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Makina Commit 1
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Lines changed: 648 additions & 2 deletions

‎TensorFlow.NET.sln‎

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Microsoft Visual Studio Solution File, Format Version 12.00
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# Visual Studio Version 16
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VisualStudioVersion = 16.0.28803.156
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# Visual Studio 15
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VisualStudioVersion = 15.0.28307.168
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MinimumVisualStudioVersion = 10.0.40219.1
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.UnitTest", "test\TensorFlowNET.UnitTest\TensorFlowNET.UnitTest.csproj", "{029A8CF1-CF95-4DCB-98AA-9D3D96A83B3E}"
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.Examples", "test\TensorFlowNET.Examples\TensorFlowNET.Examples.csproj", "{1FE60088-157C-4140-91AB-E96B915E4BAE}"
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.Core", "src\TensorFlowNET.Core\TensorFlowNET.Core.csproj", "{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}"
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EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "MakinaNET.Core", "src\MakinaNET.Core\MakinaNET.Core.csproj", "{6FF518EB-523F-4930-919E-05011EFCED6F}"
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EndProject
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Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "MakinaNET.Example", "src\MakinaNET.Example\MakinaNET.Example.csproj", "{17E1AC16-9E0E-4545-905A-E92C6300C7AF}"
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EndProject
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Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "MakinaNET.UnitTest", "src\MakinaNET.Test\MakinaNET.UnitTest.csproj", "{02F54D9F-617B-4B15-9D21-E21AF66693B1}"
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EndProject
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Global
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GlobalSection(SolutionConfigurationPlatforms) = preSolution
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Debug|Any CPU = Debug|Any CPU
@@ -27,6 +33,18 @@ Global
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{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Debug|Any CPU.Build.0 = Debug|Any CPU
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{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Release|Any CPU.ActiveCfg = Release|Any CPU
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{FD682AC0-7B2D-45D3-8B0D-C6D678B04144}.Release|Any CPU.Build.0 = Release|Any CPU
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{6FF518EB-523F-4930-919E-05011EFCED6F}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
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{6FF518EB-523F-4930-919E-05011EFCED6F}.Debug|Any CPU.Build.0 = Debug|Any CPU
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{6FF518EB-523F-4930-919E-05011EFCED6F}.Release|Any CPU.ActiveCfg = Release|Any CPU
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{6FF518EB-523F-4930-919E-05011EFCED6F}.Release|Any CPU.Build.0 = Release|Any CPU
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{17E1AC16-9E0E-4545-905A-E92C6300C7AF}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
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{17E1AC16-9E0E-4545-905A-E92C6300C7AF}.Debug|Any CPU.Build.0 = Debug|Any CPU
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{17E1AC16-9E0E-4545-905A-E92C6300C7AF}.Release|Any CPU.ActiveCfg = Release|Any CPU
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{17E1AC16-9E0E-4545-905A-E92C6300C7AF}.Release|Any CPU.Build.0 = Release|Any CPU
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{02F54D9F-617B-4B15-9D21-E21AF66693B1}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
45+
{02F54D9F-617B-4B15-9D21-E21AF66693B1}.Debug|Any CPU.Build.0 = Debug|Any CPU
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{02F54D9F-617B-4B15-9D21-E21AF66693B1}.Release|Any CPU.ActiveCfg = Release|Any CPU
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{02F54D9F-617B-4B15-9D21-E21AF66693B1}.Release|Any CPU.Build.0 = Release|Any CPU
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EndGlobalSection
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GlobalSection(SolutionProperties) = preSolution
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HideSolutionNode = FALSE

‎src/MakinaNET.Core/Core.cs‎

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using System;
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using System.Collections.Generic;
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using System.Text;
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using Tensorflow;
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namespace Makina
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{
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public static class Makina
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{
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public static Tensor create_tensor(int[] shape, float mean = 0, float stddev = 1, TF_DataType dtype = TF_DataType.TF_FLOAT, int? seed = null, string name = null)
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{
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return tf.truncated_normal(shape: shape, mean: mean, stddev: stddev, dtype: dtype, seed: seed, name: name);
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}
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}
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}

