Merge pull request #490 from deepakkumar1984/master

Metrics and loss methods skeletonized

TensorFlow.NET.sln

@@ -11,6 +11,8 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "UnitTest", "test\TensorFlow
 EndProject
 Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "Tensorflow.Keras", "src\TensorFlowNET.Keras\Tensorflow.Keras.csproj", "{6268B461-486A-460B-9B3C-86493CBBAAF7}"
 EndProject
+Project("{FAE04EC0-301F-11D3-BF4B-00C04F79EFBC}") = "Tensorflow.Keras.UnitTest", "test\Tensorflow.Keras.UnitTest\Tensorflow.Keras.UnitTest.csproj", "{EB92DD90-6346-41FB-B967-2B33A860AD98}"
+EndProject
 Global
 	GlobalSection(SolutionConfigurationPlatforms) = preSolution
 		Debug|Any CPU = Debug|Any CPU
@@ -51,6 +53,14 @@ Global
 		{6268B461-486A-460B-9B3C-86493CBBAAF7}.Release|Any CPU.Build.0 = Release|Any CPU
 		{6268B461-486A-460B-9B3C-86493CBBAAF7}.Release|x64.ActiveCfg = Release|Any CPU
 		{6268B461-486A-460B-9B3C-86493CBBAAF7}.Release|x64.Build.0 = Release|Any CPU
+		{EB92DD90-6346-41FB-B967-2B33A860AD98}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{EB92DD90-6346-41FB-B967-2B33A860AD98}.Debug|Any CPU.Build.0 = Debug|Any CPU
+		{EB92DD90-6346-41FB-B967-2B33A860AD98}.Debug|x64.ActiveCfg = Debug|Any CPU
+		{EB92DD90-6346-41FB-B967-2B33A860AD98}.Debug|x64.Build.0 = Debug|Any CPU
+		{EB92DD90-6346-41FB-B967-2B33A860AD98}.Release|Any CPU.ActiveCfg = Release|Any CPU
+		{EB92DD90-6346-41FB-B967-2B33A860AD98}.Release|Any CPU.Build.0 = Release|Any CPU
+		{EB92DD90-6346-41FB-B967-2B33A860AD98}.Release|x64.ActiveCfg = Release|Any CPU
+		{EB92DD90-6346-41FB-B967-2B33A860AD98}.Release|x64.Build.0 = Release|Any CPU
 	EndGlobalSection
 	GlobalSection(SolutionProperties) = preSolution
 		HideSolutionNode = FALSE

src/TensorFlowNET.Keras/Args.cs

@@ -0,0 +1,29 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras
+{
+    public class Args
+    {
+        private List<object> args = new List<object>();
+
+        public object this[int index]
+        {
+            get
+            {
+                return args.Count < index ? args[index] : null;
+            }
+        }
+
+        public T Get<T>(int index)
+        {
+            return args.Count < index ? (T)args[index] : default(T);
+        }
+
+        public void Add<T>(T arg)
+        {
+            args.Add(arg);
+        }
+    }
+}

src/TensorFlowNET.Keras/Engine/CallContext.cs

@@ -20,7 +20,7 @@ namespace Tensorflow.Keras.Engine
 
         }
 
-        public void enter(Layer layer, Tensor[] inputs, Graph build_graph, bool training, Saving saving = null) => throw new NotImplementedException();
+        public void enter(Layer layer, Tensor[] inputs, Graph build_graph, bool training) => throw new NotImplementedException();
 
         public bool training_arg_passed_to_call(string[] argspec, Dictionary<string, object> args, Dictionary<string, object>  kwargs) => throw new NotImplementedException();
 

src/TensorFlowNET.Keras/Engine/Node.cs

@@ -4,7 +4,7 @@ using System.Text;
 
 namespace Tensorflow.Keras.Engine
 {
-    class Node
+    public class Node
     {
     }
 }

src/TensorFlowNET.Keras/Engine/Saving.cs

@@ -1,10 +0,0 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
-
-namespace Tensorflow.Keras.Engine
-{
-    public class Saving
-    {
-    }
-}

src/TensorFlowNET.Keras/Engine/Sequential.cs

@@ -4,7 +4,8 @@ using System.Text;
 
 namespace Tensorflow.Keras.Engine
 {
-    class Sequential
+    public class Sequential
     {
+        
     }
 }

src/TensorFlowNET.Keras/KwArgs.cs

@@ -0,0 +1,43 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras
+{
+    public class KwArgs
+    {
+        private Dictionary<string, object> args = new Dictionary<string, object>();
+
+        public object this[string name]
+        {
+            get
+            {
+                return args.ContainsKey(name) ? args[name] : null;
+            }
+            set
+            {
+                args[name] = value;
+            }
+        }
+
+        public T Get<T>(string name)
+        {
+            if (!args.ContainsKey(name))
+                return default(T);
+
+            return (T)args[name];
+        }
+
+        public static explicit operator KwArgs(ValueTuple<string, object>[] p)
+        {
+            KwArgs kwArgs = new KwArgs();
+            kwArgs.args = new Dictionary<string, object>();
+            foreach (var item in p)
+            {
+                kwArgs.args[item.Item1] = item.Item2;
+            }
+
+            return kwArgs;
+        }
+    }
+}

