add tf.keras

TensorFlow.NET.sln

@@ -11,9 +11,7 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.Core", "src\T
 EndProject
 Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.Visualization", "TensorFlowNET.Visualization\TensorFlowNET.Visualization.csproj", "{4BB2ABD1-635E-41E4-B534-CB5B6A2D754D}"
 EndProject
-Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "NumSharp.Core", "..\NumSharp\src\NumSharp.Core\NumSharp.Core.csproj", "{E8340C61-12C1-4BEE-A340-403E7C1ACD82}"
-EndProject
-Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "scikit-learn", "..\scikit-learn.net\src\scikit-learn\scikit-learn.csproj", "{199DDAD8-4A6F-43B3-A560-C0393619E304}"
+Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "NumSharp.Core", "..\NumSharp\src\NumSharp.Core\NumSharp.Core.csproj", "{1F4C5683-48B4-4328-8171-E9F7ABAA2E72}"
 EndProject
 Global
 	GlobalSection(SolutionConfigurationPlatforms) = preSolution
@@ -37,14 +35,10 @@ Global
 		{4BB2ABD1-635E-41E4-B534-CB5B6A2D754D}.Debug|Any CPU.Build.0 = Debug|Any CPU
 		{4BB2ABD1-635E-41E4-B534-CB5B6A2D754D}.Release|Any CPU.ActiveCfg = Release|Any CPU
 		{4BB2ABD1-635E-41E4-B534-CB5B6A2D754D}.Release|Any CPU.Build.0 = Release|Any CPU
-		{E8340C61-12C1-4BEE-A340-403E7C1ACD82}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
-		{E8340C61-12C1-4BEE-A340-403E7C1ACD82}.Debug|Any CPU.Build.0 = Debug|Any CPU
-		{E8340C61-12C1-4BEE-A340-403E7C1ACD82}.Release|Any CPU.ActiveCfg = Release|Any CPU
-		{E8340C61-12C1-4BEE-A340-403E7C1ACD82}.Release|Any CPU.Build.0 = Release|Any CPU
-		{199DDAD8-4A6F-43B3-A560-C0393619E304}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
-		{199DDAD8-4A6F-43B3-A560-C0393619E304}.Debug|Any CPU.Build.0 = Debug|Any CPU
-		{199DDAD8-4A6F-43B3-A560-C0393619E304}.Release|Any CPU.ActiveCfg = Release|Any CPU
-		{199DDAD8-4A6F-43B3-A560-C0393619E304}.Release|Any CPU.Build.0 = Release|Any CPU
+		{1F4C5683-48B4-4328-8171-E9F7ABAA2E72}.Debug|Any CPU.ActiveCfg = Debug|Any CPU
+		{1F4C5683-48B4-4328-8171-E9F7ABAA2E72}.Debug|Any CPU.Build.0 = Debug|Any CPU
+		{1F4C5683-48B4-4328-8171-E9F7ABAA2E72}.Release|Any CPU.ActiveCfg = Release|Any CPU
+		{1F4C5683-48B4-4328-8171-E9F7ABAA2E72}.Release|Any CPU.Build.0 = Release|Any CPU
 	EndGlobalSection
 	GlobalSection(SolutionProperties) = preSolution
 		HideSolutionNode = FALSE

src/TensorFlowNET.Core/APIs/tf.init.cs

@@ -1,6 +1,7 @@
 using System;
 using System.Collections.Generic;
 using System.Text;
+using Tensorflow.Operations.Initializers;
 
