return weights for load_weights.

src/TensorFlowNET.Core/Eager/EagerTensor.Creation.cs

@@ -7,7 +7,7 @@ namespace Tensorflow.Eager
 {
     public partial class EagerTensor
     {
-        public EagerTensor(SafeTensorHandleHandle handle) : base(IntPtr.Zero)
+        public EagerTensor(SafeTensorHandleHandle handle)
         {
             _id = ops.uid();
             EagerTensorHandle = handle;

src/TensorFlowNET.Core/Operations/gen_resource_variable_ops.cs

@@ -63,7 +63,7 @@ namespace Tensorflow
         {
             if (tf.Context.executing_eagerly())
             {
-                var results = tf.Runner.TFE_FastPathExecute(tf.Context, tf.Context.DeviceName,
+                tf.Runner.TFE_FastPathExecute(tf.Context, tf.Context.DeviceName,
                     "AssignVariableOp", name,
                     null,
                     resource, value);

src/TensorFlowNET.Core/Sessions/BaseSession.cs

@@ -265,182 +265,8 @@ namespace Tensorflow
 
         private static unsafe NDArray fetchValue(IntPtr output)
         {
-            NDArray ret;
-            using (var tensor = new Tensor(output))
-            {
-                var ndims = tensor.shape;
-                var srcAddress = c_api.TF_TensorData(output).ToInt64();
-
-                if (ndims.Length == 0)
-                {
-                    switch (tensor.dtype)
-                    {
-                        case TF_DataType.TF_BOOL:
-                            ret = NDArray.Scalar(*(bool*)srcAddress);
-                            break;
-                        case TF_DataType.TF_STRING:
-                            using (var reader = new CodedInputStream(new IntPtr(srcAddress).Stream(8, (long)tensor.bytesize)))
-                                ret = new NDArray(reader.ReadBytes().ToByteArray());
-                            break;
-                        case TF_DataType.TF_UINT8:
-                            ret = NDArray.Scalar(*(byte*)srcAddress);
-                            break;
-                        case TF_DataType.TF_INT16:
-                            ret = NDArray.Scalar(*(short*)srcAddress);
-                            break;
-                        case TF_DataType.TF_INT32:
-                            ret = NDArray.Scalar(*(int*)srcAddress);
-                            break;
-                        case TF_DataType.TF_INT64:
-                            ret = NDArray.Scalar(*(long*)srcAddress);
-                            break;
-                        case TF_DataType.TF_UINT16:
-                            ret = NDArray.Scalar(*(ushort*)srcAddress);
-                            break;
-                        case TF_DataType.TF_UINT32:
-                            ret = NDArray.Scalar(*(uint*)srcAddress);
-                            break;
-                        case TF_DataType.TF_UINT64:
-                            ret = NDArray.Scalar(*(ulong*)srcAddress);
-                            break;
-                        case TF_DataType.TF_FLOAT:
-                            ret = NDArray.Scalar(*(float*)srcAddress);
-                            break;
-                        case TF_DataType.TF_DOUBLE:
-                            ret = NDArray.Scalar(*(double*)srcAddress);
-                            break;
-                        default:
-                            throw new NotImplementedException("can't fetch output");
-                    }
-                }
-                else
-                {
-                    //var size = (long) tensor.size;
-                    //var itemsize = (long) tensor.itemsize;
-                    var bytesize = (long)tensor.bytesize;
-                    var src = (void*)srcAddress;
-
-#if _REGEN
-                    #region Compute
-		            switch (tensor.dtype)
-		            {
-			            %foreach except(supported_dtypes, "Char"),except(supported_dtypes_lowercase, "char"),except(supported_dtypes_TF_DataType,"TF_STRING")%
-			            case TF_DataType.#3:
-			            {
-                            ret = new NDArray(NPTypeCode.#1, ndims, false);
-                            System.Buffer.MemoryCopy(src, #(#3=="TF_STRING"|"(byte*)ret.Unsafe.Address + 8"|"ret.Unsafe.Address"), bytesize, bytesize);
-				            break;
-			            }
-			            %
-                        case TF_DataType.TF_STRING: 
-                        {
-                            //TODO:! This is not the way to handle string[], it should be done with TF_DecodeString
-                            using (var reader = new CodedInputStream(new IntPtr(srcAddress).Stream(8, (long)tensor.bytesize)))
-                                ret = NDArray.FromString(reader.ReadString());
-                            break;
