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Define Keras interface in core project (WIP). · MSavameri/TensorFlow.NET@3370723 · GitHub

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Define Keras interface in core project (WIP).
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‎src/SciSharp.TensorFlow.Redist/README.md‎

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#### Download pre-build package
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[Mac OSX CPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-darwin-x86_64-2.4.0.tar.gz), [Linux CPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-2.4.0.tar.gz), [Linux GPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-2.4.0.tar.gz), [Windows CPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-windows-x86_64-2.4.0.tar.gz), [Windows GPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-windows-x86_64-2.4.0.zip)
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[Mac OSX CPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-darwin-x86_64-2.10.0.tar.gz), [Linux CPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-linux-x86_64-2.10.0.tar.gz), [Linux GPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-linux-x86_64-2.10.0.tar.gz), [Windows CPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-cpu-windows-x86_64-2.10.0.zip), [Windows GPU](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-gpu-windows-x86_64-2.10.0.zip)
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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.2.4.0.nupkg -k APIKEY -s https://api.nuget.org/v3/index.json -t 600`
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2. Run `dotnet nuget push SciSharp.TensorFlow.Redist.2.10.0.nupkg -k APIKEY -s https://api.nuget.org/v3/index.json -t 600`
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‎src/TensorFlowNET.Console/Program.cs‎

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var diag = new Diagnostician();
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// diag.Diagnose(@"D:\memory.txt");
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var rnn = new SimpleRnnTest();
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rnn.Run();
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// this class is used explor new features.
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var exploring = new Exploring();
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// exploring.Run();

‎src/TensorFlowNET.Console/SimpleRnnTest.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;
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using Tensorflow.NumPy;
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using static Tensorflow.Binding;
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using static Tensorflow.KerasApi;
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namespace Tensorflow
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{
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public class SimpleRnnTest
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{
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public void Run()
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{
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tf.keras = new KerasInterface();
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var inputs = np.random.random((32, 10, 8)).astype(np.float32);
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var simple_rnn = tf.keras.layers.SimpleRNN(4);
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var output = simple_rnn.Apply(inputs); // The output has shape `[32, 4]`.
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if (output.shape == (32, 4))
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{
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}
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/*simple_rnn = tf.keras.layers.SimpleRNN(
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4, return_sequences = True, return_state = True)
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# whole_sequence_output has shape `[32, 10, 4]`.
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# final_state has shape `[32, 4]`.
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whole_sequence_output, final_state = simple_rnn(inputs)*/
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}
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}
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}

‎src/TensorFlowNET.Console/Tensorflow.Console.csproj‎

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<RootNamespace>Tensorflow</RootNamespace>
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<AssemblyName>Tensorflow</AssemblyName>
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<Platforms>AnyCPU;x64</Platforms>
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<LangVersion>9.0</LangVersion>
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<LangVersion>11.0</LangVersion>
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</PropertyGroup>
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<PropertyGroup Condition="'$(Configuration)|$(Platform)'=='Debug|AnyCPU'">
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</PropertyGroup>
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<ItemGroup>
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<PackageReference Include="SciSharp.TensorFlow.Redist-Windows-GPU" Version="2.7.0" />
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<PackageReference Include="SciSharp.TensorFlow.Redist" Version="2.10.0" />
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</ItemGroup>
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<ItemGroup>

‎src/TensorFlowNET.Core/Keras/ArgsDefinition/LSTMArgs.cs‎

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‎src/TensorFlowNET.Core/Keras/ArgsDefinition/Lstm/LSTMArgs.cs‎

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using Tensorflow.Keras.ArgsDefinition.Rnn;
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namespace Tensorflow.Keras.ArgsDefinition.Lstm
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{
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public class LSTMArgs : RNNArgs
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{
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public bool UnitForgetBias { get; set; }
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public float Dropout { get; set; }
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public float RecurrentDropout { get; set; }
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public int Implementation { get; set; }
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}
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}

src/TensorFlowNET.Core/Keras/ArgsDefinition/LSTMCellArgs.cs renamed to src/TensorFlowNET.Core/Keras/ArgsDefinition/Lstm/LSTMCellArgs.cs

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namespace Tensorflow.Keras.ArgsDefinition
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namespace Tensorflow.Keras.ArgsDefinition.Lstm
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{
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public class LSTMCellArgs : LayerArgs
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{

‎src/TensorFlowNET.Core/Keras/ArgsDefinition/RNNArgs.cs‎

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‎src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/RNNArgs.cs‎

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using System.Collections.Generic;
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namespace Tensorflow.Keras.ArgsDefinition.Rnn
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{
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public class RNNArgs : LayerArgs
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{
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public interface IRnnArgCell : ILayer
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{
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object state_size { get; }
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}
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public IRnnArgCell Cell { get; set; } = null;
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public bool ReturnSequences { get; set; } = false;
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public bool ReturnState { get; set; } = false;
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public bool GoBackwards { get; set; } = false;
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public bool Stateful { get; set; } = false;
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public bool Unroll { get; set; } = false;
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public bool TimeMajor { get; set; } = false;
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public Dictionary<string, object> Kwargs { get; set; } = null;
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public int Units { get; set; }
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public Activation Activation { get; set; }
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public Activation RecurrentActivation { get; set; }
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public bool UseBias { get; set; } = true;
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public IInitializer KernelInitializer { get; set; }
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public IInitializer RecurrentInitializer { get; set; }
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public IInitializer BiasInitializer { get; set; }
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// kernel_regularizer=None,
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// recurrent_regularizer=None,
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// bias_regularizer=None,
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// activity_regularizer=None,
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// kernel_constraint=None,
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// recurrent_constraint=None,
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// bias_constraint=None,
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// dropout=0.,
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// recurrent_dropout=0.,
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// return_sequences=False,
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// return_state=False,
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// go_backwards=False,
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// stateful=False,
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// unroll=False,
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// **kwargs):
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}
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}

‎src/TensorFlowNET.Core/Keras/ArgsDefinition/Rnn/SimpleRNNArgs.cs‎

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namespace Tensorflow.Keras.ArgsDefinition.Rnn
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{
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public class SimpleRNNArgs : RNNArgs
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{
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}
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}

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