fix: error when training SimpleRNN.

src/TensorFlowNET.Core/Exceptions/NotOkStatusException.cs

@@ -0,0 +1,19 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Exceptions
+{
+    public class NotOkStatusException : TensorflowException
+    {
+        public NotOkStatusException() : base()
+        {
+
+        }
+
+        public NotOkStatusException(string message) : base(message)
+        {
+
+        }
+    }
+}

src/TensorFlowNET.Core/Operations/Operation.cs

@@ -186,7 +186,16 @@ namespace Tensorflow
         }
 
         public virtual T get_attr<T>(string name)
-            => (T)get_attr(name);
+        {
+            if (typeof(T).IsValueType)
+            {
+                return (T)Convert.ChangeType(get_attr(name), typeof(T));
+            }
+            else
+            {
+                return (T)get_attr(name);
+            }
+        }
 
         internal unsafe TF_DataType _get_attr_type(string name)
         {

src/TensorFlowNET.Core/Operations/gen_math_ops.cs

@@ -4633,8 +4633,9 @@ public static class gen_math_ops
                 var _fast_path_result = tf.Runner.TFE_FastPathExecute(new FastPathOpExecInfo(_ctx, "MatMul", name) { args = new object[] { a, b }, attrs = new Dictionary<string, object>() { ["transpose_a"] = transpose_a, ["transpose_b"] = transpose_b } });
                 return _fast_path_result[0];
             }
-            catch (Exception)
+            catch (Exception ex)
             {
+                Console.WriteLine();
             }
             try
             {

src/TensorFlowNET.Core/Status/Status.cs

@@ -17,6 +17,7 @@
 using System;
 using System.Diagnostics;
 using System.Runtime.CompilerServices;
+using Tensorflow.Exceptions;
 using Tensorflow.Util;
 using static Tensorflow.c_api;
 
@@ -88,7 +89,7 @@ namespace Tensorflow
                         case TF_Code.TF_INVALID_ARGUMENT:
                             throw new InvalidArgumentError(message);
                         default:
-                            throw new TensorflowException(message);
+                            throw new NotOkStatusException(message);
                     }
                 }
             }

src/TensorFlowNET.Keras/IsExternalInit.cs

@@ -0,0 +1,4 @@
+namespace System.Runtime.CompilerServices
+{
+    internal static class IsExternalInit { }
+}

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

@@ -11,6 +11,7 @@ using Tensorflow.Common.Extensions;
 using System.Linq.Expressions;
 using Tensorflow.Keras.Utils;
 using Tensorflow.Common.Types;
+using System.Runtime.CompilerServices;
 // from tensorflow.python.distribute import distribution_strategy_context as ds_context;
 
 namespace Tensorflow.Keras.Layers.Rnn
@@ -30,7 +31,19 @@ namespace Tensorflow.Keras.Layers.Rnn
         private int _num_constants;
         protected IVariableV1 _kernel;
         protected IVariableV1 _bias;
-        protected IRnnCell _cell;
+        private IRnnCell _cell;
+        protected IRnnCell Cell
+        {
+            get
+            {
+                return _cell;
+            }
+            init
+            {
+                _cell = value;
+                _self_tracked_trackables.Add(_cell);
+            }
+        }
 
         public RNN(RNNArgs args) : base(PreConstruct(args))
         {
@@ -40,14 +53,14 @@ namespace Tensorflow.Keras.Layers.Rnn
             // if is StackedRnncell
             if (args.Cells != null)
             {
-                _cell = new StackedRNNCells(new StackedRNNCellsArgs
+                Cell = new StackedRNNCells(new StackedRNNCellsArgs
                 {
                     Cells = args.Cells
                 });
             }
             else
             {
-                _cell = args.Cell;
+                Cell = args.Cell;
             }
 
             // get input_shape
@@ -65,7 +78,7 @@ namespace Tensorflow.Keras.Layers.Rnn
                 if (_states == null)
                 {
                     // CHECK(Rinne): check if this is correct.
-                    var nested = _cell.StateSize.MapStructure<Tensor?>(x => null);
+                    var nested = Cell.StateSize.MapStructure<Tensor?>(x => null);
                     _states = nested.AsNest().ToTensors();
                 }
                 return _states;
@@ -83,7 +96,7 @@ namespace Tensorflow.Keras.Layers.Rnn
             }
 
             // state_size is a array of ints or a positive integer
-            var state_size = _cell.StateSize.ToSingleShape();
+            var state_size = Cell.StateSize.ToSingleShape();
 
             // TODO(wanglongzhi2001),flat_output_size应该是什么类型的，Shape还是Tensor
             Func<Shape, Shape> _get_output_shape;
@@ -110,12 +123,12 @@ namespace Tensorflow.Keras.Layers.Rnn
                 return output_shape;
             };
 