 namespace Tensorflow
 {
@@ -24,128 +25,5 @@ namespace Tensorflow
                   default_name,
                   values,
                   auxiliary_name_scope);
-
-        public class Zeros : IInitializer
-        {
-            private TF_DataType dtype;
-
-            public Zeros(TF_DataType dtype = TF_DataType.TF_FLOAT)
-            {
-                this.dtype = dtype;
-            }
-
-            public Tensor call(TensorShape shape, TF_DataType dtype = TF_DataType.DtInvalid)
-            {
-                if (dtype == TF_DataType.DtInvalid)
-                    dtype = this.dtype;
-
-                return array_ops.zeros(shape, dtype);
-            }
-
-            public object get_config()
-            {
-                return new { dtype = dtype.name() };
-            }
-        }
-
-        /// <summary>
-        /// Initializer capable of adapting its scale to the shape of weights tensors.
-        /// </summary>
-        public class VarianceScaling : IInitializer
-        {
-            protected float _scale;
-            protected string _mode;
-            protected string _distribution;
-            protected int? _seed;
-            protected TF_DataType _dtype;
-
-            public VarianceScaling(float scale = 1.0f,
-                string mode = "fan_in",
-                string distribution= "truncated_normal",
-                int? seed = null,
-                TF_DataType dtype = TF_DataType.TF_FLOAT)
-            {
-                if (scale < 0)
-                    throw new ValueError("`scale` must be positive float.");
-                _scale = scale;
-                _mode = mode;
-                _distribution = distribution;
-                _seed = seed;
-                _dtype = dtype;
-            }
-
-            public Tensor call(TensorShape shape, TF_DataType dtype)
-            {
-                var (fan_in, fan_out) = _compute_fans(shape);
-                if (_mode == "fan_in")
-                    _scale /= Math.Max(1, fan_in);
-                else if (_mode == "fan_out")
-                    _scale /= Math.Max(1, fan_out);
-                else
-                    _scale /= Math.Max(1, (fan_in + fan_out) / 2);
-
-                if (_distribution == "normal" || _distribution == "truncated_normal")
-                {
-                    throw new NotImplementedException("truncated_normal");
-                }
-                else if(_distribution == "untruncated_normal")
-                {
-                    throw new NotImplementedException("truncated_normal");
-                }
-                else
-                {
-                    var limit = Math.Sqrt(3.0f * _scale);
-                    return random_ops.random_uniform(shape, (float)-limit, (float)limit, dtype, seed: _seed);
-                }
-            }
-
-            private (int, int) _compute_fans(int[] shape)
-            {
-                if (shape.Length < 1)
-                    return (1, 1);
-                if (shape.Length == 1)
-                    return (shape[0], shape[0]);
-                if (shape.Length == 2)
-                    return (shape[0], shape[1]);
-                else
-                    throw new NotImplementedException("VarianceScaling._compute_fans");
-            }
-
-            public virtual object get_config()
-            {
-                return new
-                {
-                    scale = _scale,
-                    mode = _mode,
-                    distribution = _distribution,
-                    seed = _seed,
-                    dtype = _dtype
-                };
-            }
-        }
-
-        public class GlorotUniform : VarianceScaling
-        {
-            public GlorotUniform(float scale = 1.0f,
-                string mode = "fan_avg",
-                string distribution = "uniform",
-                int? seed = null,
-                TF_DataType dtype = TF_DataType.TF_FLOAT) : base(scale, mode, distribution, seed, dtype)
-            {
-
-            }
-
-            public object get_config()
-            {
-                return new
-                {
-                    scale = _scale,
-                    mode = _mode,
-                    distribution = _distribution,
-                    seed = _seed,
-                    dtype = _dtype
-                };
-            }
-        }
     }
 }

src/TensorFlowNET.Core/APIs/tf.random.cs

@@ -22,5 +22,12 @@ namespace Tensorflow
             TF_DataType dtype = TF_DataType.TF_FLOAT,
             int? seed = null,
             string name = null) => random_ops.random_normal(shape, mean, stddev, dtype, seed, name);
+
+        public static Tensor random_uniform(int[] shape,
+            float minval = 0,
+            float? maxval = null,
+            TF_DataType dtype = TF_DataType.TF_FLOAT,
+            int? seed = null,
+            string name = null) => random_ops.random_uniform(shape, minval, maxval, dtype, seed, name);
     }
 }

