers.build_word_dataset(TRAIN_PATH, word_dict, WORD_MAX_LEN);
-            }
-
-            Console.WriteLine("\tDONE ");
-
-            var (train_x, valid_x, train_y, valid_y) = train_test_split(x, y, test_size: 0.15f);
-            Console.WriteLine("Training set size: " + train_x.shape[0]);
-            Console.WriteLine("Test set size: " + valid_x.shape[0]);
-        }
-
-        public Graph ImportGraph()
-        {
-            var graph = tf.Graph().as_default();
-
-            // download graph meta data
-            var meta_file = "word_cnn.meta";
-            var meta_path = Path.Combine("graph", meta_file);
-            if (File.GetLastWriteTime(meta_path) < new DateTime(2019, 05, 11))
-            {
-                // delete old cached file which contains errors
-                Console.WriteLine("Discarding cached file: " + meta_path);
-                if(File.Exists(meta_path))
-                    File.Delete(meta_path);
-            }
-            var url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/graph/" + meta_file;
-            Web.Download(url, "graph", meta_file);
-
-            Console.WriteLine("Import graph...");
-            tf.train.import_meta_graph(Path.Join("graph", meta_file));
-            Console.WriteLine("\tDONE ");
-
-            return graph;
-        }
-
-        public Graph BuildGraph()
-        {
-            var graph = tf.Graph().as_default();
-
-            switch (ModelName)
-            {
-                case "word_cnn":
-                    textModel = new WordCnn(vocabulary_size, WORD_MAX_LEN, NUM_CLASS);
-                    break;
-                case "char_cnn":
-                    textModel = new CharCnn(alphabet_size, CHAR_MAX_LEN, NUM_CLASS);
-                    break;
-            }
-
-            return graph;
-        }
-
-        public void Train(Session sess)
-        {
-            var graph = tf.get_default_graph();
-
-            sess.run(tf.global_variables_initializer());
-            var saver = tf.train.Saver(tf.global_variables());
-
-            var train_batches = batch_iter(train_x, train_y, BATCH_SIZE, NUM_EPOCHS);
-            var num_batches_per_epoch = (len(train_x) - 1) / BATCH_SIZE + 1;
-
-            Tensor is_training = graph.OperationByName("is_training");
-            Tensor model_x = graph.OperationByName("x");
-            Tensor model_y = graph.OperationByName("y");
-            Tensor loss = graph.OperationByName("loss/Mean");
-            Operation optimizer = graph.OperationByName("loss/Adam");
-            Tensor global_step = graph.OperationByName("Variable");
-            Tensor accuracy = graph.OperationByName("accuracy/accuracy");
-
-            var sw = new Stopwatch();
-            sw.Start();
-
-            int step = 0;
-            foreach (var (x_batch, y_batch, total) in train_batches)
-            {
-                (_, step, loss_value) = sess.run((optimizer, global_step, loss),
-                    (model_x, x_batch), (model_y, y_batch), (is_training, true));
-                if (step % 10 == 0)
-                {
-                    Console.WriteLine($"Training on batch {step}/{total} loss: {loss_value.ToString("0.0000")} {sw.ElapsedMilliseconds}ms.");
-                    sw.Restart();
-                }
-
-                if (step % 100 == 0)
-                {
-                    // Test accuracy with validation data for each epoch.
-                    var valid_batches = batch_iter(valid_x, valid_y, BATCH_SIZE, 1);
-                    var (sum_accuracy, cnt) = (0.0f, 0);
-                    foreach (var (valid_x_batch, valid_y_batch, total_validation_batches) in valid_batches)
-                    {
-                        var valid_feed_dict = new FeedDict
-                        {
-                            [model_x] = valid_x_batch,
-                            [model_y] = valid_y_batch,
-                            [is_training] = false
-                        };
-                        float accuracy_value = sess.run(accuracy, (model_x, valid_x_batch), (model_y, valid_y_batch), (is_training, false));
-                        sum_accuracy += accuracy_value;
-                        cnt += 1;
-                    }
-
-                    var valid_accuracy = sum_accuracy / cnt;
-
-                    print($"\nValidation Accuracy = {valid_accuracy.ToString("P")}\n");
-                    
-                    // Save model
-                    if (valid_accuracy > max_accuracy)
-                    {
-                        max_accuracy = valid_accuracy;
-                        saver.save(sess, $"{dataDir}/word_cnn.ckpt", global_step: step);
-                        print("Model is saved.\n");
-                    }
-                }
-            }
-        }
-
-        public void Predict(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Test(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-    }
-}

test/TensorFlowNET.Examples/TextProcessing/DataHelpers.cs

@@ -1,218 +0,0 @@
-using NumSharp;
-using System;
-using System.Collections.Generic;
-using System.IO;
-using System.Linq;
-using System.Security.Cryptography;
-using System.Text;
-using System.Text.RegularExpressions;
-using TensorFlowNET.Examples.Utility;
-
-namespace TensorFlowNET.Examples
-{
-    public class DataHelpers
-    {
-        public static Dictionary<string, int> build_word_dict(string path)
-        {
-            var contents = File.ReadAllLines(path);
-
-            var words = new List<string>();
-            foreach (var content in contents)
-                words.AddRange(clean_str(content).Split(' ').Where(x => x.Length > 1));
-            var word_counter = words.GroupBy(x => x)
-                .Select(x => new { Word = x.Key, Count = x.Count() })
-                .OrderByDescending(x => x.Count)
-                .ToArray();
-
-            var word_dict = new Dictionary<string, int>();
-            word_dict["<pad>"] = 0;
-            word_dict["<unk>"] = 1;
-            word_dict["<eos>"] = 2;
-            foreach (var word in word_counter)
-                word_dict[word.Word] = word_dict.Count;
-
-            return word_dict;
-        }
-
-        public static (int[][], int[]) build_word_dataset(string path, Dictionary<string, int> word_dict, int document_max_len)
-        {
-            var contents = File.ReadAllLines(path);
-            var x = contents.Select(c => (clean_str(c) + " <eos>")
-                .Split(' ').Take(document_max_len)
-                .Select(w => word_dict.ContainsKey(w) ? word_dict[w] : word_dict["<unk>"]).ToArray())
-                .ToArray();
-
-            for (int i = 0; i < x.Length; i++)
-                if (x[i].Length == document_max_len)
-                    x[i][document_max_len - 1] = word_dict["<eos>"];
-                else
-                    Array.Resize(ref x[i], document_max_len);
-
-            var y = contents.Select(c => int.Parse(c.Substring(0, c.IndexOf(','))) - 1).ToArray();
-
-            return (x, y);
-        }
-
-        public static (int[][], int[], int) build_char_dataset(string path, string model, int document_max_len, int? limit = null, bool shuffle=true)
-        {
-            string alphabet = "abcdefghijklmnopqrstuvwxyz0123456789-,;.!?:’'\"/|_#$%ˆ&*˜‘+=<>()[]{} ";
-            /*if (step == "train")
-                df = pd.read_csv(TRAIN_PATH, names =["class", "title", "content"]);*/
-            var char_dict = new Dictionary<string, int>();
-            char_dict["<pad>"] = 0;
-            char_dict["<unk>"] = 1;
-            foreach (char c in alphabet)
-                char_dict[c.ToString()] = char_dict.Count;
-            var contents = File.ReadAllLines(path);
-            if (shuffle)
-                new Random(17).Shuffle(contents);
-            //File.WriteAllLines("text_classification/dbpedia_csv/train_6400.csv", contents.Take(6400));
-            var size = limit == null ? contents.Length : limit.Value;
-
-            var x = new int[size][];
-            var y = new int[size];
-            var tenth = size / 10;
-            var percent = 0;
-            for (int i = 0; i < size; i++)
-            {
-                if ((i + 1) % tenth == 0)
-                {
-                    percent += 10;
-                    Console.WriteLine($"\t{percent}%");
-                }
-
-                string[] parts = contents[i].ToLower().Split(",\"").ToArray();
-                string content = parts[2];
-                content = content.Substring(0, content.Length - 1);
-                var a = new int[document_max_len];
-                for (int j = 0; j < document_max_len; j++)
-                {
-                    if (j >= content.Length)
-                        a[j] = char_dict["<pad>"];
-                    else
-                        a[j] = char_dict.ContainsKey(content[j].ToString()) ? char_dict[content[j].ToString()] : char_dict["<unk>"];
-                }
-                x[i] = a;
-                y[i] = int.Parse(parts[0]);
-            }
-
-            return (x, y, alphabet.Length + 2);
-        }
-
-        /// <summary>
-        /// Loads MR polarity data from files, splits the data into words and generates labels.
-        /// Returns split sentences and labels.
-        /// </summary>
-        /// <param name="positive_data_file"></param>
-        /// <param name="negative_data_file"></param>
-        /// <returns></returns>
-        public static (string[], NDArray) load_data_and_labels(string positive_data_file, string negative_data_file)
-        {
-            Directory.CreateDirectory("CnnTextClassification");
-            Utility.Web.Download(positive_data_file, "CnnTextClassification", "rt -polarity.pos");
-            Utility.Web.Download(negative_data_file, "CnnTextClassification", "rt-polarity.neg");
-
-            // Load data from files
-            var positive_examples = File.ReadAllLines("CnnTextClassification/rt-polarity.pos")
-                .Select(x => x.Trim())
-                .ToArray();
-
-            var negative_examples = File.ReadAllLines("CnnTextClassification/rt-polarity.neg")
-                .Select(x => x.Trim())
-                .ToArray();
-
-            var x_text = new List<string>();
-            x_text.AddRange(positive_examples);
-            x_text.AddRange(negative_examples);
-            x_text = x_text.Select(x => clean_str(x)).ToList();
-
-            var positive_labels = positive_examples.Select(x => new int[2] { 0, 1 }).ToArray();
-            var negative_labels = negative_examples.Select(x => new int[2] { 1, 0 }).ToArray();
-            var y = np.concatenate(new NDArray[] { new int[][][] { positive_labels, negative_labels } });
-            return (x_text.ToArray(), y);
-        }
-
-        private static string clean_str(string str)
-        {
-            str = Regex.Replace(str, "[^A-Za-z0-9(),!?]", " ");
-            str = Regex.Replace(str, ",", " ");
-            return str;
-        }
-
-        /// <summary>
-        /// Padding
-        /// </summary>
-        /// <param name="sequences"></param>
-        /// <param name="pad_tok">the char to pad with</param>
-        /// <returns>a list of list where each sublist has same length</returns>
-        public static (int[][], int[]) pad_sequences(int[][] sequences, int pad_tok = 0)
-        {
-            int max_length = sequences.Select(x => x.Length).Max();
-            return _pad_sequences(sequences, pad_tok, max_length);
-        }
-
-        public static (int[][][], int[][]) pad_sequences(int[][][] sequences, int pad_tok = 0)
-        {
-            int max_length_word = sequences.Select(x => x.Select(w => w.Length).Max()).Max();
-            int[][][] sequence_padded;
-            var sequence_length = new int[sequences.Length][];
-            for (int i = 0; i < sequences.Length; i++)
-            {
-                // all words are same length now
-                var (sp, sl) = _pad_sequences(sequences[i], pad_tok, max_length_word);
-                sequence_length[i] = sl;
-            }
-
-            int max_length_sentence = sequences.Select(x => x.Length).Max();
-            (sequence_padded, _) = _pad_sequences(sequences, np.repeat(pad_tok, max_length_word).GetData<int>().ToArray(), max_length_sentence);
-            (sequence_length, _) = _pad_sequences(sequence_length, 0, max_length_sentence);
-
-            return (sequence_padded, sequence_length);
-        }
-
-        private static (int[][], int[]) _pad_sequences(int[][] sequences, int pad_tok, int max_length)
-        {
-            var sequence_length = new int[sequences.Length];
-            for (int i = 0; i < sequences.Length; i++)
-            {
-                sequence_length[i] = sequences[i].Length;
-                Array.Resize(ref sequences[i], max_length);
-            }
-
-            return (sequences, sequence_length);
-        }
-
-        private static (int[][][], int[]) _pad_sequences(int[][][] sequences, int[] pad_tok, int max_length)
-        {
-            var sequence_length = new int[sequences.Length];
-            for (int i = 0; i < sequences.Length; i++)
-            {
-                sequence_length[i] = sequences[i].Length;
-                Array.Resize(ref sequences[i], max_length);
-                for (int j = 0; j < max_length - sequence_length[i]; j++)
-                {
-                    sequences[i][max_length - j - 1] = new int[pad_tok.Length];
-                    Array.Copy(pad_tok, sequences[i][max_length - j - 1], pad_tok.Length);
-                }
-            }
-
-            return (sequences, sequence_length);
-        }
-
-        public static string CalculateMD5Hash(string input)
-        {
-            // step 1, calculate MD5 hash from input
-            MD5 md5 = System.Security.Cryptography.MD5.Create();
-            byte[] inputBytes = System.Text.Encoding.ASCII.GetBytes(input);
-            byte[] hash = md5.ComputeHash(inputBytes);
-
-            // step 2, convert byte array to hex string
-            StringBuilder sb = new StringBuilder();
-            for (int i = 0; i < hash.Length; i++)
-            {
-                sb.Append(hash[i].ToString("X2"));
-            }
-            return sb.ToString();
-        }
-    }
-}

