solve conflict

TensorFlow.NET.sln

@@ -13,7 +13,7 @@ Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "TensorFlowNET.Utility", "sr
 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}") = "KerasNET.Core", "..\Keras.NET\src\KerasNET.Core\KerasNET.Core.csproj", "{E2F0C39C-D706-4CF5-AE00-81FB447F949D}"
+Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "NumSharp.Core", "..\NumSharp\src\NumSharp.Core\NumSharp.Core.csproj", "{0AB4662E-7E3C-455F-BF0C-23D56CBE74F3}"
 EndProject
 Project("{9A19103F-16F7-4668-BE54-9A1E7A4F7556}") = "NumSharp.Core", "..\NumSharp\src\NumSharp.Core\NumSharp.Core.csproj", "{62360571-D1F7-473C-B81F-F03CA59E37DD}"
 EndProject

src/TensorFlowNET.Core/APIs/keras.preprocessing.cs

@@ -0,0 +1,15 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow.Keras;
+using Tensorflow.Keras.Engine;
+
+namespace Tensorflow
+{
+    public static partial class keras
+    {
+        public static Preprocessing preprocessing => new Preprocessing();
+        public static Sequence sequence = new Sequence();
+        public static Sequential Sequential() => new Sequential();
+    }
+}

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

@@ -0,0 +1,17 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras.Engine
+{
+    /// <summary>
+    /// Base layer class.
+    /// A layer is a class implementing common neural networks operations, such
+    /// as convolution, batch norm, etc. These operations require managing weights,
+    /// losses, updates, and inter-layer connectivity.
+    /// </summary>
+    public class Layer : CheckpointableBase
+    {
+
+    }
+}

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

@@ -0,0 +1,10 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras.Engine
+{
+    internal class Model : Network
+    {
+    }
+}

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

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

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

@@ -0,0 +1,24 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras.Engine
+{
+    public class Sequential : Network, IPython
+    {
+        public void Dispose()
+        {
+            throw new NotImplementedException();
+        }
+
+        public void __enter__()
+        {
+            throw new NotImplementedException();
+        }
+
+        public void __exit__()
+        {
+            throw new NotImplementedException();
+        }
+    }
+}

src/TensorFlowNET.Core/Keras/Preprocessing.cs

@@ -0,0 +1,11 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+
+namespace Tensorflow.Keras
+{
+    public class Preprocessing
+    {
+        public Sequence sequence => new Sequence();
+    }
+}

src/TensorFlowNET.Core/Keras/Sequence.cs

@@ -0,0 +1,77 @@
+using NumSharp.Core;
+using System;
+using System.Collections.Generic;
+using System.Linq;
+using System.Text;
+using System.Text.RegularExpressions;
+using System.Threading.Tasks;
+
+namespace Tensorflow.Keras
+{
+    public class Sequence
+    {
+        /// <summary>
+        /// Pads sequences to the same length.
+        /// https://keras.io/preprocessing/sequence/
+        /// https://faroit.github.io/keras-docs/1.2.0/preprocessing/sequence/
+        /// </summary>
+        /// <param name="sequences">List of lists, where each element is a sequence.</param>
+        /// <param name="maxlen">Int, maximum length of all sequences.</param>
+        /// <param name="dtype">Type of the output sequences.</param>
+        /// <param name="padding">String, 'pre' or 'post':</param>
+        /// <param name="truncating">String, 'pre' or 'post'</param>
+        /// <param name="value">Float or String, padding value.</param>
+        /// <returns></returns>
+        public NDArray pad_sequences(NDArray sequences, 
+            int? maxlen = null,
+            string dtype = "int32",
+            string padding = "pre",
+            string truncating = "pre",
+            object value = null)
+        {
+            int[] length = new int[sequences.size];
+            switch (sequences.dtype.Name)
+            {
+                case "Object":
+                    for (int i = 0; i < sequences.size; i++)
+                    {
+                        switch (sequences.Data<object>(i))
+                        {
+                            case string data:
+                                length[i] = Regex.Matches(data, ",").Count;
+                                break;
+                        }
+                    }
+                    break;
+                case "Int32":
+                    for (int i = 0; i < sequences.size; i++)
+                        length[i] = Regex.Matches(sequences.Data<object>(i).ToString(), ",").Count;
+                    break;
+                default:
+                    throw new NotImplementedException($"pad_sequences: {sequences.dtype.Name}");
+            }
+
+            if (maxlen == null)
+                maxlen = length.Max();
+
+            if (value == null)
+                value = 0f;
+
+            var nd = new NDArray(np.int32, new Shape(sequences.size, maxlen.Value));
+            for (int i = 0; i < nd.shape[0]; i++)
+            {
+                switch(sequences[i])
+                {
+                    case int[] data:
+                        for (int j = 0; j < nd.shape[1]; j++)
+                            nd[i, j] = j < data.Length ? data[j] : value;
+                        break;
+                    default:
+                        throw new NotImplementedException("pad_sequences");
+                }
+            }
+
+            return nd;
+        }
+    }
+}

src/TensorFlowNET.Core/TensorFlowNET.Core.csproj

@@ -52,7 +52,7 @@ Docs: https://tensorflownet.readthedocs.io</Description>
   </ItemGroup>
 