src/TensorFlowNET.Core/Keras/Initializers.cs

@@ -0,0 +1,20 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow.Operations.Initializers;
+
+namespace Tensorflow.Keras
+{
+    public class Initializers
+    {
+        /// <summary>
+        /// He normal initializer.
+        /// </summary>
+        /// <param name="seed"></param>
+        /// <returns></returns>
+        public IInitializer he_normal(int? seed = null)
+        {
+            return new VarianceScaling(scale: 20f, mode: "fan_in", distribution: "truncated_normal", seed: seed);
+        }
+    }
+}

src/TensorFlowNET.Core/Keras/tf.keras.cs

@@ -0,0 +1,15 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow.Keras;
+
+namespace Tensorflow
+{
+    public static partial class tf
+    {
+        public static class keras
+        {
+            public static Initializers initializers => new Initializers();
+        }
+    }
+}

src/TensorFlowNET.Core/Operations/Initializers/GlorotUniform.cs

@@ -0,0 +1,30 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Operations.Initializers
+{
+    public class GlorotUniform : VarianceScaling
+    {
+        public GlorotUniform(float scale = 1.0f,
+            string mode = "fan_avg",
+            string distribution = "uniform",
+            int? seed = null,
+            TF_DataType dtype = TF_DataType.TF_FLOAT) : base(scale, mode, distribution, seed, dtype)
+        {
+
+        }
+
+        public object get_config()
+        {
+            return new
+            {
+                scale = _scale,
+                mode = _mode,
+                distribution = _distribution,
+                seed = _seed,
+                dtype = _dtype
+            };
+        }
+    }
+}

src/TensorFlowNET.Core/Operations/IInitializer.cs → src/TensorFlowNET.Core/Operations/Initializers/IInitializer.cs

@@ -6,7 +6,7 @@ namespace Tensorflow
 {
     public interface IInitializer
     {
-        Tensor call(TensorShape shape, TF_DataType dtype);
+        Tensor call(TensorShape shape, TF_DataType dtype = TF_DataType.DtInvalid);
         object get_config();
     }
 }

src/TensorFlowNET.Core/Operations/Initializers/TruncatedNormal.cs

@@ -0,0 +1,41 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Operations.Initializers
+{
+    public class TruncatedNormal : IInitializer
+    {
+        private float mean;
+        private float stddev;
+        private int? seed;
+        private TF_DataType dtype;
+
+        public TruncatedNormal(float mean = 0.0f, 
+            float stddev = 1.0f, 
+            int? seed = null, 
+            TF_DataType dtype = TF_DataType.TF_FLOAT)
+        {
+            this.mean = mean;
+            this.stddev = stddev;
+            this.seed = seed;
+            this.dtype = dtype;
+        }
+
+        public Tensor call(TensorShape shape, TF_DataType dtype)
+        {
+            throw new NotImplementedException("");
+        }
+
+        public object get_config()
+        {
+            return new
+            {
+                mean = mean,
+                stddev = stddev,
+                seed = seed,
+                dtype = dtype.name()
+            };
+        }
+    }
+}