test/TensorFlowNET.Examples/TextProcessing/NER/BiLstmCrfNer.cs

@@ -1,59 +0,0 @@
-using System;
-using System.IO;
-using Tensorflow;
-using Tensorflow.Estimators;
-
-namespace TensorFlowNET.Examples
-{
-    /// <summary>
-    /// Bidirectional LSTM-CRF Models for Sequence Tagging 
-    /// https://github.com/guillaumegenthial/tf_ner/tree/master/models/lstm_crf
-    /// </summary>
-    public class BiLstmCrfNer : IExample
-    {
-        public bool Enabled { get; set; } = false;
-        public bool IsImportingGraph { get; set; } = false;
-
-        public string Name => "bi-LSTM + CRF NER";
-
-        public bool Run()
-        {
-            PrepareData();
-            return false;
-        }
-
-        public void PrepareData()
-        {
-            var hp = new HyperParams("BiLstmCrfNer");
-            hp.filepath_words = Path.Combine(hp.data_root_dir, "vocab.words.txt");
-            hp.filepath_chars = Path.Combine(hp.data_root_dir, "vocab.chars.txt");
-            hp.filepath_tags = Path.Combine(hp.data_root_dir, "vocab.tags.txt");
-            hp.filepath_glove = Path.Combine(hp.data_root_dir, "glove.npz");
-        }
-
-        public Graph ImportGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public Graph BuildGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Train(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Predict(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Test(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-    }
-}

test/TensorFlowNET.Examples/TextProcessing/NER/CRF.cs

@@ -1,55 +0,0 @@
-using System;
-using Tensorflow;
-
-namespace TensorFlowNET.Examples
-{
-    /// <summary>
-    /// The CRF module implements a linear-chain CRF layer for learning to predict tag sequences.
-    /// This variant of the CRF is factored into unary potentials for every element 
-    /// in the sequence and binary potentials for every transition between output tags.
-    /// 
-    /// tensorflow\contrib\crf\python\ops\crf.py
-    /// </summary>
-    public class CRF : IExample
-    {
-        public bool Enabled { get; set; } = true;
-        public bool IsImportingGraph { get; set; } = false;
-
-        public string Name => "CRF";
-
-        public bool Run()
-        {
-            return true;
-        }
-
-        public void PrepareData()
-        {
-
-        }
-
-        public Graph ImportGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public Graph BuildGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Train(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Predict(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Test(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-    }
-}

test/TensorFlowNET.Examples/TextProcessing/NER/LstmCrfNer.cs

@@ -1,233 +0,0 @@
-using NumSharp;
-using System;
-using System.Collections.Generic;
-using System.IO;
-using System.Linq;
-using Tensorflow;
-using Tensorflow.Estimators;
-using TensorFlowNET.Examples.Utility;
-using static Tensorflow.Binding;
-using static TensorFlowNET.Examples.DataHelpers;
-
-namespace TensorFlowNET.Examples.Text.NER
-{
-    /// <summary>
-    /// A NER model using Tensorflow (LSTM + CRF + chars embeddings).
-    /// State-of-the-art performance (F1 score between 90 and 91).
-    /// 
-    /// https://github.com/guillaumegenthial/sequence_tagging
-    /// </summary>
-    public class LstmCrfNer : IExample
-    {
-        public bool Enabled { get; set; } = true;
-        public bool IsImportingGraph { get; set; } = true;
-
-        public string Name => "LSTM + CRF NER";
-
-        HyperParams hp;
-        
-        int nwords, nchars, ntags;
-        CoNLLDataset dev, train;
-
-        Tensor word_ids_tensor;
-        Tensor sequence_lengths_tensor;
-        Tensor char_ids_tensor;
-        Tensor word_lengths_tensor;
-        Tensor labels_tensor;
-        Tensor dropout_tensor;
-        Tensor lr_tensor;
-        Operation train_op;
-        Tensor loss;
-        Tensor merged;
-
-        public bool Run()
-        {
-            PrepareData();
-            var graph = tf.Graph().as_default();
-
-            tf.train.import_meta_graph("graph/lstm_crf_ner.meta");
-
-            float loss_value = 0f;
-
-            //add_summary();
-            word_ids_tensor = graph.OperationByName("word_ids");
-            sequence_lengths_tensor = graph.OperationByName("sequence_lengths");
-            char_ids_tensor = graph.OperationByName("char_ids");
-            word_lengths_tensor = graph.OperationByName("word_lengths");
-            labels_tensor = graph.OperationByName("labels");
-            dropout_tensor = graph.OperationByName("dropout");
-            lr_tensor = graph.OperationByName("lr");
-            train_op = graph.OperationByName("train_step/Adam");
-            loss = graph.OperationByName("Mean");
-            //merged = graph.OperationByName("Merge/MergeSummary");
-
-            var init = tf.global_variables_initializer();
-
-            using (var sess = tf.Session())
-            {
-                sess.run(init);
-
-                foreach (var epoch in range(hp.epochs))
-                {
-                    Console.Write($"Epoch {epoch + 1} out of {hp.epochs}, ");
-                    loss_value = run_epoch(sess, train, dev, epoch);
-                    print($"train loss: {loss_value}");
-                }
-            }
-
-            return loss_value < 0.1;
-        }
-
-        private float run_epoch(Session sess, CoNLLDataset train, CoNLLDataset dev, int epoch)
-        {
-            float accuracy = 0;
-            // iterate over dataset
-            var batches = minibatches(train, hp.batch_size);
-            foreach (var(words, labels) in batches)
-            {
-                var (fd, _) = get_feed_dict(words, labels, hp.lr, hp.dropout);
-                (_, accuracy) = sess.run((train_op, loss), feed_dict: fd);
-            }
-
-            return accuracy;
-        }
-
-        private IEnumerable<((int[][], int[])[], int[][])> minibatches(CoNLLDataset data, int minibatch_size)
-        {
-            var x_batch = new List<(int[][], int[])>();
-            var y_batch = new List<int[]>();
-            foreach(var (x, y) in data.GetItems())
-            {
-                if (len(y_batch) == minibatch_size)
-                {
-                    yield return (x_batch.ToArray(), y_batch.ToArray());
-                    x_batch.Clear();
-                    y_batch.Clear();
-                }
-
-                var x3 = (x.Select(x1 => x1.Item1).ToArray(), x.Select(x2 => x2.Item2).ToArray());
-                x_batch.Add(x3);
-                y_batch.Add(y);
-            }
-
-            if (len(y_batch) > 0)
-                yield return (x_batch.ToArray(), y_batch.ToArray());
-        }
-
-        /// <summary>
-        /// Given some data, pad it and build a feed dictionary
-        /// </summary>
-        /// <param name="words">
-        /// list of sentences. A sentence is a list of ids of a list of
-        /// words. A word is a list of ids
-        /// </param>
-        /// <param name="labels">list of ids</param>
-        /// <param name="lr">learning rate</param>
-        /// <param name="dropout">keep prob</param>
-        private (FeedItem[], int[]) get_feed_dict((int[][], int[])[] words, int[][] labels, float lr = 0f, float dropout = 0f)
-        {
-            int[] sequence_lengths;
-            int[][] word_lengths;
-            int[][] word_ids;
-            int[][][] char_ids;
-
-            if (true) // use_chars
-            {
-                (char_ids, word_ids) = (words.Select(x => x.Item1).ToArray(), words.Select(x => x.Item2).ToArray());
-                (word_ids, sequence_lengths) = pad_sequences(word_ids, pad_tok: 0);
-                (char_ids, word_lengths) = pad_sequences(char_ids, pad_tok: 0);
-            }
-
-            // build feed dictionary
-            var feeds = new List<FeedItem>();
-            feeds.Add(new FeedItem(word_ids_tensor, np.array(word_ids)));
-            feeds.Add(new FeedItem(sequence_lengths_tensor, np.array(sequence_lengths)));
-            
-            if(true) // use_chars
-            {
-                feeds.Add(new FeedItem(char_ids_tensor, np.array(char_ids)));
-                feeds.Add(new FeedItem(word_lengths_tensor, np.array(word_lengths)));
-            }
-
-            (labels, _) = pad_sequences(labels, 0);
-            feeds.Add(new FeedItem(labels_tensor, np.array(labels)));
-
-            feeds.Add(new FeedItem(lr_tensor, lr));
-
-            feeds.Add(new FeedItem(dropout_tensor, dropout));
-
-            return (feeds.ToArray(), sequence_lengths);
-        }
-
-        public void PrepareData()
-        {
-            hp = new HyperParams("LstmCrfNer")
-            {
-                epochs = 50,
-                dropout = 0.5f,
-                batch_size = 20,
-                lr_method = "adam",
-                lr = 0.001f,
-                lr_decay = 0.9f,
-                clip = false,
-                epoch_no_imprv = 3,
-                hidden_size_char = 100,
-                hidden_size_lstm = 300
-            };
-            hp.filepath_dev = hp.filepath_test = hp.filepath_train = Path.Combine(hp.data_root_dir, "test.txt");
-
-            // Loads vocabulary, processing functions and embeddings
-            hp.filepath_words = Path.Combine(hp.data_root_dir, "words.txt");
-            hp.filepath_tags = Path.Combine(hp.data_root_dir, "tags.txt");
-            hp.filepath_chars = Path.Combine(hp.data_root_dir, "chars.txt");
-
-            string url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/data/lstm_crf_ner.zip";
-            Web.Download(url, hp.data_root_dir, "lstm_crf_ner.zip");
-            Compress.UnZip(Path.Combine(hp.data_root_dir, "lstm_crf_ner.zip"), hp.data_root_dir);
-
-            // 1. vocabulary
-            /*vocab_tags = load_vocab(hp.filepath_tags);
-            
-
-            nwords = vocab_words.Count;
-            nchars = vocab_chars.Count;
-            ntags = vocab_tags.Count;*/
-
-            // 2. get processing functions that map str -> id
-            dev = new CoNLLDataset(hp.filepath_dev, hp);
-            train = new CoNLLDataset(hp.filepath_train, hp);
-
-            // download graph meta data
-            var meta_file = "lstm_crf_ner.meta";
-            var meta_path = Path.Combine("graph", meta_file);
-            url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/graph/" + meta_file;
-            Web.Download(url, "graph", meta_file);
-
-        }
-
-        public Graph ImportGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public Graph BuildGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Train(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Predict(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Test(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-    }
-}

