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1: Start example of Full Connected Neural Network. · MSavameri/TensorFlow.NET@5ff096c · GitHub

  1. MSavameri
  2. TensorFlow.NET

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Commit 5ff096c

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Oceania2018
committed
1: Start example of Full Connected Neural Network.
2: Upgrade Microsoft.ML.TensorFlow.Redist to v1.14.
1 parent 316ad5f commit 5ff096c

6 files changed

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‎src/TensorFlowNET.Core/TensorFlowNET.Core.csproj‎

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<ItemGroup>
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<PackageReference Include="Google.Protobuf" Version="3.8.0" />
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<PackageReference Include="Microsoft.ML.TensorFlow.Redist" Version="0.13.0" />
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<PackageReference Include="Microsoft.ML.TensorFlow.Redist" Version="0.14.0" />
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<PackageReference Include="NumSharp" Version="0.10.3" />
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</ItemGroup>
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‎test/KerasNET.Test/Keras.UnitTest.csproj‎

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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="Microsoft.NET.Test.Sdk" Version="16.1.1" />
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<PackageReference Include="Microsoft.NET.Test.Sdk" Version="16.2.0" />
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<PackageReference Include="MSTest.TestAdapter" Version="1.4.0" />
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<PackageReference Include="MSTest.TestFramework" Version="1.4.0" />
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</ItemGroup>

‎test/TensorFlowNET.Examples/ImageProcess/DigitRecognitionNN.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 TensorFlowNET.Examples.Utility;
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namespace TensorFlowNET.Examples.ImageProcess
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{
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/// <summary>
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/// Neural Network classifier for Hand Written Digits
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/// Sample Neural Network architecture with two layers implemented for classifying MNIST digits
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/// http://www.easy-tensorflow.com/tf-tutorials/neural-networks
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/// </summary>
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public class DigitRecognitionNN : IExample
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{
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public bool Enabled { get; set; } = true;
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public bool IsImportingGraph { get; set; } = false;
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public string Name => "Digits Recognition Neural Network";
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const int img_h = 28;
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const int img_w = 28;
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int img_size_flat = img_h * img_w; // 784, the total number of pixels
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int n_classes = 10; // Number of classes, one class per digit
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int training_epochs = 10;
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int? train_size = null;
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int validation_size = 5000;
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int? test_size = null;
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int batch_size = 100;
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Datasets mnist;
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public bool Run()
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{
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PrepareData();
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return true;
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}
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public Graph BuildGraph()
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{
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throw new NotImplementedException();
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}
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public Graph ImportGraph()
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{
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throw new NotImplementedException();
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}
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public bool Predict()
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{
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throw new NotImplementedException();
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}
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public void PrepareData()
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{
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mnist = MnistDataSet.read_data_sets("mnist", one_hot: true, train_size: train_size, validation_size: validation_size, test_size: test_size);
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}
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public bool Train()
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{
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throw new NotImplementedException();
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}
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}
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}

‎test/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj‎

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<PackageReference Include="Newtonsoft.Json" Version="12.0.2" />
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<PackageReference Include="SharpZipLib" Version="1.1.0" />
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<PackageReference Include="System.Drawing.Common" Version="4.5.1" />
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<PackageReference Include="TensorFlow.NET" Version="0.8.2" />
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<PackageReference Include="TensorFlow.NET" Version="0.8.3" />
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</ItemGroup>
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<ItemGroup>

‎test/TensorFlowNET.Examples/TextProcess/CnnTextClassification.cs‎

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valid_y = y[new Slice(start: train_size)];
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Console.WriteLine("\tDONE");
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train_x = np.Load<int[,]>(Path.Join("word_cnn", "train_x.npy"));
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valid_x = np.Load<int[,]>(Path.Join("word_cnn", "valid_x.npy"));
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train_y = np.Load<int[]>(Path.Join("word_cnn", "train_y.npy"));
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valid_y = np.Load<int[]>(Path.Join("word_cnn", "valid_y.npy"));
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return (train_x, valid_x, train_y, valid_y);
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}
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‎test/TensorFlowNET.UnitTest/TensorFlowNET.UnitTest.csproj‎

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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="Microsoft.NET.Test.Sdk" Version="16.1.1" />
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<PackageReference Include="Microsoft.NET.Test.Sdk" Version="16.2.0" />
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<PackageReference Include="MSTest.TestAdapter" Version="1.4.0" />
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<PackageReference Include="MSTest.TestFramework" Version="1.4.0" />
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</ItemGroup>

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