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Mnist dataset
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‎test/TensorFlowNET.Examples/LogisticRegression.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
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{
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/// <summary>
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/// A logistic regression learning algorithm example using TensorFlow library.
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/// This example is using the MNIST database of handwritten digits
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/// https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/2_BasicModels/logistic_regression.py
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/// </summary>
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public class LogisticRegression : Python, IExample
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{
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public void Run()
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{
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PrepareData();
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}
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private void PrepareData()
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{
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MnistDataSet.read_data_sets("logistic_regression", one_hot: true);
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}
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}
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}

‎test/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj

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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="DevExpress.Xpo" Version="18.2.6" />
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<PackageReference Include="NumSharp" Version="0.8.0" />
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<PackageReference Include="SharpZipLib" Version="1.1.0" />
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<PackageReference Include="TensorFlow.NET" Version="0.4.2" />

‎test/TensorFlowNET.Examples/Utility/Compress.cs

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using ICSharpCode.SharpZipLib.GZip;
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using ICSharpCode.SharpZipLib.Core;
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using ICSharpCode.SharpZipLib.GZip;
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using ICSharpCode.SharpZipLib.Tar;
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using System;
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using System.IO;
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{
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public class Compress
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{
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public static void ExtractGZip(string gzipFileName, string targetDir)
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{
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// Use a 4K buffer. Any larger is a waste.
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byte[] dataBuffer = new byte[4096];
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using (System.IO.Stream fs = new FileStream(gzipFileName, FileMode.Open, FileAccess.Read))
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{
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using (GZipInputStream gzipStream = new GZipInputStream(fs))
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{
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// Change this to your needs
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string fnOut = Path.Combine(targetDir, Path.GetFileNameWithoutExtension(gzipFileName));
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using (FileStream fsOut = File.Create(fnOut))
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{
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StreamUtils.Copy(gzipStream, fsOut, dataBuffer);
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}
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}
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}
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}
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public static void UnZip(String gzArchiveName, String destFolder)
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{
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var flag = gzArchiveName.Split(Path.DirectorySeparatorChar).Last().Split('.').First() + ".bin";

‎test/TensorFlowNET.Examples/Utility/DataSet.cs

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using NumSharp.Core;
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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 TensorFlowNET.Examples.Utility
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{
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public class DataSet
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{
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private int _num_examples;
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public DataSet(NDArray images, NDArray labels, TF_DataType dtype, bool reshape)
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{
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_num_examples = images.shape[0];
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images = images.reshape(images.shape[0], images.shape[1] * images.shape[2]);
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images = np.multiply(images, 1.0f / 255.0f);
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}
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}
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}

