TensorFlow.NET/src/TensorFlowNET.Keras/Layers/RNN.cs

using System;
using System.Collections.Generic;
using Tensorflow.Keras.ArgsDefinition;
using Tensorflow.Keras.Engine;

namespace Tensorflow.Keras.Layers
{
    public class RNN : Layer
    {
        private RNNArgs args;

        public RNN(RNNArgs args) : base(PreConstruct(args))
        {
            this.args = args;
            SupportsMasking = true;

            // The input shape is unknown yet, it could have nested tensor inputs, and
            // the input spec will be the list of specs for nested inputs, the structure
            // of the input_spec will be the same as the input.

            //self.input_spec = None
            //self.state_spec = None
            //self._states = None
            //self.constants_spec = None
            //self._num_constants = 0

            //if stateful:
            //  if ds_context.has_strategy():
            //    raise ValueError('RNNs with stateful=True not yet supported with '
            //                     'tf.distribute.Strategy.')
        }

        private static RNNArgs PreConstruct(RNNArgs args)
        {
            if (args.Kwargs == null)
            {
                args.Kwargs = new Dictionary<string, object>();
            }

            // If true, the output for masked timestep will be zeros, whereas in the
            // false case, output from previous timestep is returned for masked timestep.
            var zeroOutputForMask = (bool)args.Kwargs.Get("zero_output_for_mask", false);

            object input_shape;
            var propIS = args.Kwargs.Get("input_shape", null);
            var propID = args.Kwargs.Get("input_dim", null);
            var propIL = args.Kwargs.Get("input_length", null);

            if (propIS == null && (propID != null || propIL != null))
            {
                input_shape = (
                    propIL ?? new NoneValue(),  // maybe null is needed here 
                    propID ?? new NoneValue()); // and here
                args.Kwargs["input_shape"] = input_shape;
            }

            return args;
        }

        public RNN New(LayerRnnCell 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(IList<RnnCell> 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 = new StackedRNNCells(new StackedRNNCellsArgs { Cells = cell }),
                    ReturnSequences = return_sequences,
                    ReturnState = return_state,
                    GoBackwards = go_backwards,
                    Stateful = stateful,
                    Unroll = unroll,
                    TimeMajor = time_major
                });


        protected Tensor get_initial_state(Tensor inputs)
        {
            return _generate_zero_filled_state_for_cell(null, null);
        }

        Tensor _generate_zero_filled_state_for_cell(LSTMCell cell, Tensor batch_size)
        {
            throw new NotImplementedException("");
        }
    }
}