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Oceania2018Oceania2018
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Tensorflow Hub
1 parent d4b7ff7 commit 3516079

15 files changed

Lines changed: 657 additions & 3 deletions

‎TensorFlow.NET.sln‎

Lines changed: 81 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -11,12 +11,20 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "UnitTest", "test\TensorFlow
1111
EndProject
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Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Tensorflow.Keras", "src\TensorFlowNET.Keras\Tensorflow.Keras.csproj", "{6268B461-486A-460B-9B3C-86493CBBAAF7}"
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14-
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14+
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16+
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17+
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18+
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@@ -25,6 +33,14 @@ Global
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@@ -33,6 +49,14 @@ Global
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{3A6EB896-604F-4E25-B677-B8103BCF3D2E}.Publish|Any CPU.ActiveCfg = Release|Any CPU
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{3A6EB896-604F-4E25-B677-B8103BCF3D2E}.Publish|Any CPU.Build.0 = Release|Any CPU
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{3A6EB896-604F-4E25-B677-B8103BCF3D2E}.Publish|x64.ActiveCfg = Release|Any CPU
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{3A6EB896-604F-4E25-B677-B8103BCF3D2E}.Publish|x64.Build.0 = Release|Any CPU
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@@ -41,6 +65,14 @@ Global
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@@ -49,6 +81,14 @@ Global
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{6268B461-486A-460B-9B3C-86493CBBAAF7}.Debug|Any CPU.Build.0 = Debug|Any CPU
5082
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{6268B461-486A-460B-9B3C-86493CBBAAF7}.Debug-Minimal|Any CPU.ActiveCfg = Debug|Any CPU
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{6268B461-486A-460B-9B3C-86493CBBAAF7}.Debug-Minimal|x64.ActiveCfg = Debug|Any CPU
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{6268B461-486A-460B-9B3C-86493CBBAAF7}.Debug-Minimal|x64.Build.0 = Debug|Any CPU
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{6268B461-486A-460B-9B3C-86493CBBAAF7}.Publish|Any CPU.Build.0 = Release|Any CPU
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{6268B461-486A-460B-9B3C-86493CBBAAF7}.Publish|x64.ActiveCfg = Release|Any CPU
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{6268B461-486A-460B-9B3C-86493CBBAAF7}.Release|Any CPU.ActiveCfg = Release|Any CPU
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@@ -57,10 +97,50 @@ Global
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EndGlobalSection
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GlobalSection(SolutionProperties) = preSolution
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HideSolutionNode = FALSE

‎src/TensorFlowNET.Core/Binding.Util.cs‎

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@@ -88,7 +88,7 @@ public static int len(object a)
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case ICollection arr:
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return arr.Count;
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case NDArray ndArray:
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return ndArray.shape[0];
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return ndArray.ndim == 0 ? 1 : ndArray.shape[0];
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case IEnumerable enumerable:
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}

‎src/TensorFlowNET.Core/TensorFlow.Binding.csproj‎

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@@ -60,10 +60,13 @@ https://tensorflownet.readthedocs.io</Description>
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<ItemGroup>
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<PackageReference Include="Google.Protobuf" Version="3.11.2" />
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<PackageReference Include="NumSharp" Version="0.20.5" />
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</ItemGroup>
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<ItemGroup>
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<Folder Include="Keras\Initializers\" />
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</ItemGroup>
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<ItemGroup>
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<ProjectReference Include="..\..\..\NumSharp\src\NumSharp.Core\NumSharp.Core.csproj" />
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</ItemGroup>
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</Project>

‎src/TensorFlowNET.Hub/DataSetBase.cs‎

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,13 @@
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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 NumSharp;
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namespace Tensorflow.Hub
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{
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public abstract class DataSetBase : IDataSet
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{
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public NDArray Data { get; protected set; }
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public NDArray Labels { get; protected set; }
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}
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}

‎src/TensorFlowNET.Hub/Datasets.cs‎

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Original file line numberDiff line numberDiff line change
@@ -0,0 +1,46 @@
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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 NumSharp;
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namespace Tensorflow.Hub
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{
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public class Datasets<TDataSet> where TDataSet : IDataSet
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{
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public TDataSet Train { get; private set; }
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public TDataSet Validation { get; private set; }
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public TDataSet Test { get; private set; }
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public Datasets(TDataSet train, TDataSet validation, TDataSet test)
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{
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Train = train;
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Validation = validation;
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Test = test;
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}
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public (NDArray, NDArray) Randomize(NDArray x, NDArray y)
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{
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var perm = np.random.permutation(y.shape[0]);
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np.random.shuffle(perm);
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return (x[perm], y[perm]);
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}
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/// <summary>
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/// selects a few number of images determined by the batch_size variable (if you don't know why, read about Stochastic Gradient Method)
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/// </summary>
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/// <param name="x"></param>
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/// <param name="y"></param>
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/// <param name="start"></param>
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/// <param name="end"></param>
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/// <returns></returns>
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public (NDArray, NDArray) GetNextBatch(NDArray x, NDArray y, int start, int end)
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{
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var slice = new Slice(start, end);
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var x_batch = x[slice];
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var y_batch = y[slice];
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return (x_batch, y_batch);
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}
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}
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}

