remove unnecessary variables.

src/TensorFlowNET.Core/Data/MnistModelLoader.cs

@@ -13,10 +13,8 @@ namespace Tensorflow
         private const string TEST_IMAGES = "t10k-images-idx3-ubyte.gz";
         private const string TEST_LABELS = "t10k-labels-idx1-ubyte.gz";
 
-        public static async Task<Datasets<MnistDataSet>> LoadAsync(string trainDir, bool oneHot = false, int? trainSize = null, int? validationSize = null, int? testSize = null, bool showProgressInConsole = false)
+        public async Task<Datasets<MnistDataSet>> LoadAsync(string trainDir, bool oneHot = false, int? trainSize = null, int? validationSize = null, int? testSize = null, bool showProgressInConsole = false)
         {
-            var loader = new MnistModelLoader();
-
             var setting = new ModelLoadSetting
             {
                 TrainDir = trainDir,
@@ -33,7 +31,7 @@ namespace Tensorflow
             if (testSize.HasValue)
                 setting.TestSize = testSize.Value;
 
-            return await loader.LoadAsync(setting);
+            return await LoadAsync(setting);
         }
 
         public async Task<Datasets<MnistDataSet>> LoadAsync(ModelLoadSetting setting)

src/TensorFlowNET.Core/Eager/EagerRunner.TFE_Execute.cs

@@ -42,7 +42,7 @@ namespace Tensorflow.Eager
             int num_outputs)
         {
             var status = tf.Status;
-            using var op = GetOp(ctx, op_name, status);
+            var op = GetOp(ctx, op_name, status);
             c_api.TFE_OpSetDevice(op, device_name, status.Handle);
             if (status.ok())
             {

src/TensorFlowNET.Core/Eager/EagerRunner.TFE_FastPathExecute.cs

@@ -15,7 +15,9 @@ namespace Tensorflow.Eager
     /// </summary>
     public partial class EagerRunner
     {
-        UnorderedMap<Context, SafeOpHandle> thread_local_eager_operation_map = new UnorderedMap<Context, SafeOpHandle>();
+        UnorderedMap<string, SafeOpHandle> thread_local_eager_operation_map = new UnorderedMap<string, SafeOpHandle>();
+        public void ClearEagerOperationMap()
+            => thread_local_eager_operation_map.Clear();
 
         public Tensor[] TFE_FastPathExecute(FastPathOpExecInfo op_exec_info)
         {
@@ -31,7 +33,7 @@ namespace Tensorflow.Eager
             op_exec_info.run_callbacks = op_exec_info.run_gradient_callback || op_exec_info.run_post_exec_callbacks;
 
             var status = tf.Status;
-            using var op = GetOp(op_exec_info.ctx, op_exec_info.op_name, status);
+            var op = GetOp(op_exec_info.ctx, op_exec_info.op_name, status);
 
             var op_def = tf.get_default_graph().GetOpDef(op_exec_info.op_name);
 
@@ -56,8 +58,8 @@ namespace Tensorflow.Eager
                 }
             }
 
-            c_api.TFE_OpSetDevice(op, op_exec_info.device_name, status.Handle);
-            status.Check(true);
+            // c_api.TFE_OpSetDevice(op, op_exec_info.device_name, status.Handle);
+            // status.Check(true);
 
             // Add inferred attrs and inputs.
             for (int i = 0; i < op_def.InputArg.Count; i++)
@@ -145,7 +147,6 @@ namespace Tensorflow.Eager
 
             var flat_result = retVals.Select(x => new EagerTensor(x)).ToArray();
 
-
             if (op_exec_info.run_callbacks)
             {
                 RunCallbacks(op_exec_info,
@@ -158,19 +159,19 @@ namespace Tensorflow.Eager
 