test/TensorFlowNET.Examples/TextProcessing/NamedEntityRecognition.cs

@@ -1,51 +0,0 @@
-using System;
-using Tensorflow;
-
-namespace TensorFlowNET.Examples
-{
-    /// <summary>
-    /// https://github.com/guillaumegenthial/tf_ner
-    /// </summary>
-    public class NamedEntityRecognition : IExample
-    {
-        public bool Enabled { get; set; } = false;
-        public string Name => "NER";
-        public bool IsImportingGraph { get; set; } = false;
-
-
-        public void Train(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void PrepareData()
-        {
-            throw new NotImplementedException();
-        }
-
-        public Graph ImportGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public Graph BuildGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public bool Run()
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Predict(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Test(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-    }
-}

test/TensorFlowNET.Examples/TextProcessing/Word2Vec.cs

@@ -1,243 +0,0 @@
-using NumSharp;
-using System;
-using System.Collections.Generic;
-using System.IO;
-using System.Linq;
-using Tensorflow;
-using TensorFlowNET.Examples.Utility;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples
-{
-    /// <summary>
-    /// Implement Word2Vec algorithm to compute vector representations of words.
-    /// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/word2vec.py
-    /// </summary>
-    public class Word2Vec : IExample
-    {
-        public bool Enabled { get; set; } = true;
-        public string Name => "Word2Vec";
-        public bool IsImportingGraph { get; set; } = true;
-
-        // Training Parameters
-        float learning_rate = 0.1f;
-        int batch_size = 128;
-        int num_steps = 30000; //3000000;
-        int display_step = 1000; //10000;
-        int eval_step = 5000;//200000;
-
-        // Evaluation Parameters
-        string[] eval_words = new string[] { "five", "of", "going", "hardware", "american", "britain" };
-        string[] text_words;
-        List<WordId> word2id;
-        int[] data;
-
-        // Word2Vec Parameters
-        int embedding_size = 200; // Dimension of the embedding vector
-        int max_vocabulary_size = 50000; // Total number of different words in the vocabulary
-        int min_occurrence = 10; // Remove all words that does not appears at least n times
-        int skip_window = 3; // How many words to consider left and right
-        int num_skips = 2; // How many times to reuse an input to generate a label
-        int num_sampled = 64; // Number of negative examples to sample
-
-        int data_index = 0;
-        int top_k = 8; // number of nearest neighbors
-        float average_loss = 0;
-
-        public bool Run()
-        {
-            PrepareData();
-
-            var graph = tf.Graph().as_default();
-
-            tf.train.import_meta_graph($"graph{Path.DirectorySeparatorChar}word2vec.meta");
-
-            // Input data
-            Tensor X = graph.OperationByName("Placeholder");
-            // Input label
-            Tensor Y = graph.OperationByName("Placeholder_1");
-
-            // Compute the average NCE loss for the batch
-            Tensor loss_op = graph.OperationByName("Mean");
-            // Define the optimizer
-            var train_op = graph.OperationByName("GradientDescent");
-            Tensor cosine_sim_op = graph.OperationByName("MatMul_1");
-
-            // Initialize the variables (i.e. assign their default value)
-            var init = tf.global_variables_initializer();
-
-            using (var sess = tf.Session(graph))
-            {
-                // Run the initializer
-                sess.run(init);
-
-                var x_test = (from word in eval_words
-                              join id in word2id on word equals id.Word into wi
-                              from wi2 in wi.DefaultIfEmpty()
-                              select wi2 == null ? 0 : wi2.Id).ToArray();
-
-                foreach (var step in range(1, num_steps + 1))
-                {
-                    // Get a new batch of data
-                    var (batch_x, batch_y) = next_batch(batch_size, num_skips, skip_window);
-
-                    (_, float loss) = sess.run((train_op, loss_op), (X, batch_x), (Y, batch_y));
-                    average_loss += loss;
-
-                    if (step % display_step == 0 || step == 1)
-                    {
-                        if (step > 1)
-                            average_loss /= display_step;
-
-                        print($"Step {step}, Average Loss= {average_loss.ToString("F4")}");
-                        average_loss = 0;
-                    }
-
-                    // Evaluation
-                    if (step % eval_step == 0 || step == 1)
-                    {
-                        print("Evaluation...");
-                        var sim = sess.run(cosine_sim_op, (X, x_test));
-                        foreach(var i in range(len(eval_words)))
-                        {
-                            var nearest = (0f - sim[i]).argsort<float>()
-                                .Data<int>()
-                                .Skip(1)
-                                .Take(top_k)
-                                .ToArray();
-                            string log_str = $"\"{eval_words[i]}\" nearest neighbors:";
-                            foreach (var k in range(top_k))
-                                log_str = $"{log_str} {word2id.First(x => x.Id == nearest[k]).Word},";
-                            print(log_str);
-                        }
-                    }
-                }
-            }
-
-            return average_loss < 100;
-        }
-
-        // Generate training batch for the skip-gram model
-        private (NDArray, NDArray) next_batch(int batch_size, int num_skips, int skip_window)
-        {
-            var batch = np.ndarray(new Shape(batch_size), dtype: np.int32);
-            var labels = np.ndarray((batch_size, 1), dtype: np.int32);
-            // get window size (words left and right + current one)
-            int span = 2 * skip_window + 1;
-            var buffer = new Queue<int>(span);
-            if (data_index + span > data.Length)
-                data_index = 0;
-            data.Skip(data_index).Take(span).ToList().ForEach(x => buffer.Enqueue(x));
-            data_index += span;
-
-            foreach (var i in range(batch_size / num_skips))
-            {
-                var context_words = range(span).Where(x => x != skip_window).ToArray();
-                var words_to_use = new int[] { 1, 6 };
-                foreach(var (j, context_word) in enumerate(words_to_use))
-                {
-                    batch[i * num_skips + j] = buffer.ElementAt(skip_window);
-                    labels[i * num_skips + j, 0] = buffer.ElementAt(context_word);
-                }
-
-                if (data_index == len(data))
-                {
-                    //buffer.extend(data[0:span]);
-                    data_index = span;
-                }
-                else
-                {
-                    buffer.Enqueue(data[data_index]);
-                    data_index += 1;
-                }
-            }
-
-            // Backtrack a little bit to avoid skipping words in the end of a batch
-            data_index = (data_index + len(data) - span) % len(data);
-
-            return (batch, labels);
-        }
-
-        public void PrepareData()
-        {
-            // Download graph meta
-            var url = "https://github.com/SciSharp/TensorFlow.NET/raw/master/graph/word2vec.meta";
-            Web.Download(url, "graph", "word2vec.meta");
-
-            // Download a small chunk of Wikipedia articles collection
-            url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/data/text8.zip";
-            Web.Download(url, "word2vec", "text8.zip");
-            // Unzip the dataset file. Text has already been processed
-            Compress.UnZip($"word2vec{Path.DirectorySeparatorChar}text8.zip", "word2vec");
-
-            int wordId = 0;
-            text_words = File.ReadAllText($"word2vec{Path.DirectorySeparatorChar}text8").Trim().ToLower().Split();
-            // Build the dictionary and replace rare words with UNK token
-            word2id = text_words.GroupBy(x => x)
-                .Select(x => new WordId
-                {
-                    Word = x.Key,
-                    Occurrence = x.Count()
-                })
-                .Where(x => x.Occurrence >= min_occurrence) // Remove samples with less than 'min_occurrence' occurrences
-                .OrderByDescending(x => x.Occurrence) // Retrieve the most common words
-                .Select(x => new WordId
-                {
-                    Word = x.Word,
-                    Id = ++wordId, // Assign an id to each word
-                    Occurrence = x.Occurrence
-                })
-                .ToList();
-
-            // Retrieve a word id, or assign it index 0 ('UNK') if not in dictionary
-            data = (from word in text_words
-                        join id in word2id on word equals id.Word into wi
-                        from wi2 in wi.DefaultIfEmpty()
-                        select wi2 == null ? 0 : wi2.Id).ToArray();
-
-            word2id.Insert(0, new WordId { Word = "UNK", Id = 0, Occurrence = data.Count(x => x == 0) });
-
-            print($"Words count: {text_words.Length}");
-            print($"Unique words: {text_words.Distinct().Count()}");
-            print($"Vocabulary size: {word2id.Count}");
-            print($"Most common words: {string.Join(", ", word2id.Take(10))}");
-        }
-
-        public Graph ImportGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public Graph BuildGraph()
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Train(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Predict(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        public void Test(Session sess)
-        {
-            throw new NotImplementedException();
-        }
-
-        private class WordId
-        {
-            public string Word { get; set; }
-            public int Id { get; set; }
-            public int Occurrence { get; set; }
-
-            public override string ToString()
-            {
-                return Word + " " + Id + " " + Occurrence;
-            }
-        }
-    }
-}