‎src/MakinaNET.Core/IInitializer.cs‎

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using System;
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using System.Collections.Generic;
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using System.Text;
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namespace Makina
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{
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interface IInitializer
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{
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}
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}

‎src/MakinaNET.Core/Initializer/BaseInitializer.cs‎

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using System;
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using System.Collections.Generic;
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using System.Text;
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using Tensorflow;
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using Tensorflow.Layers;
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namespace Makina.Initializer
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{
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class BaseInitializer : IInitializer
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{
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public int seed;
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}
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}

‎src/MakinaNET.Core/Layers/Dense.cs‎

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using System;
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using System.Collections.Generic;
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using System.Text;
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using System.Linq;
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using Tensorflow;
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using static Makina.Makina;
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using Makina;
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using NumSharp;
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using Tensorflow.Operations.Activation;
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namespace Makina.Layers
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{
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public class Dense : ILayer
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{
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RefVariable W;
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int units;
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TensorShape WShape;
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string name;
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IActivation activation;
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public Dense(int units, string name = null, IActivation activation = null)
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{
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this.activation = activation;
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this.units = units;
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this.name = (string.IsNullOrEmpty(name) || string.IsNullOrWhiteSpace(name))?this.GetType().Name + "_" + this.GetType().GUID:name;
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}
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public ILayer __build__(TensorShape input_shape, int seed = 1, float stddev = -1f)
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{
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Console.WriteLine("Building Layer \"" + name + "\" ...");
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if (stddev == -1)
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stddev = (float)(1 / Math.Sqrt(2));
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var dim = input_shape.Dimensions;
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var input_dim = dim[dim.Length - 1];
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W = tf.Variable(create_tensor(new int[] { input_dim, units }, seed: seed, stddev: (float)stddev));
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WShape = new TensorShape(W.shape);
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return this;
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}
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public Tensor __call__(Tensor x)
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{
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var dot = tf.matmul(x, W);
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if (this.activation != null)
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dot = activation.Activate(dot);
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Console.WriteLine("Calling Layer \"" + name + "(" + np.array(dot.getShape().Dimensions).ToString() + ")\" ...");
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return dot;
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}
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public TensorShape __shape__()
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{
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return WShape;
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}
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public TensorShape output_shape(TensorShape input_shape)
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{
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var output_shape = input_shape.Dimensions;
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output_shape[output_shape.Length - 1] = units;
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return new TensorShape(output_shape);
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}
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}
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}

‎src/MakinaNET.Core/Layers/ILayer.cs‎

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using System;
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using System.Collections.Generic;
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using System.Text;
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using Tensorflow;
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using NumSharp;
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namespace Makina.Layers
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{
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public interface ILayer
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{
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TensorShape __shape__();
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ILayer __build__(TensorShape input_shape, int seed = 1, float stddev = -1f);
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Tensor __call__(Tensor x);
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TensorShape output_shape(TensorShape input_shape);
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}
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}

‎src/MakinaNET.Core/MakinaNET.Core.csproj‎

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<Project Sdk="Microsoft.NET.Sdk">
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<PropertyGroup>
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<TargetFramework>netstandard2.0</TargetFramework>
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<AssemblyName>Makina</AssemblyName>
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<RootNamespace>Makina</RootNamespace>
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</PropertyGroup>
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<ItemGroup>
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<ProjectReference Include="..\TensorFlowNET.Core\TensorFlowNET.Core.csproj" />
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</ItemGroup>
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</Project>