-                        }
-			            default:
-				            throw new NotSupportedException();
-		            }
-                    #endregion
-#else
-
-                    #region Compute
-
-                    switch (tensor.dtype)
-                    {
-                        case TF_DataType.TF_BOOL:
-                            {
-                                ret = new NDArray(NPTypeCode.Boolean, ndims, false);
-                                System.Buffer.MemoryCopy(src, ret.Unsafe.Address, bytesize, bytesize);
-                                break;
-                            }
-
-                        case TF_DataType.TF_UINT8:
-                            {
-                                ret = new NDArray(NPTypeCode.Byte, ndims, false);
-                                System.Buffer.MemoryCopy(src, ret.Unsafe.Address, bytesize, bytesize);
-                                break;
-                            }
-
-                        case TF_DataType.TF_INT16:
-                            {
-                                ret = new NDArray(NPTypeCode.Int16, ndims, false);
-                                System.Buffer.MemoryCopy(src, ret.Unsafe.Address, bytesize, bytesize);
-                                break;
-                            }
-
-                        case TF_DataType.TF_UINT16:
-                            {
-                                ret = new NDArray(NPTypeCode.UInt16, ndims, false);
-                                System.Buffer.MemoryCopy(src, ret.Unsafe.Address, bytesize, bytesize);
-                                break;
-                            }
-
-                        case TF_DataType.TF_INT32:
-                            {
-                                ret = new NDArray(NPTypeCode.Int32, ndims, false);
-                                System.Buffer.MemoryCopy(src, ret.Unsafe.Address, bytesize, bytesize);
-                                break;
-                            }
-
-                        case TF_DataType.TF_UINT32:
-                            {
-                                ret = new NDArray(NPTypeCode.UInt32, ndims, false);
-                                System.Buffer.MemoryCopy(src, ret.Unsafe.Address, bytesize, bytesize);
-                                break;
-                            }
-
-                        case TF_DataType.TF_INT64:
-                            {
-                                ret = new NDArray(NPTypeCode.Int64, ndims, false);
-                                System.Buffer.MemoryCopy(src, ret.Unsafe.Address, bytesize, bytesize);
-                                break;
-                            }
-
-                        case TF_DataType.TF_UINT64:
-                            {
-                                ret = new NDArray(NPTypeCode.UInt64, ndims, false);
-                                System.Buffer.MemoryCopy(src, ret.Unsafe.Address, bytesize, bytesize);
-                                break;
-                            }
-
-                        case TF_DataType.TF_DOUBLE:
-                            {
-                                ret = new NDArray(NPTypeCode.Double, ndims, false);
-                                System.Buffer.MemoryCopy(src, ret.Unsafe.Address, bytesize, bytesize);
-                                break;
-                            }
-
-                        case TF_DataType.TF_FLOAT:
-                            {
-                                ret = new NDArray(NPTypeCode.Single, ndims, false);
-                                System.Buffer.MemoryCopy(src, ret.Unsafe.Address, bytesize, bytesize);
-                                break;
-                            }
-
-                        case TF_DataType.TF_STRING:
-                            {
-                                throw new NotImplementedException();
-                                //TODO:! This is not the way to handle string[], it should be done with TF_DecodeString
-#pragma warning disable CS0162 // Unreachable code detected
-                                using (var reader = new CodedInputStream(new IntPtr(srcAddress).Stream(8, (long)tensor.bytesize)))
-#pragma warning restore CS0162 // Unreachable code detected
-                                    ret = NDArray.FromString(reader.ReadString());
-                                break;
-                            }
-
-                        default:
-                            throw new NotSupportedException();
-                    }
-
-                    #endregion
-
-#endif
-                }
-            }
-
-            return ret;
+            var tensor = new Tensor(output);
+            return tensor.numpy();
         }
 