test/TensorFlowNET.Examples/TextProcessing/cnn_models/CharCnn.cs

@@ -1,147 +0,0 @@
-using Tensorflow;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples.Text
-{
-    public class CharCnn : ITextModel
-    {
-        public CharCnn(int alphabet_size, int document_max_len, int num_class)
-        {
-            var learning_rate = 0.001f;
-            var filter_sizes = new int[] { 7, 7, 3, 3, 3, 3 };
-            var num_filters = 256;
-            var kernel_initializer = tf.truncated_normal_initializer(stddev: 0.05f);
-
-            var x = tf.placeholder(tf.int32, new TensorShape(-1, document_max_len), name: "x");
-            var y = tf.placeholder(tf.int32, new TensorShape(-1), name: "y");
-            var is_training = tf.placeholder(tf.@bool, new TensorShape(), name: "is_training");
-            var global_step = tf.Variable(0, trainable: false);
-            var keep_prob = tf.where(is_training, 0.5f, 1.0f);
-
-            var x_one_hot = tf.one_hot(x, alphabet_size);
-            var x_expanded = tf.expand_dims(x_one_hot, -1);
-
-            // ============= Convolutional Layers =============
-            Tensor pool1 = null, pool2 = null;
-            Tensor conv3 = null, conv4 = null, conv5 = null, conv6 = null;
-            Tensor h_pool = null;
-
-            tf_with(tf.name_scope("conv-maxpool-1"), delegate
-            {
-                var conv1 = tf.layers.conv2d(x_expanded,
-                    filters: num_filters,
-                    kernel_size: new[] { filter_sizes[0], alphabet_size },
-                    kernel_initializer: kernel_initializer,
-                    activation: tf.nn.relu());
-
-                pool1 = tf.layers.max_pooling2d(conv1,
-                    pool_size: new[] { 3, 1 },
-                    strides: new[] { 3, 1 });
-                pool1 = tf.transpose(pool1, new[] { 0, 1, 3, 2 });
-            });
-
-            tf_with(tf.name_scope("conv-maxpool-2"), delegate
-            {
-                var conv2 = tf.layers.conv2d(pool1,
-                    filters: num_filters,
-                    kernel_size: new[] {filter_sizes[1], num_filters },
-                    kernel_initializer: kernel_initializer,
-                    activation: tf.nn.relu());
-
-                pool2 = tf.layers.max_pooling2d(conv2,
-                    pool_size: new[] { 3, 1 },
-                    strides: new[] { 3, 1 });
-                pool2 = tf.transpose(pool2, new[] { 0, 1, 3, 2 });
-            });
-
-            tf_with(tf.name_scope("conv-3"), delegate
-            {
-                conv3 = tf.layers.conv2d(pool2,
-                    filters: num_filters,
-                    kernel_size: new[] { filter_sizes[2], num_filters },
-                    kernel_initializer: kernel_initializer,
-                    activation: tf.nn.relu());
-                conv3 = tf.transpose(conv3, new[] { 0, 1, 3, 2 });
-            });
-
-            tf_with(tf.name_scope("conv-4"), delegate
-            {
-                conv4 = tf.layers.conv2d(conv3,
-                    filters: num_filters,
-                    kernel_size: new[] { filter_sizes[3], num_filters },
-                    kernel_initializer: kernel_initializer,
-                    activation: tf.nn.relu());
-                conv4 = tf.transpose(conv4, new[] { 0, 1, 3, 2 });
-            });
-
-            tf_with(tf.name_scope("conv-5"), delegate
-            {
-                conv5 = tf.layers.conv2d(conv4,
-                    filters: num_filters,
-                    kernel_size: new[] { filter_sizes[4], num_filters },
-                    kernel_initializer: kernel_initializer,
-                    activation: tf.nn.relu());
-                conv5 = tf.transpose(conv5, new[] { 0, 1, 3, 2 });
-            });
-
-            tf_with(tf.name_scope("conv-maxpool-6"), delegate
-            {
-                conv6 = tf.layers.conv2d(conv5,
-                    filters: num_filters,
-                    kernel_size: new[] { filter_sizes[5], num_filters },
-                    kernel_initializer: kernel_initializer,
-                    activation: tf.nn.relu());
-
-                var pool6 = tf.layers.max_pooling2d(conv6,
-                    pool_size: new[] { 3, 1 },
-                    strides: new[] { 3, 1 });
-                pool6 = tf.transpose(pool6, new[] { 0, 2, 1, 3 });
-
-                h_pool = tf.reshape(pool6, new[] { -1, 34 * num_filters });
-            });
-
-            // ============= Fully Connected Layers =============
-            Tensor fc1_out = null, fc2_out = null;
-            Tensor logits = null;
-            Tensor predictions = null;
-
-            tf_with(tf.name_scope("fc-1"), delegate
-            {
-                fc1_out = tf.layers.dense(h_pool,
-                    1024,
-                    activation: tf.nn.relu(),
-                    kernel_initializer: kernel_initializer);
-            });
-
-            tf_with(tf.name_scope("fc-2"), delegate
-            {
-                fc2_out = tf.layers.dense(fc1_out,
-                    1024,
-                    activation: tf.nn.relu(),
-                    kernel_initializer: kernel_initializer);
-            });
-
-            tf_with(tf.name_scope("fc-3"), delegate
-            {
-                logits = tf.layers.dense(fc2_out,
-                    num_class,
-                    kernel_initializer: kernel_initializer);
-                predictions = tf.argmax(logits, -1, output_type: tf.int32);
-            });
-
-            tf_with(tf.name_scope("loss"), delegate
-            {
-                var y_one_hot = tf.one_hot(y, num_class);
-                var loss = tf.reduce_mean(
-                    tf.nn.softmax_cross_entropy_with_logits_v2(logits: logits, labels: y_one_hot));
-                var optimizer = tf.train.AdamOptimizer(learning_rate).minimize(loss, global_step: global_step);
-            });
-
-            tf_with(tf.name_scope("accuracy"), delegate
-            {
-                var correct_predictions = tf.equal(predictions, y);
-                var accuracy = tf.reduce_mean(tf.cast(correct_predictions, tf.float32), name: "accuracy");
-            });
-        }
-    }
-}

test/TensorFlowNET.Examples/TextProcessing/cnn_models/ITextModel.cs

@@ -1,6 +0,0 @@
-namespace TensorFlowNET.Examples.Text
-{
-    interface ITextModel
-    {
-    }
-}

test/TensorFlowNET.Examples/TextProcessing/cnn_models/VdCnn.cs

@@ -1,171 +0,0 @@
-using System.Collections.Generic;
-using System.Linq;
-using Tensorflow;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples.Text
-{
-    public class VdCnn : ITextModel
-    {
-        private int embedding_size;
-        private int[] filter_sizes;
-        private int[] num_filters;
-        private int[] num_blocks;
-        private float learning_rate;
-        private IInitializer cnn_initializer;
-        private IInitializer fc_initializer;
-        public Tensor x { get; private set; }
-        public Tensor y { get; private set; }
-        public Tensor is_training { get; private set; }
-        private RefVariable global_step;
-        private RefVariable embeddings;
-        private Tensor x_emb;
-        private Tensor x_expanded;
-        private Tensor logits;
-        private Tensor predictions;
-        private Tensor loss;
-
-        public VdCnn(int alphabet_size, int document_max_len, int num_class)
-        {
-            embedding_size = 16;
-            filter_sizes = new int[] { 3, 3, 3, 3, 3 };
-            num_filters = new int[] { 64, 64, 128, 256, 512 };
-            num_blocks = new int[] { 2, 2, 2, 2 };
-            learning_rate = 0.001f;
-            cnn_initializer = tensorflow.keras.initializers.he_normal();
-            fc_initializer = tf.truncated_normal_initializer(stddev: 0.05f);
-
-            x = tf.placeholder(tf.int32, new TensorShape(-1, document_max_len), name: "x");
-            y = tf.placeholder(tf.int32, new TensorShape(-1), name: "y");
-            is_training = tf.placeholder(tf.@bool, new TensorShape(), name: "is_training");
-            global_step = tf.Variable(0, trainable: false);
-
-            // Embedding Layer
-            tf_with(tf.name_scope("embedding"), delegate
-            {
-                var init_embeddings = tf.random_uniform(new int[] { alphabet_size, embedding_size }, -1.0f, 1.0f);
-                embeddings = tf.get_variable("embeddings", initializer: init_embeddings);
-                x_emb = tf.nn.embedding_lookup(embeddings, x);
-                x_expanded = tf.expand_dims(x_emb, -1);
-            });
-
-            Tensor conv0 = null;
-            Tensor conv1 = null;
-            Tensor conv2 = null;
-            Tensor conv3 = null;
-            Tensor conv4 = null;
-            Tensor h_flat = null;
-            Tensor fc1_out = null;
-            Tensor fc2_out = null;
-
-            // First Convolution Layer
-            tf_with(tf.variable_scope("conv-0"), delegate
-            {
-                conv0 = tf.layers.conv2d(x_expanded,
-                    filters: num_filters[0],
-                    kernel_size: new int[] { filter_sizes[0], embedding_size },
-                    kernel_initializer: cnn_initializer,
-                    activation: tf.nn.relu());
-
-                conv0 = tf.transpose(conv0, new int[] { 0, 1, 3, 2 });
-            });
-
-            tf_with(tf.name_scope("conv-block-1"), delegate {
-                conv1 = conv_block(conv0, 1);
-            });
-
-            tf_with(tf.name_scope("conv-block-2"), delegate {
-                conv2 = conv_block(conv1, 2);
-            });
-
-            tf_with(tf.name_scope("conv-block-3"), delegate {
-                conv3 = conv_block(conv2, 3);
-            });
-
-            tf_with(tf.name_scope("conv-block-4"), delegate
-            {
-                conv4 = conv_block(conv3, 4, max_pool: false);
-            });
-
-            // ============= k-max Pooling =============
-            tf_with(tf.name_scope("k-max-pooling"), delegate
-            {
-                var h = tf.transpose(tf.squeeze(conv4, new int[] { -1 }), new int[] { 0, 2, 1 });
-                var top_k = tf.nn.top_k(h, k: 8, sorted: false)[0];
-                h_flat = tf.reshape(top_k, new int[] { -1, 512 * 8 });
-            });
-
-            // ============= Fully Connected Layers =============
-            tf_with(tf.name_scope("fc-1"), scope =>
-            {
-                fc1_out = tf.layers.dense(h_flat, 2048, activation: tf.nn.relu(), kernel_initializer: fc_initializer);
-            });
-
-            tf_with(tf.name_scope("fc-2"), scope =>
-            {
-                fc2_out = tf.layers.dense(fc1_out, 2048, activation: tf.nn.relu(), kernel_initializer: fc_initializer);
-            });
-
-            tf_with(tf.name_scope("fc-3"), scope =>
-            {
-                logits = tf.layers.dense(fc2_out, num_class, activation: null, kernel_initializer: fc_initializer);
-                predictions = tf.argmax(logits, -1, output_type: tf.int32);
-            });
-
-            // ============= Loss and Accuracy =============
-            tf_with(tf.name_scope("loss"), delegate
-            {
-                var y_one_hot = tf.one_hot(y, num_class);
-                loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits: logits, labels: y_one_hot));
-
-                var update_ops = tf.get_collection<object>(tf.GraphKeys.UPDATE_OPS);
-                tf_with(tf.control_dependencies(update_ops.Select(x => (Operation)x).ToArray()), delegate
-                {
-                    var adam = tf.train.AdamOptimizer(learning_rate);
-                    adam.minimize(loss, global_step: global_step);
-                });
-            });
-        }
-
-        private Tensor conv_block(Tensor input, int i, bool max_pool = true)
-        {
-            return tf_with(tf.variable_scope($"conv-block-{i}"), delegate
-            {
-                Tensor conv = null;
-                // Two "conv-batch_norm-relu" layers.
-                foreach (var j in Enumerable.Range(0, 2))
-                {
-                    tf_with(tf.variable_scope($"conv-{j}"), delegate
-                    {
-                        // convolution
-                        conv = tf.layers.conv2d(
-                            input,
-                            filters: num_filters[i],
-                            kernel_size: new int[] { filter_sizes[i], num_filters[i - 1] },
-                            kernel_initializer: cnn_initializer,
-                            activation: null);
-                        // batch normalization
-                        conv = tf.layers.batch_normalization(conv, training: is_training);
-                        // relu
-                        conv = tf.nn.relu(conv);
-                        conv = tf.transpose(conv, new int[] { 0, 1, 3, 2 });
-                    });
-                }
-                
-                if (max_pool)
-                {
-                    // Max pooling
-                    return tf.layers.max_pooling2d(
-                        conv,
-                        pool_size: new int[] { 3, 1 },
-                        strides: new int[] { 2, 1 },
-                        padding: "SAME");
-                }
-                else
-                {
-                    return conv;
-                }
-            });
-        }
-    }
-}