src/TensorFlowNET.Keras/Losses/Loss.cs

@@ -6,5 +6,36 @@ namespace Tensorflow.Keras.Losses
 {
     public abstract class Loss
     {
+        public static Tensor mean_squared_error(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
+
+        public static Tensor mean_absolute_error(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
+
+        public static Tensor mean_absolute_percentage_error(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
+
+        public static Tensor mean_squared_logarithmic_error(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
+
+        public static Tensor _maybe_convert_labels(Tensor y_true) => throw new NotImplementedException();
+
+        public static Tensor squared_hinge(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
+
+        public static Tensor hinge(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
+
+        public static Tensor categorical_hinge(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
+
+        public static Tensor huber_loss(Tensor y_true, Tensor y_pred, float delta = 1) => throw new NotImplementedException();
+
+        public static Tensor logcosh(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
+
+        public static Tensor categorical_crossentropy(Tensor y_true, Tensor y_pred, bool from_logits = false, float label_smoothing = 0) => throw new NotImplementedException();
+
+        public static Tensor sparse_categorical_crossentropy(Tensor y_true, Tensor y_pred, bool from_logits = false, float axis = -1) => throw new NotImplementedException();
+
+        public static Tensor binary_crossentropy(Tensor y_true, Tensor y_pred, bool from_logits = false, float label_smoothing = 0) => throw new NotImplementedException();
+
+        public static Tensor kullback_leibler_divergence(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
+
+        public static Tensor poisson(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
+
+        public static Tensor cosine_similarity(Tensor y_true, Tensor y_pred, int axis = -1) => throw new NotImplementedException();
     }
 }

src/TensorFlowNET.Keras/Metrics/AUC.cs

@@ -1,10 +1,41 @@
 using System;
+using System.Collections;
 using System.Collections.Generic;
 using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class AUC
+    public class AUC : Metric
     {
+        public AUC(int num_thresholds= 200, string curve= "ROC", string summation_method= "interpolation",
+                    string name= null, string dtype= null, float thresholds= 0.5f,
+                    bool multi_label= false, Tensor label_weights= null) : base(name, dtype)
+        {
+            throw new NotImplementedException();
+        }
+
+        private void _build(TensorShape shape) => throw new NotImplementedException();
+
+        public Tensor interpolate_pr_auc() => throw new NotImplementedException();
+
+        public override Tensor result()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override void update_state(Args args, KwArgs kwargs)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override void reset_states()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Hashtable get_config()
+        {
+            throw new NotImplementedException();
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/Accuracy.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class Accuracy
+    public class Accuracy : MeanMetricWrapper
     {
+        public Accuracy(string name = "accuracy", string dtype = null)
+            : base(Metric.accuracy, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/BinaryAccuracy.cs

@@ -4,7 +4,16 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class BinaryAccuracy
+    public class BinaryAccuracy : MeanMetricWrapper
     {
+        public BinaryAccuracy(string name = "binary_accuracy", string dtype = null, float threshold = 0.5f)
+            : base(Fn, name, dtype)
+        {
+        }
+
+        internal static Tensor Fn(Tensor y_true, Tensor y_pred)
+        {
+            return Metric.binary_accuracy(y_true, y_pred);
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/BinaryCrossentropy.cs

@@ -4,7 +4,16 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class BinaryCrossentropy
+    public class BinaryCrossentropy : MeanMetricWrapper
     {
+        public BinaryCrossentropy(string name = "binary_crossentropy", string dtype = null, bool from_logits = false, float label_smoothing = 0)
+            : base(Fn, name, dtype)
+        {
+        }
+
+        internal static Tensor Fn(Tensor y_true, Tensor y_pred)
+        {
+            return Losses.Loss.binary_crossentropy(y_true, y_pred);
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/CategoricalAccuracy.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class CategoricalAccuracy
+    public class CategoricalAccuracy : MeanMetricWrapper
     {
+        public CategoricalAccuracy(string name = "categorical_accuracy", string dtype = null)
+            : base(Metric.categorical_accuracy, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/CategoricalCrossentropy.cs

@@ -4,7 +4,16 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class CategoricalCrossentropy
+    public class CategoricalCrossentropy : MeanMetricWrapper
     {
+        public CategoricalCrossentropy(string name = "categorical_crossentropy", string dtype = null, bool from_logits = false, float label_smoothing = 0)
+            : base(Fn, name, dtype)
+        {
+        }
+
+        internal static Tensor Fn(Tensor y_true, Tensor y_pred)
+        {
+            return Losses.Loss.categorical_crossentropy(y_true, y_pred);
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/CategoricalHinge.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class CategoricalHinge
+    public class CategoricalHinge : MeanMetricWrapper
     {
+        public CategoricalHinge(string name = "categorical_hinge", string dtype = null)
+            : base(Losses.Loss.categorical_hinge, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/CosineSimilarity.cs