src/TensorFlowNET.Core/Operations/Initializers/VarianceScaling.cs

@@ -0,0 +1,82 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Operations.Initializers
+{
+    /// <summary>
+    /// Initializer capable of adapting its scale to the shape of weights tensors.
+    /// </summary>
+    public class VarianceScaling : IInitializer
+    {
+        protected float _scale;
+        protected string _mode;
+        protected string _distribution;
+        protected int? _seed;
+        protected TF_DataType _dtype;
+
+        public VarianceScaling(float scale = 1.0f,
+            string mode = "fan_in",
+            string distribution = "truncated_normal",
+            int? seed = null,
+            TF_DataType dtype = TF_DataType.TF_FLOAT)
+        {
+            if (scale < 0)
+                throw new ValueError("`scale` must be positive float.");
+            _scale = scale;
+            _mode = mode;
+            _distribution = distribution;
+            _seed = seed;
+            _dtype = dtype;
+        }
+
+        public Tensor call(TensorShape shape, TF_DataType dtype)
+        {
+            var (fan_in, fan_out) = _compute_fans(shape);
+            if (_mode == "fan_in")
+                _scale /= Math.Max(1, fan_in);
+            else if (_mode == "fan_out")
+                _scale /= Math.Max(1, fan_out);
+            else
+                _scale /= Math.Max(1, (fan_in + fan_out) / 2);
+
+            if (_distribution == "normal" || _distribution == "truncated_normal")
+            {
+                throw new NotImplementedException("truncated_normal");
+            }
+            else if (_distribution == "untruncated_normal")
+            {
+                throw new NotImplementedException("truncated_normal");
+            }
+            else
+            {
+                var limit = Math.Sqrt(3.0f * _scale);
+                return random_ops.random_uniform(shape, (float)-limit, (float)limit, dtype, seed: _seed);
+            }
+        }
+
+        private (int, int) _compute_fans(int[] shape)
+        {
+            if (shape.Length < 1)
+                return (1, 1);
+            if (shape.Length == 1)
+                return (shape[0], shape[0]);
+            if (shape.Length == 2)
+                return (shape[0], shape[1]);
+            else
+                throw new NotImplementedException("VarianceScaling._compute_fans");
+        }
+
+        public virtual object get_config()
+        {
+            return new
+            {
+                scale = _scale,
+                mode = _mode,
+                distribution = _distribution,
+                seed = _seed,
+                dtype = _dtype
+            };
+        }
+    }
+}

src/TensorFlowNET.Core/Operations/Initializers/Zeros.cs

@@ -0,0 +1,29 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Operations.Initializers
+{
+    public class Zeros : IInitializer
+    {
+        private TF_DataType dtype;
+
+        public Zeros(TF_DataType dtype = TF_DataType.TF_FLOAT)
+        {
+            this.dtype = dtype;
+        }
+
+        public Tensor call(TensorShape shape, TF_DataType dtype = TF_DataType.DtInvalid)
+        {
+            if (dtype == TF_DataType.DtInvalid)
+                dtype = this.dtype;
+
+            return array_ops.zeros(shape, dtype);
+        }
+
+        public object get_config()
+        {
+            return new { dtype = dtype.name() };
+        }
+    }
+}

src/TensorFlowNET.Core/Operations/embedding_ops.cs

@@ -0,0 +1,14 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow
+{
+    public class embedding_ops
+    {
+        public Tensor _embedding_lookup_and_transform()
+        {
+            throw new NotImplementedException("");
+        }
+    }
+}

src/TensorFlowNET.Core/TensorFlowNET.Core.csproj

@@ -43,7 +43,7 @@ Fixed import name scope issue.</PackageReleaseNotes>
   </ItemGroup>
 
   <ItemGroup>
-    <PackageReference Include="Google.Protobuf" Version="3.6.1" />
+    <PackageReference Include="Google.Protobuf" Version="3.7.0" />
     <PackageReference Include="NumSharp" Version="0.7.3" />
   </ItemGroup>
 

src/TensorFlowNET.Core/Variables/VariableScope.cs

@@ -33,7 +33,7 @@ namespace Tensorflow
             string name, 
             TensorShape shape = null, 
             TF_DataType dtype = TF_DataType.DtInvalid,
-            IInitializer initializer = null,
+            object initializer = null, // IInitializer or Tensor
             bool? trainable = null,
             VariableSynchronization synchronization = VariableSynchronization.AUTO,
             VariableAggregation aggregation= VariableAggregation.NONE)