test/TensorFlowNET.Examples/TextProcessing/cnn_models/WordCnn.cs

@@ -1,99 +0,0 @@
-/*****************************************************************************
-   Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
-******************************************************************************/
-
-using System.Collections.Generic;
-using Tensorflow;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.Examples.Text
-{
-    public class WordCnn : ITextModel
-    {
-        public WordCnn(int vocabulary_size, int document_max_len, int num_class)
-        {
-            var embedding_size = 128;
-            var learning_rate = 0.001f;
-            var filter_sizes = new int[3, 4, 5];
-            var num_filters = 100;
-
-            var x = tf.placeholder(tf.int32, new TensorShape(-1, document_max_len), name: "x");
-            var y = tf.placeholder(tf.int32, new TensorShape(-1), name: "y");
-            var is_training = tf.placeholder(tf.@bool, new TensorShape(), name: "is_training");
-            var global_step = tf.Variable(0, trainable: false);
-            var keep_prob = tf.where(is_training, 0.5f, 1.0f);
-            Tensor x_emb = null;
-
-            tf_with(tf.name_scope("embedding"), scope =>
-            {
-                var init_embeddings = tf.random_uniform(new int[] { vocabulary_size, embedding_size });
-                var embeddings = tf.get_variable("embeddings", initializer: init_embeddings);
-                x_emb = tf.nn.embedding_lookup(embeddings, x);
-                x_emb = tf.expand_dims(x_emb, -1);
-            });
-
-            var pooled_outputs = new List<Tensor>();
-            for (int len = 0; len < filter_sizes.Rank; len++)
-            {
-                int filter_size = filter_sizes.GetLength(len);
-                var conv = tf.layers.conv2d(
-                    x_emb,
-                    filters: num_filters,
-                    kernel_size: new int[] { filter_size, embedding_size },
-                    strides: new int[] { 1, 1 },
-                    padding: "VALID",
-                    activation: tf.nn.relu());
-
-                var pool = tf.layers.max_pooling2d(
-                    conv,
-                    pool_size: new[] { document_max_len - filter_size + 1, 1 },
-                    strides: new[] { 1, 1 },
-                    padding: "VALID");
-
-                pooled_outputs.Add(pool);
-            }
-
-            var h_pool = tf.concat(pooled_outputs, 3);
-            var h_pool_flat = tf.reshape(h_pool, new TensorShape(-1, num_filters * filter_sizes.Rank));
-            Tensor h_drop = null;
-            tf_with(tf.name_scope("dropout"), delegate
-            {
-                h_drop = tf.nn.dropout(h_pool_flat, keep_prob);
-            });
-
-            Tensor logits = null;
-            Tensor predictions = null;
-            tf_with(tf.name_scope("output"), delegate
-            {
-                logits = tf.layers.dense(h_drop, num_class);
-                predictions = tf.argmax(logits, -1, output_type: tf.int32);
-            });
-
-            tf_with(tf.name_scope("loss"), delegate
-            {
-                var sscel = tf.nn.sparse_softmax_cross_entropy_with_logits(logits: logits, labels: y);
-                var loss = tf.reduce_mean(sscel);
-                var adam = tf.train.AdamOptimizer(learning_rate);
-                var optimizer = adam.minimize(loss, global_step: global_step);
-            });
-
-            tf_with(tf.name_scope("accuracy"), delegate
-            {
-                var correct_predictions = tf.equal(predictions, y);
-                var accuracy = tf.reduce_mean(tf.cast(correct_predictions, TF_DataType.TF_FLOAT), name: "accuracy");
-            });
-        }
-    }
-}

test/TensorFlowNET.Examples/Utility/ArrayShuffling.cs

@@ -1,20 +0,0 @@
-using System;
-
-namespace TensorFlowNET.Examples.Utility
-{
-    public static class ArrayShuffling
-    {
-        public static T[] Shuffle<T>(this Random rng, T[] array)
-        {
-            int n = array.Length;
-            while (n > 1)
-            {
-                int k = rng.Next(n--);
-                T temp = array[n];
-                array[n] = array[k];
-                array[k] = temp;
-            }
-            return array;
-        }
-    }
-}

test/TensorFlowNET.Examples/Utility/CoNLLDataset.cs

@@ -1,105 +0,0 @@
-using System.Collections.Generic;
-using System.IO;
-using System.Linq;
-using Tensorflow.Estimators;
-
-namespace TensorFlowNET.Examples.Utility
-{
-    public class CoNLLDataset
-    {
-        static Dictionary<string, int> vocab_chars;
-        static Dictionary<string, int> vocab_words;
-        static Dictionary<string, int> vocab_tags;
-
-        HyperParams _hp;
-        string _path;
-
-        public CoNLLDataset(string path, HyperParams hp)
-        {
-            if (vocab_chars == null)
-                vocab_chars = load_vocab(hp.filepath_chars);
-
-            if (vocab_words == null)
-                vocab_words = load_vocab(hp.filepath_words);
-
-            if (vocab_tags == null)
-                vocab_tags = load_vocab(hp.filepath_tags);
-
-            _path = path;
-        }
-
-        private (int[], int) processing_word(string word)
-        {
-            var char_ids = word.ToCharArray().Select(x => vocab_chars[x.ToString()]).ToArray();
-
-            // 1. preprocess word
-            if (true) // lowercase
-                word = word.ToLower();
-            if (false) // isdigit
-                word = "$NUM$";
-
-            // 2. get id of word
-            int id = vocab_words.GetValueOrDefault(word, vocab_words["$UNK$"]);
-            
-            return (char_ids, id);
-        }
-
-        private int processing_tag(string word)
-        {
-            // 1. preprocess word
-            if (false) // lowercase
-                word = word.ToLower();
-            if (false) // isdigit
-                word = "$NUM$";
-
-            // 2. get id of word
-            int id = vocab_tags.GetValueOrDefault(word, -1);
-
-            return id;
-        }
-
-        private Dictionary<string, int> load_vocab(string filename)
-        {
-            var dict = new Dictionary<string, int>();
-            int i = 0;
-            File.ReadAllLines(filename)
-                .Select(x => dict[x] = i++)
-                .Count();
-            return dict;
-        }
-
-        public IEnumerable<((int[], int)[], int[])> GetItems()
-        {
-            var lines = File.ReadAllLines(_path);
-
-            int niter = 0;
-            var words = new List<(int[], int)>();
-            var tags = new List<int>();
-
-            foreach (var l in lines)
-            {
-                string line = l.Trim();
-                if (string.IsNullOrEmpty(line) || line.StartsWith("-DOCSTART-"))
-                {
-                    if (words.Count > 0)
-                    {
-                        niter++;
-                        yield return (words.ToArray(), tags.ToArray());
-                        words.Clear();
-                        tags.Clear();
-                    }
-                }
-                else
-                {
-                    var ls = line.Split(' ');
-                    // process word
-                    var word = processing_word(ls[0]);
-                    var tag = processing_tag(ls[1]);
-
-                    words.Add(word);
-                    tags.Add(tag);
-                }
-            }
-        }
-    }
-}

test/TensorFlowNET.Examples/Utility/Compress.cs

@@ -1,102 +0,0 @@
-/*****************************************************************************
-   Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
-******************************************************************************/
-
-using ICSharpCode.SharpZipLib.Core;
-using ICSharpCode.SharpZipLib.GZip;
-using ICSharpCode.SharpZipLib.Tar;
-using System;
-using System.IO;
-using System.IO.Compression;
-using System.Linq;
-using System.Threading;
-using System.Threading.Tasks;
-
-namespace TensorFlowNET.Examples.Utility
-{
-    public class Compress
-    {
-        public static void ExtractGZip(string gzipFileName, string targetDir)
-        {
-            // Use a 4K buffer. Any larger is a waste.    
-            byte[] dataBuffer = new byte[4096];
-
-            using (System.IO.Stream fs = new FileStream(gzipFileName, FileMode.Open, FileAccess.Read))
-            {
-                using (GZipInputStream gzipStream = new GZipInputStream(fs))
-                {
-                    // Change this to your needs
-                    string fnOut = Path.Combine(targetDir, Path.GetFileNameWithoutExtension(gzipFileName));
-
-                    using (FileStream fsOut = File.Create(fnOut))
-                    {
-                        StreamUtils.Copy(gzipStream, fsOut, dataBuffer);
-                    }
-                }
-            }
-        }
-
-        public static void UnZip(String gzArchiveName, String destFolder)
-        {
-            var flag = gzArchiveName.Split(Path.DirectorySeparatorChar).Last().Split('.').First() + ".bin";
-            if (File.Exists(Path.Combine(destFolder, flag))) return;
-
-            Console.WriteLine($"Extracting.");
-            var task = Task.Run(() =>
-            {
-                ZipFile.ExtractToDirectory(gzArchiveName, destFolder);
-            });
-
-            while (!task.IsCompleted)
-            {
-                Thread.Sleep(200);
-                Console.Write(".");
-            }
-
-            File.Create(Path.Combine(destFolder, flag));
-            Console.WriteLine("");
-            Console.WriteLine("Extracting is completed.");
-        }
-
-        public static void ExtractTGZ(String gzArchiveName, String destFolder)
-        {
-            var flag = gzArchiveName.Split(Path.DirectorySeparatorChar).Last().Split('.').First() + ".bin";
-            if (File.Exists(Path.Combine(destFolder, flag))) return;
-
-            Console.WriteLine($"Extracting.");
-            var task = Task.Run(() =>
-            {
-                using (var inStream = File.OpenRead(gzArchiveName))
-                {
-                    using (var gzipStream = new GZipInputStream(inStream))
-                    {
-                        using (TarArchive tarArchive = TarArchive.CreateInputTarArchive(gzipStream))
-                            tarArchive.ExtractContents(destFolder);
-                    }
-                }
-            });
-
-            while (!task.IsCompleted)
-            {
-                Thread.Sleep(200);
-                Console.Write(".");
-            }
-
-            File.Create(Path.Combine(destFolder, flag));
-            Console.WriteLine("");
-            Console.WriteLine("Extracting is completed.");
-        }
-    }
-}

