8000 Merge pull request #1149 from Wanglongzhi2001/master · SciSharp/TensorFlow.NET@fa2d2dc · GitHub
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Merge pull request #1149 from Wanglongzhi2001/master
feat: add Bidirectional layer
2 parents fa5d19d + 0c9437a commit fa2d2dc

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11 files changed

+428
-18
lines changed

11 files changed

+428
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lines changed

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

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using Newtonsoft.Json;
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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.NumPy;
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namespace Tensorflow.Keras.ArgsDefinition
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{
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public class BidirectionalArgs : AutoSerializeLayerArgs
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{
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[JsonProperty("layer")]
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public ILayer Layer { get; set; }
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[JsonProperty("merge_mode")]
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public string? MergeMode { get; set; }
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[JsonProperty("backward_layer")]
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public ILayer BackwardLayer { get; set; }
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public NDArray Weights { get; set; }
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}
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}

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

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@@ -5,5 +5,10 @@ public class LSTMArgs : RNNArgs
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// TODO: maybe change the `RNNArgs` and implement this class.
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public bool UnitForgetBias { get; set; }
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public int Implementation { get; set; }
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public LSTMArgs Clone()
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{
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return (LSTMArgs)MemberwiseClone();
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}
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}
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}

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

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public bool ZeroOutputForMask { get; set; } = false;
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[JsonProperty("recurrent_dropout")]
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public float RecurrentDropout { get; set; } = .0f;
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public RNNArgs Clone()
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{
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return (RNNArgs)MemberwiseClone();
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}
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}
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}

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

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using Newtonsoft.Json;
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using System;
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using System.Collections.Generic;
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using System.Runtime.CompilerServices;
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using System.Text;
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namespace Tensorflow.Keras.ArgsDefinition
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{
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public class WrapperArgs : AutoSerializeLayerArgs
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{
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[JsonProperty("layer")]
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public ILayer Layer { get; set; }
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public WrapperArgs(ILayer layer)
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{
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Layer = layer;
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}
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public static implicit operator WrapperArgs(BidirectionalArgs args)
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=> new WrapperArgs(args.Layer);
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}
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}

‎src/TensorFlowNET.Core/Keras/Layers/ILayersApi.cs

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@@ -258,7 +258,19 @@ public IRnnCell GRUCell(
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float dropout = 0f,
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float recurrent_dropout = 0f,
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bool reset_after = true);
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/// <summary>
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/// Bidirectional wrapper for RNNs.
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/// </summary>
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/// <param name="layer">`keras.layers.RNN` instance, such as `keras.layers.LSTM` or `keras.layers.GRU`</param>
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/// automatically.</param>
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/// <returns></returns>
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public ILayer Bidirectional(
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ILayer layer,
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string merge_mode = "concat",
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NDArray weights = null,
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ILayer backward_layer = null);
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public ILayer Subtract();
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}
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}

‎src/TensorFlowNET.Keras/Layers/LayersApi.cs

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ResetAfter = reset_after
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});
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public ILayer Bidirectional(
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ILayer layer,
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string merge_mode = "concat",
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NDArray weights = null,
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ILayer backward_layer = null)
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=> new Bidirectional(new BidirectionalArgs
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{
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Layer = layer,
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MergeMode = merge_mode,
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Weights = weights,
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BackwardLayer = backward_layer
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});
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/// <summary>
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///
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/// </summary>

‎src/TensorFlowNET.Keras/Layers/Rnn/BaseWrapper.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 Tensorflow.Keras.ArgsDefinition;
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using Tensorflow.Keras.Saving;
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namespace Tensorflow.Keras.Layers
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{
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/// <summary>
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/// Abstract wrapper base class. Wrappers take another layer and augment it in various ways.
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/// Do not use this class as a layer, it is only an abstract base class.
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/// Two usable wrappers are the `TimeDistributed` and `Bidirectional` wrappers.
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/// </summary>
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public abstract class Wrapper: Layer
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{
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public ILayer _layer;
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public Wrapper(WrapperArgs args):base(args)
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{
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_layer = args.Layer;
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}
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public virtual void Build(KerasShapesWrapper input_shape)
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{
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if (!_layer.Built)
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{
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_layer.build(input_shape);
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}
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built = true;
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}
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}
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}

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