         SafeOpHandle GetOp(Context ctx, string op_or_function_name, Status status)
         {
-            /*if (thread_local_eager_operation_map.find(ctx, out var op))
+            if (thread_local_eager_operation_map.find(op_or_function_name, out var op))
                 c_api.TFE_OpReset(op, op_or_function_name, ctx.DeviceName, status.Handle);
             else
             {
                 op = c_api.TFE_NewOp(ctx.Handle, op_or_function_name, status.Handle);
-                thread_local_eager_operation_map[ctx] = op;
+                thread_local_eager_operation_map[op_or_function_name] = op;
             }
 
-            status.Check(true);
-            return op;*/
-            var op = c_api.TFE_NewOp(ctx.Handle, op_or_function_name, status.Handle);
             status.Check(true);
             return op;
+            /*var op = c_api.TFE_NewOp(ctx.Handle, op_or_function_name, status.Handle);
+            status.Check(true);
+            return op;*/
         }
 
         bool HasAccumulator()
@@ -268,16 +269,7 @@ namespace Tensorflow.Eager
 
             if (attr_value == null)
             {
-                if (is_list != 0)
-#pragma warning disable CS0642 // Possible mistaken empty statement
-                    ;
-#pragma warning restore CS0642 // Possible mistaken empty statement
-                //SetOpAttrListDefault
-                else
-#pragma warning disable CS0642 // Possible mistaken empty statement
-                    ;
-#pragma warning restore CS0642 // Possible mistaken empty statement
-                //SetOpAttrScalarDefault
+
             }
             else
             {

src/TensorFlowNET.Core/Eager/IEagerRunner.cs

@@ -39,5 +39,7 @@ namespace Tensorflow.Eager
         bool MustRecordGradient();
 
         int TapeSetPossibleGradientTypes(params Tensor[] args);
+
+        void ClearEagerOperationMap();
     }
 }

src/TensorFlowNET.Core/Framework/tensor_shape.cs

@@ -44,14 +44,14 @@ namespace Tensorflow.Framework
                 return true;
             }
 
-            if (other.IsSparseTensor)
+            if (other is SparseTensor)
             {
                 return self.dtype.is_compatible_with(other.dtype);
             }
 
             return self.dtype.is_compatible_with(other.dtype) &&
                    _shape_is_compatible_0dim(self.shape, other.shape) &&
-                   !self.IsSparseTensor;
+                   !(self is SparseTensor);
         }
 
         public static Dimension dimension_at_index(Shape shape, int index)

src/TensorFlowNET.Core/Sessions/BaseSession.cs

@@ -30,10 +30,6 @@ namespace Tensorflow
     public class BaseSession : DisposableObject
     {
         protected Graph _graph;
-        protected bool _opened;
-        protected bool _closed;
-        protected int _current_version;
-        protected byte[] _target;
         public Graph graph => _graph;
 
         public BaseSession(IntPtr handle, Graph g)
@@ -46,18 +42,15 @@ namespace Tensorflow
         {
             _graph = g ?? ops.get_default_graph();
             if (!_graph.building_function)
-                _graph.as_default();
-            _target = Encoding.UTF8.GetBytes(target);
-
-            using (var opts = new SessionOptions(target, config))
             {
-                lock (Locks.ProcessWide)
-                {
-                    status = status ?? new Status();
-                    _handle = c_api.TF_NewSession(_graph, opts.Handle, status.Handle);
-                    status.Check(true);
-                }
+                if (ops.get_default_graph() != _graph)
+                    _graph.as_default();
             }
+            
+            using var opts = new SessionOptions(target, config);
+            status = status ?? tf.Status;
+            _handle = c_api.TF_NewSession(_graph, opts.Handle, status.Handle);
+            status.Check(true);
         }
 
         public virtual void run(Operation op, params FeedItem[] feed_dict)