test/TensorFlowNET.Examples/Utility/PbtxtParser.cs

@@ -1,81 +0,0 @@
-/*****************************************************************************
-   Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
-******************************************************************************/
-
-using Newtonsoft.Json;
-using System.Collections.Generic;
-
-namespace TensorFlowNET.Examples.Utility
-{
-    public class PbtxtItem
-    {
-        public string name { get; set; }
-        public int id { get; set; }
-        public string display_name { get; set; }
-    }
-    public class PbtxtItems
-    {
-        public List<PbtxtItem> items { get; set; }
-    }
-
-    public class PbtxtParser
-    {
-        public static PbtxtItems ParsePbtxtFile(string filePath)
-        {
-            string line;
-            string newText = "{\"items\":[";
-
-            using (System.IO.StreamReader reader = new System.IO.StreamReader(filePath))
-            {
-
-                while ((line = reader.ReadLine()) != null)
-                {
-                    string newline = string.Empty;
-
-                    if (line.Contains("{"))
-                    {
-                        newline = line.Replace("item", "").Trim();
-                        //newText += line.Insert(line.IndexOf("=") + 1, "\"") + "\",";
-                        newText += newline;
-                    }
-                    else if (line.Contains("}"))
-                    {
-                        newText = newText.Remove(newText.Length - 1);
-                        newText += line;
-                        newText += ",";
-                    }
-                    else
-                    {
-                        newline = line.Replace(":", "\":").Trim();
-                        newline = "\"" + newline;// newline.Insert(0, "\"");
-                        newline += ",";
-
-                        newText += newline;
-                    }
-
-                }
-
-                newText = newText.Remove(newText.Length - 1);
-                newText += "]}";
-
-                reader.Close();
-            }
-
-            PbtxtItems items = JsonConvert.DeserializeObject<PbtxtItems>(newText);
-
-            return items;
-        }
-    }
-}

test/TensorFlowNET.Examples/Utility/Web.cs

@@ -1,57 +0,0 @@
-/*****************************************************************************
-   Copyright 2018 The TensorFlow.NET Authors. All Rights Reserved.
-
-   Licensed under the Apache License, Version 2.0 (the "License");
-   you may not use this file except in compliance with the License.
-   You may obtain a copy of the License at
-
-       http://www.apache.org/licenses/LICENSE-2.0
-
-   Unless required by applicable law or agreed to in writing, software
-   distributed under the License is distributed on an "AS IS" BASIS,
-   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-   See the License for the specific language governing permissions and
-   limitations under the License.
-******************************************************************************/
-
-using System;
-using System.IO;
-using System.Linq;
-using System.Net;
-using System.Threading;
-using System.Threading.Tasks;
-
-namespace TensorFlowNET.Examples.Utility
-{
-    public class Web
-    {
-        public static bool Download(string url, string destDir, string destFileName)
-        {
-            if (destFileName == null)
-                destFileName = url.Split(Path.DirectorySeparatorChar).Last();
-
-            Directory.CreateDirectory(destDir);
-
-            string relativeFilePath = Path.Combine(destDir, destFileName);
-
-            if (File.Exists(relativeFilePath))
-            {
-                Console.WriteLine($"{relativeFilePath} already exists.");
-                return false;
-            }
-
-            var wc = new WebClient();
-            Console.WriteLine($"Downloading {relativeFilePath}");
-            var download = Task.Run(() => wc.DownloadFile(url, relativeFilePath));
-            while (!download.IsCompleted)
-            {
-                Thread.Sleep(1000);
-                Console.Write(".");
-            }
-            Console.WriteLine("");
-            Console.WriteLine($"Downloaded {relativeFilePath}");
-
-            return true;
-        }
-    }
-}

test/TensorFlowNET.Examples/python/binary_text_classification.py

@@ -1,115 +0,0 @@
-
-from __future__ import absolute_import, division, print_function
-
-import tensorflow as tf
-from tensorflow import keras
-
-import numpy as np
-
-print(tf.__version__)
-
-imdb = keras.datasets.imdb
-
-(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)
-
-print("Training entries: {}, labels: {}".format(len(train_data), len(train_labels)))
-print(train_data[0])
-len(train_data[0]), len(train_data[1])
-
-# A dictionary mapping words to an integer index
-word_index = imdb.get_word_index()
-
-# The first indices are reserved
-word_index = {k:(v+3) for k,v in word_index.items()} 
-word_index["<PAD>"] = 0
-word_index["<START>"] = 1
-word_index["<UNK>"] = 2  # unknown
-word_index["<UNUSED>"] = 3
-
-reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])
-
-def decode_review(text):
-    return ' '.join([reverse_word_index.get(i, '?') for i in text])
-
-decode_review(train_data[0])
-
-
-train_data = keras.preprocessing.sequence.pad_sequences(train_data,
-                                                        value=word_index["<PAD>"],
-                                                        padding='post',
-                                                        maxlen=256)
-
-test_data = keras.preprocessing.sequence.pad_sequences(test_data,
-                                                       value=word_index["<PAD>"],
-                                                       padding='post',
-                                                       maxlen=256)
-
-
-print(train_data[0])
-
-# input shape is the vocabulary count used for the movie reviews (10,000 words)
-vocab_size = 10000
-
-model = keras.Sequential()
-model.add(keras.layers.Embedding(vocab_size, 16))
-model.add(keras.layers.GlobalAveragePooling1D())
-model.add(keras.layers.Dense(16, activation=tf.nn.relu))
-model.add(keras.layers.Dense(1, activation=tf.nn.sigmoid))
-
-model.summary()
-
-model.compile(optimizer='adam',
-              loss='binary_crossentropy',
-              metrics=['accuracy'])
-
-
-x_val = train_data[:10000]
-partial_x_train = train_data[10000:]
-
-y_val = train_labels[:10000]
-partial_y_train = train_labels[10000:]
-
-history = model.fit(partial_x_train,
-                    partial_y_train,
-                    epochs=20,
-                    batch_size=512,
-                    validation_data=(x_val, y_val),
-                    verbose=1)
-
-results = model.evaluate(test_data, test_labels)
-print(results)
-
-history_dict = history.history
-history_dict.keys()
-
-import matplotlib.pyplot as plt
-
-acc = history_dict['acc']
-val_acc = history_dict['val_acc']
-loss = history_dict['loss']
-val_loss = history_dict['val_loss']
-
-epochs = range(1, len(acc) + 1)
-
-# "bo" is for "blue dot"
-plt.plot(epochs, loss, 'bo', label='Training loss')
-# b is for "solid blue line"
-plt.plot(epochs, val_loss, 'b', label='Validation loss')
-plt.title('Training and validation loss')
-plt.xlabel('Epochs')
-plt.ylabel('Loss')
-plt.legend()
-
-plt.show()
-
-
-plt.clf()   # clear figure
-
-plt.plot(epochs, acc, 'bo', label='Training acc')
-plt.plot(epochs, val_acc, 'b', label='Validation acc')
-plt.title('Training and validation accuracy')
-plt.xlabel('Epochs')
-plt.ylabel('Accuracy')
-plt.legend()
-
-plt.show()

test/TensorFlowNET.Examples/python/linear_regression.py

@@ -1,107 +0,0 @@
-'''
-A linear regression learning algorithm example using TensorFlow library.
-Author: Aymeric Damien
-Project: https://github.com/aymericdamien/TensorFlow-Examples/
-'''
-
-from __future__ import print_function
-
-import tensorflow as tf
-import numpy
-import matplotlib.pyplot as plt
-rng = numpy.random
-
-# Parameters
-learning_rate = 0.01
-training_epochs = 1000
-display_step = 10
-
-# Training Data
-train_X = numpy.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.59,2.167,
-                         7.042,10.791,5.313,7.997,5.654,9.27,3.1])
-train_Y = numpy.asarray([1.7,2.76,2.09,3.19,1.694,1.573,3.366,2.596,2.53,1.221,
-                         2.827,3.465,1.65,2.904,2.42,2.94,1.3])
-n_samples = train_X.shape[0]
-
-if False:
-    # tf Graph Input
-    X = tf.placeholder("float")
-    Y = tf.placeholder("float")
-
-    # Set model weights
-    W = tf.Variable(-0.06, name="weight")
-    b = tf.Variable(-0.73, name="bias")
-
-    # Construct a linear model
-    mul = tf.multiply(X, W)
-    pred = tf.add(mul, b)
-
-    # Mean squared error
-    sub = pred-Y
-    pow = tf.pow(sub, 2)
-
-    reduce = tf.reduce_sum(pow)
-    cost = reduce/(2*n_samples)
-    # Gradient descent
-    #  Note, minimize() knows to modify W and b because Variable objects are trainable=True by default
-    grad = tf.train.GradientDescentOptimizer(learning_rate)
-    optimizer = grad.minimize(cost)
-    # tf.train.export_meta_graph(filename='save_model.meta');
-else:
-    # tf Graph Input
-    new_saver = tf.train.import_meta_graph("linear_regression.meta")
-    nodes = tf.get_default_graph()._nodes_by_name;
-    optimizer = nodes["GradientDescent"]
-    cost = nodes["truediv"].outputs[0]
-    X = nodes["Placeholder"].outputs[0]
-    Y = nodes["Placeholder_1"].outputs[0]
-    W = nodes["weight"].outputs[0]
-    b = nodes["bias"].outputs[0]
-    pred = nodes["Add"].outputs[0]
-
-# Initialize the variables (i.e. assign their default value)
-init = tf.global_variables_initializer()
-
-# Start training
-with tf.Session() as sess:
-
-    # Run the initializer
-    sess.run(init)
-
-    # Fit all training data
-    for epoch in range(training_epochs):
-        for (x, y) in zip(train_X, train_Y):
-            sess.run(optimizer, feed_dict={X: x, Y: y})
-
-        # Display logs per epoch step
-        if (epoch+1) % display_step == 0:
-            c = sess.run(cost, feed_dict={X: train_X, Y:train_Y})
-            print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c), \
-                "W=", sess.run(W), "b=", sess.run(b))
-
-    print("Optimization Finished!")
-    training_cost = sess.run(cost, feed_dict={X: train_X, Y: train_Y})
-    print("Training cost=", training_cost, "W=", sess.run(W), "b=", sess.run(b), '\n')
-
-    # Graphic display
-    plt.plot(train_X, train_Y, 'ro', label='Original data')
-    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
-    plt.legend()
-    plt.show()
-
-    # Testing example, as requested (Issue #2)
-    test_X = numpy.asarray([6.83, 4.668, 8.9, 7.91, 5.7, 8.7, 3.1, 2.1])
-    test_Y = numpy.asarray([1.84, 2.273, 3.2, 2.831, 2.92, 3.24, 1.35, 1.03])
-
-    print("Testing... (Mean square loss Comparison)")
-    testing_cost = sess.run(
-        tf.reduce_sum(tf.pow(pred - Y, 2)) / (2 * test_X.shape[0]),
-        feed_dict={X: test_X, Y: test_Y})  # same function as cost above
-    print("Testing cost=", testing_cost)
-    print("Absolute mean square loss difference:", abs(
-        training_cost - testing_cost))
-
-    plt.plot(test_X, test_Y, 'bo', label='Testing data')
-    plt.plot(train_X, sess.run(W) * train_X + sess.run(b), label='Fitted line')
-    plt.legend()
-    plt.show()

