Blank SimpleRNN and test for it

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

@@ -1,6 +1,21 @@
-namespace Tensorflow.Keras.ArgsDefinition
+using System.Collections.Generic;
+
+namespace Tensorflow.Keras.ArgsDefinition
 {
     public class RNNArgs : LayerArgs
     {
+        public interface IRnnArgCell : ILayer
+        {
+            object state_size { get; }
+        }
+
+        public IRnnArgCell Cell { get; set; } = null;
+        public bool ReturnSequences { get; set; } = false;
+        public bool ReturnState { get; set; } = false;
+        public bool GoBackwards { get; set; } = false;
+        public bool Stateful { get; set; } = false;
+        public bool Unroll { get; set; } = false;
+        public bool TimeMajor { get; set; } = false;
+        public Dictionary<string, object> Kwargs { get; set; } = null;
     }
 }

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

@@ -0,0 +1,30 @@
+namespace Tensorflow.Keras.ArgsDefinition
+{
+    public class SimpleRNNArgs : RNNArgs
+    {
+        public int Units { get; set; }
+        public Activation Activation { get; set; }
+		
+		// units,
+		// activation='tanh',
+		// use_bias=True,
+		// kernel_initializer='glorot_uniform',
+		// recurrent_initializer='orthogonal',
+		// bias_initializer='zeros',
+		// kernel_regularizer=None,
+		// recurrent_regularizer=None,
+		// bias_regularizer=None,
+		// activity_regularizer=None,
+		// kernel_constraint=None,
+		// recurrent_constraint=None,
+		// bias_constraint=None,
+		// dropout=0.,
+		// recurrent_dropout=0.,
+		// return_sequences=False,
+		// return_state=False,
+		// go_backwards=False,
+		// stateful=False,
+		// unroll=False,
+		// **kwargs):
+    }
+}

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

@@ -0,0 +1,9 @@
+using System.Collections.Generic;
+
+namespace Tensorflow.Keras.ArgsDefinition
+{
+    public class StackedRNNCellsArgs : LayerArgs
+    {
+        public IList<RnnCell> Cells { get; set; }
+    }
+}

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

@@ -46,7 +46,7 @@ namespace Tensorflow
     /// matching structure of Tensors having shape `[batch_size].concatenate(s)`
     /// for each `s` in `self.batch_size`.
     /// </summary>
-    public abstract class RnnCell : ILayer
+    public abstract class RnnCell : ILayer, RNNArgs.IRnnArgCell
     {
         /// <summary>
         /// Attribute that indicates whether the cell is a TF RNN cell, due the slight

src/TensorFlowNET.Keras/Layers/LayersApi.cs

@@ -1,4 +1,5 @@
 using NumSharp;
+using System.Collections.Generic;
 using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Engine;
 using static Tensorflow.Binding;
@@ -327,6 +328,24 @@ namespace Tensorflow.Keras.Layers
                 Alpha = alpha
             });
 
+        public Layer SimpleRNN(int units) => SimpleRNN(units, "tanh");
+
+        public Layer SimpleRNN(int units,
+            Activation activation = null)
+                => new SimpleRNN(new SimpleRNNArgs
+                {
+                    Units = units,
+                    Activation = activation
+                });
+
+        public Layer SimpleRNN(int units,
+            string activation = "tanh")
+                => new SimpleRNN(new SimpleRNNArgs
+                {
+                    Units = units,
+                    Activation = GetActivationByName(activation)
+                });
+
         public Layer LSTM(int units,
             Activation activation = null,
             Activation recurrent_activation = null,

src/TensorFlowNET.Keras/Layers/RNN.cs

@@ -1,4 +1,5 @@
 using System;
+using System.Collections.Generic;
 using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Engine;
 
@@ -6,12 +7,93 @@ namespace Tensorflow.Keras.Layers
 {
     public class RNN : Layer
     {
-        public RNN(RNNArgs args)
-        : base(args)
+        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);

src/TensorFlowNET.Keras/Layers/SimpleRNN.cs

@@ -0,0 +1,14 @@
+using Tensorflow.Keras.ArgsDefinition;
+
+namespace Tensorflow.Keras.Layers
+{
+    public class SimpleRNN : RNN
+    {
+
+        public SimpleRNN(RNNArgs args) : base(args)
+        {
+
+        }
+
+    }
+}

