feat: implement GRU layer

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

@@ -0,0 +1,29 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras.ArgsDefinition
+{
+    public class GRUArgs : AutoSerializeLayerArgs
+    {
+        public int Units { get; set; }
+        public Activation Activation { get; set; }
+        public Activation RecurrentActivation { get; set; }
+        public bool UseBias { get; set; } = true;
+        public float Dropout { get; set; } = .0f;
+        public float RecurrentDropout { get; set; } = .0f;
+        public IInitializer KernelInitializer { get; set; }
+        public IInitializer RecurrentInitializer { get; set; }
+        public IInitializer BiasInitializer { get; set; }
+        public bool ReturnSequences { get;set; }
+        public bool ReturnState { get;set; }
+        public bool GoBackwards { get;set; }
+        public bool Stateful { get;set; }
+        public bool Unroll { get;set; }
+        public bool TimeMajor { get;set; }
+        public bool ResetAfter { get;set; }
+        public int Implementation { get; set; } = 2;
+
+    }
+
+}

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

@@ -0,0 +1,13 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras.ArgsDefinition
+{
+    public class GRUOptionalArgs
+    {
+        public string Identifier => "GRU";
+
+        public Tensor Mask { get; set; } = null;
+    }
+}

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

@@ -259,6 +259,25 @@ namespace Tensorflow.Keras.Layers
             float recurrent_dropout = 0f, 
             bool reset_after = true);
 
+        public ILayer GRU(
+            int units,
+            string activation = "tanh",
+            string recurrent_activation = "sigmoid",
+            bool use_bias = true,
+            string kernel_initializer = "glorot_uniform",
+            string recurrent_initializer = "orthogonal",
+            string bias_initializer = "zeros",
+            float dropout = 0f,
+            float recurrent_dropout = 0f,
+            bool return_sequences = false,
+            bool return_state = false,
+            bool go_backwards = false,
+            bool stateful = false,
+            bool unroll = false,
+            bool time_major = false,
+            bool reset_after = true
+            );
+
         /// <summary>
         /// Bidirectional wrapper for RNNs.
         /// </summary>

src/TensorFlowNET.Keras/Layers/LayersApi.cs

@@ -784,7 +784,7 @@ namespace Tensorflow.Keras.Layers
             string recurrent_activation = "sigmoid",
             bool use_bias = true,
             string kernel_initializer = "glorot_uniform",
-            string recurrent_initializer = "orthogonal", // TODO(Wanglongzhi2001),glorot_uniform has not been developed.
+            string recurrent_initializer = "orthogonal",
             string bias_initializer = "zeros",
             bool unit_forget_bias = true,
             float dropout = 0f,
@@ -908,6 +908,65 @@ namespace Tensorflow.Keras.Layers
                 ResetAfter = reset_after
             });
 