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

@@ -26,80 +26,6 @@ namespace Tensorflow
     [SuppressMessage("ReSharper", "InvokeAsExtensionMethod")]
     public partial class Tensor
     {
-        public unsafe void CopyTo(NDArray nd)
-        {
-            //if (!nd.Shape.IsContiguous)
-                //throw new ArgumentException("NDArray has to be contiguous (ndarray.Shape.IsContiguous).");
-
-            var length = (int)(nd.size * nd.dtypesize);
-
-            switch (nd.dtype)
-            {
-                /*case NumpyDType.Boolean:
-                    {
-                        CopyTo(new Span<bool>(nd.Address.ToPointer(), length));
-                        break;
-                    }
-                case NumpyDType.Byte:
-                    {
-                        CopyTo(new Span<byte>(nd.Address.ToPointer(), length));
-                        break;
-                    }
-                case NumpyDType.Int16:
-                    {
-                        CopyTo(new Span<short>(nd.Address.ToPointer(), length));
-                        break;
-                    }
-                case NumpyDType.UInt16:
-                    {
-                        CopyTo(new Span<ushort>(nd.Address.ToPointer(), length));
-                        break;
-                    }
-                case NumpyDType.Int32:
-                    {
-                        CopyTo(new Span<int>(nd.Address.ToPointer(), length));
-                        break;
-                    }
-                case NumpyDType.UInt32:
-                    {
-                        CopyTo(new Span<uint>(nd.Address.ToPointer(), length));
-                        break;
-                    }
-                case NumpyDType.Int64:
-                    {
-                        CopyTo(new Span<long>(nd.Address.ToPointer(), length));
-                        break;
-                    }
-                case NumpyDType.UInt64:
-                    {
-                        CopyTo(new Span<ulong>(nd.Address.ToPointer(), length));
-                        break;
-                    }
-                case NumpyDType.Char:
-                    {
-                        CopyTo(new Span<char>(nd.Address.ToPointer(), length));
-                        break;
-                    }
-                case NumpyDType.Double:
-                    {
-                        CopyTo(new Span<double>(nd.Address.ToPointer(), length));
-                        break;
-                    }
-                case NumpyDType.Single:
-                    {
-                        CopyTo(new Span<float>(nd.Address.ToPointer(), length));
-                        break;
-                    }*/
-                default:
-                    throw new NotSupportedException();
-            }
-        }
-
-        public void CopyTo<T>(Span<T> destination) where T : unmanaged
-        {
-            throw new NotImplementedException("");
-        }
-
         public TensorSpec ToTensorSpec()
             => new TensorSpec(shape, dtype, name);
     }

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

@@ -32,7 +32,7 @@ namespace Tensorflow
 
         public Tensor()
         {
-            isCreatedInGraphMode = !tf.executing_eagerly();
+            _isCreatedInGraphMode = !tf.executing_eagerly();
         }
 
         /// <summary>
@@ -45,7 +45,7 @@ namespace Tensorflow
             if (clone && handle != null)
                 _handle = TF_NewTensor(shape, dtype, data: TensorDataPointer.ToPointer());
                 
-            isCreatedInGraphMode = !tf.executing_eagerly();
+            _isCreatedInGraphMode = !tf.executing_eagerly();
         }
 
         /// <summary>
@@ -59,13 +59,13 @@ namespace Tensorflow
         public unsafe Tensor(IntPtr data_ptr, Shape shape, TF_DataType dtype)
         {
             _handle = TF_NewTensor(shape, dtype, data: data_ptr.ToPointer());
-            isCreatedInGraphMode = !tf.executing_eagerly();
+            _isCreatedInGraphMode = !tf.executing_eagerly();
         }
 
         public unsafe Tensor(NDArray nd)
         {
             _handle = TF_NewTensor(nd.shape, nd.dtype, nd.data.ToPointer());
-            isCreatedInGraphMode = !tf.executing_eagerly();
+            _isCreatedInGraphMode = !tf.executing_eagerly();
         }
 