@@ -4,7 +4,16 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class CosineSimilarity
+    public class CosineSimilarity : MeanMetricWrapper
     {
+        public CosineSimilarity(string name = "cosine_similarity", string dtype = null, int axis = -1)
+            : base(Fn, name, dtype)
+        {
+        }
+
+        internal static Tensor Fn(Tensor y_true, Tensor y_pred)
+        {
+            return Metric.cosine_proximity(y_true, y_pred);
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/FalseNegatives.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class FalseNegatives
+    public class FalseNegatives : _ConfusionMatrixConditionCount
     {
+        public FalseNegatives(float thresholds = 0.5F, string name = null, string dtype = null)
+            : base(Utils.MetricsUtils.ConfusionMatrix.FALSE_NEGATIVES, thresholds, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/FalsePositives.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class FalsePositives
+    public class FalsePositives : _ConfusionMatrixConditionCount
     {
+        public FalsePositives(float thresholds = 0.5F, string name = null, string dtype = null) 
+            : base(Utils.MetricsUtils.ConfusionMatrix.FALSE_POSITIVES, thresholds, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/Hinge.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class Hinge
+    public class Hinge : MeanMetricWrapper
     {
+        public Hinge(string name = "hinge", string dtype = null)
+            : base(Losses.Loss.hinge, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/KLDivergence.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class KLDivergence
+    public class KLDivergence : MeanMetricWrapper
     {
+        public KLDivergence(string name = "kullback_leibler_divergence", string dtype = null)
+            : base(Losses.Loss.logcosh, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/LogCoshError.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class LogCoshError
+    public class LogCoshError : MeanMetricWrapper
     {
+        public LogCoshError(string name = "logcosh", string dtype = null)
+            : base(Losses.Loss.logcosh, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/Mean.cs

@@ -4,7 +4,12 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class Mean
+    public class Mean : Reduce
     {
+        public Mean(string name, string dtype = null)
+           : base(Reduction.MEAN, name, dtype)
+        {
+        }
+
     }
 }

src/TensorFlowNET.Keras/Metrics/MeanAbsoluteError.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class MeanAbsoluteError
+    public class MeanAbsoluteError : MeanMetricWrapper
     {
+        public MeanAbsoluteError(string name = "mean_absolute_error", string dtype = null)
+            : base(Losses.Loss.mean_absolute_error, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/MeanAbsolutePercentageError.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class MeanAbsolutePercentageError
+    public class MeanAbsolutePercentageError : MeanMetricWrapper
     {
+        public MeanAbsolutePercentageError(string name = "mean_absolute_percentage_error", string dtype = null)
+            : base(Losses.Loss.mean_absolute_percentage_error, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/MeanIoU.cs

@@ -1,10 +1,34 @@
 using System;
+using System.Collections;
 using System.Collections.Generic;
 using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class MeanIoU
+    public class MeanIoU : Metric
     {
+        public MeanIoU(int num_classes, string name, string dtype) : base(name, dtype)
+        {
+        }
+
+        public override void reset_states()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Hashtable get_config()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Tensor result()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override void update_state(Args args, KwArgs kwargs)
+        {
+            throw new NotImplementedException();
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/MeanMetricWrapper.cs

@@ -1,10 +1,25 @@
 using System;
+using System.Collections;
 using System.Collections.Generic;
 using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class MeanMetricWrapper
+    public class MeanMetricWrapper : Mean
     {
+        public MeanMetricWrapper(Func<Tensor, Tensor, Tensor> fn, string name, string dtype = null) : base(name, dtype)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Tensor result()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Hashtable get_config()
+        {
+            throw new NotImplementedException();
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/MeanRelativeError.cs

@@ -1,10 +1,30 @@
 using System;
+using System.Collections;
 using System.Collections.Generic;
 using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class MeanRelativeError
+    public class MeanRelativeError : Metric
     {
+        public MeanRelativeError(Tensor normalizer, string name, string dtype) : base(name, dtype)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Tensor result()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override void update_state(Args args, KwArgs kwargs)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Hashtable get_config()
+        {
+            throw new NotImplementedException();
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/MeanSquaredError.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class MeanSquaredError
+    public class MeanSquaredError : MeanMetricWrapper
     {
+        public MeanSquaredError(string name = "mean_squared_error", string dtype = null)
+            : base(Losses.Loss.mean_squared_error, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/MeanSquaredLogarithmicError.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class MeanSquaredLogarithmicError
+    public class MeanSquaredLogarithmicError : MeanMetricWrapper
     {
+        public MeanSquaredLogarithmicError(string name = "mean_squared_logarithmic_error", string dtype = null)
+            : base(Losses.Loss.mean_squared_logarithmic_error, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/MeanTensor.cs