src/TensorFlowNET.Core/Variables/_VariableStore.cs

@@ -23,7 +23,7 @@ namespace Tensorflow
         public RefVariable get_variable(string name,
             TensorShape shape = null,
             TF_DataType dtype = TF_DataType.TF_FLOAT,
-            IInitializer initializer = null,
+            object initializer = null, // IInitializer or Tensor
             bool? trainable = null,
             bool validate_shape = true,
             VariableSynchronization synchronization = VariableSynchronization.AUTO,
@@ -45,7 +45,7 @@ namespace Tensorflow
         private RefVariable _true_getter(string name,
             TensorShape shape = null,
             TF_DataType dtype = TF_DataType.TF_FLOAT,
-            IInitializer initializer = null,
+            object initializer = null,
             bool? trainable = null,
             bool validate_shape = true,
             VariableSynchronization synchronization = VariableSynchronization.AUTO,
@@ -53,14 +53,32 @@ namespace Tensorflow
         {
             bool is_scalar = shape.NDim == 0;
 
-            return _get_single_variable(name: name, 
-                shape: shape, 
+            if (initializer is IInitializer init)
+            {
+                return _get_single_variable(name: name,
+                shape: shape,
                 dtype: dtype,
-                initializer: initializer,
+                initializer: init,
                 trainable: trainable,
                 validate_shape: validate_shape,
                 synchronization: synchronization,
                 aggregation: aggregation);
+            }
+            else if (initializer is Tensor tensor)
+            {
+                return _get_single_variable(name: name,
+                shape: shape,
+                dtype: dtype,
+                initializer: tensor,
+                trainable: trainable,
+                validate_shape: validate_shape,
+                synchronization: synchronization,
+                aggregation: aggregation);
+            }
+            else
+            {
+                throw new NotImplementedException("_true_getter");
+            }
         }
 
         private RefVariable _get_single_variable(string name,
@@ -125,5 +143,45 @@ namespace Tensorflow
 
             return v;
         }
+
+        private RefVariable _get_single_variable(string name,
+            TensorShape shape = null,
+            TF_DataType dtype = TF_DataType.DtInvalid,
+            Tensor initializer = null,
+            bool reuse = false,
+            bool? trainable = null,
+            bool validate_shape = false,
+            bool? use_resource = null,
+            VariableSynchronization synchronization = VariableSynchronization.AUTO,
+            VariableAggregation aggregation = VariableAggregation.NONE)
+        {
+            if (use_resource == null)
+                use_resource = false;
+
+            if (_vars.ContainsKey(name))
+            {
+                if (!reuse)
+                {
+                    var var = _vars[name];
+
+                }
+                throw new NotImplementedException("_get_single_variable");
+            }
+
+            RefVariable v = null;
+            // Create the variable.
+            ops.init_scope();
+            {
+                var init_val = initializer;
+                v = new RefVariable(init_val,
+                    name: name,
+                    validate_shape: validate_shape,
+                    trainable: trainable.Value);
+            }
+
+            _vars[name] = v;
+
+            return v;
+        }
     }
 }

src/TensorFlowNET.Core/Variables/tf.variable.cs

@@ -15,7 +15,7 @@ namespace Tensorflow
         public static RefVariable get_variable(string name,
             TensorShape shape = null,
             TF_DataType dtype = TF_DataType.DtInvalid,
-            IInitializer initializer = null,
+            object initializer = null, // IInitializer or Tensor
             bool? trainable = null,
             VariableSynchronization synchronization = VariableSynchronization.AUTO,
             VariableAggregation aggregation = VariableAggregation.NONE)

src/TensorFlowNET.Core/tf.cs

@@ -12,20 +12,27 @@ namespace Tensorflow
         public static TF_DataType float16 = TF_DataType.TF_HALF;
         public static TF_DataType float32 = TF_DataType.TF_FLOAT;
         public static TF_DataType float64 = TF_DataType.TF_DOUBLE;
+        public static TF_DataType boolean = TF_DataType.TF_BOOL;
         public static TF_DataType chars = TF_DataType.TF_STRING;
 
         public static Context context = new Context(new ContextOptions(), new Status());
 
         public static Session defaultSession;
 