test/TensorFlowNET.Examples/python/logistic_regression.py

@@ -1,100 +0,0 @@
-'''
-A logistic regression learning algorithm example using TensorFlow library.
-This example is using the MNIST database of handwritten digits
-(http://yann.lecun.com/exdb/mnist/)
-Author: Aymeric Damien
-Project: https://github.com/aymericdamien/TensorFlow-Examples/
-'''
-
-from __future__ import print_function
-
-import tensorflow as tf
-
-# Import MNIST data
-from tensorflow.examples.tutorials.mnist import input_data
-mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
-
-# Parameters
-learning_rate = 0.01
-training_epochs = 10
-batch_size = 100
-display_step = 1
-
-# tf Graph Input
-x = tf.placeholder(tf.float32, [None, 784]) # mnist data image of shape 28*28=784
-y = tf.placeholder(tf.float32, [None, 10]) # 0-9 digits recognition => 10 classes
-
-# Set model weights
-W = tf.Variable(tf.zeros([784, 10]))
-b = tf.Variable(tf.zeros([10]))
-
-# Construct model
-pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax
-
-# Minimize error using cross entropy
-cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))
-# Gradient Descent
-optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
-
-# Initialize the variables (i.e. assign their default value)
-init = tf.global_variables_initializer()
-
-# Start training
-with tf.Session() as sess:
-
-    # Run the initializer
-    sess.run(init)
-
-    # Training cycle
-    for epoch in range(training_epochs):
-        avg_cost = 0.
-        total_batch = int(mnist.train.num_examples/batch_size)
-        # Loop over all batches
-        for i in range(total_batch):
-            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
-            # Run optimization op (backprop) and cost op (to get loss value)
-            _, c = sess.run([optimizer, cost], feed_dict={x: batch_xs,
-                                                          y: batch_ys})
-            # Compute average loss
-            avg_cost += c / total_batch
-        # Display logs per epoch step
-        if (epoch+1) % display_step == 0:
-            print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))
-
-    print("Optimization Finished!")
-
-    # Test model
-    correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
-    # Calculate accuracy
-    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
-    print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))
-
-    # predict
-    # results = sess.run(pred, feed_dict={x: batch_xs[:1]})
-
-    # save model
-    saver = tf.train.Saver()
-    save_path = saver.save(sess, "logistic_regression/model.ckpt")
-    tf.train.write_graph(sess.graph.as_graph_def(),'logistic_regression','model.pbtxt', as_text=True)
-
-    freeze_graph.freeze_graph(input_graph = 'logistic_regression/model.pbtxt', 
-                              input_saver = "", 
-                              input_binary = False, 
-                              input_checkpoint = 'logistic_regression/model.ckpt', 
-                              output_node_names = "Softmax",
-                              restore_op_name = "save/restore_all", 
-                              filename_tensor_name = "save/Const:0",
-                              output_graph = 'logistic_regression/model.pb', 
-                              clear_devices = True, 
-                              initializer_nodes = "")
-
-    # restoring the model
-    saver = tf.train.import_meta_graph('logistic_regression/tensorflowModel.ckpt.meta')
-    saver.restore(sess,tf.train.latest_checkpoint('logistic_regression'))
-
-    # predict
-    # pred = graph._nodes_by_name["Softmax"]
-    # output = pred.outputs[0]
-    # x = graph._nodes_by_name["Placeholder"]
-    # input = x.outputs[0]
-    # results = sess.run(output, feed_dict={input: batch_xs[:1]})

test/TensorFlowNET.Examples/python/meta_graph.py

@@ -1,67 +0,0 @@
-
-import tensorflow as tf
-import math
-
-# Creates an inference graph.
-# Hidden 1
-images = tf.constant(1.2, tf.float32, shape=[100, 28])
-with tf.name_scope("hidden1"):
-  weights = tf.Variable(
-      tf.truncated_normal([28, 128],
-                          stddev=1.0 / math.sqrt(float(28))),
-      name="weights")
-  biases = tf.Variable(tf.zeros([128]),
-                       name="biases")
-  hidden1 = tf.nn.relu(tf.matmul(images, weights) + biases)
-# Hidden 2
-with tf.name_scope("hidden2"):
-  weights = tf.Variable(
-      tf.truncated_normal([128, 32],
-                          stddev=1.0 / math.sqrt(float(128))),
-      name="weights")
-  biases = tf.Variable(tf.zeros([32]),
-                       name="biases")
-  hidden2 = tf.nn.relu(tf.matmul(hidden1, weights) + biases)
-# Linear
-with tf.name_scope("softmax_linear"):
-  weights = tf.Variable(
-      tf.truncated_normal([32, 10],
-                          stddev=1.0 / math.sqrt(float(32))),
-      name="weights")
-  biases = tf.Variable(tf.zeros([10]),
-                       name="biases")
-  logits = tf.matmul(hidden2, weights) + biases
-  tf.add_to_collection("logits", logits)
-
-init_all_op = tf.global_variables_initializer()
-
-with tf.Session() as sess:
-  # Initializes all the variables.
-  sess.run(init_all_op)
-  # Runs to logit.
-  sess.run(logits)
-  # Creates a saver.
-  saver0 = tf.train.Saver()
-  saver0.save(sess, 'my-save-dir/my-model-10000')
-  # Generates MetaGraphDef.
-  saver0.export_meta_graph('my-save-dir/my-model-10000.meta')
-
-
-# Then later import it and extend it to a training graph.
-with tf.Session() as sess:
-  new_saver = tf.train.import_meta_graph('my-save-dir/my-model-10000.meta')
-  new_saver.restore(sess, 'my-save-dir/my-model-10000')
-  # Addes loss and train.
-  labels = tf.constant(0, tf.int32, shape=[100], name="labels")
-  batch_size = tf.size(labels)
-  logits = tf.get_collection("logits")[0]
-  loss = tf.losses.sparse_softmax_cross_entropy(labels=labels,
-                                                logits=logits)
-
-  tf.summary.scalar('loss', loss)
-  # Creates the gradient descent optimizer with the given learning rate.
-  optimizer = tf.train.GradientDescentOptimizer(0.01)
-
-  # Runs train_op.
-  train_op = optimizer.minimize(loss)
-  sess.run(train_op)

test/TensorFlowNET.Examples/python/minst_lstm.py

@@ -1,78 +0,0 @@
-import tensorflow as tf
-from tensorflow.contrib import rnn
-
-#import mnist dataset
-from tensorflow.examples.tutorials.mnist import input_data
-mnist=input_data.read_data_sets("/tmp/data/",one_hot=True)
-
-#define constants
-#unrolled through 28 time steps
-time_steps=28
-#hidden LSTM units
-num_units=128
-#rows of 28 pixels
-n_input=28
-#learning rate for adam
-learning_rate=0.001
-#mnist is meant to be classified in 10 classes(0-9).
-n_classes=10
-#size of batch
-batch_size=128
-
-
-#weights and biases of appropriate shape to accomplish above task
-out_weights=tf.Variable(tf.random_normal([num_units,n_classes]))
-out_bias=tf.Variable(tf.random_normal([n_classes]))
-
-#defining placeholders
-#input image placeholder
-x=tf.placeholder("float",[None,time_steps,n_input])
-#input label placeholder
-y=tf.placeholder("float",[None,n_classes])
-
-#processing the input tensor from [batch_size,n_steps,n_input] to "time_steps" number of [batch_size,n_input] tensors
-input=tf.unstack(x ,time_steps,1)
-
-#defining the network
-lstm_layer=rnn.BasicLSTMCell(num_units,forget_bias=1)
-outputs,_=rnn.static_rnn(lstm_layer,input,dtype="float32")
-
-#converting last output of dimension [batch_size,num_units] to [batch_size,n_classes] by out_weight multiplication
-prediction=tf.matmul(outputs[-1],out_weights)+out_bias
-
-#loss_function
-loss=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction,labels=y))
-#optimization
-opt=tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)
-
-#model evaluation
-correct_prediction=tf.equal(tf.argmax(prediction,1),tf.argmax(y,1))
-accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
-
-#initialize variables
-init=tf.global_variables_initializer()
-with tf.Session() as sess:
-    sess.run(init)
-    iter=1
-    while iter<800:
-        batch_x,batch_y=mnist.train.next_batch(batch_size=batch_size)
-
-        batch_x=batch_x.reshape((batch_size,time_steps,n_input))
-
-        sess.run(opt, feed_dict={x: batch_x, y: batch_y})
-
-        if iter %10==0:
-            acc=sess.run(accuracy,feed_dict={x:batch_x,y:batch_y})
-            los=sess.run(loss,feed_dict={x:batch_x,y:batch_y})
-            print("For iter ",iter)
-            print("Accuracy ",acc)
-            print("Loss ",los)
-            print("__________________")
-
-        iter=iter+1
-
-    #calculating test accuracy
-    test_data = mnist.test.images[:128].reshape((-1, time_steps, n_input))
-    test_label = mnist.test.labels[:128]
-    print("Testing Accuracy:", sess.run(accuracy, feed_dict={x: test_data, y: test_label}))
-

test/TensorFlowNET.Examples/python/minst_rnn.py

@@ -1,48 +0,0 @@
-import tensorflow as tf
-
-# hyperparameters
-n_neurons = 128
-learning_rate = 0.001
-batch_size = 128
-n_epochs = 10
-# parameters
-n_steps = 28 # 28 rows
-n_inputs = 28 # 28 cols
-n_outputs = 10 # 10 classes
-# build a rnn model
-X = tf.placeholder(tf.float32, [None, n_steps, n_inputs])
-y = tf.placeholder(tf.int32, [None])
-cell = tf.nn.rnn_cell.BasicRNNCell(num_units=n_neurons)
-output, state = tf.nn.dynamic_rnn(cell, X, dtype=tf.float32)
-logits = tf.layers.dense(state, n_outputs)
-cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y, logits=logits)
-loss = tf.reduce_mean(cross_entropy)
-optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(loss)
-prediction = tf.nn.in_top_k(logits, y, 1)
-accuracy = tf.reduce_mean(tf.cast(prediction, tf.float32))
-
-# input data
-from tensorflow.examples.tutorials.mnist import input_data
-mnist = input_data.read_data_sets("MNIST_data/")
-X_test = mnist.test.images # X_test shape: [num_test, 28*28]
-X_test = X_test.reshape([-1, n_steps, n_inputs])
-y_test = mnist.test.labels
-
-# initialize the variables
-init = tf.global_variables_initializer()
-# train the model
-with tf.Session() as sess:
-    sess.run(init)
-    n_batches = mnist.train.num_examples // batch_size
-    for epoch in range(n_epochs):
-        for batch in range(n_batches):
-            X_train, y_train = mnist.train.next_batch(batch_size)
-            X_train = X_train.reshape([-1, n_steps, n_inputs])
-            sess.run(optimizer, feed_dict={X: X_train, y: y_train})
-        loss_train, acc_train = sess.run(
-            [loss, accuracy], feed_dict={X: X_train, y: y_train})
-        print('Epoch: {}, Train Loss: {:.3f}, Train Acc: {:.3f}'.format(
-            epoch + 1, loss_train, acc_train))
-    loss_test, acc_test = sess.run(
-        [loss, accuracy], feed_dict={X: X_test, y: y_test})
-    print('Test Loss: {:.3f}, Test Acc: {:.3f}'.format(loss_test, acc_test))