‎test/TensorFlowNET.Examples/Utility/MnistDataSet.cs

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using ICSharpCode.SharpZipLib.Core;
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using ICSharpCode.SharpZipLib.GZip;
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using NumSharp.Core;
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using System;
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using System.Collections.Generic;
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using System.IO;
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using System.Linq;
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using System.Text;
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using Tensorflow;
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namespace TensorFlowNET.Examples.Utility
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{
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public class MnistDataSet
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{
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private const string DEFAULT_SOURCE_URL = "https://storage.googleapis.com/cvdf-datasets/mnist/";
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private const string TRAIN_IMAGES = "train-images-idx3-ubyte.gz";
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private const string TRAIN_LABELS = "train-labels-idx1-ubyte.gz";
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private const string TEST_IMAGES = "t10k-images-idx3-ubyte.gz";
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private const string TEST_LABELS = "t10k-labels-idx1-ubyte.gz";
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public static void read_data_sets(string train_dir,
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bool one_hot = false,
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TF_DataType dtype = TF_DataType.DtInvalid,
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bool reshape = true,
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int validation_size = 5000,
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string source_url = DEFAULT_SOURCE_URL)
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{
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Web.Download(source_url + TRAIN_IMAGES, train_dir, TRAIN_IMAGES);
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Compress.ExtractGZip(Path.Join(train_dir, TRAIN_IMAGES), train_dir);
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var train_images = extract_images(Path.Join(train_dir, TRAIN_IMAGES.Split('.')[0]));
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Web.Download(source_url + TRAIN_LABELS, train_dir, TRAIN_LABELS);
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Compress.ExtractGZip(Path.Join(train_dir, TRAIN_LABELS), train_dir);
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var train_labels = extract_labels(Path.Join(train_dir, TRAIN_LABELS.Split('.')[0]), one_hot: one_hot);
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Web.Download(source_url + TEST_IMAGES, train_dir, TEST_IMAGES);
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Compress.ExtractGZip(Path.Join(train_dir, TEST_IMAGES), train_dir);
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var test_images = extract_images(Path.Join(train_dir, TEST_IMAGES.Split('.')[0]));
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Web.Download(source_url + TEST_LABELS, train_dir, TEST_LABELS);
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Compress.ExtractGZip(Path.Join(train_dir, TEST_LABELS), train_dir);
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var test_labels = extract_labels(Path.Join(train_dir, TEST_LABELS.Split('.')[0]), one_hot: one_hot);
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int end = train_images.shape[0];
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var validation_images = train_images[np.arange(validation_size)];
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var validation_labels = train_labels[np.arange(validation_size)];
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train_images = train_images[np.arange(validation_size, end)];
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train_labels = train_labels[np.arange(validation_size, end)];
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var train = new DataSet(train_images, train_labels, dtype, reshape);
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}
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public static NDArray extract_images(string file)
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{
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using (var bytestream = new FileStream(file, FileMode.Open))
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{
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var magic = _read32(bytestream);
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if (magic != 2051)
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throw new ValueError($"Invalid magic number {magic} in MNIST image file: {file}");
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var num_images = _read32(bytestream);
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var rows = _read32(bytestream);
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var cols = _read32(bytestream);
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var buf = new byte[rows * cols * num_images];
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bytestream.Read(buf, 0, buf.Length);
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var data = np.frombuffer(buf, np.uint8);
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data = data.reshape((int)num_images, (int)rows, (int)cols, 1);
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return data;
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}
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}
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public static NDArray extract_labels(string file, bool one_hot = false, int num_classes = 10)
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{
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using (var bytestream = new FileStream(file, FileMode.Open))
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{
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var magic = _read32(bytestream);
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if (magic != 2049)
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throw new ValueError($"Invalid magic number {magic} in MNIST label file: {file}");
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var num_items = _read32(bytestream);
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var buf = new byte[num_items];
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bytestream.Read(buf, 0, buf.Length);
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var labels = np.frombuffer(buf, np.uint8);
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if (one_hot)
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return dense_to_one_hot(labels, num_classes);
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return labels;
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}
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}
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private static NDArray dense_to_one_hot(NDArray labels_dense, int num_classes)
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{
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var num_labels = labels_dense.shape[0];
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var index_offset = np.arange(num_labels) * num_classes;
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var labels_one_hot = np.zeros(num_labels, num_classes);
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for(int row = 0; row < num_labels; row++)
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{
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var col = labels_dense.Data<byte>(row);
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labels_one_hot[row, col] = 1;
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}
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return labels_one_hot;
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}
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private static uint _read32(FileStream bytestream)
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{
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var buffer = new byte[sizeof(uint)];
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var count = bytestream.Read(buffer, 0, 4);
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return np.frombuffer(buffer, ">u4").Data<uint>(0);
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}
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}
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}

‎test/TensorFlowNET.Examples/python/logistic_regression.py

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'''
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A logistic regression learning algorithm example using TensorFlow library.
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This example is using the MNIST database of handwritten digits
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(http://yann.lecun.com/exdb/mnist/)
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Author: Aymeric Damien
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Project: https://github.com/aymericdamien/TensorFlow-Examples/
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'''
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from __future__ import print_function
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import tensorflow as tf
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# Import MNIST data
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from tensorflow.examples.tutorials.mnist import input_data
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mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)
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# Parameters
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learning_rate = 0.01
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training_epochs = 25
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batch_size = 100
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display_step = 1
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# tf Graph Input
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x = tf.placeholder(tf.float32, [None, 784]) # mnist data image of shape 28*28=784
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y = tf.placeholder(tf.float32, [None, 10]) # 0-9 digits recognition => 10 classes
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# Set model weights
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W = tf.Variable(tf.zeros([784, 10]))
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b = tf.Variable(tf.zeros([10]))
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# Construct model
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pred = tf.nn.softmax(tf.matmul(x, W) + b) # Softmax
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# Minimize error using cross entropy
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cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))
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# Gradient Descent
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optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
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# Initialize the variables (i.e. assign their default value)
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init = tf.global_variables_initializer()
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# Start training
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with tf.Session() as sess:
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# Run the initializer
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sess.run(init)
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# Training cycle
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for epoch in range(training_epochs):
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avg_cost = 0.
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total_batch = int(mnist.train.num_examples/batch_size)
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# Loop over all batches
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for i in range(total_batch):
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batch_xs, batch_ys = mnist.train.next_batch(batch_size)
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# Run optimization op (backprop) and cost op (to get loss value)
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_, c = sess.run([optimizer, cost], feed_dict={x: batch_xs,
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y: batch_ys})
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# Compute average loss
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avg_cost += c / total_batch
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# Display logs per epoch step
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if (epoch+1) % display_step == 0:
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print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(avg_cost))
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print("Optimization Finished!")
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# Test model
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correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
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# Calculate accuracy
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accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
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print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))

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