+        /// <summary>
+        /// Gated Recurrent Unit - Cho et al. 2014.
+        /// </summary>
+        /// <param name="units">Positive integer, dimensionality of the output space.</param>
+        /// <param name="activation">Activation function to use. If you pass `None`, no activation is applied.(ie. "linear" activation: `a(x) = x`).</param>
+        /// <param name="recurrent_activation">Activation function to use for the recurrent step. If you pass `None`, no activation is applied. (ie. "linear" activation: `a(x) = x`).</param>
+        /// <param name="use_bias">Boolean, (default `True`), whether the layer uses a bias vector.</param>
+        /// <param name="kernel_initializer">Initializer for the `kernel` weights matrix, used for the linear transformation of the inputs. Default: `glorot_uniform`.</param>
+        /// <param name="recurrent_initializer">Initializer for the `recurrent_kernel` weights matrix, used for the linear transformation of the recurrent state. Default: `orthogonal`.</param>
+        /// <param name="bias_initializer">Initializer for the bias vector. Default: `zeros`.</param>
+        /// <param name="dropout">Float between 0 and 1. Fraction of the units to drop for the linear transformation of the inputs. Default: 0.</param>
+        /// <param name="recurrent_dropout">Float between 0 and 1. Fraction of the units to drop for the linear transformation of the recurrent state. Default: 0.</param>
+        /// <param name="implementation"></param>
+        /// <param name="return_sequences">Boolean. Whether to return the last output in the output sequence, or the full sequence. Default: `False`.</param>
+        /// <param name="return_state">Boolean. Whether to return the last state in addition to the output. Default: `False`.</param>
+        /// <param name="go_backwards">Boolean (default `False`). If True, process the input sequence backwards and return the reversed sequence.</param>
+        /// <param name="stateful">Boolean (default False). If True, the last state for each sample at index i in a batch will be used as initial state for the sample of index i in the following batch.</param>
+        /// <param name="unroll">Boolean (default False). If True, the network will be unrolled, else a symbolic loop will be used. Unrolling can speed-up a RNN,</param>
+        /// <param name="time_major">The shape format of the `inputs` and `outputs` tensors.</param>
+        /// <param name="reset_after">GRU convention (whether to apply reset gate after or before matrix multiplication). False = "before", True = "after" (default and cuDNN compatible).</param>
+        /// <returns></returns>
+        public ILayer GRU(
+            int units,
+            string activation = "tanh",
+            string recurrent_activation = "sigmoid",
+            bool use_bias = true,
+            string kernel_initializer = "glorot_uniform",
+            string recurrent_initializer = "orthogonal",
+            string bias_initializer = "zeros",
+            float dropout = 0f,
+            float recurrent_dropout = 0f,
+            bool return_sequences = false,
+            bool return_state = false,
+            bool go_backwards = false,
+            bool stateful = false,
+            bool unroll = false,
+            bool time_major = false,
+            bool reset_after = true
+            )
+                => new GRU(new GRUArgs
+                {
+                    Units = units,
+                    Activation = keras.activations.GetActivationFromName(activation),
+                    RecurrentActivation = keras.activations.GetActivationFromName(recurrent_activation),
+                    KernelInitializer = GetInitializerByName(kernel_initializer),
+                    RecurrentInitializer = GetInitializerByName(recurrent_initializer),
+                    BiasInitializer = GetInitializerByName(bias_initializer),
+                    UseBias = use_bias,
+                    Dropout = dropout,
+                    RecurrentDropout = recurrent_dropout,
+                    ReturnSequences = return_sequences,
+                    ReturnState = return_state,
+                    GoBackwards = go_backwards,
+                    Stateful = stateful,
+                    TimeMajor = time_major,
+                    Unroll = unroll,
+                    ResetAfter = reset_after
+                });
+
         public ILayer Bidirectional(
         ILayer layer,
         string merge_mode = "concat",

