ICallback

src/TensorFlowNET.Console/Tensorflow.Console.csproj

@@ -20,7 +20,7 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="SciSharp.TensorFlow.Redist" Version="2.10.0" />
+    <PackageReference Include="SciSharp.TensorFlow.Redist" Version="2.11.0" />
   </ItemGroup>
 
   <ItemGroup>

src/TensorFlowNET.Core/Keras/Engine/ICallback.cs

@@ -0,0 +1,15 @@
+namespace Tensorflow.Keras.Engine;
+
+public interface ICallback
+{
+    Dictionary<string, List<float>> history { get; set; }
+    void on_train_begin();
+    void on_epoch_begin(int epoch);
+    void on_train_batch_begin(long step);
+    void on_train_batch_end(long end_step, Dictionary<string, float> logs);
+    void on_epoch_end(int epoch, Dictionary<string, float> epoch_logs);
+    void on_predict_begin();
+    void on_predict_batch_begin(long step);
+    void on_predict_batch_end(long end_step, Dictionary<string, Tensors> logs);
+    void on_predict_end();
+}

src/TensorFlowNET.Core/Keras/Engine/IModel.cs

@@ -1,6 +1,65 @@
-namespace Tensorflow.Keras.Engine
+using Tensorflow.Functions;
+using Tensorflow.Keras.Losses;
+using Tensorflow.Keras.Saving;
+using Tensorflow.NumPy;
+
+namespace Tensorflow.Keras.Engine;
+
+public interface IModel : ILayer
 {
-    public interface IModel
-    {
-    }
+    void compile(IOptimizer optimizer = null,
+            ILossFunc loss = null,
+            string[] metrics = null);
+
+    void compile(string optimizer, string loss, string[] metrics);
+
+    ICallback fit(NDArray x, NDArray y,
+            int batch_size = -1,
+            int epochs = 1,
+            int verbose = 1,
+            float validation_split = 0f,
+            bool shuffle = true,
+            int initial_epoch = 0,
+            int max_queue_size = 10,
+            int workers = 1,
+            bool use_multiprocessing = false);
+
+    void save(string filepath,
+            bool overwrite = true,
+            bool include_optimizer = true,
+            string save_format = "tf",
+            SaveOptions? options = null,
+            ConcreteFunction? signatures = null,
+            bool save_traces = true);
+
+    void save_weights(string filepath, 
+        bool overwrite = true, 
+        string save_format = null, 
+        object options = null);
+
+    void load_weights(string filepath, 
+        bool by_name = false, 
+        bool skip_mismatch = false, 
+        object options = null);
+
+    void evaluate(NDArray x, NDArray y,
+            int batch_size = -1,
+            int verbose = 1,
+            int steps = -1,
+            int max_queue_size = 10,
+            int workers = 1,
+            bool use_multiprocessing = false,
+            bool return_dict = false);
+
+    Tensors predict(Tensor x,
+            int batch_size = -1,
+            int verbose = 0,
+            int steps = -1,
+            int max_queue_size = 10,
+            int workers = 1,
+            bool use_multiprocessing = false);
+
+    void summary(int line_length = -1, float[] positions = null);
+
+    IKerasConfig get_config();
 }

src/TensorFlowNET.Core/Keras/Engine/IOptimizer.cs

@@ -0,0 +1,13 @@
+namespace Tensorflow.Keras.Engine;
+
+public interface IOptimizer
+{
+    Tensor[] aggregate_gradients(IEnumerable<(Tensor, IVariableV1)> grads_and_vars);
+    Tensor[] clip_gradients(Tensor[] grads);
+    void apply_gradients((Tensor, ResourceVariable) grads_and_vars,
+            string name = null,
+            bool experimental_aggregate_gradients = true);
+    void apply_gradients(IEnumerable<(Tensor, ResourceVariable)> grads_and_vars,
+            string name = null,
+            bool experimental_aggregate_gradients = true);
+}