@@ -4,7 +4,44 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class MeanTensor
+    public class MeanTensor : Metric
     {
+        public int total
+        {
+            get
+            {
+                throw new NotImplementedException();
+            }
+        }
+
+        public int count
+        {
+            get
+            {
+                throw new NotImplementedException();
+            }
+        }
+
+        public MeanTensor(int num_classes, string name = "mean_tensor", string dtype = null) : base(name, dtype)
+        {
+        }
+
+
+        private void _build(TensorShape shape) => throw new NotImplementedException();
+
+        public override void reset_states()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Tensor result()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override void update_state(Args args, KwArgs kwargs)
+        {
+            throw new NotImplementedException();
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/Metric.cs

@@ -1,10 +1,63 @@
 using System;
+using System.Collections;
 using System.Collections.Generic;
 using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    public abstract class Metric
+    public abstract class Metric : Layers.Layer
     {
+        public string dtype
+        {
+            get
+            {
+                throw new NotImplementedException();
+            }
+        }
+
+        public Metric(string name, string dtype)
+        {
+            throw new NotImplementedException();
+        }
+
+        public void __new__ (Metric cls, Args args, KwArgs kwargs) => throw new NotImplementedException();
+
+        public Tensor __call__(Metric cls, Args args, KwArgs kwargs) => throw new NotImplementedException();
+
+        public virtual Hashtable get_config() => throw new NotImplementedException();
+
+        public virtual void reset_states() => throw new NotImplementedException();
+
+        public abstract void update_state(Args args, KwArgs kwargs);
+
+        public abstract Tensor result();
+
+        public void add_weight(string name, TensorShape shape= null, VariableAggregation aggregation= VariableAggregation.Sum,
+                                VariableSynchronization synchronization = VariableSynchronization.OnRead, Initializers.Initializer initializer= null, 
+                                string dtype= null) => throw new NotImplementedException();
+
+        public static Tensor accuracy(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
+
+        public static Tensor binary_accuracy(Tensor y_true, Tensor y_pred, float threshold = 0.5f) => throw new NotImplementedException();
+
+        public static Tensor categorical_accuracy(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
+
+        public static Tensor sparse_categorical_accuracy(Tensor y_true, Tensor y_pred) => throw new NotImplementedException();
+
+        public static Tensor top_k_categorical_accuracy(Tensor y_true, Tensor y_pred, int k = 5) => throw new NotImplementedException();
+
+        public static Tensor sparse_top_k_categorical_accuracy(Tensor y_true, Tensor y_pred, int k = 5) => throw new NotImplementedException();
+
+        public static Tensor cosine_proximity(Tensor y_true, Tensor y_pred, int axis = -1) => throw new NotImplementedException();
+
+        public static Metric clone_metric(Metric metric) => throw new NotImplementedException();
+
+        public static Metric[] clone_metrics(Metric[] metric) => throw new NotImplementedException();
+
+        public static string serialize(Metric metric) => throw new NotImplementedException();
+
+        public static Metric deserialize(string config, object custom_objects = null) => throw new NotImplementedException();
+
+        public static Metric get(object identifier) => throw new NotImplementedException();
     }
 }

src/TensorFlowNET.Keras/Metrics/Poisson.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class Poisson
+    public class Poisson : MeanMetricWrapper
     {
+        public Poisson(string name = "logcosh", string dtype = null)
+            : base(Losses.Loss.logcosh, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/Precision.cs

@@ -1,10 +1,41 @@
 using System;
+using System.Collections;
 using System.Collections.Generic;
 using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class Precision
+    public class Precision : Metric
     {
+        public Precision(float? thresholds = null, int? top_k = null, int? class_id = null, string name = null, string dtype = null) : base(name, dtype)
+        {
+            throw new NotImplementedException();
+        }
+
+        public Precision(float[] thresholds = null, int? top_k = null, int? class_id = null, string name = null, string dtype = null) : base(name, dtype)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Tensor result()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override void update_state(Args args, KwArgs kwargs)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override void reset_states()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Hashtable get_config()
+        {
+            throw new NotImplementedException();
+        }
+
     }
 }

src/TensorFlowNET.Keras/Metrics/PrecisionAtRecall.cs

@@ -1,10 +1,25 @@
 using System;
+using System.Collections;
 using System.Collections.Generic;
 using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class PrecisionAtRecall
+    public class PrecisionAtRecall : SensitivitySpecificityBase
     {
+        public PrecisionAtRecall(float recall, int num_thresholds = 200, string name = null, string dtype = null) : base(recall, num_thresholds, name, dtype)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Tensor result()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Hashtable get_config()
+        {
+            throw new NotImplementedException();
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/Recall.cs