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

@@ -0,0 +1,168 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow.Keras.ArgsDefinition;
+using Tensorflow.Common.Extensions;
+using Tensorflow.Common.Types;
+using Tensorflow.Keras.Saving;
+
+
+namespace Tensorflow.Keras.Layers
+{
+    public class GRU : RNN
+    {
+        GRUArgs _args;
+        private static GRUCell _cell;
+
+        bool _return_runtime;
+        public GRUCell Cell { get => _cell; }
+        public int units { get => _args.Units; }
+        public Activation activation { get => _args.Activation; }
+        public Activation recurrent_activation { get => _args.RecurrentActivation; }
+        public bool use_bias { get => _args.UseBias; }
+        public float dropout { get => _args.Dropout; }
+        public float recurrent_dropout { get => _args.RecurrentDropout; }
+        public IInitializer kernel_initializer { get => _args.KernelInitializer; }
+        public IInitializer recurrent_initializer { get => _args.RecurrentInitializer; }
+        public IInitializer bias_initializer { get => _args.BiasInitializer; }
+        public int implementation { get => _args.Implementation; }
+        public bool reset_after { get => _args.ResetAfter; }
+
+        public GRU(GRUArgs args) : base(CreateCell(args), PreConstruct(args))
+        {
+            _args = args;
+
+            if (_args.Implementation == 0)
+            {
+                // Use the red output to act as a warning message that can also be used under the release version
+                Console.ForegroundColor = ConsoleColor.Red; 
+                Console.WriteLine("Warning: `implementation=0` has been deprecated, "+
+                    "and now defaults to `implementation=2`."+
+                    "Please update your layer call.");
+                Console.ResetColor();
+            }
+
+            GRUCell cell = new GRUCell(new GRUCellArgs
+            {
+                Units = _args.Units,
+                Activation = _args.Activation,
+                RecurrentActivation = _args.RecurrentActivation,
+                UseBias = _args.UseBias,
+                Dropout = _args.Dropout,
+                RecurrentDropout = _args.RecurrentDropout,
+                KernelInitializer = _args.KernelInitializer,
+                RecurrentInitializer = _args.RecurrentInitializer,
+                BiasInitializer = _args.BiasInitializer,
+                ResetAfter = _args.ResetAfter,
+                Implementation = _args.Implementation
+            });
+            _cell = cell;
+        }
+
+        protected override Tensors Call(Tensors inputs, Tensors initial_state = null, bool? training = null, IOptionalArgs? optional_args = null)
+        {
+            GRUOptionalArgs? gru_optional_args = optional_args as GRUOptionalArgs;
+            if (optional_args is not null && gru_optional_args is null)
+            {
+                throw new ArgumentException("The type of optional args should be `GRUOptionalArgs`.");
+            }
+            Tensors? mask = gru_optional_args?.Mask;
+
+            // Not support ragger input temporarily;
+            int row_length = 0;
+            bool is_ragged_input = false;
+
+            _validate_args_if_ragged(is_ragged_input, mask);
+
+            // GRU does not support constants.Ignore it during process.
+             (inputs, initial_state, _) = this._process_inputs(inputs, initial_state, null);
+
+            if (mask.Length > 1)
+            {
+                mask = mask[0];
+            }
+
+            var input_shape = inputs.shape;
+            var timesteps = _args.TimeMajor ? input_shape[0] : input_shape[1];
+
+
+            // TODO(Wanglongzhi2001), finish _could_use_gpu_kernel part
+            Func<Tensors, Tensors, (Tensors, Tensors)> step = (cell_inputs, cell_states) =>
+            {
+                var res = Cell.Apply(cell_inputs, cell_states, training is null ? true : training.Value);
+                var (output, state) = res;
+                return (output, state);
+            };
+
+            var (last_output, outputs, states) = keras.backend.rnn(
+                step,
+                inputs,
+                initial_state,
+                constants: null,
+                go_backwards: _args.GoBackwards,
+                mask: mask,
+                unroll: _args.Unroll,
+                input_length: ops.convert_to_tensor(timesteps),
+                time_major: _args.TimeMajor,
+                zero_output_for_mask: base.Args.ZeroOutputForMask,
+                return_all_outputs: _args.ReturnSequences
+            );
+
+            Tensors output;
+            if (_args.ReturnSequences)
+            {
+                output = outputs;   
+            }
+            else
+            {
+                output = last_output;
+            }
+
+            if (_args.ReturnState)
+            {
+                output = new Tensors { output, states };
+            }
+            return output;
+        }
+
+        private static IRnnCell CreateCell(GRUArgs gruArgs)
+        {
+            return new GRUCell(new GRUCellArgs
+            {
+                Units = gruArgs.Units,
+                Activation = gruArgs.Activation,
+                RecurrentActivation = gruArgs.RecurrentActivation,
+                UseBias = gruArgs.UseBias,
+                Dropout = gruArgs.Dropout,
+                RecurrentDropout = gruArgs.RecurrentDropout,
+                KernelInitializer = gruArgs.KernelInitializer,
+                RecurrentInitializer = gruArgs.RecurrentInitializer,
+                BiasInitializer = gruArgs.BiasInitializer,
+                ResetAfter = gruArgs.ResetAfter,
+                Implementation = gruArgs.Implementation
+            });
+        }
+
+        private static RNNArgs PreConstruct(GRUArgs args)
+        {
+            return new RNNArgs
+            {
+                ReturnSequences = args.ReturnSequences,
+                ReturnState = args.ReturnState,
+                GoBackwards = args.GoBackwards,
+                Stateful = args.Stateful,
+                Unroll = args.Unroll,
+                TimeMajor = args.TimeMajor,
+                Units = args.Units,
+                Activation = args.Activation,
+                RecurrentActivation = args.RecurrentActivation,
+                UseBias = args.UseBias,
+                Dropout = args.Dropout,
+                RecurrentDropout = args.RecurrentDropout,
+                KernelInitializer = args.KernelInitializer,
+                RecurrentInitializer = args.RecurrentInitializer,
+                BiasInitializer = args.BiasInitializer
+            };
+        }
+    }
+}