-        public static RefVariable Variable<T>(T data, string name = null, TF_DataType dtype = TF_DataType.DtInvalid)
+        public static RefVariable Variable<T>(T data, 
+            bool trainable = true,
+            string name = null, 
+            TF_DataType dtype = TF_DataType.DtInvalid)
         {
-            return Tensorflow.variable_scope.default_variable_creator(data, name: name, dtype: TF_DataType.DtInvalid);
+            return Tensorflow.variable_scope.default_variable_creator(data, 
+                trainable: trainable,
+                name: name,
+                dtype: TF_DataType.DtInvalid);
         }
 
-        public static unsafe Tensor placeholder(TF_DataType dtype, TensorShape shape = null)
+        public static unsafe Tensor placeholder(TF_DataType dtype, TensorShape shape = null, string name = null)
         {
-            return gen_array_ops.placeholder(dtype, shape);
+            return gen_array_ops.placeholder(dtype, shape, name);
         }
 
         public static void enable_eager_execution()

test/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj

@@ -13,7 +13,6 @@
 
   <ItemGroup>
     <ProjectReference Include="..\..\..\NumSharp\src\NumSharp.Core\NumSharp.Core.csproj" />
-    <ProjectReference Include="..\..\..\scikit-learn.net\src\scikit-learn\scikit-learn.csproj" />
     <ProjectReference Include="..\..\src\TensorFlowNET.Core\TensorFlowNET.Core.csproj" />
   </ItemGroup>
 

test/TensorFlowNET.Examples/TextClassification/DataHelpers.cs

@@ -77,7 +77,7 @@ namespace TensorFlowNET.Examples.CnnTextClassification
 
             var positive_labels = positive_examples.Select(x => new int[2] { 0, 1 }).ToArray();
             var negative_labels = negative_examples.Select(x => new int[2] { 1, 0 }).ToArray();
-            var y = np.array(1);// np.concatenate(new int[][][] { positive_labels, negative_labels });
+            var y = np.concatenate(new int[][][] { positive_labels, negative_labels });
             return (x_text.ToArray(), y);
         }
 

test/TensorFlowNET.Examples/TextClassification/TextClassificationTrain.cs

@@ -5,6 +5,7 @@ using System.IO;
 using System.Linq;
 using System.Text;
 using Tensorflow;
+using TensorFlowNET.Examples.TextClassification;
 using TensorFlowNET.Examples.Utility;
 
 namespace TensorFlowNET.Examples.CnnTextClassification
@@ -18,13 +19,21 @@ namespace TensorFlowNET.Examples.CnnTextClassification
         private string dataFileName = "dbpedia_csv.tar.gz";
 
         private const int CHAR_MAX_LEN = 1014;
+        private const int NUM_CLASS = 2;
 
         public void Run()
         {
             download_dbpedia();
             Console.WriteLine("Building dataset...");
             var (x, y, alphabet_size) = DataHelpers.build_char_dataset("train", "vdcnn", CHAR_MAX_LEN);
-            //var (train_x, valid_x, train_y, valid_y) = train_test_split(x, y, test_size: 0.15);
+
+            var (train_x, valid_x, train_y, valid_y) = train_test_split(x, y, test_size: 0.15f);
+
+            with(tf.Session(), sess =>
+            {
+                new VdCnn(alphabet_size, CHAR_MAX_LEN, NUM_CLASS);
+
+            });
         }
 