src/TensorFlowNET.Core/Keras/IKerasApi.cs

@@ -1,6 +1,7 @@
 using System;
 using System.Collections.Generic;
 using System.Text;
+using Tensorflow.Keras.Engine;
 using Tensorflow.Keras.Layers;
 using Tensorflow.Keras.Losses;
 using Tensorflow.Keras.Metrics;
@@ -13,5 +14,13 @@ namespace Tensorflow.Keras
         public ILossesApi losses { get; }
         public IMetricsApi metrics { get; }
         public IInitializersApi initializers { get; }
+
+        /// <summary>
+        /// `Model` groups layers into an object with training and inference features.
+        /// </summary>
+        /// <param name="input"></param>
+        /// <param name="output"></param>
+        /// <returns></returns>
+        public IModel Model(Tensors inputs, Tensors outputs, string name = null);
     }
 }

src/TensorFlowNET.Core/Keras/Layers/ILayer.cs

@@ -1,6 +1,4 @@
-using System.Collections.Generic;
-using Tensorflow.Keras.ArgsDefinition;
-using Tensorflow.Keras.Engine;
+using Tensorflow.Keras.Engine;
 using Tensorflow.Keras.Saving;
 using Tensorflow.Training;
 
@@ -15,7 +13,7 @@ namespace Tensorflow.Keras
         List<ILayer> Layers { get; }
         List<INode> InboundNodes { get; }
         List<INode> OutboundNodes { get; }
-        Tensors Apply(Tensors inputs, Tensor state = null, bool is_training = false);
+        Tensors Apply(Tensors inputs, Tensor state = null, bool training = false);
         List<IVariableV1> TrainableVariables { get; }
         List<IVariableV1> TrainableWeights { get; }
         List<IVariableV1> NonTrainableWeights { get; }

src/TensorFlowNET.Core/Tensorflow.Binding.csproj

@@ -5,7 +5,7 @@
     <AssemblyName>Tensorflow.Binding</AssemblyName>
     <RootNamespace>Tensorflow</RootNamespace>
     <TargetTensorFlow>2.10.0</TargetTensorFlow>
-    <Version>0.100.4</Version>
+    <Version>1.0.0</Version>
     <LangVersion>10.0</LangVersion>
     <Nullable>enable</Nullable>
     <Authors>Haiping Chen, Meinrad Recheis, Eli Belash</Authors>
@@ -20,7 +20,7 @@
     <Description>Google's TensorFlow full binding in .NET Standard.
 Building, training and infering deep learning models.
 https://tensorflownet.readthedocs.io</Description>
-    <AssemblyVersion>0.100.4.0</AssemblyVersion>
+    <AssemblyVersion>1.0.0.0</AssemblyVersion>
     <PackageReleaseNotes>
 		tf.net 0.100.x and above are based on tensorflow native 2.10.0
 
@@ -38,7 +38,7 @@ https://tensorflownet.readthedocs.io</Description>
 		tf.net 0.7x.x aligns with TensorFlow v2.7.x native library.
 		tf.net 0.10x.x aligns with TensorFlow v2.10.x native library.
 	</PackageReleaseNotes>
-    <FileVersion>0.100.4.0</FileVersion>
+    <FileVersion>1.0.0.0</FileVersion>
     <PackageLicenseFile>LICENSE</PackageLicenseFile>
     <PackageRequireLicenseAcceptance>true</PackageRequireLicenseAcceptance>
     <SignAssembly>true</SignAssembly>

src/TensorFlowNET.Keras/Callbacks/CallbackList.cs

@@ -1,63 +1,63 @@
 using System;
 using System.Collections.Generic;
 using System.Text;
+using Tensorflow.Keras.Engine;
 