src/TensorFlowNET.Keras/Layers/StackedRNNCells.cs

@@ -0,0 +1,125 @@
+using System;
+using System.Collections.Generic;
+using Tensorflow.Keras.ArgsDefinition;
+using Tensorflow.Keras.Engine;
+
+namespace Tensorflow.Keras.Layers
+{
+    public class StackedRNNCells : Layer, RNNArgs.IRnnArgCell
+    {
+        public IList<RnnCell> Cells { get; set; }
+
+        public StackedRNNCells(StackedRNNCellsArgs args) : base(args)
+        {
+            Cells = args.Cells;
+            //Cells.reverse_state_order = kwargs.pop('reverse_state_order', False);
+            //    self.reverse_state_order = kwargs.pop('reverse_state_order', False)
+            //    if self.reverse_state_order:
+            //      logging.warning('reverse_state_order=True in StackedRNNCells will soon '
+            //                      'be deprecated. Please update the code to work with the '
+            //                      'natural order of states if you rely on the RNN states, '
+            //                      'eg RNN(return_state=True).')
+            //    super(StackedRNNCells, self).__init__(**kwargs)
+            throw new NotImplementedException("");
+        }
+
+        public object state_size
+        {
+            get => throw new NotImplementedException();
+        }
+
+        //@property
+        //def state_size(self) :
+        //    return tuple(c.state_size for c in
+        //                 (self.cells[::- 1] if self.reverse_state_order else self.cells))
+
+        //  @property
+        //  def output_size(self) :
+        //    if getattr(self.cells[-1], 'output_size', None) is not None:
+        //      return self.cells[-1].output_size
+        //    elif _is_multiple_state(self.cells[-1].state_size) :
+        //      return self.cells[-1].state_size[0]
+        //    else:
+        //      return self.cells[-1].state_size
+
+        //  def get_initial_state(self, inputs= None, batch_size= None, dtype= None) :
+        //    initial_states = []
+        //    for cell in self.cells[::- 1] if self.reverse_state_order else self.cells:
+        //      get_initial_state_fn = getattr(cell, 'get_initial_state', None)
+        //      if get_initial_state_fn:
+        //        initial_states.append(get_initial_state_fn(
+        //            inputs=inputs, batch_size=batch_size, dtype=dtype))
+        //      else:
+        //        initial_states.append(_generate_zero_filled_state_for_cell(
+        //            cell, inputs, batch_size, dtype))
+
+        //    return tuple(initial_states)
+
+        //  def call(self, inputs, states, constants= None, training= None, ** kwargs):
+        //    # Recover per-cell states.
+        //    state_size = (self.state_size[::- 1]
+        //                  if self.reverse_state_order else self.state_size)
+        //    nested_states = nest.pack_sequence_as(state_size, nest.flatten(states))
+
+        //    # Call the cells in order and store the returned states.
+        //    new_nested_states = []
+        //    for cell, states in zip(self.cells, nested_states) :
+        //      states = states if nest.is_nested(states) else [states]
+        //# TF cell does not wrap the state into list when there is only one state.
+        //    is_tf_rnn_cell = getattr(cell, '_is_tf_rnn_cell', None) is not None
+        //      states = states[0] if len(states) == 1 and is_tf_rnn_cell else states
+        //      if generic_utils.has_arg(cell.call, 'training'):
+        //        kwargs['training'] = training
+        //      else:
+        //        kwargs.pop('training', None)
+        //      # Use the __call__ function for callable objects, eg layers, so that it
+        //      # will have the proper name scopes for the ops, etc.
+        //      cell_call_fn = cell.__call__ if callable(cell) else cell.call
+        //      if generic_utils.has_arg(cell.call, 'constants'):
+        //        inputs, states = cell_call_fn(inputs, states,
+        //                                      constants= constants, ** kwargs)
+        //      else:
+        //        inputs, states = cell_call_fn(inputs, states, ** kwargs)
+        //      new_nested_states.append(states)
+
+        //    return inputs, nest.pack_sequence_as(state_size,
+        //                                         nest.flatten(new_nested_states))
+
+        //  @tf_utils.shape_type_conversion
+        //  def build(self, input_shape) :
+        //    if isinstance(input_shape, list) :
+        //      input_shape = input_shape[0]
+        //    for cell in self.cells:
+        //      if isinstance(cell, Layer) and not cell.built:
+        //        with K.name_scope(cell.name):
+        //          cell.build(input_shape)
+        //          cell.built = True
+        //      if getattr(cell, 'output_size', None) is not None:
+        //        output_dim = cell.output_size
+        //      elif _is_multiple_state(cell.state_size) :
+        //        output_dim = cell.state_size[0]
+        //      else:
+        //        output_dim = cell.state_size
+        //      input_shape = tuple([input_shape[0]] +
+        //                          tensor_shape.TensorShape(output_dim).as_list())
+        //    self.built = True
+
+        //  def get_config(self) :
+        //    cells = []
+        //    for cell in self.cells:
+        //      cells.append(generic_utils.serialize_keras_object(cell))
+        //    config = {'cells': cells
+        //}
+        //base_config = super(StackedRNNCells, self).get_config()
+        //    return dict(list(base_config.items()) + list(config.items()))
+
+        //  @classmethod
+        //  def from_config(cls, config, custom_objects = None):
+        //    from tensorflow.python.keras.layers import deserialize as deserialize_layer  # pylint: disable=g-import-not-at-top
+        //    cells = []
+        //    for cell_config in config.pop('cells'):
+        //      cells.append(
+        //          deserialize_layer(cell_config, custom_objects = custom_objects))
+        //    return cls(cells, **config)
+    }
+}

test/TensorFlowNET.Keras.UnitTest/Layers/LayersTest.cs

@@ -36,7 +36,7 @@ namespace TensorFlowNET.Keras.UnitTest
             var model = keras.Model(inputs, outputs, name: "mnist_model");
             model.summary();
         }
-        
+
         /// <summary>
         /// Custom layer test, used in Dueling DQN
         /// </summary>
@@ -45,10 +45,10 @@ namespace TensorFlowNET.Keras.UnitTest
         {
             var layers = keras.layers;
             var inputs = layers.Input(shape: 24);
-            var x = layers.Dense(128, activation:"relu").Apply(inputs);
+            var x = layers.Dense(128, activation: "relu").Apply(inputs);
             var value = layers.Dense(24).Apply(x);
             var adv = layers.Dense(1).Apply(x);
-            
+
             var mean = adv - tf.reduce_mean(adv, axis: 1, keepdims: true);
             adv = layers.Subtract().Apply((adv, mean));
             var outputs = layers.Add().Apply((value, adv));
@@ -105,9 +105,14 @@ namespace TensorFlowNET.Keras.UnitTest
         }
 
         [TestMethod]
+        [Ignore]
         public void SimpleRNN()
         {
-
+            var inputs = np.random.rand(32, 10, 8).astype(np.float32);
+            var simple_rnn = keras.layers.SimpleRNN(4);
+            var output = simple_rnn.Apply(inputs);
+            Assert.AreEqual((32, 4), output.shape);
         }
+
     }
 }