TextClassification example: use imported graph (in progress)

test/TensorFlowNET.Examples/Text/DataHelpers.cs

@@ -13,8 +13,10 @@ namespace TensorFlowNET.Examples.CnnTextClassification
         private const string TRAIN_PATH = "text_classification/dbpedia_csv/train.csv";
         private const string TEST_PATH = "text_classification/dbpedia_csv/test.csv";
 
-        public static (int[][], int[], int) build_char_dataset(string step, string model, int document_max_len, int? limit=null)
+        public static (int[][], int[], int) build_char_dataset(string step, string model, int document_max_len, int? limit = null)
         {
+            if (model != "vd_cnn")
+                throw new NotImplementedException(model);
             string alphabet = "abcdefghijklmnopqrstuvwxyz0123456789-,;.!?:’'\"/|_#$%ˆ&*˜‘+=<>()[]{} ";
             /*if (step == "train")
                 df = pd.read_csv(TRAIN_PATH, names =["class", "title", "content"]);*/
@@ -41,8 +43,8 @@ namespace TensorFlowNET.Examples.CnnTextClassification
                         x[i][j] = char_dict["<pad>"];
                     else
                         x[i][j] = char_dict.ContainsKey(content[j].ToString()) ? char_dict[content[j].ToString()] : char_dict["<unk>"];
-                }
-                    
+                }
+
                 y[i] = int.Parse(parts[0]);
             }
 

test/TensorFlowNET.Examples/Text/TextClassificationTrain.cs

@@ -4,6 +4,8 @@ using System.IO;
 using System.Linq;
 using System.Text;
 using Tensorflow;
+using Tensorflow.Keras.Engine;
+using TensorFlowNET.Examples.Text.cnn_models;
 using TensorFlowNET.Examples.TextClassification;
 using TensorFlowNET.Examples.Utility;
 
@@ -15,7 +17,7 @@ namespace TensorFlowNET.Examples.CnnTextClassification
     public class TextClassificationTrain : Python, IExample
     {
         public int Priority => 100;
-        public bool Enabled { get; set; }= false;
+        public bool Enabled { get; set; } = false;
         public string Name => "Text Classification";
         public int? DataLimit = null;
         public bool ImportGraph { get; set; } = true;
@@ -23,22 +25,68 @@ namespace TensorFlowNET.Examples.CnnTextClassification
         private string dataDir = "text_classification";
         private string dataFileName = "dbpedia_csv.tar.gz";
 
+        public string model_name = "vd_cnn"; // word_cnn | char_cnn | vd_cnn | word_rnn | att_rnn | rcnn
+
         private const int CHAR_MAX_LEN = 1014;
         private const int NUM_CLASS = 2;
+        protected float loss_value = 0;
 
         public bool Run()
         {
             PrepareData();
+            return with(tf.Session(), sess =>
+            {
+                if (ImportGraph)
+                    return RunWithImportedGraph(sess);
+                else
+                    return RunWithBuiltGraph(sess);
+            });
+        }
+
+        protected virtual bool RunWithImportedGraph(Session sess)
+        {
+            var graph = tf.Graph().as_default();
+            Console.WriteLine("Building dataset...");
+            var (x, y, alphabet_size) = DataHelpers.build_char_dataset("train", model_name, CHAR_MAX_LEN, DataLimit);
+
+            var (train_x, valid_x, train_y, valid_y) = train_test_split(x, y, test_size: 0.15f);
+
+            var meta_file = model_name + "_untrained.meta";
+            tf.train.import_meta_graph(Path.Join("graph", meta_file));
+
+            //sess.run(tf.global_variables_initializer());
+
+            Tensor is_training = graph.get_operation_by_name("is_training");
+            Tensor model_x = graph.get_operation_by_name("x");
+            Tensor model_y = graph.get_operation_by_name("y");
+            //Tensor loss = graph.get_operation_by_name("loss");
+            //Tensor accuracy = graph.get_operation_by_name("accuracy");
+            return false;
+        }
+
+        protected virtual bool RunWithBuiltGraph(Session session)
+        {
             Console.WriteLine("Building dataset...");
-            var (x, y, alphabet_size) = DataHelpers.build_char_dataset("train", "vdcnn", CHAR_MAX_LEN, DataLimit);
+            var (x, y, alphabet_size) = DataHelpers.build_char_dataset("train", model_name, CHAR_MAX_LEN, DataLimit);
 
             var (train_x, valid_x, train_y, valid_y) = train_test_split(x, y, test_size: 0.15f);
 
-            return with(tf.Session(), sess =>
-            {
-                new VdCnn(alphabet_size, CHAR_MAX_LEN, NUM_CLASS);
-                return false;
-            });
+            ITextClassificationModel model = null;
+            switch (model_name) // word_cnn | char_cnn | vd_cnn | word_rnn | att_rnn | rcnn
+            {
+                case "word_cnn":
+                case "char_cnn":
+                case "word_rnn":
+                case "att_rnn":
+                case "rcnn":
+                    throw new NotImplementedException();
+                    break;
+                case "vd_cnn":
+                    model=new VdCnn(alphabet_size, CHAR_MAX_LEN, NUM_CLASS);
+                    break;
+            }
+            // todo train the model
+            return false;
         }
 
         private (int[][], int[][], int[], int[]) train_test_split(int[][] x, int[] y, float test_size = 0.3f)
@@ -53,7 +101,7 @@ namespace TensorFlowNET.Examples.CnnTextClassification
             var train_y = new List<int>();
             var valid_y = new List<int>();
 
-            for (int i = 0; i< classes; i++)
+            for (int i = 0; i < classes; i++)
             {
                 for (int j = 0; j < samples; j++)
                 {
@@ -79,6 +127,14 @@ namespace TensorFlowNET.Examples.CnnTextClassification
             string url = "https://github.com/le-scientifique/torchDatasets/raw/master/dbpedia_csv.tar.gz";
             Web.Download(url, dataDir, dataFileName);
             Compress.ExtractTGZ(Path.Join(dataDir, dataFileName), dataDir);
+
+            if (ImportGraph)
+            {
+                // download graph meta data
+                var meta_file = model_name + "_untrained.meta";
+                url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/graph/" + meta_file;
+                Web.Download(url, "graph", meta_file);
+            }
         }
     }
 }

test/TensorFlowNET.Examples/Text/cnn_models/ITextClassificationModel.cs

@@ -0,0 +1,14 @@
+using System;
+using System.Collections.Generic;
+using System.Text;
+using Tensorflow;
+
+namespace TensorFlowNET.Examples.Text.cnn_models
+{
+    interface ITextClassificationModel
+    {
+        Tensor is_training { get;  }
+        Tensor x { get;}
+        Tensor y { get; }
+    }
+}

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

@@ -3,10 +3,11 @@ using System.Collections.Generic;
 using System.Linq;
 using System.Text;
 using Tensorflow;
+using TensorFlowNET.Examples.Text.cnn_models;
 
 namespace TensorFlowNET.Examples.TextClassification
 {
-    public class VdCnn : Python
+    public class VdCnn : Python, ITextClassificationModel
     {
         private int embedding_size;
         private int[] filter_sizes;
@@ -15,9 +16,9 @@ namespace TensorFlowNET.Examples.TextClassification
         private float learning_rate;
         private IInitializer cnn_initializer;
         private IInitializer fc_initializer;
-        private Tensor x;
-        private Tensor y;
-        private Tensor is_training;
+        public Tensor x { get; private set; }
+        public Tensor y { get; private set; }
+        public Tensor is_training { get; private set; }
         private RefVariable global_step;
         private RefVariable embeddings;
         private Tensor x_emb;