         /// <summary>

src/TensorFlowNET.Core/Tensorflow.Binding.csproj

@@ -82,7 +82,7 @@ TensorFlow .NET v0.3x is focused on making more Keras API works</PackageReleaseN
   <ItemGroup>
     <PackageReference Include="MethodBoundaryAspect.Fody" Version="2.0.138" />
     <PackageReference Include="Microsoft.Extensions.DependencyInjection" Version="5.0.1" />
-    <PackageReference Include="NumSharp.Lite" Version="0.1.11" />
+    <PackageReference Include="NumSharp.Lite" Version="0.1.12" />
     <PackageReference Include="Protobuf.Text" Version="0.4.0" />
     <PackageReference Include="Serilog.Sinks.Console" Version="3.1.1" />
   </ItemGroup>

src/TensorFlowNET.Core/Tensors/Tensor.Creation.cs

@@ -48,6 +48,11 @@ namespace Tensorflow
 
         public IntPtr TensorDataPointer => _handle == IntPtr.Zero ? IntPtr.Zero : TF_TensorData(_handle);
 
+        public Tensor()
+        {
+
+        }
+
         /// <summary>
         ///     Create a Tensor object from an existing TF handle
         /// </summary>
@@ -56,6 +61,9 @@ namespace Tensorflow
         {
             _handle = handle;
             //no need to set AllocationType = AllocationType.None;
+#if TRACK_TENSOR_LIFE
+            print($"New Tensor 0x{_handle.ToString("x16")} {AllocationType} String Data: 0x{TensorDataPointer.ToString("x16")}");
+#endif
         }
 
         public Tensor(int value)

src/TensorFlowNET.Core/Tensors/Tensor.Value.cs

@@ -163,6 +163,9 @@ namespace Tensorflow
                     break;
                 case TF_DataType.TF_STRING:
                     return np.array(StringBytes()[0]);
+                case TF_DataType.TF_UINT8:
+                    storage = new UnmanagedStorage(NPTypeCode.Byte);
+                    break;
                 case TF_DataType.TF_INT32:
                     storage = new UnmanagedStorage(NPTypeCode.Int32);
                     break;
@@ -186,23 +189,6 @@ namespace Tensorflow
             return new NDArray(storage);
         }
 
-        /*protected unsafe NDArray GetScalar(TF_DataType dtype)
-        {
-            switch(dtype)
-            {
-                case TF_DataType.TF_STRING:
-                    return (NDArray)StringData()[0];
-                case TF_DataType.TF_INT32:
-                    return *(int*)buffer;
-                case TF_DataType.TF_FLOAT:
-                    return *(float*)buffer;
-                case TF_DataType.TF_DOUBLE:
-                    return *(double*)buffer;
-                default:
-                    return BufferToArray();
-            }
-        }*/
-
         /// <summary>
         /// Copies the memory of current buffer onto newly allocated array.
         /// </summary>
@@ -210,7 +196,6 @@ namespace Tensorflow
         public unsafe byte[] BufferToArray()
         {
             // ReSharper disable once LocalVariableHidesMember
-            var bytesize = (long)this.bytesize;
             var data = new byte[bytesize];
             fixed (byte* dst = data)
                 System.Buffer.MemoryCopy(buffer.ToPointer(), dst, bytesize, bytesize);

src/TensorFlowNET.Core/Variables/BaseResourceVariable.cs

@@ -100,6 +100,9 @@ namespace Tensorflow
             if (read_value)
                 return gen_resource_variable_ops.read_variable_op(handle, dtype);
 
+            if (assign_op == null)
+                return null;
+
             return assign_op;
         }
 

src/TensorFlowNET.Keras/Engine/Model.Training.cs

@@ -3,13 +3,14 @@ using System.Collections.Generic;
 using System.Text;
 using HDF.PInvoke;
 using HDF5CSharp;
+using NumSharp;
 using Tensorflow.Keras.Saving;
 
 namespace Tensorflow.Keras.Engine
 {
      public partial class Model
     {
-        public void load_weights(string filepath, bool by_name = false, bool skip_mismatch = false, object options = null)
+        public List<(IVariableV1, NDArray)> load_weights(string filepath, bool by_name = false, bool skip_mismatch = false, object options = null)
         {
             long fileId = Hdf5.OpenFile(filepath, true);
 