‎src/MakinaNET.Core/Model.cs‎

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using Makina.Layers;
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using NumSharp;
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using System;
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using System.Collections.Generic;
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using System.Text;
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using Tensorflow;
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using static Makina.Makina;
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using static Tensorflow.Python;
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namespace Makina
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{
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public class Model
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{
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public Tensor Flow;
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List<ILayer> layer_stack;
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public TensorShape InputShape;
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public Model()
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{
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layer_stack = new List<ILayer>();
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}
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public Model Add(ILayer layer)
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{
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layer_stack.Add(layer);
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return this;
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}
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public Model Add(IEnumerable<ILayer> layers)
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{
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layer_stack.AddRange(layers);
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return this;
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}
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public Tensor getFlow()
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{
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try
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{
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return Flow;
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}
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catch (Exception ex)
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{
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return null;
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}
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}
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public (Operation, Tensor, Tensor) make_graph(Tensor features, Tensor labels)
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{
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// TODO : Creating Loss Functions And Optimizers.....
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#region Model Layers Graph
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/*
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var stddev = 1 / Math.Sqrt(2);
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var d1 = new Dense(num_hidden);
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d1.__build__(features.getShape());
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var hidden_activations = tf.nn.relu(d1.__call__(features));
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var d1_output = d1.output_shape(features.getShape());
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var d2 = new Dense(1);
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d2.__build__(d1.output_shape(features.getShape()), seed: 17, stddev: (float)(1/ Math.Sqrt(num_hidden)));
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var logits = d2.__call__(hidden_activations);
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var predictions = tf.sigmoid(tf.squeeze(logits));
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*/
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#endregion
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#region Model Graph Form Layer Stack
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var flow_shape = features.getShape();
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Flow = features;
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for (int i = 0; i < layer_stack.Count; i++)
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{
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layer_stack[i].__build__(flow_shape);
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flow_shape = layer_stack[i].output_shape(flow_shape);
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Flow = layer_stack[i].__call__(Flow);
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}
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var predictions = tf.sigmoid(tf.squeeze(Flow));
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#endregion
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#region loss and optimizer
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var loss = tf.reduce_mean(tf.square(predictions - tf.cast(labels, tf.float32)), name: "loss");
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var gs = tf.Variable(0, trainable: false, name: "global_step");
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var train_op = tf.train.GradientDescentOptimizer(0.2f).minimize(loss, global_step: gs);
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#endregion
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return (train_op, loss, gs);
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}
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public float train(int num_steps, (NDArray, NDArray) training_dataset)
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{
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var (X, Y) = training_dataset;
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var x_shape = X.shape;
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var batch_size = x_shape[0];
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var graph = tf.Graph().as_default();
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var features = tf.placeholder(tf.float32, new TensorShape(batch_size, 2));
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var labels = tf.placeholder(tf.float32, new TensorShape(batch_size));
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var (train_op, loss, gs) = this.make_graph(features, labels);
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var init = tf.global_variables_initializer();
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float loss_value = 0;
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with(tf.Session(graph), sess =>
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{
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sess.run(init);
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var step = 0;
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while (step < num_steps)
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{
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var result = sess.run(
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new ITensorOrOperation[] { train_op, gs, loss },
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new FeedItem(features, X),
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new FeedItem(labels, Y));
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loss_value = result[2];
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step = result[1];
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if (step % 1000 == 0)
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Console.WriteLine($"Step {step} loss: {loss_value}");
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}
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Console.WriteLine($"Final loss: {loss_value}");
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});
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return loss_value;
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}
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}
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}

‎src/MakinaNET.Example/App.config‎

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<?xml version="1.0" encoding="utf-8"?>
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<configuration>
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<startup>
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<supportedRuntime version="v4.0" sku=".NETFramework,Version=v4.7.2" />
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</startup>
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<runtime>
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<assemblyBinding xmlns="urn:schemas-microsoft-com:asm.v1">
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<dependentAssembly>
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<assemblyIdentity name="System.Numerics.Vectors" publicKeyToken="b03f5f7f11d50a3a" culture="neutral" />
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<bindingRedirect oldVersion="0.0.0.0-4.1.4.0" newVersion="4.1.4.0" />
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</dependentAssembly>
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</assemblyBinding>
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</runtime>
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</configuration>

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