@@ -1,10 +1,41 @@
 using System;
+using System.Collections;
 using System.Collections.Generic;
 using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class Recall
+    public class Recall : Metric
     {
+        public Recall(float? thresholds = null, int? top_k = null, int? class_id = null, string name = null, string dtype = null) : base(name, dtype)
+        {
+            throw new NotImplementedException();
+        }
+
+        public Recall(float[] thresholds = null, int? top_k = null, int? class_id = null, string name = null, string dtype = null) : base(name, dtype)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Tensor result()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override void update_state(Args args, KwArgs kwargs)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override void reset_states()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Hashtable get_config()
+        {
+            throw new NotImplementedException();
+        }
+
     }
 }

src/TensorFlowNET.Keras/Metrics/Reduce.cs

@@ -4,7 +4,22 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class Reduce
+    public class Reduce : Metric
     {
+        public Reduce(string reduction, string name, string dtype= null)
+            : base(name, dtype)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Tensor result()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override void update_state(Args args, KwArgs kwargs)
+        {
+            throw new NotImplementedException();
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/RootMeanSquaredError.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class RootMeanSquaredError
+    public class RootMeanSquaredError : Mean
     {
+        public RootMeanSquaredError(string name = "root_mean_squared_error", string dtype = null)
+            : base(name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/SensitivityAtSpecificity.cs

@@ -1,10 +1,25 @@
 using System;
+using System.Collections;
 using System.Collections.Generic;
 using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class SensitivityAtSpecificity
+    public class SensitivityAtSpecificity : SensitivitySpecificityBase
     {
+        public SensitivityAtSpecificity(float specificity, int num_thresholds = 200, string name = null, string dtype = null) : base(specificity, num_thresholds, name, dtype)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Tensor result()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Hashtable get_config()
+        {
+            throw new NotImplementedException();
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/SensitivitySpecificityBase.cs

@@ -4,7 +4,26 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class SensitivitySpecificityBase
+    public class SensitivitySpecificityBase : Metric
     {
+        public SensitivitySpecificityBase(float value, int num_thresholds= 200, string name = null, string dtype = null) : base(name, dtype)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Tensor result()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override void update_state(Args args, KwArgs kwargs)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override void reset_states()
+        {
+            throw new NotImplementedException();
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/SparseCategoricalAccuracy.cs

@@ -4,7 +4,12 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class SparseCategoricalAccuracy
+    public class SparseCategoricalAccuracy : MeanMetricWrapper
     {
+        public SparseCategoricalAccuracy(string name = "sparse_categorical_accuracy", string dtype = null)
+            : base(Metric.sparse_categorical_accuracy, name, dtype)
+        {
+        }
+
     }
 }

src/TensorFlowNET.Keras/Metrics/SparseCategoricalCrossentropy.cs

@@ -4,7 +4,16 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class SparseCategoricalCrossentropy
+    public class SparseCategoricalCrossentropy : MeanMetricWrapper
     {
+        public SparseCategoricalCrossentropy(string name = "sparse_categorical_crossentropy", string dtype = null, bool from_logits = false, int axis = -1)
+            : base(Fn, name, dtype)
+        {
+        }
+
+        internal static Tensor Fn(Tensor y_true, Tensor y_pred)
+        {
+            return Losses.Loss.sparse_categorical_crossentropy(y_true, y_pred);
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/SparseTopKCategoricalAccuracy.cs

@@ -4,7 +4,17 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class SparseTopKCategoricalAccuracy
+    public class SparseTopKCategoricalAccuracy : MeanMetricWrapper
     {
+        public SparseTopKCategoricalAccuracy(int k = 5, string name = "sparse_top_k_categorical_accuracy", string dtype = null)
+            : base(Fn, name, dtype)
+        {
+            
+        }
+
+        internal static Tensor Fn(Tensor y_true, Tensor y_pred)
+        {
+            return Metric.sparse_top_k_categorical_accuracy(y_true, y_pred);
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/SquaredHinge.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class SquaredHinge
+    public class SquaredHinge : MeanMetricWrapper
     {
+        public SquaredHinge(string name = "squared_hinge", string dtype = null)
+            : base(Losses.Loss.squared_hinge, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/Sum.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class Sum
+    public class Sum : Reduce
     {
+        public Sum(string name, string dtype = null)
+           : base(Reduction.SUM, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/SumOverBatchSize.cs

@@ -4,7 +4,10 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class SumOverBatchSize
+    public class SumOverBatchSize : Reduce
     {
+        public SumOverBatchSize(string name = "sum_over_batch_size", string dtype = null) : base(Reduction.SUM_OVER_BATCH_SIZE, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/SumOverBatchSizeMetricWrapper.cs

@@ -1,10 +1,25 @@
 using System;
+using System.Collections;
 using System.Collections.Generic;
 using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class SumOverBatchSizeMetricWrapper
+    public class SumOverBatchSizeMetricWrapper : SumOverBatchSize
     {
+        public SumOverBatchSizeMetricWrapper(Func<Tensor, Tensor, Tensor> fn, string name, string dtype = null)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override void update_state(Args args, KwArgs kwargs)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Hashtable get_config()
+        {
+            throw new NotImplementedException();
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/TopKCategoricalAccuracy.cs

