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Merge pull request #1144 from Wanglongzhi2001/master
fix: fix the bug of load LSTM model and add test
2 parents 3006c86 + 68772b2 commit d452d8c

34 files changed

+81
-40
lines changed

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

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using System.Collections.Generic;
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using System.Text;
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namespace Tensorflow.Keras.ArgsDefinition.Rnn
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namespace Tensorflow.Keras.ArgsDefinition
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{
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public class GRUCellArgs : AutoSerializeLayerArgs
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{

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

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namespace Tensorflow.Keras.ArgsDefinition.Rnn
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namespace Tensorflow.Keras.ArgsDefinition
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{
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public class LSTMArgs : RNNArgs
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{

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

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using Newtonsoft.Json;
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using static Tensorflow.Binding;
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namespace Tensorflow.Keras.ArgsDefinition.Rnn
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namespace Tensorflow.Keras.ArgsDefinition
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{
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// TODO: complete the implementation
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public class LSTMCellArgs : AutoSerializeLayerArgs

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

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using Newtonsoft.Json;
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using System.Collections.Generic;
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using Tensorflow.Keras.Layers.Rnn;
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using Tensorflow.Keras.Layers;
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namespace Tensorflow.Keras.ArgsDefinition.Rnn
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namespace Tensorflow.Keras.ArgsDefinition
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{
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// TODO(Rinne): add regularizers.
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public class RNNArgs : AutoSerializeLayerArgs
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public int? InputDim { get; set; }
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public int? InputLength { get; set; }
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// TODO: Add `num_constants` and `zero_output_for_mask`.
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[JsonProperty("units")]
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public int Units { get; set; }
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[JsonProperty("activation")]
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public Activation Activation { get; set; }
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[JsonProperty("recurrent_activation")]
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public Activation RecurrentActivation { get; set; }
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[JsonProperty("use_bias")]
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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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[JsonProperty("dropout")]
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public float Dropout { get; set; } = .0f;
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[JsonProperty("zero_output_for_mask")]
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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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}
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}

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

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using System.Text;
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using Tensorflow.Common.Types;
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namespace Tensorflow.Keras.ArgsDefinition.Rnn
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namespace Tensorflow.Keras.ArgsDefinition
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{
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public class RnnOptionalArgs: IOptionalArgs
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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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namespace Tensorflow.Keras.ArgsDefinition
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{
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public class SimpleRNNArgs : RNNArgs
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{

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

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using Newtonsoft.Json;
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namespace Tensorflow.Keras.ArgsDefinition.Rnn
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namespace Tensorflow.Keras.ArgsDefinition
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{
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public class SimpleRNNCellArgs: AutoSerializeLayerArgs
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{

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

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using System.Collections.Generic;
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using Tensorflow.Keras.Layers.Rnn;
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using Tensorflow.Keras.Layers;
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namespace Tensorflow.Keras.ArgsDefinition.Rnn
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namespace Tensorflow.Keras.ArgsDefinition
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{
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public class StackedRNNCellsArgs : LayerArgs
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{

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

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using System;
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using Tensorflow.Framework.Models;
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using Tensorflow.Keras.Engine;
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using Tensorflow.Keras.Layers.Rnn;
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using Tensorflow.Keras.Layers;
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using Tensorflow.NumPy;
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using static Google.Protobuf.Reflection.FieldDescriptorProto.Types;
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‎src/TensorFlowNET.Core/Keras/Layers/Rnn/IRnnCell.cs

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using System.Text;
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using Tensorflow.Common.Types;
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namespace Tensorflow.Keras.Layers.Rnn
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namespace Tensorflow.Keras.Layers
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{
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public interface IRnnCell: ILayer
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{

‎src/TensorFlowNET.Core/Keras/Layers/Rnn/IStackedRnnCells.cs

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using System.Collections.Generic;
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using System.Text;
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namespace Tensorflow.Keras.Layers.Rnn
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namespace Tensorflow.Keras.Layers
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{
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public interface IStackedRnnCells : IRnnCell
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{

‎src/TensorFlowNET.Core/Operations/NnOps/RNNCell.cs

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using Tensorflow.Common.Types;
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using Tensorflow.Keras;
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using Tensorflow.Keras.ArgsDefinition;
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using Tensorflow.Keras.ArgsDefinition.Rnn;
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using Tensorflow.Keras.Engine;
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using Tensorflow.Keras.Layers.Rnn;
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using Tensorflow.Keras.Layers;
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using Tensorflow.Keras.Saving;
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using Tensorflow.NumPy;
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using Tensorflow.Operations;

‎src/TensorFlowNET.Core/ops.cs

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if (tf.Context.executing_eagerly())
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return true;
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else
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throw new NotImplementedException("");
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// TODO(Wanglongzhi2001), implement the false case
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return true;
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//throw new NotImplementedException("");
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}
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public static bool inside_function()