-namespace Tensorflow.Keras.Callbacks
+namespace Tensorflow.Keras.Callbacks;
+
+public class CallbackList
 {
-    public class CallbackList
-    {
-        List<ICallback> callbacks = new List<ICallback>();
-        public History History => callbacks[0] as History;
-
-        public CallbackList(CallbackParams parameters)
-        {
-            callbacks.Add(new History(parameters));
-            callbacks.Add(new ProgbarLogger(parameters));
-        }
-
-        public void on_train_begin()
-        {
-            callbacks.ForEach(x => x.on_train_begin());
-        }
-
-        public void on_epoch_begin(int epoch)
-        {
-            callbacks.ForEach(x => x.on_epoch_begin(epoch));
-        }
-
-        public void on_train_batch_begin(long step)
-        {
-            callbacks.ForEach(x => x.on_train_batch_begin(step));
-        }
-
-        public void on_train_batch_end(long end_step, Dictionary<string, float> logs)
-        {
-            callbacks.ForEach(x => x.on_train_batch_end(end_step, logs));
-        }
-
-        public void on_epoch_end(int epoch, Dictionary<string, float> epoch_logs)
-        {
-            callbacks.ForEach(x => x.on_epoch_end(epoch, epoch_logs));
-        }
-
-        public void on_predict_begin()
-        {
-            callbacks.ForEach(x => x.on_predict_begin());
-        }
-
-        public void on_predict_batch_begin(long step)
-        {
-            callbacks.ForEach(x => x.on_predict_batch_begin(step));
-        }
-
-        public void on_predict_batch_end(long end_step, Dictionary<string, Tensors> logs)
-        {
-            callbacks.ForEach(x => x.on_predict_batch_end(end_step, logs));
-        }
-
-        public void on_predict_end()
-        {
-            callbacks.ForEach(x => x.on_predict_end());
-        }
+    List<ICallback> callbacks = new List<ICallback>();
+    public History History => callbacks[0] as History;
+
+    public CallbackList(CallbackParams parameters)
+    {
+        callbacks.Add(new History(parameters));
+        callbacks.Add(new ProgbarLogger(parameters));
+    }
+
+    public void on_train_begin()
+    {
+        callbacks.ForEach(x => x.on_train_begin());
+    }
+
+    public void on_epoch_begin(int epoch)
+    {
+        callbacks.ForEach(x => x.on_epoch_begin(epoch));
+    }
+
+    public void on_train_batch_begin(long step)
+    {
+        callbacks.ForEach(x => x.on_train_batch_begin(step));
+    }
+
+    public void on_train_batch_end(long end_step, Dictionary<string, float> logs)
+    {
+        callbacks.ForEach(x => x.on_train_batch_end(end_step, logs));
+    }
+
+    public void on_epoch_end(int epoch, Dictionary<string, float> epoch_logs)
+    {
+        callbacks.ForEach(x => x.on_epoch_end(epoch, epoch_logs));
+    }
+
+    public void on_predict_begin()
+    {
+        callbacks.ForEach(x => x.on_predict_begin());
+    }
+
+    public void on_predict_batch_begin(long step)
+    {
+        callbacks.ForEach(x => x.on_predict_batch_begin(step));
+    }
+
+    public void on_predict_batch_end(long end_step, Dictionary<string, Tensors> logs)
+    {
+        callbacks.ForEach(x => x.on_predict_batch_end(end_step, logs));
+    }
+
+    public void on_predict_end()
+    {
+        callbacks.ForEach(x => x.on_predict_end());
     }
 }

src/TensorFlowNET.Keras/Callbacks/History.cs

@@ -1,73 +1,70 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
+using Tensorflow.Keras.Engine;
 
-namespace Tensorflow.Keras.Callbacks
+namespace Tensorflow.Keras.Callbacks;
+
+public class History : ICallback
 {
-    public class History : ICallback
+    List<int> epochs;
+    CallbackParams _parameters;
+    public Dictionary<string, List<float>> history { get; set; }
+
+    public History(CallbackParams parameters)
     {
-        List<int> epochs;
-        CallbackParams _parameters;
-        public Dictionary<string, List<float>> history { get; set; }
+        _parameters = parameters;
+    }
 
-        public History(CallbackParams parameters)
-        {
-            _parameters = parameters;
-        }
+    public void on_train_begin()
+    {
+        epochs = new List<int>();
+        history = new Dictionary<string, List<float>>();
+    }
 
-        public void on_train_begin()
-        {
-            epochs = new List<int>();
-            history = new Dictionary<string, List<float>>();
-        }
+    public void on_epoch_begin(int epoch)
+    {
 
-        public void on_epoch_begin(int epoch)
-        {
+    }
 
-        }
+    public void on_train_batch_begin(long step)
+    {
+        
+    }
 
-        public void on_train_batch_begin(long step)
-        {
-            
-        }
+    public void on_train_batch_end(long end_step, Dictionary<string, float> logs)
+    {
+    }
 
-        public void on_train_batch_end(long end_step, Dictionary<string, float> logs)
-        {
-        }
+    public void on_epoch_end(int epoch, Dictionary<string, float> epoch_logs)
+    {
+        epochs.Add(epoch);
 