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

@@ -25,8 +25,8 @@ namespace Tensorflow.Keras.Layers
         private RNNArgs _args;
         private object _input_spec = null; // or NoneValue??
         private object _state_spec = null;
-        private Tensors _states = null;
         private object _constants_spec = null;
+        private Tensors _states = null;
         private int _num_constants;
         protected IVariableV1 _kernel;
         protected IVariableV1 _bias;
@@ -469,7 +469,7 @@ namespace Tensorflow.Keras.Layers
             return (inputs, initial_state, constants);
         }
 
-        private void _validate_args_if_ragged(bool is_ragged_input, Tensors mask)
+        protected void _validate_args_if_ragged(bool is_ragged_input, Tensors mask)
         {
             if (!is_ragged_input)
             {
@@ -528,44 +528,6 @@ namespace Tensorflow.Keras.Layers
             throw new NotImplementedException();
         }
 
-        // 好像不能cell不能传接口类型
-        //public RNN New(IRnnArgCell cell,
-        //    bool return_sequences = false,
-        //    bool return_state = false,
-        //    bool go_backwards = false,
-        //    bool stateful = false,
-        //    bool unroll = false,
-        //    bool time_major = false)
-        //        => new RNN(new RNNArgs
-        //        {
-        //            Cell = cell,
-        //            ReturnSequences = return_sequences,
-        //            ReturnState = return_state,
-        //            GoBackwards = go_backwards,
-        //            Stateful = stateful,
-        //            Unroll = unroll,
-        //            TimeMajor = time_major
-        //        });
-
-        //public RNN New(List<IRnnArgCell> cell,
-        //    bool return_sequences = false,
-        //    bool return_state = false,
-        //    bool go_backwards = false,
-        //    bool stateful = false,
-        //    bool unroll = false,
-        //    bool time_major = false)
-        //        => new RNN(new RNNArgs
-        //        {
-        //            Cell = cell,
-        //            ReturnSequences = return_sequences,
-        //            ReturnState = return_state,
-        //            GoBackwards = go_backwards,
-        //            Stateful = stateful,
-        //            Unroll = unroll,
-        //            TimeMajor = time_major
-        //        });
-
-
         protected Tensors get_initial_state(Tensors inputs)
         {
             var input = inputs[0];

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

@@ -146,6 +146,15 @@ namespace Tensorflow.Keras.UnitTest.Layers
 
         }
 
+        [TestMethod]
+        public void GRU()
+        {
+            var inputs = tf.ones((32, 10, 8));
+            var gru = tf.keras.layers.GRU(4);
+            var output = gru.Apply(inputs);
+            Assert.AreEqual((32, 4), output.shape);
+        }
+
         [TestMethod]
         public void Bidirectional()
         {