         #region scala
@@ -107,13 +107,13 @@ namespace Tensorflow
             _value_index = value_index;
             _override_dtype = dtype;
             _id = ops.uid();
-            isCreatedInGraphMode = !tf.executing_eagerly();
+            _isCreatedInGraphMode = !tf.executing_eagerly();
         }
 
         protected unsafe void InitTensor(Shape shape, TF_DataType dtype)
         {
             _handle = TF_NewTensor(shape, dtype, null);
-            isCreatedInGraphMode = !tf.executing_eagerly();
+            _isCreatedInGraphMode = !tf.executing_eagerly();
         }
 
         protected unsafe void InitTensor(Shape shape, byte[] bytes, TF_DataType dtype)
@@ -122,12 +122,12 @@ namespace Tensorflow
                 _handle = StringTensor(new byte[][] { bytes }, Shape.Scalar);
             else
                 _handle = TF_NewTensor(bytes, shape, dtype);
-            isCreatedInGraphMode = !tf.executing_eagerly();
+            _isCreatedInGraphMode = !tf.executing_eagerly();
         }
 
         protected unsafe void InitTensor(Array array, Shape? shape = null)
         {
-            isCreatedInGraphMode = !tf.executing_eagerly();
+            _isCreatedInGraphMode = !tf.executing_eagerly();
 
             shape = shape ?? array.GetShape();
             var dtype = array.GetDataType();

src/TensorFlowNET.Core/Tensors/Tensor.cs

@@ -17,11 +17,8 @@
 using Tensorflow.NumPy;
 using System;
 using System.Diagnostics.CodeAnalysis;
-using System.Globalization;
 using System.Linq;
-using System.Runtime.InteropServices;
 using Tensorflow.Eager;
-using Tensorflow.Framework;
 using Tensorflow.Keras.Engine;
 using static Tensorflow.Binding;
 
@@ -97,12 +94,9 @@ namespace Tensorflow
         /// </summary>
         public SafeTensorHandleHandle EagerTensorHandle => _eagerTensorHandle;
 
-        protected bool isCreatedInGraphMode;
+        protected bool _isCreatedInGraphMode;
         
-        public bool IsCreatedInGraphMode => isCreatedInGraphMode;
-        public bool IsSparseTensor => this is SparseTensor;
-
-        public Tensor TensorShape => tf.shape(this);
+        public bool IsCreatedInGraphMode => _isCreatedInGraphMode;
 
         /// <summary>
         ///     Returns the shape of a tensor.
@@ -157,7 +151,6 @@ namespace Tensorflow
         /// Keras History: (Layer, (node_index, tensor_index))
         /// </summary>
         public KerasHistory KerasHistory { get; set; }
-        public Tensor KerasMask { get; set; }
 
         /// <summary>
         ///     Updates the shape of this tensor.

src/TensorFlowNET.Core/ops.cs

@@ -383,6 +383,9 @@ namespace Tensorflow
         public static void reset_uid()
         {
             uid_number = -1;
+            graph_uid_number = -1;
+            uid_number_for_function = 0;
+            uid_number_for_layer = 0;
         }
 
         public static void colocate_with(bool ignore_existing = false)

src/TensorFlowNET.Keras/BackendImpl.cs

@@ -126,9 +126,10 @@ namespace Tensorflow.Keras
             PER_GRAPH_LAYER_NAME_UIDS.Clear();
             _CURRENT_SCRATCH_GRAPH = null;
             _GRAPH = null;
-            
+
             ops.set_default_session(tf.Session(ops.get_default_graph()));
             tf.enable_eager_execution();
+            tf.Runner.ClearEagerOperationMap();
 
             GC.Collect();
             GC.WaitForPendingFinalizers();

src/TensorFlowNET.Keras/Engine/Functional.cs

@@ -12,11 +12,6 @@ namespace Tensorflow.Keras.Engine
     /// </summary>
     public partial class Functional : Model
     {
-        Shape _build_input_shape;
-        bool _compute_output_and_mask_jointly;
-        bool _expects_training_arg;
-        bool _expects_mask_arg;
-        bool _autocast;
         List<ILayer> _output_layers;
         List<ILayer> _input_layers;
         List<KerasHistory> _input_coordinates;
@@ -49,12 +44,6 @@ namespace Tensorflow.Keras.Engine
             this.inputs = inputs;
             this.outputs = outputs;
             built = true;
-            _build_input_shape = inputs.shape;
-            _compute_output_and_mask_jointly = true;
-            _expects_training_arg = true;
-            _expects_mask_arg = true;
-            // A graph network does not autocast inputs, as its layers will cast them instead.
-            _autocast = false;
 