-        public void on_epoch_end(int epoch, Dictionary<string, float> epoch_logs)
+        foreach (var log in epoch_logs)
         {
-            epochs.Add(epoch);
-
-            foreach (var log in epoch_logs)
+            if (!history.ContainsKey(log.Key))
             {
-                if (!history.ContainsKey(log.Key))
-                {
-                    history[log.Key] = new List<float>();
-                }
-                history[log.Key].Add((float)log.Value);
+                history[log.Key] = new List<float>();
             }
+            history[log.Key].Add((float)log.Value);
         }
+    }
 
-        public void on_predict_begin()
-        {
-            epochs = new List<int>();
-            history = new Dictionary<string, List<float>>();
-        }
+    public void on_predict_begin()
+    {
+        epochs = new List<int>();
+        history = new Dictionary<string, List<float>>();
+    }
 
-        public void on_predict_batch_begin(long step)
-        {
-            
-        }
+    public void on_predict_batch_begin(long step)
+    {
+        
+    }
 
-        public void on_predict_batch_end(long end_step, Dictionary<string, Tensors> logs)
-        {
-            
-        }
+    public void on_predict_batch_end(long end_step, Dictionary<string, Tensors> logs)
+    {
+        
+    }
 
-        public void on_predict_end()
-        {
-            
-        }
+    public void on_predict_end()
+    {
+        
     }
 }

src/TensorFlowNET.Keras/Callbacks/ICallback.cs

@@ -1,19 +0,0 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
-
-namespace Tensorflow.Keras.Callbacks
-{
-    public interface ICallback
-    {
-        void on_train_begin();
-        void on_epoch_begin(int epoch);
-        void on_train_batch_begin(long step);
-        void on_train_batch_end(long end_step, Dictionary<string, float> logs);
-        void on_epoch_end(int epoch, Dictionary<string, float> epoch_logs);
-        void on_predict_begin();
-        void on_predict_batch_begin(long step);
-        void on_predict_batch_end(long end_step, Dictionary<string, Tensors> logs);
-        void on_predict_end();
-    }
-}

src/TensorFlowNET.Keras/Callbacks/ProgbarLogger.cs

@@ -1,8 +1,5 @@
-using System;
-using System.Collections.Generic;
-using System.Diagnostics;
-using System.Linq;
-using System.Text;
+using System.Diagnostics;
+using Tensorflow.Keras.Engine;
 
 namespace Tensorflow.Keras.Callbacks
 {
@@ -13,6 +10,8 @@ namespace Tensorflow.Keras.Callbacks
         CallbackParams _parameters;
         Stopwatch _sw;
 
+        public Dictionary<string, List<float>> history { get; set; }
+
         public ProgbarLogger(CallbackParams parameters)
         {
             _parameters = parameters;

src/TensorFlowNET.Keras/Engine/Functional.cs

@@ -348,7 +348,7 @@ namespace Tensorflow.Keras.Engine
                     var layer_inputs = node.MapArguments(tensor_dict);
 
                     tf.Logger.Debug($"Depth {depth}: {node.Layer}: {node.Layer.Name}");
-                    var outputs = node.Layer.Apply(layer_inputs, is_training: training ?? false);
+                    var outputs = node.Layer.Apply(layer_inputs, training: training ?? false);
                     foreach (var output in outputs.Where(x => x != null))
                         tf.Logger.Information($"Depth {depth}: {node.Layer}: {node.Layer.Name} {output.shape}");
                     // Update tensor_dict for next or later input

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

@@ -1,5 +1,4 @@
-using System;
-using Tensorflow.Keras.ArgsDefinition;
+using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Losses;
 using Tensorflow.Keras.Metrics;
 using Tensorflow.Keras.Optimizers;
@@ -11,7 +10,7 @@ namespace Tensorflow.Keras.Engine
         LossesContainer compiled_loss;
         MetricsContainer compiled_metrics;
 
-        public void compile(OptimizerV2 optimizer = null, 
+        public void compile(IOptimizer optimizer = null, 
             ILossFunc loss = null, 
             string[] metrics = null)
         {
@@ -32,7 +31,7 @@ namespace Tensorflow.Keras.Engine
             _is_compiled = true;
         }
 