@@ -4,7 +4,16 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class TopKCategoricalAccuracy
+    public class TopKCategoricalAccuracy : MeanMetricWrapper
     {
+        public TopKCategoricalAccuracy(int k = 5, string name = "top_k_categorical_accuracy", string dtype = null)
+            : base(Fn, name, dtype)
+        {
+        }
+
+        internal static Tensor Fn(Tensor y_true, Tensor y_pred)
+        {
+            return Metric.top_k_categorical_accuracy(y_true, y_pred);
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/TrueNegatives.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class TrueNegatives
+    public class TrueNegatives : _ConfusionMatrixConditionCount
     {
+        public TrueNegatives(float thresholds = 0.5F, string name = null, string dtype = null)
+            : base(Utils.MetricsUtils.ConfusionMatrix.TRUE_NEGATIVES, thresholds, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/TruePositives.cs

@@ -4,7 +4,11 @@ using System.Text;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class TruePositives
+    public class TruePositives : _ConfusionMatrixConditionCount
     {
+        public TruePositives(float thresholds = 0.5F, string name = null, string dtype = null)
+            : base(Utils.MetricsUtils.ConfusionMatrix.TRUE_POSITIVES, thresholds, name, dtype)
+        {
+        }
     }
 }

src/TensorFlowNET.Keras/Metrics/_ConfusionMatrixConditionCount.cs

@@ -1,10 +1,37 @@
 using System;
+using System.Collections;
 using System.Collections.Generic;
 using System.Text;
+using static Tensorflow.Keras.Utils.MetricsUtils;
 
 namespace Tensorflow.Keras.Metrics
 {
-    class _ConfusionMatrixConditionCount
+    public class _ConfusionMatrixConditionCount : Metric
     {
+        public _ConfusionMatrixConditionCount(string confusion_matrix_cond, float thresholds= 0.5f, string name= null, string dtype= null)
+            : base(name, dtype)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Tensor result()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override void update_state(Args args, KwArgs kwargs)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override void reset_states()
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Hashtable get_config()
+        {
+            throw new NotImplementedException();
+        }
     }
 }

src/TensorFlowNET.Keras/Models.cs

@@ -1,14 +1,42 @@
-using System;
+using Keras.Layers;
+using System;
+using System.Collections;
 using System.Collections.Generic;
 using System.Text;
+using Tensorflow.Keras.Engine;
 
 namespace Tensorflow.Keras
 {
     class Models
     {
-        public class Model : Keras.Engine.Training.Model
-        {
+        public class Model : Keras.Engine.Training.Model{}
 
-        }
+        public static Layer share_weights(Layer layer) => throw new NotImplementedException();
+
+        private static Layer _clone_layer(Layer layer) => throw new NotImplementedException();
+
+        private static Layer _insert_ancillary_layers(Model model, Layer ancillary_layers, string[] metrics_names, Node[] new_nodes) => throw new NotImplementedException();
+
+        private static Node[] _make_new_nodes(Node[] nodes_by_depth, Func<Layer, Layer> layer_fn, Hashtable layer_map, Hashtable tensor_map) => throw new NotImplementedException();
+
+        private static Model _clone_functional_model(Model model, Tensor[] input_tensors = null, Func<Layer, Layer> layer_fn = null) => throw new NotImplementedException();
+
+        private static (Hashtable, Layer[]) _clone_layers_and_model_config(Model model, Layer[] input_layers, Func<Layer, Layer> layer_fn) => throw new NotImplementedException();
+
+        private static (Layer[], Layer[]) _remove_ancillary_layers(Model model, Hashtable layer_map, Layer[] layers) => throw new NotImplementedException();
+
+        private static Sequential _clone_sequential_model(Model model, Tensor[] input_tensors = null, Func<Layer, Layer> layer_fn = null) => throw new NotImplementedException();
+
+        public static Model clone_model(Model model, Tensor[] input_tensors = null, Func<Layer, Layer> layer_fn = null) => throw new NotImplementedException();
+
+        private static void _in_place_subclassed_model_reset(Model model) => throw new NotImplementedException();
+
+        private static void _reset_build_compile_trackers(Model model) => throw new NotImplementedException();
+
+        public static void in_place_subclassed_model_state_restoration(Model model) => throw new NotImplementedException();
+
+        public static void clone_and_build_model(Model model, Tensor[] input_tensors= null, Tensor[] target_tensors= null, object custom_objects= null,
+                                        bool compile_clone= true, bool in_place_reset= false, VariableV1 optimizer_iterations= null, Hashtable optimizer_config= null)
+            => throw new NotImplementedException();
     }
 }

src/TensorFlowNET.Keras/Ops.cs

@@ -1,10 +0,0 @@
-using System;
-using System.Collections.Generic;
-using System.Text;
-
-namespace Tensorflow.Keras
-{
-    class Ops
-    {
-    }
-}

src/TensorFlowNET.Keras/Optimizer/Adadelta.cs

@@ -0,0 +1,25 @@
+using System;
+using System.Collections;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras
+{
+    public class Adadelta : Optimizer
+    {
+        public Adadelta(float lr= 0.01f, float rho = 0.95f, float? epsilon = null, float decay = 0) : base(null)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Tensor[] get_updates(Tensor loss, variables @params)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Hashtable get_config()
+        {
+            throw new NotImplementedException();
+        }
+    }
+}