-            Type type = _cell.GetType();
+            Type type = Cell.GetType();
             PropertyInfo output_size_info = type.GetProperty("output_size");
             Shape output_shape;
             if (output_size_info != null)
             {
-                output_shape = nest.map_structure(_get_output_shape, _cell.OutputSize.ToSingleShape());
+                output_shape = nest.map_structure(_get_output_shape, Cell.OutputSize.ToSingleShape());
                 // TODO(wanglongzhi2001),output_shape应该简单的就是一个元组还是一个Shape类型
                 output_shape = (output_shape.Length == 1 ? (int)output_shape[0] : output_shape);
             }
@@ -171,7 +184,9 @@ namespace Tensorflow.Keras.Layers.Rnn
 
         public override void build(KerasShapesWrapper input_shape)
         {
-            object get_input_spec(Shape shape)
+            input_shape = new KerasShapesWrapper(input_shape.Shapes[0]);
+
+            InputSpec get_input_spec(Shape shape)
             {
                 var input_spec_shape = shape.as_int_list();
 
@@ -213,10 +228,13 @@ namespace Tensorflow.Keras.Layers.Rnn
             // numpy inputs.
 
 
-            if (!_cell.Built)
+            if (Cell is Layer layer && !layer.Built)
             {
-                _cell.build(input_shape);
+                layer.build(input_shape);
+                layer.Built = true;
             }
+
+            this.built = true;
         }
 
         /// <summary>
@@ -247,10 +265,10 @@ namespace Tensorflow.Keras.Layers.Rnn
 
             (inputs, initial_state, constants) = _process_inputs(inputs, initial_state, constants);
 
-            _maybe_reset_cell_dropout_mask(_cell);
-            if (_cell is StackedRNNCells)
+            _maybe_reset_cell_dropout_mask(Cell);
+            if (Cell is StackedRNNCells)
             {
-                var stack_cell = _cell as StackedRNNCells;
+                var stack_cell = Cell as StackedRNNCells;
                 foreach (IRnnCell cell in stack_cell.Cells)
                 {
                     _maybe_reset_cell_dropout_mask(cell);
@@ -300,10 +318,10 @@ namespace Tensorflow.Keras.Layers.Rnn
             bool is_tf_rnn_cell = false;
             if (constants is not null)
             {
-                if (!_cell.SupportOptionalArgs)
+                if (!Cell.SupportOptionalArgs)
                 {
                     throw new ValueError(
-                          $"RNN cell {_cell} does not support constants." +
+                          $"RNN cell {Cell} does not support constants." +
                           $"Received: constants={constants}");
                 }
 
@@ -312,7 +330,7 @@ namespace Tensorflow.Keras.Layers.Rnn
                     constants = new Tensors(states.TakeLast(_num_constants).ToArray());
                     states = new Tensors(states.SkipLast(_num_constants).ToArray());
                     states = len(states) == 1 && is_tf_rnn_cell ? new Tensors(states[0]) : states;
-                    var (output, new_states) = _cell.Apply(inputs, states, optional_args: new RnnOptionalArgs() { Constants = constants });
+                    var (output, new_states) = Cell.Apply(inputs, states, optional_args: new RnnOptionalArgs() { Constants = constants });
                     return (output, new_states.Single);
                 };
             }
@@ -321,7 +339,7 @@ namespace Tensorflow.Keras.Layers.Rnn
                 step = (inputs, states) =>
                 {
                     states = len(states) == 1 && is_tf_rnn_cell ? new Tensors(states.First()) : states;
-                    var (output, new_states) = _cell.Apply(inputs, states);
+                    var (output, new_states) = Cell.Apply(inputs, states);
                     return (output, new_states);
                 };
             }
@@ -562,7 +580,7 @@ namespace Tensorflow.Keras.Layers.Rnn
             var batch_size = _args.TimeMajor ? input_shape[1] : input_shape[0];
             var dtype = input.dtype;
 
-            Tensors init_state = _cell.GetInitialState(null, batch_size, dtype);
+            Tensors init_state = Cell.GetInitialState(null, batch_size, dtype);
 
             return init_state;
         }

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

@@ -32,19 +32,5 @@ namespace Tensorflow.Keras.Layers.Rnn
             });
             return args;
         }
-
-        public override void build(KerasShapesWrapper input_shape)
-        {
-            var single_shape = input_shape.ToSingleShape();
-            var input_dim = single_shape[-1];
-            _buildInputShape = input_shape;
-
-            _kernel = add_weight("kernel", (single_shape[-1], args.Units),
-                initializer: args.KernelInitializer
-                //regularizer = self.kernel_regularizer,
-                //constraint = self.kernel_constraint,
-                //caching_device = default_caching_device,
-            );
-        }
     }
 }

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

@@ -77,6 +77,11 @@ namespace Tensorflow.Keras.UnitTest.Layers
             var output = keras.layers.Dense(10).Apply(x);
             var model = keras.Model(inputs, output);
             model.summary();
+
+            model.compile(keras.optimizers.Adam(), keras.losses.SparseCategoricalCrossentropy());
+            var datax = np.ones((16, 10, 8), dtype: dtypes.float32);
+            var datay = np.ones((16));
+            model.fit(datax, datay, epochs: 20);
         }
         [TestMethod]
         public void RNNForSimpleRNNCell()