-        public void compile(OptimizerV2 optimizer = null,
+        public void compile(IOptimizer optimizer = null,
             ILossFunc loss = null,
             IMetricFunc[] metrics = null)
         {

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

@@ -22,7 +22,7 @@ namespace Tensorflow.Keras.Engine
         /// <param name="verbose"></param>
         /// <param name="validation_split"></param>
         /// <param name="shuffle"></param>
-        public History fit(NDArray x, NDArray y,
+        public ICallback fit(NDArray x, NDArray y,
             int batch_size = -1,
             int epochs = 1,
             int verbose = 1,

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

@@ -51,11 +51,11 @@ namespace Tensorflow.Keras.Engine
             return dict;
         }
 
-        void _minimize(GradientTape tape, OptimizerV2 optimizer, Tensor loss, List<IVariableV1> trainable_variables)
+        void _minimize(GradientTape tape, IOptimizer optimizer, Tensor loss, List<IVariableV1> trainable_variables)
         {
             var gradients = tape.gradient(loss, trainable_variables);
-            gradients = optimizer._aggregate_gradients(zip(gradients, trainable_variables));
-            gradients = optimizer._clip_gradients(gradients);
+            gradients = optimizer.aggregate_gradients(zip(gradients, trainable_variables));
+            gradients = optimizer.clip_gradients(gradients);
 
             optimizer.apply_gradients(zip(gradients, trainable_variables.Select(x => x as ResourceVariable)),
                 experimental_aggregate_gradients: false);

src/TensorFlowNET.Keras/Engine/Model.cs

@@ -1,13 +1,7 @@
-using System.Collections.Generic;
-using System.Linq;
-using Tensorflow.Keras.ArgsDefinition;
-using Tensorflow.Keras.Engine.DataAdapters;
+using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Losses;
-using Tensorflow.Keras.Optimizers;
 using Tensorflow.Keras.Saving.SavedModel;
 using Tensorflow.Train;
-using static Tensorflow.Binding;
-using static Tensorflow.KerasApi;
 
 namespace Tensorflow.Keras.Engine
 {
@@ -25,7 +19,7 @@ namespace Tensorflow.Keras.Engine
 #pragma warning restore CS0414 // The field 'Model._is_compiled' is assigned but its value is never used
 #pragma warning restore CS0108 // Member hides inherited member; missing new keyword
         ILossFunc loss;
-        OptimizerV2 optimizer;
+        IOptimizer optimizer;
         IVariableV1 _steps_per_execution;
         protected bool _is_graph_network;
         protected Tensors inputs;
@@ -39,7 +33,7 @@ namespace Tensorflow.Keras.Engine
 
         public bool IsGraphNetwork => _is_graph_network;
         
-        public OptimizerV2 Optimizer
+        public IOptimizer Optimizer
         {
             get => optimizer;
             set => optimizer = value;

src/TensorFlowNET.Keras/KerasInterface.cs

@@ -46,7 +46,7 @@ namespace Tensorflow.Keras
         /// <param name="input"></param>
         /// <param name="output"></param>
         /// <returns></returns>
-        public Functional Model(Tensors inputs, Tensors outputs, string name = null)
+        public IModel Model(Tensors inputs, Tensors outputs, string name = null)
             => new Functional(inputs, outputs, name: name);
 
         /// <summary>

src/TensorFlowNET.Keras/Optimizers/IOptimizer.cs

@@ -1,6 +0,0 @@
-namespace Tensorflow.Keras.Optimizers
-{
-    public interface IOptimizer
-    {
-    }
-}

src/TensorFlowNET.Keras/Optimizers/OptimizerV2.cs

@@ -1,10 +1,7 @@
-using System;
-using System.Collections.Generic;
-using System.Linq;
-using Tensorflow.Keras.ArgsDefinition;
+using Tensorflow.Keras.ArgsDefinition;
+using Tensorflow.Keras.Engine;
 using Tensorflow.Keras.Utils;
 using Tensorflow.Train;
-using static Tensorflow.Binding;
 
 namespace Tensorflow.Keras.Optimizers
 {
@@ -114,12 +111,12 @@ namespace Tensorflow.Keras.Optimizers
             });
         }
 