‎src/TensorFlowNET.Keras/Layers/LayersApi.cs

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using Tensorflow.Framework.Models;
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using Tensorflow.Keras.ArgsDefinition;
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using Tensorflow.Keras.ArgsDefinition.Core;
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using Tensorflow.Keras.ArgsDefinition.Rnn;
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using Tensorflow.Keras.Engine;
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using Tensorflow.Keras.Layers.Rnn;
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using Tensorflow.Keras.Layers;
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using Tensorflow.NumPy;
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using static Tensorflow.Binding;
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using static Tensorflow.KerasApi;

‎src/TensorFlowNET.Keras/Layers/Rnn/DropoutRNNCellMixin.cs

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using Tensorflow.Keras.Engine;
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using Tensorflow.Keras.Utils;
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namespace Tensorflow.Keras.Layers.Rnn
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namespace Tensorflow.Keras.Layers
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{
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public abstract class DropoutRNNCellMixin: Layer, IRnnCell
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{

‎src/TensorFlowNET.Keras/Layers/Rnn/GRUCell.cs

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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.ArgsDefinition.Rnn;
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using Tensorflow.Common.Extensions;
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using Tensorflow.Common.Types;
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using Tensorflow.Keras.Saving;
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namespace Tensorflow.Keras.Layers.Rnn
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namespace Tensorflow.Keras.Layers
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{
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/// <summary>
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/// Cell class for the GRU layer.

‎src/TensorFlowNET.Keras/Layers/Rnn/LSTM.cs

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using System.Linq;
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using Tensorflow.Keras.ArgsDefinition.Rnn;
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using Tensorflow.Keras.ArgsDefinition;
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using Tensorflow.Keras.Engine;
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using Tensorflow.Common.Types;
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using Tensorflow.Common.Extensions;
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namespace Tensorflow.Keras.Layers.Rnn
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namespace Tensorflow.Keras.Layers
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{
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/// <summary>
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/// Long Short-Term Memory layer - Hochreiter 1997.

‎src/TensorFlowNET.Keras/Layers/Rnn/LSTMCell.cs

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using System.Diagnostics;
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using Tensorflow.Common.Extensions;
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using Tensorflow.Common.Types;
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using Tensorflow.Keras.ArgsDefinition.Rnn;
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using Tensorflow.Keras.ArgsDefinition;
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using Tensorflow.Keras.Engine;
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using Tensorflow.Keras.Saving;
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using Tensorflow.Keras.Utils;
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namespace Tensorflow.Keras.Layers.Rnn
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namespace Tensorflow.Keras.Layers
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{
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/// <summary>
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/// Cell class for the LSTM layer.

‎src/TensorFlowNET.Keras/Layers/Rnn/RNN.cs

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using System.Collections.Generic;
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using System.Reflection;
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using Tensorflow.Keras.ArgsDefinition;
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using Tensorflow.Keras.ArgsDefinition.Rnn;
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using Tensorflow.Keras.Engine;
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using Tensorflow.Keras.Saving;
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using Tensorflow.Util;
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using System.Runtime.CompilerServices;
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// from tensorflow.python.distribute import distribution_strategy_context as ds_context;
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namespace Tensorflow.Keras.Layers.Rnn
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namespace Tensorflow.Keras.Layers
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{
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/// <summary>
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/// Base class for recurrent layers.
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186185
public override void build(KerasShapesWrapper input_shape)
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{
187+
_buildInputShape = input_shape;
188188
input_shape = new KerasShapesWrapper(input_shape.Shapes[0]);
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InputSpec get_input_spec(Shape shape)

‎src/TensorFlowNET.Keras/Layers/Rnn/RnnBase.cs

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using Tensorflow.Keras.ArgsDefinition;
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using Tensorflow.Keras.Engine;
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namespace Tensorflow.Keras.Layers.Rnn
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namespace Tensorflow.Keras.Layers
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{
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public abstract class RnnBase: Layer
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{

‎src/TensorFlowNET.Keras/Layers/Rnn/SimpleRNN.cs

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using System.Data;
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using Tensorflow.Keras.ArgsDefinition.Rnn;
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using Tensorflow.Keras.ArgsDefinition;
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using Tensorflow.Keras.Saving;
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using Tensorflow.Operations.Activation;
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using static HDF.PInvoke.H5Z;
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using static Tensorflow.ApiDef.Types;
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namespace Tensorflow.Keras.Layers.Rnn
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namespace Tensorflow.Keras.Layers
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{
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public class SimpleRNN : RNN
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{

‎src/TensorFlowNET.Keras/Layers/Rnn/SimpleRNNCell.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.Rnn;
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using Tensorflow.Keras.ArgsDefinition;
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using Tensorflow.Keras.Engine;
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using Tensorflow.Keras.Saving;
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using Tensorflow.Common.Types;
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using Tensorflow.Common.Extensions;
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using Tensorflow.Keras.Utils;
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using Tensorflow.Graphs;
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namespace Tensorflow.Keras.Layers.Rnn
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namespace Tensorflow.Keras.Layers
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{
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/// <summary>
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/// Cell class for SimpleRNN.

