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Oceania2018Oceania2018
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0.20.x-preview1, add tf.keras namespace.
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Lines changed: 233 additions & 18 deletions

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‎src/TensorFlowNET.Console/TensorFlowNET.Console.csproj‎

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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="SciSharp.TensorFlow.Redist" Version="2.2.0.1" />
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<PackageReference Include="SciSharp.TensorFlow.Redist" Version="2.2.0.2" />
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</ItemGroup>
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<ItemGroup>

‎src/TensorFlowNET.Core/APIs/c_api.cs‎

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/// </summary>
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public partial class c_api
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{
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public const string TensorFlowLibName = @"D:\SciSharp\tensorflow-google\bazel-bin\tensorflow\tensorflow.dll";
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public const string TensorFlowLibName = "tensorflow";
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public static string StringPiece(IntPtr handle)
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{

‎src/TensorFlowNET.Core/APIs/tf.data.cs‎

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/*****************************************************************************
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Copyright 2020 The TensorFlow.NET Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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******************************************************************************/
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using NumSharp;
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namespace Tensorflow
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{
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public partial class tensorflow
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{
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public DataOps data { get; } = new DataOps();
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public class DataOps
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{
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public TensorSliceDataset Dataset { get; } = new TensorSliceDataset();
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}
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}
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}

‎src/TensorFlowNET.Core/APIs/tf.keras.cs‎

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/*****************************************************************************
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Copyright 2020 Haiping Chen. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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******************************************************************************/
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using NumSharp;
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namespace Tensorflow
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{
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public partial class tensorflow
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{
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public KerasApi keras { get; } = new KerasApi();
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}
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}

‎src/TensorFlowNET.Core/Data/DatasetOps.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
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{
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public class DatasetOps
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{
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}
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}

‎src/TensorFlowNET.Core/Data/TensorSliceDataset.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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namespace Tensorflow
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{
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public class TensorSliceDataset
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{
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public TensorSliceDataset(params NDArray[] elements)
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{
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}
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public TensorSliceDataset from_tensor_slices(params NDArray[] elements)
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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/Datasets/DatasetPass.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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namespace Tensorflow.Keras.Datasets
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{
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public class DatasetPass
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{
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public (NDArray, NDArray) Train { get; set; }
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public (NDArray, NDArray) Test { get; set; }
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public void Deconstruct(out NDArray x_train, out NDArray y_train, out NDArray x_test, out NDArray y_test)
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{
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x_train = Train.Item1;
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y_train = Train.Item2;
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x_test = Test.Item1;
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y_test = Test.Item2;
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}
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public void Deconstruct(out (NDArray, NDArray) train, out (NDArray, NDArray) test)
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{
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train = Train;
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test = Test;
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}
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}
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}

‎src/TensorFlowNET.Core/Keras/Datasets/KerasDataset.cs‎

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/*****************************************************************************
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Copyright 2020 Haiping Chen. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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******************************************************************************/
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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.Datasets
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{
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public class KerasDataset
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{
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public Mnist mnist { get; } = new Mnist();
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}
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}

‎src/TensorFlowNET.Core/Keras/Datasets/Mnist.cs‎

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/*****************************************************************************
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Copyright 2020 Haiping Chen. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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******************************************************************************/
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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.IO;
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using System.Net;
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using System.Text;
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namespace Tensorflow.Keras.Datasets
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{
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public class Mnist
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{
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string origin_folder = "https://storage.googleapis.com/tensorflow/tf-keras-datasets/";
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string file_name = "mnist.npz";
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/// <summary>
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/// Loads the [MNIST dataset](http://yann.lecun.com/exdb/mnist/).
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/// </summary>
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/// <returns></returns>
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public DatasetPass load_data()
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{
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var file = Download();
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var bytes = File.ReadAllBytes(file);
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var datax = LoadX(bytes);
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var datay = LoadY(bytes);
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return new DatasetPass
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{
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Train = (datax.Item1, datay.Item1),
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Test = (datax.Item2, datay.Item2)
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};
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}
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(NDArray, NDArray) LoadX(byte[] bytes)
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{
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var y = np.Load_Npz<byte[,,]>(bytes);
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return (y["x_train.npy"], y["x_test.npy"]);
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}
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(NDArray, NDArray) LoadY(byte[] bytes)
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{
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var y = np.Load_Npz<byte[]>(bytes);
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return (y["y_train.npy"], y["y_test.npy"]);
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}
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string Download()
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{
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var fileSaveTo = Path.Combine(Path.GetTempPath(), file_name);
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if (File.Exists(fileSaveTo))
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{
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Console.WriteLine($"The file {fileSaveTo} already exists");
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return fileSaveTo;
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}
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using var wc = new WebClient();
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wc.DownloadFileTaskAsync(origin_folder + file_name, fileSaveTo).Wait();
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return fileSaveTo;
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}
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}
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}

‎src/TensorFlowNET.Core/Keras/KerasApi.cs‎

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using System.Data;
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using Tensorflow.Keras;
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using Tensorflow.Keras.Datasets;
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namespace Tensorflow
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{
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public class KerasApi
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{
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public KerasDataset datasets { get; } = new KerasDataset();
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public Initializers initializers { get; } = new Initializers();
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}
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}

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