-        public Tensor[] _aggregate_gradients(IEnumerable<(Tensor, IVariableV1)> grads_and_vars)
+        public Tensor[] aggregate_gradients(IEnumerable<(Tensor, IVariableV1)> grads_and_vars)
         {
             return grads_and_vars.Select(x => x.Item1).ToArray();
         }
 
-        public Tensor[] _clip_gradients(Tensor[] grads)
+        public Tensor[] clip_gradients(Tensor[] grads)
         {
             return grads;
         }

src/TensorFlowNET.Keras/Saving/SavedModel/Save.cs

@@ -33,7 +33,7 @@ public partial class KerasSavedModelUtils
             }
         }
 
-        OptimizerV2? orig_optimizer = null;
+        IOptimizer? orig_optimizer = null;
         if (!include_optimizer)
         {
             orig_optimizer = model.Optimizer;

src/TensorFlowNET.Keras/Tensorflow.Keras.csproj

@@ -7,7 +7,7 @@
     <Nullable>enable</Nullable>
     <RootNamespace>Tensorflow.Keras</RootNamespace>
     <Platforms>AnyCPU;x64</Platforms>
-    <Version>0.10.4</Version>
+    <Version>1.0.0</Version>
     <Authors>Haiping Chen</Authors>
     <Product>Keras for .NET</Product>
     <Copyright>Apache 2.0, Haiping Chen 2023</Copyright>
@@ -37,8 +37,8 @@ Keras is an API designed for human beings, not machines. Keras follows best prac
     <RepositoryType>Git</RepositoryType>
     <SignAssembly>true</SignAssembly>
     <AssemblyOriginatorKeyFile>Open.snk</AssemblyOriginatorKeyFile>
-    <AssemblyVersion>0.10.4.0</AssemblyVersion>
-    <FileVersion>0.10.4.0</FileVersion>
+    <AssemblyVersion>1.0.0.0</AssemblyVersion>
+    <FileVersion>1.0.0.0</FileVersion>
     <PackageLicenseFile>LICENSE</PackageLicenseFile>
     <Configurations>Debug;Release;GPU</Configurations>
   </PropertyGroup>
@@ -73,7 +73,7 @@ Keras is an API designed for human beings, not machines. Keras follows best prac
     <PackageReference Include="HDF5-CSharp" Version="1.16.3" />
     <PackageReference Include="MethodBoundaryAspect.Fody" Version="2.0.148" />
     <PackageReference Include="Newtonsoft.Json" Version="13.0.2" />
-    <PackageReference Include="SharpZipLib" Version="1.4.1" />
+    <PackageReference Include="SharpZipLib" Version="1.4.2" />
   </ItemGroup>
 
   <ItemGroup>

src/TensorFlowNet.Benchmarks/TensorBenchmark.cs

@@ -1,10 +1,8 @@
 using BenchmarkDotNet.Attributes;
-using Tensorflow;
-using Tensorflow.Eager;
 
 namespace TensorFlowBenchmark
 {
-    [SimpleJob(launchCount: 1, warmupCount: 1, targetCount: 10)]
+    [SimpleJob(launchCount: 1, warmupCount: 1)]
     [MinColumn, MaxColumn, MeanColumn, MedianColumn]
     public class TensorBenchmark
     {

src/TensorFlowNet.Benchmarks/Tensorflow.Benchmark.csproj

@@ -36,8 +36,8 @@
   </ItemGroup>
 
   <ItemGroup>
-    <PackageReference Include="BenchmarkDotNet" Version="0.13.1" />
-    <PackageReference Include="SciSharp.TensorFlow.Redist" Version="2.7.0" />
+    <PackageReference Include="BenchmarkDotNet" Version="0.13.5" />
+    <PackageReference Include="SciSharp.TensorFlow.Redist" Version="2.11.0" />
   </ItemGroup>
 