   <ItemGroup>
-    <Folder Include="APIs\Keras\" />
+    <ProjectReference Include="..\..\..\NumSharp\src\NumSharp.Core\NumSharp.Core.csproj" />
   </ItemGroup>
 
   <ItemGroup>

src/TensorFlowNET.Core/Train/Checkpointable/CheckpointableBase.cs

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

test/TensorFlowNET.Examples/TensorFlowNET.Examples.csproj

@@ -11,7 +11,6 @@
   </ItemGroup>
 
   <ItemGroup>
-    <ProjectReference Include="..\..\..\Keras.NET\src\KerasNET.Core\KerasNET.Core.csproj" />
     <ProjectReference Include="..\..\..\NumSharp\src\NumSharp.Core\NumSharp.Core.csproj" />
     <ProjectReference Include="..\..\src\TensorFlowNET.Core\TensorFlowNET.Core.csproj" />
     <ProjectReference Include="..\..\src\TensorFlowNET.Utility\TensorFlowNET.Utility.csproj" />

test/TensorFlowNET.Examples/TextClassificationWithMovieReviews.cs

@@ -1,12 +1,10 @@
 using System;
 using System.Collections.Generic;
 using System.IO;
-using System.Text;
 using Tensorflow;
 using NumSharp.Core;
 using Newtonsoft.Json;
 using System.Linq;
-using Keras;
 using System.Text.RegularExpressions;
 
 namespace TensorFlowNET.Examples
@@ -24,11 +22,21 @@ namespace TensorFlowNET.Examples
 
             // A dictionary mapping words to an integer index
             var word_index = GetWordIndex();
-
+            
             train_data = keras.preprocessing.sequence.pad_sequences(train_data,
                 value: word_index["<PAD>"],
                 padding: "post",
                 maxlen: 256);
+
+            test_data = keras.preprocessing.sequence.pad_sequences(test_data,
+                value: word_index["<PAD>"],
+                padding: "post",
+                maxlen: 256);
+
+            // input shape is the vocabulary count used for the movie reviews (10,000 words)
+            int vocab_size = 10000;
+
+            var model = keras.Sequential();
         }
 
         private ((NDArray, NDArray), (NDArray, NDArray)) PrepareData()
@@ -46,42 +54,48 @@ namespace TensorFlowNET.Examples
             var x_train = ReadData(Path.Join(dir, "x_train.txt"));
             var labels_train = ReadData(Path.Join(dir, "y_train.txt"));
             var indices_train = ReadData(Path.Join(dir, "indices_train.txt"));
-            // x_train = x_train[indices_train];
-            // labels_train = labels_train[indices_train];
+            x_train = x_train[indices_train];
+            labels_train = labels_train[indices_train];
 
             var x_test = ReadData(Path.Join(dir, "x_test.txt"));
             var labels_test = ReadData(Path.Join(dir, "y_test.txt"));
             var indices_test = ReadData(Path.Join(dir, "indices_test.txt"));
-            // x_test = x_test[indices_test];
-            // labels_test = labels_test[indices_test];
+            x_test = x_test[indices_test];
+            labels_test = labels_test[indices_test];
 
             // not completed
-            /*var xs = x_train.hstack(x_test);
+            var xs = x_train.hstack(x_test);
             var labels = labels_train.hstack(labels_test);
 
             var idx = x_train.size;
             var y_train = labels_train;
             var y_test = labels_test;
 
-            return ((x_train, y_train), (x_test, y_test));*/
-
-            throw new NotImplementedException();
+            return ((x_train, y_train), (x_test, y_test));
         }
 
-        private int[][] ReadData(string file)
+        private NDArray ReadData(string file)
         {
-            var lines = new List<int[]>();
+            var lines = File.ReadAllLines(file);
+            var nd = new NDArray(lines[0].StartsWith("[") ? typeof(object) : np.int32, new Shape(lines.Length));
 
-            foreach(var line in File.ReadAllLines(file))
+            if (lines[0].StartsWith("["))
             {
-                var matches = Regex.Matches(line, @"\d+,*");
-                var data = new int[matches.Count];
-                for (int i = 0; i < data.Length; i++)
-                    data[i] = Convert.ToInt32(matches[i].Value.Trim(','));
-                lines.Add(data.ToArray());
+                for (int i = 0; i < lines.Length; i++)
+                {
+                    var matches = Regex.Matches(lines[i], @"\d+\s*");
+                    var data = new int[matches.Count];
+                    for (int j = 0; j < data.Length; j++)
+                        data[j] = Convert.ToInt32(matches[j].Value);
+                    nd[i] = data.ToArray();
+                }
             }
-
-            return lines.ToArray();
+            else
+            {
+                for (int i = 0; i < lines.Length; i++)
+                    nd[i] = Convert.ToInt32(lines[i]);
+            }
+            return nd;
         }
 
         private Dictionary<string, int> GetWordIndex()