src/TensorFlowNET.Keras/Optimizer/Adagrad.cs

@@ -0,0 +1,25 @@
+using System;
+using System.Collections;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras
+{
+    public class Adagrad : Optimizer
+    {
+        public Adagrad(float lr= 0.01f, float? epsilon = null, float decay = 0) : base(null)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Tensor[] get_updates(Tensor loss, variables @params)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Hashtable get_config()
+        {
+            throw new NotImplementedException();
+        }
+    }
+}

src/TensorFlowNET.Keras/Optimizer/Adam.cs

@@ -0,0 +1,25 @@
+using System;
+using System.Collections;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras
+{
+    public class Adam : Optimizer
+    {
+        public Adam(float lr= 0.001f, float beta_1 = 0.9f, float beta_2 = 0.99f, float? epsilon = null, float decay = 0) : base(null)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Tensor[] get_updates(Tensor loss, variables @params)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Hashtable get_config()
+        {
+            throw new NotImplementedException();
+        }
+    }
+}

src/TensorFlowNET.Keras/Optimizer/Adamax.cs

@@ -0,0 +1,25 @@
+using System;
+using System.Collections;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras
+{
+    public class Adamax : Optimizer
+    {
+        public Adamax(float lr = 0.002f, float beta_1 = 0.9f, float beta_2 = 0.999f, float? epsilon = null, float decay = 0) : base(null)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Tensor[] get_updates(Tensor loss, variables @params)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Hashtable get_config()
+        {
+            throw new NotImplementedException();
+        }
+    }
+}

src/TensorFlowNET.Keras/Optimizer/Nadam.cs

@@ -0,0 +1,25 @@
+using System;
+using System.Collections;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras
+{
+    public class Nadam : Optimizer
+    {
+        public Nadam(float lr = 0.002f, float beta_1 = 0.9f, float beta_2 = 0.999f, float? epsilon = null, float schedule_decay = 0.004f) : base(null)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Tensor[] get_updates(Tensor loss, variables @params)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Hashtable get_config()
+        {
+            throw new NotImplementedException();
+        }
+    }
+}

src/TensorFlowNET.Keras/Optimizer/Optimizer.cs

@@ -0,0 +1,36 @@
+using NumSharp;
+using System;
+using System.Collections;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras
+{
+    public class Optimizer
+    {
+        public Optimizer(KwArgs kwargs)
+        {
+            throw new NotImplementedException();
+        }
+
+        public virtual Tensor[] get_updates(Tensor loss, variables @params)
+        {
+            return null;
+        }
+
+        public virtual Tensor[] get_gradients(Tensor loss, variables @params) => throw new NotImplementedException();
+
+        public virtual void set_weights(NDArray[] weights) => throw new NotImplementedException();
+
+        public virtual NDArray[] get_weights() => throw new NotImplementedException();
+
+        public virtual Hashtable get_config() => throw new NotImplementedException();
+
+        public static string serialize(Optimizer optimizer) => throw new NotImplementedException();
+
+        public static Optimizer deserialize(string config, object custom_objects = null) => throw new NotImplementedException();
+
+        public static Optimizer get(object identifier) => throw new NotImplementedException();
+
+    }
+}

src/TensorFlowNET.Keras/Optimizer/RMSprop.cs

@@ -0,0 +1,25 @@
+using System;
+using System.Collections;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras
+{
+    public class RMSprop : Optimizer
+    {
+        public RMSprop(float lr= 0.01f, float rho = 0f, float? epsilon = null, float decay = 0) : base(null)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Tensor[] get_updates(Tensor loss, variables @params)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Hashtable get_config()
+        {
+            throw new NotImplementedException();
+        }
+    }
+}

src/TensorFlowNET.Keras/Optimizer/SGD.cs

@@ -0,0 +1,25 @@
+using System;
+using System.Collections;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras
+{
+    public class SGD : Optimizer
+    {
+        public SGD(float lr= 0.01f, float momentum= 0, float decay= 0, bool nesterov= false) : base(null)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Tensor[] get_updates(Tensor loss, variables @params)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Hashtable get_config()
+        {
+            throw new NotImplementedException();
+        }
+    }
+}

src/TensorFlowNET.Keras/OptimizersV2/BaseOptimizerV2.cs → src/TensorFlowNET.Keras/OptimizersV2/OptimizerV2.cs