   <ItemGroup>

src/TensorFlowNet.Benchmarks/Unmanaged/StructCastBenchmark.cs

@@ -16,7 +16,7 @@ namespace TensorFlowBenchmark.Unmanaged
         }
     }
 
-    [SimpleJob(launchCount: 1, warmupCount: 2, targetCount: 10)]
+    [SimpleJob(launchCount: 1, warmupCount: 2)]
     [MinColumn, MaxColumn, MeanColumn, MedianColumn]
     public unsafe class StructCastBenchmark
     {

test/TensorFlowNET.Graph.UnitTest/TensorFlowNET.Graph.UnitTest.csproj

@@ -27,11 +27,11 @@
     <PackageReference Include="Microsoft.NET.Test.Sdk" Version="17.0.0" />
     <PackageReference Include="MSTest.TestAdapter" Version="2.2.8" />
     <PackageReference Include="MSTest.TestFramework" Version="2.2.8" />
-    <PackageReference Include="coverlet.collector" Version="3.1.0">
+    <PackageReference Include="coverlet.collector" Version="3.2.0">
       <PrivateAssets>all</PrivateAssets>
       <IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
     </PackageReference>
-    <PackageReference Include="SciSharp.TensorFlow.Redist" Version="2.7.0" />
+    <PackageReference Include="SciSharp.TensorFlow.Redist" Version="2.11.0" />
   </ItemGroup>
 
   <ItemGroup>

test/TensorFlowNET.Keras.UnitTest/Gradient.cs

@@ -11,17 +11,17 @@ namespace TensorFlowNET.Keras.UnitTest;
 [TestClass]
 public class GradientTest
 {
-    public Model get_actor(int num_states)
+    public IModel get_actor(int num_states)
     {
-        var inputs = keras.layers.Input(shape: num_states);
-        var outputs = keras.layers.Dense(1, activation: keras.activations.Tanh).Apply(inputs);
+        var inputs = tf.keras.layers.Input(shape: num_states);
+        var outputs = tf.keras.layers.Dense(1, activation: keras.activations.Tanh).Apply(inputs);
 
-        Model model = keras.Model(inputs, outputs);
+        var model = tf.keras.Model(inputs, outputs);
 
         return model;
     }
 
-    public Model get_critic(int num_states, int num_actions)
+    public IModel get_critic(int num_states, int num_actions)
     {
         // State as input
         var state_input = keras.layers.Input(shape: num_states);
@@ -33,7 +33,7 @@ public class GradientTest
 
         var outputs = keras.layers.Dense(1).Apply(concat);
 
-        Model model = keras.Model(new Tensors(state_input, action_input), outputs);
+        var model = tf.keras.Model(new Tensors(state_input, action_input), outputs);
         model.summary();
 
         return model;

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

@@ -22,7 +22,7 @@ namespace TensorFlowNET.Keras.UnitTest
             Assert.AreEqual(model.Layers.Count, new_model.Layers.Count);
         }
 
-        Functional GetFunctionalModel()
+        IModel GetFunctionalModel()
         {
             // Create a simple model.
             var inputs = keras.Input(shape: 32);

test/TensorFlowNET.Keras.UnitTest/MultiThreadsTest.cs

@@ -51,7 +51,7 @@ namespace TensorFlowNET.Keras.UnitTest
             //Sanity check without multithreading
             for (int i = 0; i < 2; i++)
             {
-                Functional clone = BuildModel();
+                var clone = BuildModel();
                 clone.load_weights(savefile);
 
                 //Predict something
@@ -71,7 +71,7 @@ namespace TensorFlowNET.Keras.UnitTest
             });
         }
 
-        Functional BuildModel()
+        IModel BuildModel()
         {
             tf.Context.reset_context();
             var inputs = keras.Input(shape: 2);
@@ -81,7 +81,7 @@ namespace TensorFlowNET.Keras.UnitTest
             var outputs = DenseLayer.Apply(inputs);
 
             // build keras model
-            Functional model = keras.Model(inputs, outputs, name: Guid.NewGuid().ToString());
+            var model = tf.keras.Model(inputs, outputs, name: Guid.NewGuid().ToString());
             // show model summary
             model.summary();
 