‎src/TensorFlowNET.Hub/IDataSet.cs‎

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Original file line numberDiff line numberDiff line change
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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 NumSharp;
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namespace Tensorflow.Hub
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{
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public interface IDataSet
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{
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NDArray Data { get; }
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NDArray Labels { get; }
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}
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}

‎src/TensorFlowNET.Hub/IModelLoader.cs‎

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using System;
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using System.Threading.Tasks;
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using System.Collections.Generic;
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using System.Text;
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using NumSharp;
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7+
namespace Tensorflow.Hub
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{
9+
public interface IModelLoader<TDataSet>
10+
where TDataSet : IDataSet
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{
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Task<Datasets<TDataSet>> LoadAsync(ModelLoadSetting setting);
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}
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}

‎src/TensorFlowNET.Hub/MnistDataSet.cs‎

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using System;
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using System.Collections.Generic;
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using System.Diagnostics;
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using System.Text;
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using NumSharp;
6+
using Tensorflow;
7+
8+
namespace Tensorflow.Hub
9+
{
10+
public class MnistDataSet : DataSetBase
11+
{
12+
public int NumOfExamples { get; private set; }
13+
public int EpochsCompleted { get; private set; }
14+
public int IndexInEpoch { get; private set; }
15+
16+
public MnistDataSet(NDArray images, NDArray labels, Type dataType, bool reshape)
17+
{
18+
EpochsCompleted = 0;
19+
IndexInEpoch = 0;
20+
21+
NumOfExamples = images.shape[0];
22+
23+
images = images.reshape(images.shape[0], images.shape[1] * images.shape[2]);
24+
images = images.astype(dataType);
25+
// for debug np.multiply performance
26+
var sw = new Stopwatch();
27+
sw.Start();
28+
images = np.multiply(images, 1.0f / 255.0f);
29+
sw.Stop();
30+
Console.WriteLine($"{sw.ElapsedMilliseconds}ms");
31+
Data = images;
32+
33+
labels = labels.astype(dataType);
34+
Labels = labels;
35+
}
36+
37+
public (NDArray, NDArray) GetNextBatch(int batch_size, bool fake_data = false, bool shuffle = true)
38+
{
39+
if (IndexInEpoch >= NumOfExamples)
40+
IndexInEpoch = 0;
41+
42+
var start = IndexInEpoch;
43+
// Shuffle for the first epoch
44+
if(EpochsCompleted == 0 && start == 0 && shuffle)
45+
{
46+
var perm0 = np.arange(NumOfExamples);
47+
np.random.shuffle(perm0);
48+
Data = Data[perm0];
49+
Labels = Labels[perm0];
50+
}
51+
52+
// Go to the next epoch
53+
if (start + batch_size > NumOfExamples)
54+
{
55+
// Finished epoch
56+
EpochsCompleted += 1;
57+
58+
// Get the rest examples in this epoch
59+
var rest_num_examples = NumOfExamples - start;
60+
var images_rest_part = Data[np.arange(start, NumOfExamples)];
61+
var labels_rest_part = Labels[np.arange(start, NumOfExamples)];
62+
// Shuffle the data
63+
if (shuffle)
64+
{
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var perm = np.arange(NumOfExamples);
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np.random.shuffle(perm);
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Data = Data[perm];
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Labels = Labels[perm];
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}
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start = 0;
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IndexInEpoch = batch_size - rest_num_examples;
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var end = IndexInEpoch;
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var images_new_part = Data[np.arange(start, end)];
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var labels_new_part = Labels[np.arange(start, end)];
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return (np.concatenate(new[] { images_rest_part, images_new_part }, axis: 0),
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np.concatenate(new[] { labels_rest_part, labels_new_part }, axis: 0));
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}
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else
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{
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IndexInEpoch += batch_size;
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var end = IndexInEpoch;
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return (Data[np.arange(start, end)], Labels[np.arange(start, end)]);
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}
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}
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}
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}

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