         public void download_dbpedia()
@@ -33,5 +42,38 @@ namespace TensorFlowNET.Examples.CnnTextClassification
             Web.Download(url, dataDir, dataFileName);
             Compress.ExtractTGZ(Path.Join(dataDir, dataFileName), dataDir);
         }
+
+        private (int[][], int[][], int[], int[]) train_test_split(int[][] x, int[] y, float test_size = 0.3f)
+        {
+            int len = x.Length;
+            int classes = y.Distinct().Count();
+            int samples = len / classes;
+            int train_size = int.Parse((samples * (1 - test_size)).ToString());
+
+            var train_x = new List<int[]>();
+            var valid_x = new List<int[]>();
+            var train_y = new List<int>();
+            var valid_y = new List<int>();
+
+            for (int i = 0; i< classes; i++)
+            {
+                for (int j = 0; j < samples; j++)
+                {
+                    int idx = i * samples + j;
+                    if (idx < train_size + samples * i)
+                    {
+                        train_x.Add(x[idx]);
+                        train_y.Add(y[idx]);
+                    }
+                    else
+                    {
+                        valid_x.Add(x[idx]);
+                        valid_y.Add(y[idx]);
+                    }
+                }
+            }
+
+            return (train_x.ToArray(), valid_x.ToArray(), train_y.ToArray(), valid_y.ToArray());
+        }
     }
 }

test/TensorFlowNET.Examples/TextClassification/cnn_models/VdCnn.cs

@@ -0,0 +1,44 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow;
+
+namespace TensorFlowNET.Examples.TextClassification
+{
+    public class VdCnn : Python
+    {
+        private int embedding_size;
+        private int[] filter_sizes;
+        private int[] num_filters;
+        private int[] num_blocks;
+        private float learning_rate;
+        private IInitializer cnn_initializer;
+        private Tensor x;
+        private Tensor y;
+        private Tensor is_training;
+        private RefVariable global_step;
+        private RefVariable embeddings;
+        private Tensor x_emb;
+
+        public VdCnn(int alphabet_size, int document_max_len, int num_class)
+        {
+            embedding_size = 16;
+            filter_sizes = new int[] { 3, 3, 3, 3, 3 };
+            num_filters = new int[] { 64, 64, 128, 256, 512 };
+            num_blocks = new int[] { 2, 2, 2, 2 };
+            learning_rate = 0.001f;
+            cnn_initializer = tf.keras.initializers.he_normal();
+            x = tf.placeholder(tf.int32, new TensorShape(-1, document_max_len), name: "x");
+            y = tf.placeholder(tf.int32, new TensorShape(-1), name: "y");
+            is_training = tf.placeholder(tf.boolean, new TensorShape(), name: "is_training");
+            global_step = tf.Variable(0, trainable: false);
+
+            with(tf.name_scope("embedding"), delegate
+            {
+                var init_embeddings = tf.random_uniform(new int[] { alphabet_size, embedding_size }, -1.0f, 1.0f);
+                embeddings = tf.get_variable("embeddings", initializer: init_embeddings);
+                // x_emb = tf.nn.embedding_lookup(embeddings, x);
+            });
+        }
+    }
+}

test/TensorFlowNET.UnitTest/TensorFlowNET.UnitTest.csproj

@@ -16,14 +16,15 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Microsoft.NET.Test.Sdk" Version="15.9.0" />
+    <PackageReference Include="Microsoft.NET.Test.Sdk" Version="16.0.1" />
     <PackageReference Include="MSTest.TestAdapter" Version="1.4.0" />
     <PackageReference Include="MSTest.TestFramework" Version="1.4.0" />
     <PackageReference Include="NumSharp" Version="0.7.3" />
-    <PackageReference Include="TensorFlow.NET" Version="0.3.0" />
+    <PackageReference Include="TensorFlow.NET" Version="0.4.2" />
   </ItemGroup>
 
   <ItemGroup>
+    <ProjectReference Include="..\..\..\NumSharp\src\NumSharp.Core\NumSharp.Core.csproj" />
     <ProjectReference Include="..\..\src\TensorFlowNET.Core\TensorFlowNET.Core.csproj" />
   </ItemGroup>
 

test/TensorFlowNET.UnitTest/VariableTest.cs

@@ -26,7 +26,7 @@ namespace TensorFlowNET.UnitTest
         [TestMethod]
         public void StringVar()
         {
-            var mammal1 = tf.Variable("Elephant", "var1", tf.chars);
+            var mammal1 = tf.Variable("Elephant", name: "var1", dtype: tf.chars);
             var mammal2 = tf.Variable("Tiger");
         }