@@ -17,20 +18,20 @@ namespace Tensorflow.Keras.Engine
             bool lsuccess = Hdf5.GroupExists(fileId, "layer_names");
 
             if (!lsuccess && msuccess)
-            {
                 fileId = H5G.open(fileId, "model_weights");
-            }
+
             if (by_name)
-            {
                 //fdf5_format.load_weights_from_hdf5_group_by_name();
                 throw new NotImplementedException("");
-            }
             else
             {
-                hdf5_format.load_weights_from_hdf5_group(fileId, Layers);
+                var weights = hdf5_format.load_weights_from_hdf5_group(fileId, Layers);
+                Hdf5.CloseFile(fileId);
+                // return a reference to prevent GC collect Variable.
+                return weights;
             }
-            Hdf5.CloseFile(fileId);
         }
+
         public void save_weights(string filepath, bool overwrite = true, string save_format = null, object options = null)
         {
             long fileId = Hdf5.CreateFile(filepath);

src/TensorFlowNET.Keras/Saving/hdf5_format.cs

@@ -71,18 +71,20 @@ namespace Tensorflow.Keras.Saving
             var target_class = layer.GetType().Name;
             return weights;
         }
+
         public static void save_optimizer_weights_to_hdf5_group(long filepath = -1, Dictionary<string, object> custom_objects = null, bool compile = false)
         {
 
         }
+
         public static void load_optimizer_weights_from_hdf5_group(long filepath = -1, Dictionary<string, object> custom_objects = null, bool compile = false)
         {
 
         }
 
-        public static void load_weights_from_hdf5_group(long f, List<ILayer> layers)
+        public static List<(IVariableV1, NDArray)> load_weights_from_hdf5_group(long f, List<ILayer> layers)
         {
-            string original_keras_version = "2.4.0";
+            string original_keras_version = "2.5.0";
             string original_backend = null;
             if (Hdf5.AttributeExists(f, "keras_version"))
             {
@@ -156,15 +158,19 @@ namespace Tensorflow.Keras.Saving
             }
 
             keras.backend.batch_set_value(weight_value_tuples);
+            return weight_value_tuples;
         }
+
         public static void toarrayf4(long filepath = -1, Dictionary<string, object> custom_objects = null, bool compile = false)
         {
 
         }
+
         public static void load_weights_from_hdf5_group_by_name(long filepath = -1, Dictionary<string, object> custom_objects = null, bool compile = false)
         {
 
         }
+
         public static void save_weights_to_hdf5_group(long f, List<ILayer> layers)
         {
             List<string> layerName=new List<string>();
@@ -260,8 +266,8 @@ namespace Tensorflow.Keras.Saving
                 WriteAttrs(f, getType,name, data);
               
             }
-
         }
+
         private static void WriteDataset(long f,  string name, Tensor data)
         {
             switch (data.dtype)
@@ -283,6 +289,7 @@ namespace Tensorflow.Keras.Saving
                     break;
             }
         }
+
         private static void WriteAttrs(long f,string typename, string name, Array data)
         {
             switch (typename)
@@ -307,6 +314,7 @@ namespace Tensorflow.Keras.Saving
                     break;
             }
         }
+
         private static List<List<object>> Split(Array list, int chunkSize)
         {
             var splitList = new List<List<object>>();
@@ -327,6 +335,7 @@ namespace Tensorflow.Keras.Saving
 
             return splitList;
         }
+
         public static string[] load_attributes_from_hdf5_group(long group, string name)
         {
             if (Hdf5.AttributeExists(group, name))
@@ -337,6 +346,7 @@ namespace Tensorflow.Keras.Saving
             }
             return null;
         }
+
         public static void load_attributes_from_hdf5_group(long filepath = -1, Dictionary<string, object> custom_objects = null, bool compile = false)
         {