test/TensorFlowNET.Keras.UnitTest/SaveModel/SequentialModelSave.cs

@@ -3,9 +3,7 @@ using System.Collections.Generic;
 using System.Diagnostics;
 using Tensorflow;
 using Tensorflow.Keras;
-using Tensorflow.Keras.ArgsDefinition;
 using Tensorflow.Keras.Engine;
-using Tensorflow.Keras.Layers;
 using Tensorflow.Keras.Losses;
 using Tensorflow.Keras.Optimizers;
 using Tensorflow.NumPy;
@@ -20,14 +18,16 @@ public class SequentialModelSave
     [TestMethod]
     public void SimpleModelFromAutoCompile()
     {
-        var inputs = new KerasInterface().Input((28, 28, 1));
-        var x = new Flatten(new FlattenArgs()).Apply(inputs);
-        x = new Dense(new DenseArgs() { Units = 100, Activation = tf.nn.relu }).Apply(x);
-        x = new LayersApi().Dense(units: 10).Apply(x);
-        var outputs = new LayersApi().Softmax(axis: 1).Apply(x);
-        var model = new KerasInterface().Model(inputs, outputs);
-
-        model.compile(new Adam(0.001f), new LossesApi().SparseCategoricalCrossentropy(), new string[] { "accuracy" });
+        var inputs = tf.keras.layers.Input((28, 28, 1));
+        var x = tf.keras.layers.Flatten().Apply(inputs);
+        x = tf.keras.layers.Dense(100, activation: tf.nn.relu).Apply(x);
+        x = tf.keras.layers.Dense(units: 10).Apply(x);
+        var outputs = tf.keras.layers.Softmax(axis: 1).Apply(x);
+        var model = tf.keras.Model(inputs, outputs);
+
+        model.compile(new Adam(0.001f), 
+            tf.keras.losses.SparseCategoricalCrossentropy(), 
+            new string[] { "accuracy" });
 
         var data_loader = new MnistModelLoader();
         var num_epochs = 1;

test/TensorFlowNET.Keras.UnitTest/Tensorflow.Keras.UnitTest.csproj

@@ -16,11 +16,11 @@
     <PackageReference Include="Microsoft.NET.Test.Sdk" Version="17.0.0" />
     <PackageReference Include="MSTest.TestAdapter" Version="2.2.8" />
     <PackageReference Include="MSTest.TestFramework" Version="2.2.8" />
-    <PackageReference Include="coverlet.collector" Version="3.1.0">
+    <PackageReference Include="coverlet.collector" Version="3.2.0">
       <PrivateAssets>all</PrivateAssets>
       <IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
     </PackageReference>
-    <PackageReference Include="SciSharp.TensorFlow.Redist" Version="2.7.0" />
+    <PackageReference Include="SciSharp.TensorFlow.Redist" Version="2.11.0" />
   </ItemGroup>
 
   <ItemGroup>

test/TensorFlowNET.Native.UnitTest/Tensorflow.Native.UnitTest.csproj

@@ -1,4 +1,4 @@
-<Project Sdk="Microsoft.NET.Sdk">
+<Project Sdk="Microsoft.NET.Sdk">
 
   <PropertyGroup>
     <TargetFramework>net6.0</TargetFramework>
@@ -47,11 +47,11 @@
     <PackageReference Include="Microsoft.NET.Test.Sdk" Version="17.0.0" />
     <PackageReference Include="MSTest.TestAdapter" Version="2.2.8" />
     <PackageReference Include="MSTest.TestFramework" Version="2.2.8" />
-    <PackageReference Include="coverlet.collector" Version="3.1.0">
+    <PackageReference Include="coverlet.collector" Version="3.2.0">
       <PrivateAssets>all</PrivateAssets>
       <IncludeAssets>runtime; build; native; contentfiles; analyzers; buildtransitive</IncludeAssets>
     </PackageReference>
-    <PackageReference Include="SciSharp.TensorFlow.Redist" Version="2.7.0" />
+    <PackageReference Include="SciSharp.TensorFlow.Redist" Version="2.11.0" />
     <PackageReference Include="SciSharp.TensorFlow.Redist-Lite" Version="2.6.0" />
   </ItemGroup>
 

test/TensorFlowNET.UnitTest/Tensorflow.Binding.UnitTest.csproj

@@ -51,7 +51,7 @@
     <PackageReference Include="Microsoft.NET.Test.Sdk" Version="17.0.0" />
     <PackageReference Include="MSTest.TestAdapter" Version="2.2.8" />
     <PackageReference Include="MSTest.TestFramework" Version="2.2.8" />
-    <PackageReference Include="SciSharp.TensorFlow.Redist" Version="2.7.0" />
+    <PackageReference Include="SciSharp.TensorFlow.Redist" Version="2.11.0" />
   </ItemGroup>
 
   <ItemGroup>