             if (outputs.Any(x => x.KerasHistory == null))
                 base_layer_utils.create_keras_history(outputs);
@@ -303,23 +292,11 @@ namespace Tensorflow.Keras.Engine
 
         protected override Tensors Call(Tensors inputs, Tensor state = null, bool? training = null)
         {
-            return run_internal_graph(inputs, training.Value);
-        }
-
-        Tensors run_internal_graph(Tensors inputs, bool training = false, Tensors mask = null)
-        {
-            if (mask == null)
-            {
-                Tensor[] masks = new Tensor[inputs.Count()];
-                foreach (var (i, input_t) in enumerate(inputs))
-                    input_t.KerasMask = masks[i];
-            }
-
             var tensor_dict = new Dictionary<long, Queue<Tensor>>();
+            // map input values
             foreach (var (x, y) in zip(this.inputs, inputs))
             {
-                var y1 = conform_to_reference_input(y, x);
-                tensor_dict[x.Id] = new Queue<Tensor>(Enumerable.Range(0, tensor_usage_count[x.Id]).Select(x => y1));
+                tensor_dict[x.Id] = new Queue<Tensor>(Enumerable.Range(0, tensor_usage_count[x.Id]).Select(x => y));
             }
 
             var depth_keys = NodesByDepth.Keys.OrderBy(x => x).Reverse().ToArray();
@@ -336,11 +313,11 @@ namespace Tensorflow.Keras.Engine
                     var layer_inputs = node.MapArguments(tensor_dict);
 
                     tf.Logger.Debug($"Depth {depth}: {node.Layer}: {node.Layer.Name}");
-                    var outputs = node.Layer.Apply(layer_inputs, is_training: training);
+                    var outputs = node.Layer.Apply(layer_inputs, is_training: training ?? false);
                     foreach (var output in outputs.Where(x => x != null))
                         tf.Logger.Information($"Depth {depth}: {node.Layer}: {node.Layer.Name} {output.shape}");
                     // Update tensor_dict for next input
-                    foreach (var (x_id, y) in zip(node.FlatOutputIds, outputs))
+                    foreach (var (x_id, y) in zip(node.Outputs.Select(x => x.Id), outputs))
                         tensor_dict[x_id] = new Queue<Tensor>(Enumerable.Range(0, tensor_usage_count[x_id]).Select(x => y));
                 }
             }
@@ -352,10 +329,5 @@ namespace Tensorflow.Keras.Engine
 
             return output_tensors;
         }
-
-        Tensor conform_to_reference_input(Tensor tensor, Tensor ref_input)
-        {
-            return tensor;
-        }
     }
 }

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

@@ -11,7 +11,6 @@ namespace Tensorflow.Keras.Engine
 {
     public partial class Model
     {
-        List<(IVariableV1, NDArray)> LoadedWeights;
         public void load_weights(string filepath, bool by_name = false, bool skip_mismatch = false, object options = null)
         {
             long fileId = Hdf5.OpenFile(filepath, true);
@@ -31,7 +30,7 @@ namespace Tensorflow.Keras.Engine
                 throw new NotImplementedException("");
             else
             {
-                LoadedWeights = hdf5_format.load_weights_from_hdf5_group(fileId, Layers);
+                hdf5_format.load_weights_from_hdf5_group(fileId, Layers);
                 Hdf5.CloseFile(fileId);
             }
         }

src/TensorFlowNET.Keras/Engine/Node.cs

@@ -33,20 +33,13 @@ namespace Tensorflow.Keras.Engine
     {
         NodeArgs args;
 