test/TensorFlowNET.Examples/python/neural_network.py

@@ -1,164 +0,0 @@
-
-# imports
-import tensorflow as tf
-import numpy as np
-import matplotlib.pyplot as plt
-
-img_h = img_w = 28             # MNIST images are 28x28
-img_size_flat = img_h * img_w  # 28x28=784, the total number of pixels
-n_classes = 10                 # Number of classes, one class per digit
-
-def load_data(mode='train'):
-    """
-    Function to (download and) load the MNIST data
-    :param mode: train or test
-    :return: images and the corresponding labels
-    """
-    from tensorflow.examples.tutorials.mnist import input_data
-    mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
-    if mode == 'train':
-        x_train, y_train, x_valid, y_valid = mnist.train.images, mnist.train.labels, \
-                                             mnist.validation.images, mnist.validation.labels
-        return x_train, y_train, x_valid, y_valid
-    elif mode == 'test':
-        x_test, y_test = mnist.test.images, mnist.test.labels
-    return x_test, y_test
-
-def randomize(x, y):
-    """ Randomizes the order of data samples and their corresponding labels"""
-    permutation = np.random.permutation(y.shape[0])
-    shuffled_x = x[permutation, :]
-    shuffled_y = y[permutation]
-    return shuffled_x, shuffled_y
-
-def get_next_batch(x, y, start, end):
-    x_batch = x[start:end]
-    y_batch = y[start:end]
-    return x_batch, y_batch
-
-# Load MNIST data
-x_train, y_train, x_valid, y_valid = load_data(mode='train')
-print("Size of:")
-print("- Training-set:\t\t{}".format(len(y_train)))
-print("- Validation-set:\t{}".format(len(y_valid)))
-
-print('x_train:\t{}'.format(x_train.shape))
-print('y_train:\t{}'.format(y_train.shape))
-print('x_train:\t{}'.format(x_valid.shape))
-print('y_valid:\t{}'.format(y_valid.shape))
-
-print(y_valid[:5, :])
-
-# Hyper-parameters
-epochs = 10             # Total number of training epochs
-batch_size = 100        # Training batch size
-display_freq = 100      # Frequency of displaying the training results
-learning_rate = 0.001   # The optimization initial learning rate
-
-h1 = 200                # number of nodes in the 1st hidden layer
-
-# weight and bais wrappers
-def weight_variable(name, shape):
-    """
-    Create a weight variable with appropriate initialization
-    :param name: weight name
-    :param shape: weight shape
-    :return: initialized weight variable
-    """
-    initer = tf.truncated_normal_initializer(stddev=0.01)
-    return tf.get_variable('W_' + name,
-                           dtype=tf.float32,
-                           shape=shape,
-                           initializer=initer)
-
-
-def bias_variable(name, shape):
-    """
-    Create a bias variable with appropriate initialization
-    :param name: bias variable name
-    :param shape: bias variable shape
-    :return: initialized bias variable
-    """
-    initial = tf.constant(0., shape=shape, dtype=tf.float32)
-    return tf.get_variable('b_' + name,
-                           dtype=tf.float32,
-                           initializer=initial)
-
-def fc_layer(x, num_units, name, use_relu=True):
-    """
-    Create a fully-connected layer
-    :param x: input from previous layer
-    :param num_units: number of hidden units in the fully-connected layer
-    :param name: layer name
-    :param use_relu: boolean to add ReLU non-linearity (or not)
-    :return: The output array
-    """
-    in_dim = x.get_shape()[1]
-    W = weight_variable(name, shape=[in_dim, num_units])
-    b = bias_variable(name, [num_units])
-    layer = tf.matmul(x, W)
-    layer += b
-    if use_relu:
-        layer = tf.nn.relu(layer)
-    return layer
-
-# Create the graph for the linear model
-# Placeholders for inputs (x) and outputs(y)
-x = tf.placeholder(tf.float32, shape=[None, img_size_flat], name='X')
-y = tf.placeholder(tf.float32, shape=[None, n_classes], name='Y')
-
-# Create a fully-connected layer with h1 nodes as hidden layer
-fc1 = fc_layer(x, h1, 'FC1', use_relu=True)
-# Create a fully-connected layer with n_classes nodes as output layer
-output_logits = fc_layer(fc1, n_classes, 'OUT', use_relu=False)
-
-# Define the loss function, optimizer, and accuracy
-logits = tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=output_logits)
-loss = tf.reduce_mean(logits, name='loss')
-optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate, name='Adam-op').minimize(loss)
-correct_prediction = tf.equal(tf.argmax(output_logits, 1), tf.argmax(y, 1), name='correct_pred')
-accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name='accuracy')
-
-# Network predictions
-cls_prediction = tf.argmax(output_logits, axis=1, name='predictions')
-
-# export graph
-#tf.train.export_meta_graph(filename='neural_network.meta', graph=tf.get_default_graph(), clear_extraneous_savers= True, as_text = True)
-
-# Create the op for initializing all variables
-init = tf.global_variables_initializer()
-
-# Create an interactive session (to keep the session in the other cells)
-sess = tf.InteractiveSession()
-# Initialize all variables
-sess.run(init)
-# Number of training iterations in each epoch
-num_tr_iter = int(len(y_train) / batch_size)
-for epoch in range(epochs):
-    print('Training epoch: {}'.format(epoch + 1))
-    # Randomly shuffle the training data at the beginning of each epoch 
-    x_train, y_train = randomize(x_train, y_train)
-    for iteration in range(num_tr_iter):
-        start = iteration * batch_size
-        end = (iteration + 1) * batch_size
-        x_batch, y_batch = get_next_batch(x_train, y_train, start, end)
-
-        # Run optimization op (backprop)
-        feed_dict_batch = {x: x_batch, y: y_batch}
-        sess.run(optimizer, feed_dict=feed_dict_batch)
-
-        if iteration % display_freq == 0:
-            # Calculate and display the batch loss and accuracy
-            loss_batch, acc_batch = sess.run([loss, accuracy],
-                                             feed_dict=feed_dict_batch)
-
-            print("iter {0:3d}:\t Loss={1:.2f},\tTraining Accuracy={2:.01%}".
-                  format(iteration, loss_batch, acc_batch))
-
-    # Run validation after every epoch
-    feed_dict_valid = {x: x_valid[:1000], y: y_valid[:1000]}
-    loss_valid, acc_valid = sess.run([loss, accuracy], feed_dict=feed_dict_valid)
-    print('---------------------------------------------------------')
-    print("Epoch: {0}, validation loss: {1:.2f}, validation accuracy: {2:.01%}".
-          format(epoch + 1, loss_valid, acc_valid))
-    print('---------------------------------------------------------')

test/TensorFlowNET.UnitTest/ExamplesTests/ExamplesTest.cs

@@ -1,126 +0,0 @@
-using Microsoft.VisualStudio.TestTools.UnitTesting;
-using Tensorflow;
-using TensorFlowNET.Examples;
-using static Tensorflow.Binding;
-
-namespace TensorFlowNET.ExamplesTests
-{
-    [TestClass]
-    public class ExamplesTest
-    {
-        [TestMethod]
-        public void BasicOperations()
-        {
-            tf.Graph().as_default();
-            new BasicOperations() { Enabled = true }.Run();
-        }
-
-        [TestMethod]
-        public void HelloWorld()
-        {
-            tf.Graph().as_default();
-            new HelloWorld() { Enabled = true }.Run();
-        }
-
-        [TestMethod]
-        public void ImageRecognition()
-        {
-            tf.Graph().as_default();
-            new HelloWorld() { Enabled = true }.Run();
-        }
-
-        [Ignore]
-        [TestMethod]
-        public void InceptionArchGoogLeNet()
-        {
-            tf.Graph().as_default();
-            new InceptionArchGoogLeNet() { Enabled = true }.Run();
-        }
-
-        [Ignore]
-        [TestMethod]
-        public void KMeansClustering()
-        {
-            tf.Graph().as_default();
-            new KMeansClustering() { Enabled = true, IsImportingGraph = true, train_size = 500, validation_size = 100, test_size = 100, batch_size =100 }.Run();
-        }
-
-        [TestMethod]
-        public void LinearRegression()
-        {
-            tf.Graph().as_default();
-            new LinearRegression() { Enabled = true }.Run();
-        }
-
-        [TestMethod]
-        public void LogisticRegression()
-        {
-            tf.Graph().as_default();
-            new LogisticRegression() { Enabled = true, training_epochs=10, train_size = 500, validation_size = 100, test_size = 100 }.Run();
-        }
-
-        [Ignore]
-        [TestMethod]
-        public void NaiveBayesClassifier()
-        {
-            tf.Graph().as_default();
-            new NaiveBayesClassifier() { Enabled = false }.Run();
-        }
-
-        [Ignore]
-        [TestMethod]
-        public void NamedEntityRecognition()
-        {
-            tf.Graph().as_default();
-            new NamedEntityRecognition() { Enabled = true }.Run();
-        }
-
-        [TestMethod]
-        public void NearestNeighbor()
-        {
-            tf.Graph().as_default();
-            new NearestNeighbor() { Enabled = true, TrainSize = 500, ValidationSize = 100, TestSize = 100 }.Run();
-        }
-
-        [Ignore]
-        [TestMethod]
-        public void WordCnnTextClassification()
-            => new CnnTextClassification { Enabled = true, ModelName = "word_cnn", DataLimit =100 }.Run();
-
-        [Ignore]
-        [TestMethod]
-        public void CharCnnTextClassification()
-            => new CnnTextClassification { Enabled = true, ModelName = "char_cnn", DataLimit = 100 }.Run();
-
-        [Ignore]
-        [TestMethod]
-        public void TextClassificationWithMovieReviews()
-        {
-            tf.Graph().as_default();
-            new BinaryTextClassification() { Enabled = true }.Run();
-        }
-
-        [TestMethod]
-        public void NeuralNetXor()
-        {
-            tf.Graph().as_default();
-            Assert.IsTrue(new NeuralNetXor() { Enabled = true, IsImportingGraph = false }.Run());
-        }
-
-        [Ignore("Not working")]
-        [TestMethod]
-        public void NeuralNetXor_ImportedGraph()
-        {
-            tf.Graph().as_default();
-            Assert.IsTrue(new NeuralNetXor() { Enabled = true, IsImportingGraph = true }.Run());
-        }
-
-        [Ignore("Not working")]
-        [TestMethod]
-        public void ObjectDetection()
-        {
-            tf.Graph().as_default();
-            Assert.IsTrue(new ObjectDetection() { Enabled = true, IsImportingGraph = true }.Run());
-        }
-    }
-}

test/TensorFlowNET.UnitTest/TensorFlowNET.UnitTest.csproj

@@ -39,7 +39,6 @@
     <ProjectReference Include="..\..\src\TensorFlowHub\TensorFlowHub.csproj" />
     <ProjectReference Include="..\..\src\TensorFlowNET.Core\TensorFlowNET.Core.csproj" />
     <ProjectReference Include="..\..\src\TensorFlowText\TensorFlowText.csproj" />
-    <ProjectReference Include="..\TensorFlowNET.Examples\TensorFlowNET.Examples.csproj" />
   </ItemGroup>
 
   <ItemGroup>