@@ -4,7 +4,7 @@ using System.Text;
 
 namespace Tensorflow.Keras.OptimizersV2
 {
-    class BaseOptimizerV2
+    class OptimizerV2
     {
     }
 }

src/TensorFlowNET.Keras/Regularizers/L1L2.cs

@@ -1,10 +1,25 @@
 using System;
+using System.Collections;
 using System.Collections.Generic;
 using System.Text;
 
 namespace Tensorflow.Keras.Regularizers
 {
-    class L1L2
+    public class L1L2 : Regularizer
     {
+        public L1L2(float l1 = 0f, float l2 = 0f)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override float call(Tensor x)
+        {
+            throw new NotImplementedException();
+        }
+
+        public override Hashtable get_config()
+        {
+            throw new NotImplementedException();
+        }
     }
 }

src/TensorFlowNET.Keras/Regularizers/Regularizer.cs

@@ -1,10 +1,40 @@
 using System;
+using System.Collections;
 using System.Collections.Generic;
 using System.Text;
 
 namespace Tensorflow.Keras.Regularizers
 {
-    public class Regularizer
+    public abstract class Regularizer
     {
+        public virtual float call(Tensor x)
+        {
+            return 0f;
+        }
+
+        public static Regularizer from_config(Hashtable hashtable) => throw new NotImplementedException();
+
+        public virtual Hashtable get_config() => throw new NotImplementedException();
+
+        public static Regularizer l1(float l = 0.01f)
+        {
+            return new L1L2(l1: l);
+        }
+
+        public static Regularizer l2(float l = 0.01f)
+        {
+            return new L1L2(l2: l);
+        }
+
+        public static Regularizer l1_l2(float l1 = 0.01f, float l2 = 0.01f)
+        {
+            return new L1L2(l1, l2);
+        }
+
+        public static string serialize(Regularizer regularizer) => throw new NotImplementedException();
+
+        public static string deserialize(string config, dynamic custom_objects = null) => throw new NotImplementedException();
+
+        public static Regularizer get(object identifier) => throw new NotImplementedException();
     }
 }

src/TensorFlowNET.Keras/Utils/MetricsUtils.cs

@@ -1,10 +1,60 @@
 using System;
 using System.Collections.Generic;
+using System.Reflection;
 using System.Text;
 
 namespace Tensorflow.Keras.Utils
 {
-    class MetricsUtils
+    public class MetricsUtils
     {
+        public static class Reduction
+        {
+            public const string SUM = "sum";
+            public const string SUM_OVER_BATCH_SIZE = "sum_over_batch_size";
+            public const string WEIGHTED_MEAN = "weighted_mean";
+        }
+
+        public static class ConfusionMatrix
+        {
+            public const string TRUE_POSITIVES = "tp";
+            public const string FALSE_POSITIVES = "fp";
+            public const string TRUE_NEGATIVES = "tn";
+            public const string FALSE_NEGATIVES = "fn";
+        }
+
+        public static class AUCCurve
+        {
+            public const string ROC = "ROC";
+            public const string PR = "PR";
+
+            public static string from_str(string key) => throw new NotImplementedException();
+        }
+
+        public static class AUCSummationMethod
+        {
+            public const string INTERPOLATION = "interpolation";
+            public const string MAJORING = "majoring";
+            public const string MINORING = "minoring";
+
+            public static string from_str(string key) => throw new NotImplementedException();
+        }
+
+        public static dynamic update_state_wrapper(Func<Args, KwArgs, Func<bool>> update_state_fn) => throw new NotImplementedException();
+
+        public static dynamic result_wrapper(Func<Args, Tensor> result_fn) => throw new NotImplementedException();
+
+        public static WeakReference weakmethod(MethodInfo method) => throw new NotImplementedException();
+
+        public static void assert_thresholds_range(float[] thresholds) => throw new NotImplementedException();
+
+        public static void parse_init_thresholds(float[] thresholds, float default_threshold = 0.5f) => throw new NotImplementedException();
+
+        public static Operation update_confusion_matrix_variables(variables variables_to_update, Tensor y_true, Tensor y_pred, float[] thresholds,
+                                                    int? top_k= null,int? class_id= null, Tensor sample_weight= null, bool multi_label= false,
+                                                    Tensor label_weights= null) => throw new NotImplementedException();
+
+        private static Tensor _filter_top_k(Tensor x, int k) => throw new NotImplementedException();
+
+        private static (Tensor[], Tensor) ragged_assert_compatible_and_get_flat_values(Tensor[] values, Tensor mask = null) => throw new NotImplementedException();
     }
 }

test/Tensorflow.Keras.UnitTest/OptimizerTest.cs

@@ -0,0 +1,14 @@
+using Microsoft.VisualStudio.TestTools.UnitTesting;
+using System.Collections.Generic;
+
+namespace Tensorflow.Keras.UnitTest
+{
+    [TestClass]
+    public class OptimizerTest
+    {
+        [TestMethod]
+        public void BaseConstruct()
+        {
+        }
+    }
+}

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

@@ -0,0 +1,20 @@
+<Project Sdk="Microsoft.NET.Sdk">
+
+  <PropertyGroup>
+    <TargetFramework>netcoreapp3.1</TargetFramework>
+
+    <IsPackable>false</IsPackable>
+  </PropertyGroup>
+
+  <ItemGroup>
+    <PackageReference Include="Microsoft.NET.Test.Sdk" Version="16.2.0" />
+    <PackageReference Include="MSTest.TestAdapter" Version="2.0.0" />
+    <PackageReference Include="MSTest.TestFramework" Version="2.0.0" />
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