-        public int[] node_indices;
-        public int[] tensor_indices;
         public Tensors input_tensors => is_input ? Outputs : args.InputTensors;
         public Tensors Outputs => args.Outputs;
-        public Shape[] input_shapes;
-        public Shape[] output_shapes;
         public List<Tensor> KerasInputs { get; set; } = new List<Tensor>();
         ILayer _layer;
         public ILayer Layer => _layer;
         public bool is_input => args.InputTensors == null;
-        public long[] FlatInputIds { get; set; }
-        public long[] FlatOutputIds { get; set; }
-        bool _single_positional_tensor_passed => KerasInputs.Count() == 1;
-        Dictionary<int, long> _keras_inputs_ids_and_indices = new Dictionary<int, long>();
+
         public INode[] ParentNodes
         {
             get
@@ -74,9 +67,6 @@ namespace Tensorflow.Keras.Engine
             if (args.InputTensors != null)
                 KerasInputs.AddRange(args.InputTensors);
             
-            foreach (var (i, ele) in enumerate(KerasInputs))
-                _keras_inputs_ids_and_indices[i] = ele.Id;
-
             // Wire up Node to Layers.
             layer.InboundNodes.Add(this);
             
@@ -93,10 +83,6 @@ namespace Tensorflow.Keras.Engine
             var node_index = layer.InboundNodes.Count - 1;
             foreach (var (i, tensor) in enumerate(Outputs))
                 tensor.KerasHistory = new KerasHistory(layer, node_index, i);
-
-            // Cached for performance.
-            FlatInputIds = KerasInputs.Select(x => x.Id).ToArray();
-            FlatOutputIds = Outputs.Select(x => x.Id).ToArray();
         }
 
         /// <summary>
@@ -106,16 +92,16 @@ namespace Tensorflow.Keras.Engine
         /// <returns></returns>
         public Tensors MapArguments(Dictionary<long, Queue<Tensor>> tensor_dict)
         {
-            if (_single_positional_tensor_passed)
+            if (KerasInputs.Count() == 1)
             {
-                var kt_id = _keras_inputs_ids_and_indices[0];
+                var kt_id = KerasInputs[0].Id;
                 return tensor_dict[kt_id].Dequeue();
             }
             else
             {
                 var flat_arguments = KerasInputs.Select(x => x).ToArray();
-                foreach (var (kt_index, kt_id) in enumerate(_keras_inputs_ids_and_indices))
-                    flat_arguments[kt_index] = tensor_dict[kt_id].Dequeue();
+                foreach (var (kt_index, kt) in enumerate(KerasInputs))
+                    flat_arguments[kt_index] = tensor_dict[kt.Id].Dequeue();
 
                 return flat_arguments;
             }

src/TensorFlowNET.Keras/Saving/hdf5_format.cs

@@ -3,12 +3,10 @@ using System.Collections.Generic;
 using System.Text;
 using HDF.PInvoke;
 using Tensorflow.NumPy;
-using Tensorflow.Keras.Engine;
 using HDF5CSharp;
 using static Tensorflow.Binding;
 using static Tensorflow.KerasApi;
 using System.Linq;
-using Tensorflow.Util;
 namespace Tensorflow.Keras.Saving
 {
     public class hdf5_format
@@ -82,7 +80,7 @@ namespace Tensorflow.Keras.Saving
 
         }
 
-        public static List<(IVariableV1, NDArray)> load_weights_from_hdf5_group(long f, List<ILayer> layers)
+        public static void load_weights_from_hdf5_group(long f, List<ILayer> layers)
         {
             string original_keras_version = "2.5.0";
             string original_backend = null;
@@ -158,7 +156,6 @@ 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)

src/TensorFlowNET.Keras/Tensorflow.Keras.csproj

@@ -63,7 +63,7 @@ Keras is an API designed for human beings, not machines. Keras follows best prac
     <PackageReference Include="MethodBoundaryAspect.Fody" Version="2.0.139" />
     <PackageReference Include="Newtonsoft.Json" Version="13.0.1" />
     <PackageReference Include="SciSharp.Keras.HDF5" Version="1.1.10.500" />
-    <PackageReference Include="SharpZipLib" Version="1.3.2" />
+    <PackageReference Include="SharpZipLib" Version="1.3.3" />
   </ItemGroup>
 
   <ItemGroup>