diff --git a/src/TensorFlowNET.Keras/Layers/Rnn/Bidirectional.cs b/src/TensorFlowNET.Keras/Layers/Rnn/Bidirectional.cs
index 6114d9c7c..0566b08ad 100644
--- a/src/TensorFlowNET.Keras/Layers/Rnn/Bidirectional.cs
+++ b/src/TensorFlowNET.Keras/Layers/Rnn/Bidirectional.cs
@@ -13,17 +13,17 @@ namespace Tensorflow.Keras.Layers
     /// </summary>
     public class Bidirectional: Wrapper
     {
-        BidirectionalArgs _args;
-        RNN _forward_layer;
-        RNN _backward_layer;
-        RNN _layer;
-        bool _support_masking = true;
         int _num_constants = 0;
+        bool _support_masking = true;
         bool _return_state;
         bool _stateful;
         bool _return_sequences;
-        InputSpec _input_spec;
+        BidirectionalArgs _args;
         RNNArgs _layer_args_copy;
+        RNN _forward_layer;
+        RNN _backward_layer;
+        RNN _layer;
+        InputSpec _input_spec;
         public Bidirectional(BidirectionalArgs args):base(args)
         {
             _args = args;
@@ -66,12 +66,16 @@ public Bidirectional(BidirectionalArgs args):base(args)
 
             // Recreate the forward layer from the original layer config, so that it
             // will not carry over any state from the layer.
-            var actualType = _layer.GetType();
-            if (actualType == typeof(LSTM))
+            if (_layer is LSTM)
             {
                 var arg = _layer_args_copy as LSTMArgs;
                 _forward_layer = new LSTM(arg);
             }
+            else if(_layer is SimpleRNN)
+            {
+                var arg = _layer_args_copy as SimpleRNNArgs;
+                _forward_layer = new SimpleRNN(arg);
+            }
             // TODO(Wanglongzhi2001), add GRU if case.
             else
             {
@@ -154,12 +158,18 @@ private RNN _recreate_layer_from_config(RNN layer, bool go_backwards = false)
             {
                 config.GoBackwards = !config.GoBackwards;
             }
-            var actualType = layer.GetType();
-            if (actualType == typeof(LSTM))
+
+            if (layer is LSTM)
             {
                 var arg = config as LSTMArgs;
                 return new LSTM(arg);
             }
+            else if(layer is SimpleRNN)
+            {
+                var arg = config as SimpleRNNArgs;
+                return new SimpleRNN(arg);
+            }
+            // TODO(Wanglongzhi2001), add GRU if case.
             else
             {
                 return new RNN(cell, config);
@@ -183,7 +193,6 @@ public override void build(KerasShapesWrapper input_shape)
         protected override Tensors Call(Tensors inputs, Tensors state = null, bool? training = null, IOptionalArgs? optional_args = null)
         {
             // `Bidirectional.call` implements the same API as the wrapped `RNN`.
-
             Tensors forward_inputs;
             Tensors backward_inputs;
             Tensors forward_state;