‎src/TensorFlowNET.Keras/Layers/Rnn/StackedRNNCells.cs

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using System.Linq;
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using Tensorflow.Common.Extensions;
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using Tensorflow.Common.Types;
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using Tensorflow.Keras.ArgsDefinition.Rnn;
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using Tensorflow.Keras.ArgsDefinition;
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using Tensorflow.Keras.Engine;
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using Tensorflow.Keras.Saving;
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using Tensorflow.Keras.Utils;
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namespace Tensorflow.Keras.Layers.Rnn
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namespace Tensorflow.Keras.Layers
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{
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public class StackedRNNCells : Layer, IRnnCell
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{

‎src/TensorFlowNET.Keras/Saving/KerasObjectLoader.cs

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using Tensorflow.Keras.ArgsDefinition;
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using Tensorflow.Keras.Engine;
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using Tensorflow.Keras.Layers;
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using Tensorflow.Keras.Layers.Rnn;
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using Tensorflow.Keras.Losses;
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using Tensorflow.Keras.Metrics;
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using Tensorflow.Keras.Saving.SavedModel;

‎src/TensorFlowNET.Keras/Saving/SavedModel/serialized_attributes.cs

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using System.Linq;
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using System.Text;
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using Tensorflow.Keras.Engine;
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using Tensorflow.Keras.Layers.Rnn;
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using Tensorflow.Keras.Layers;
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using Tensorflow.Keras.Metrics;
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using Tensorflow.Train;
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‎src/TensorFlowNET.Keras/Utils/RnnUtils.cs

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using System.Diagnostics;
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using System.Text;
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using Tensorflow.Common.Types;
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using Tensorflow.Keras.Layers.Rnn;
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using Tensorflow.Keras.Layers;
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using Tensorflow.Common.Extensions;
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namespace Tensorflow.Keras.Utils

‎test/TensorFlowNET.Keras.UnitTest/Assets/lstm_from_sequential/fingerprint.pb

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����������̟땐͉��������� ��Σ�����(��ռ����2

