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release preview4. · MSavameri/TensorFlow.NET@b821d82 · GitHub

  1. MSavameri
  2. TensorFlow.NET

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Commit b821d82

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Oceania2018
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release preview4.
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‎src/SciSharp.TensorFlow.Redist/README.md‎

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On Windows, the tar command does not support extracting archives with symlinks. So when `dotnet pack` runs on Windows it will only package the Windows binaries.
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1. Run `dotnet pack SciSharp.TensorFlow.Redist.nupkgproj` under `src/SciSharp.TensorFlow.Redist` directory in Linux.
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2. Run `dotnet nuget push SciSharp.TensorFlow.Redist.1.15.0.nupkg -k APIKEY -s https://api.nuget.org/v3/index.json`
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2. Run `dotnet nuget push SciSharp.TensorFlow.Redist.2.3.0.nupkg -k APIKEY -s https://api.nuget.org/v3/index.json`
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‎src/TensorFlowNET.Core/Data/DatasetV2.cs‎

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public IEnumerator<(Tensor, Tensor)> GetEnumerator()
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{
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var ownedIterator = new OwnedIterator(this);
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using var ownedIterator = new OwnedIterator(this);
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Tensor[] results = null;
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while (true)

‎src/TensorFlowNET.Core/Data/OwnedIterator.cs‎

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/// <summary>
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/// An iterator producing tf.Tensor objects from a tf.data.Dataset.
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/// </summary>
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public class OwnedIterator : IteratorBase
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public class OwnedIterator : IteratorBase, IDisposable
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{
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IDatasetV2 _dataset;
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TensorSpec[] _element_spec;
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throw new StopIteration(ex.Message);
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}
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}
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public void Dispose()
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{
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_resource_deleter.Dispose();
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}
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}
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}

‎src/TensorFlowNET.Core/Keras/KerasApi.cs‎

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public Initializers initializers { get; } = new Initializers();
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public LayersApi layers { get; } = new LayersApi();
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public Activations activations { get; } = new Activations();
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public Preprocessing preprocessing { get; } = new Preprocessing();
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public BackendImpl backend { get; } = new BackendImpl();
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public Sequential Sequential(List<Layer> layers = null,

‎src/TensorFlowNET.Core/Keras/Preprocessing.cs‎

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‎src/TensorFlowNET.Core/Keras/Preprocessings/DatasetUtils.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 Tensorflow.Keras.Preprocessings
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{
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public partial class DatasetUtils
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{
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}
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}

‎src/TensorFlowNET.Core/Keras/Preprocessings/DatasetUtils.index_directory.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 Tensorflow.Keras.Preprocessings
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{
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public partial class DatasetUtils
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{
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/// <summary>
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/// Make list of all files in the subdirs of `directory`, with their labels.
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/// </summary>
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/// <param name="directory"></param>
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/// <param name="labels"></param>
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/// <param name="formats"></param>
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/// <param name="class_names"></param>
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/// <param name="shuffle"></param>
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/// <param name="seed"></param>
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/// <param name="follow_links"></param>
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/// <returns>
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/// file_paths, labels, class_names
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/// </returns>
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public (string[], int[], string[]) index_directory(string directory,
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string labels,
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string[] formats,
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string class_names = null,
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bool shuffle = true,
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int? seed = null,
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bool follow_links = false)
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{
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throw new NotImplementedException("");
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}
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}
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}

‎src/TensorFlowNET.Core/Keras/Preprocessings/Preprocessing.cs‎

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using Tensorflow.Keras.Preprocessings;
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namespace Tensorflow.Keras
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{
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public partial class Preprocessing
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{
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public Sequence sequence => new Sequence();
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public DatasetUtils dataset_utils => new DatasetUtils();
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}
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}

‎src/TensorFlowNET.Core/Keras/Preprocessings/Preprocessing.image_dataset_from_directory.cs‎

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using System;
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using static Tensorflow.Binding;
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namespace Tensorflow.Keras
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{
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public partial class Preprocessing
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{
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public static string[] WHITELIST_FORMATS = new[] { ".bmp", ".gif", ".jpeg", ".jpg", ".png" };
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/// <summary>
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/// Generates a `tf.data.Dataset` from image files in a directory.
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/// https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image_dataset_from_directory
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/// </summary>
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/// <param name="directory">Directory where the data is located.</param>
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/// <param name="labels"></param>
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/// <param name="label_mode"></param>
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/// <param name="class_names"></param>
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/// <param name="color_mode"></param>
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/// <param name="batch_size"></param>
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/// <param name="image_size"></param>
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/// <param name="shuffle"></param>
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/// <param name="seed"></param>
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/// <param name="validation_split"></param>
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/// <param name="subset"></param>
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/// <param name="interpolation"></param>
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/// <param name="follow_links"></param>
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/// <returns></returns>
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public Tensor image_dataset_from_directory(string directory,
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string labels = "inferred",
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string label_mode = "int",
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string class_names = null,
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string color_mode = "rgb",
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int batch_size = 32,
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TensorShape image_size = null,
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bool shuffle = true,
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int? seed = null,
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float validation_split = 0.2f,
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string subset = null,
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string interpolation = "bilinear",
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bool follow_links = false)
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{
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int num_channels = 0;
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if (color_mode == "rgb")
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num_channels = 3;
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// C:/Users/haipi/.keras/datasets/flower_photos
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var (image_paths, label_list, class_name_list) = tf.keras.preprocessing.dataset_utils.index_directory(directory,
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labels,
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WHITELIST_FORMATS,
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class_names: class_names,
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shuffle: shuffle,
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seed: seed,
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follow_links: follow_links);
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throw new NotImplementedException("");
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}
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}
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}

‎tensorflowlib/README.md‎

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### Download prebuild binary manually
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We can't found official prebuild binaries for each platform since tensorflow 2.0. If you know where we can download, please PR here.
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Tensorflow packages are built nightly and uploaded to GCS for all supported platforms. They are uploaded to the [libtensorflow-nightly](https://www.tensorflow.org/install/lang_c) GCS bucket and are indexed by operating system and date built.
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### Build from source for Windows

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