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Add layers of ZeroPadding2D and UpSampling2D. · MSavameri/TensorFlow.NET@94ad2f5 · GitHub

  1. MSavameri
  2. TensorFlow.NET

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Commit 94ad2f5

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Oceania2018
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Add layers of ZeroPadding2D and UpSampling2D.
1 parent 374ff58 commit 94ad2f5

8 files changed

Lines changed: 119 additions & 14 deletions

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‎src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/UpSampling2DArgs.cs‎

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namespace Tensorflow.Keras.ArgsDefinition
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{
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public class UpSampling2DArgs : LayerArgs
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{
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public TensorShape Size { get; set; }
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public string DataFormat { get; set; }
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/// <summary>
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/// 'nearest', 'bilinear'
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/// </summary>
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public string Interpolation { get; set; } = "nearest";
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}
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}

src/TensorFlowNET.Core/Keras/ArgsDefinition/ZeroPadding2DArgs.cs renamed to src/TensorFlowNET.Core/Keras/ArgsDefinition/Reshaping/ZeroPadding2DArgs.cs

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‎src/TensorFlowNET.Keras/Layers/LayersApi.Reshaping.cs‎

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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.Keras.ArgsDefinition;
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namespace Tensorflow.Keras.Layers
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{
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public partial class LayersApi
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{
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/// <summary>
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/// Zero-padding layer for 2D input (e.g. picture).
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/// </summary>
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/// <param name="padding"></param>
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/// <returns></returns>
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public ZeroPadding2D ZeroPadding2D(NDArray padding)
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=> new ZeroPadding2D(new ZeroPadding2DArgs
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{
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Padding = padding
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});
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/// <summary>
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/// Upsampling layer for 2D inputs.<br/>
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/// Repeats the rows and columns of the data by size[0] and size[1] respectively.
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/// </summary>
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/// <param name="size"></param>
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/// <param name="data_format"></param>
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/// <param name="interpolation"></param>
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/// <returns></returns>
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public UpSampling2D UpSampling2D(TensorShape size = null,
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string data_format = null,
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string interpolation = "nearest")
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=> new UpSampling2D(new UpSampling2DArgs
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{
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Size = size ?? (2, 2)
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});
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}
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}

‎src/TensorFlowNET.Keras/Layers/LayersApi.cs‎

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namespace Tensorflow.Keras.Layers
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{
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public class LayersApi
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public partial class LayersApi
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{
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/// <summary>
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/// Functional interface for the batch normalization layer.
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InputShape = input_shape
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});
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/// <summary>
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/// Zero-padding layer for 2D input (e.g. picture).
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/// </summary>
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/// <param name="padding"></param>
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/// <returns></returns>
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public ZeroPadding2D ZeroPadding2D(NDArray padding)
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=> new ZeroPadding2D(new ZeroPadding2DArgs
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{
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Padding = padding
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});
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public Tensor add(params Tensor[] inputs)
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=> new Add(new MergeArgs { Inputs = inputs }).Apply(inputs);
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public Add Add(params Tensor[] inputs)
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=> new Add(new MergeArgs { Inputs = inputs });
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public GlobalAveragePooling2D GlobalAveragePooling2D()
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=> new GlobalAveragePooling2D(new Pooling2DArgs { });

‎src/TensorFlowNET.Keras/Layers/Reshaping/UpSampling2D.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.Keras.ArgsDefinition;
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using Tensorflow.Keras.Engine;
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using Tensorflow.Keras.Utils;
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using static Tensorflow.Binding;
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using static Tensorflow.KerasApi;
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namespace Tensorflow.Keras.Layers
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{
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public class UpSampling2D : Layer
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{
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UpSampling2DArgs args;
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int[] size;
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string data_format;
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string interpolation => args.Interpolation;
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public UpSampling2D(UpSampling2DArgs args) : base(args)
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{
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this.args = args;
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data_format = conv_utils.normalize_data_format(args.DataFormat);
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size = conv_utils.normalize_tuple(args.Size, 2, "size");
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inputSpec = new InputSpec(ndim: 4);
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}
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protected override Tensors Call(Tensors inputs, Tensor state = null, bool is_training = false)
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{
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return keras.backend.resize_images(inputs,
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size[0], size[1],
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data_format,
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interpolation: interpolation);
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}
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}
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}

src/TensorFlowNET.Keras/Layers/ZeroPadding2D.cs renamed to src/TensorFlowNET.Keras/Layers/Reshaping/ZeroPadding2D.cs

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‎test/TensorFlowNET.UnitTest/Keras/Layers.Reshaping.Test.cs‎

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using Microsoft.VisualStudio.TestTools.UnitTesting;
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using NumSharp;
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using Tensorflow;
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using static Tensorflow.KerasApi;
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namespace TensorFlowNET.UnitTest.Keras
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{
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[TestClass]
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public class LayersReshapingTest : EagerModeTestBase
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{
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[TestMethod]
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public void ZeroPadding2D()
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{
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var input_shape = new[] { 1, 1, 2, 2 };
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var x = np.arange(np.prod(input_shape)).reshape(input_shape);
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var zero_padding_2d = keras.layers.ZeroPadding2D(new[,] { { 1, 0 }, { 1, 0 } });
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var y = zero_padding_2d.Apply(x);
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Assert.AreEqual((1, 2, 3, 2), y.shape);
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}
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[TestMethod]
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public void UpSampling2D()
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{
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var input_shape = new[] { 2, 2, 1, 3 };
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var x = np.arange(np.prod(input_shape)).reshape(input_shape);
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var y = keras.layers.UpSampling2D(size: (1, 2)).Apply(x);
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Assert.AreEqual((2, 2, 2, 3), y.shape);
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}
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}
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}

‎test/TensorFlowNET.UnitTest/Keras/LayersTest.cs‎

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using Microsoft.VisualStudio.TestTools.UnitTesting;
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using NumSharp;
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using Tensorflow;
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using static Tensorflow.KerasApi;
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namespace TensorFlowNET.UnitTest.Keras

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