‎test/TensorFlowNET.Keras.UnitTest/Assets/lstm_from_sequential/keras_metadata.pb

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�&root"_tf_keras_sequential*�&{"name": "sequential", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": false, "class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 5, 3]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_1"}}, {"class_name": "LSTM", "config": {"name": "lstm", "trainable": true, "dtype": "float32", "return_sequences": false, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 32, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 1}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}, "shared_object_id": 2}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 3}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}}, {"class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 1, "activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}}, "bias_initializer": {"class_name": "Zeros", "config": {}}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}}]}, "shared_object_id": 9, "input_spec": [{"class_name": "InputSpec", "config": {"dtype": null, "shape": {"class_name": "__tuple__", "items": [null, 5, 3]}, "ndim": 3, "max_ndim": null, "min_ndim": null, "axes": {}}}], "build_input_shape": {"class_name": "TensorShape", "items": [null, 5, 3]}, "is_graph_network": true, "full_save_spec": {"class_name": "__tuple__", "items": [[{"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 5, 3]}, "float32", "input_1"]}], {}]}, "save_spec": {"class_name": "TypeSpec", "type_spec": "tf.TensorSpec", "serialized": [{"class_name": "TensorShape", "items": [null, 5, 3]}, "float32", "input_1"]}, "keras_version": "2.12.0", "backend": "tensorflow", "model_config": {"class_name": "Sequential", "config": {"name": "sequential", "layers": [{"class_name": "InputLayer", "config": {"batch_input_shape": {"class_name": "__tuple__", "items": [null, 5, 3]}, "dtype": "float32", "sparse": false, "ragged": false, "name": "input_1"}, "shared_object_id": 0}, {"class_name": "LSTM", "config": {"name": "lstm", "trainable": true, "dtype": "float32", "return_sequences": false, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 32, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 1}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}, "shared_object_id": 2}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 3}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}, "shared_object_id": 5}, {"class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 1, "activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 6}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 7}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 8}]}}, "training_config": {"loss": "binary_crossentropy", "metrics": [[{"class_name": "MeanMetricWrapper", "config": {"name": "accuracy", "dtype": "float32", "fn": "binary_accuracy"}, "shared_object_id": 11}]], "weighted_metrics": null, "loss_weights": null, "optimizer_config": {"class_name": "Custom>Adam", "config": {"name": "Adam", "weight_decay": null, "clipnorm": null, "global_clipnorm": null, "clipvalue": null, "use_ema": false, "ema_momentum": 0.99, "ema_overwrite_frequency": null, "jit_compile": false, "is_legacy_optimizer": false, "learning_rate": 0.0010000000474974513, "beta_1": 0.9, "beta_2": 0.999, "epsilon": 1e-07, "amsgrad": false}}}}2
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� root.layer_with_weights-0"_tf_keras_rnn_layer*� {"name": "lstm", "trainable": true, "expects_training_arg": true, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "LSTM", "config": {"name": "lstm", "trainable": true, "dtype": "float32", "return_sequences": false, "return_state": false, "go_backwards": false, "stateful": false, "unroll": false, "time_major": false, "units": 32, "activation": "tanh", "recurrent_activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 1}, "recurrent_initializer": {"class_name": "Orthogonal", "config": {"gain": 1.0, "seed": null}, "shared_object_id": 2}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 3}, "unit_forget_bias": true, "kernel_regularizer": null, "recurrent_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "recurrent_constraint": null, "bias_constraint": null, "dropout": 0.0, "recurrent_dropout": 0.0, "implementation": 2}, "shared_object_id": 5, "input_spec": [{"class_name": "InputSpec", "config": {"dtype": null, "shape": {"class_name": "__tuple__", "items": [null, null, 3]}, "ndim": 3, "max_ndim": null, "min_ndim": null, "axes": {}}, "shared_object_id": 12}], "build_input_shape": {"class_name": "TensorShape", "items": [null, 5, 3]}}2
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�root.layer_with_weights-1"_tf_keras_layer*�{"name": "dense", "trainable": true, "expects_training_arg": false, "dtype": "float32", "batch_input_shape": null, "stateful": false, "must_restore_from_config": false, "preserve_input_structure_in_config": false, "autocast": true, "class_name": "Dense", "config": {"name": "dense", "trainable": true, "dtype": "float32", "units": 1, "activation": "sigmoid", "use_bias": true, "kernel_initializer": {"class_name": "GlorotUniform", "config": {"seed": null}, "shared_object_id": 6}, "bias_initializer": {"class_name": "Zeros", "config": {}, "shared_object_id": 7}, "kernel_regularizer": null, "bias_regularizer": null, "activity_regularizer": null, "kernel_constraint": null, "bias_constraint": null}, "shared_object_id": 8, "input_spec": {"class_name": "InputSpec", "config": {"dtype": null, "shape": null, "ndim": null, "max_ndim": null, "min_ndim": 2, "axes": {"-1": 32}}, "shared_object_id": 13}, "build_input_shape": {"class_name": "TensorShape", "items": [null, 32]}}2
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‎test/TensorFlowNET.Keras.UnitTest/Assets/lstm_from_sequential/saved_model.pb

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‎test/TensorFlowNET.Keras.UnitTest/Assets/lstm_from_sequential/variables/variables.data-00000-of-00001

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‎test/TensorFlowNET.Keras.UnitTest/Assets/lstm_from_sequential/variables/variables.index

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

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -6,7 +6,7 @@
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using System.Threading.Tasks;
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using Tensorflow.Common.Types;
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using Tensorflow.Keras.Engine;
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using Tensorflow.Keras.Layers.Rnn;
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using Tensorflow.Keras.Layers;
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using Tensorflow.Keras.Saving;
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using Tensorflow.NumPy;
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using Tensorflow.Train;

‎test/TensorFlowNET.Keras.UnitTest/Model/ModelLoadTest.cs

Lines changed: 14 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -1,5 +1,7 @@
1-
using Microsoft.VisualStudio.TestTools.UnitTesting;
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using Microsoft.VisualStudio.TestPlatform.Utilities;
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using Microsoft.VisualStudio.TestTools.UnitTesting;
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using System.Linq;
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using Tensorflow.Keras.Engine;
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using Tensorflow.Keras.Optimizers;
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using Tensorflow.Keras.UnitTest.Helpers;
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using Tensorflow.NumPy;
@@ -79,6 +81,17 @@ public void ModelWithSelfDefinedModule()
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model.fit(dataset.Train.Data, dataset.Train.Labels, batch_size, num_epochs);
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}
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[TestMethod]
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public void LSTMLoad()
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{
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var model = tf.keras.models.load_model(@"Assets/lstm_from_sequential");
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model.summary();
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model.compile(tf.keras.optimizers.Adam(), tf.keras.losses.MeanSquaredError(), new string[] { "accuracy" });
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var inputs = tf.random.normal(shape: (10, 5, 3));
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var outputs = tf.random.normal(shape: (10, 1));
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model.fit(inputs.numpy(), outputs.numpy(), batch_size: 10, epochs: 5, workers: 16, use_multiprocessing: true);
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}
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[Ignore]
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[TestMethod]
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public void VGG19()

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