word_cnn training completed, but saving model doesn't work.

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

@@ -6,6 +6,12 @@ namespace Tensorflow
 {
     public static partial class tf
     {
+        public static VariableV1[] global_variables(string scope = null)
+        {
+            return (ops.get_collection(ops.GraphKeys.GLOBAL_VARIABLES, scope) as List<VariableV1>)
+                .ToArray();
+        }
+
         public static Operation global_variables_initializer()
         {
             var g = variables.global_variables();

src/TensorFlowNET.Core/Train/tf.optimizers.cs

@@ -14,7 +14,7 @@ namespace Tensorflow
 
             public static Optimizer AdamOptimizer(float learning_rate) => new AdamOptimizer(learning_rate);
 
-            public static Saver Saver() => new Saver();
+            public static Saver Saver(VariableV1[] var_list = null) => new Saver(var_list: var_list);
 
             public static string write_graph(Graph graph, string logdir, string name, bool as_text = true) 
                 => graph_io.write_graph(graph, logdir, name, as_text);

test/TensorFlowNET.Examples/TextProcess/DataHelpers.cs

@@ -12,6 +12,46 @@ namespace TensorFlowNET.Examples
 {
     public class DataHelpers
     {
+        public static Dictionary<string, int> build_word_dict(string path)
+        {
+            var contents = File.ReadAllLines(path);
+
+            var words = new List<string>();
+            foreach (var content in contents)
+                words.AddRange(clean_str(content).Split(' ').Where(x => x.Length > 1));
+            var word_counter = words.GroupBy(x => x)
+                .Select(x => new { Word = x.Key, Count = x.Count() })
+                .OrderByDescending(x => x.Count)
+                .ToArray();
+
+            var word_dict = new Dictionary<string, int>();
+            word_dict["<pad>"] = 0;
+            word_dict["<unk>"] = 1;
+            word_dict["<eos>"] = 2;
+            foreach (var word in word_counter)
+                word_dict[word.Word] = word_dict.Count;
+
+            return word_dict;
+        }
+
+        public static (int[][], int[]) build_word_dataset(string path, Dictionary<string, int> word_dict, int document_max_len)
+        {
+            var contents = File.ReadAllLines(path);
+            var x = contents.Select(c => (clean_str(c) + " <eos>")
+                .Split(' ').Take(document_max_len)
+                .Select(w => word_dict.ContainsKey(w) ? word_dict[w] : word_dict["<unk>"]).ToArray())
+                .ToArray();
+
+            for (int i = 0; i < x.Length; i++)
+                if (x[i].Length == document_max_len)
+                    x[i][document_max_len - 1] = word_dict["<eos>"];
+                else
+                    Array.Resize(ref x[i], document_max_len);
+
+            var y = contents.Select(c => int.Parse(c.Substring(0, c.IndexOf(','))) - 1).ToArray();
+
+            return (x, y);
+        }
 
         public static (int[][], int[], int) build_char_dataset(string path, string model, int document_max_len, int? limit = null, bool shuffle=true)
         {
@@ -96,8 +136,8 @@ namespace TensorFlowNET.Examples
 
         private static string clean_str(string str)
         {
-            str = Regex.Replace(str, @"[^A-Za-z0-9(),!?\'\`]", " ");
-            str = Regex.Replace(str, @"\'s", " \'s");
+            str = Regex.Replace(str, "[^A-Za-z0-9(),!?]", " ");
+            str = Regex.Replace(str, ",", " ");
             return str;
         }
 

test/TensorFlowNET.Examples/TextProcess/TextClassificationTrain.cs

@@ -26,22 +26,23 @@ namespace TensorFlowNET.Examples.CnnTextClassification
         public string Name => "Text Classification";
         public int? DataLimit = null;
         public bool ImportGraph { get; set; } = true;
-        public bool UseSubset = true; // <----- set this true to use a limited subset of dbpedia
+        public bool UseSubset = false; // <----- set this true to use a limited subset of dbpedia
 
         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
+        public string model_name = "word_cnn"; // word_cnn | char_cnn | vd_cnn | word_rnn | att_rnn | rcnn
 
         private const string TRAIN_PATH = "text_classification/dbpedia_csv/train.csv";
         private const string SUBSET_PATH = "text_classification/dbpedia_csv/dbpedia_6400.csv";
         private const string TEST_PATH = "text_classification/dbpedia_csv/test.csv";
 
-        private const int CHAR_MAX_LEN = 1014;
-        private const int WORD_MAX_LEN = 1014;
         private const int NUM_CLASS = 14;
         private const int BATCH_SIZE = 64;
         private const int NUM_EPOCHS = 10;
+        private const int WORD_MAX_LEN = 100;
+        private const int CHAR_MAX_LEN = 1014;
+        
         protected float loss_value = 0;
 
         public bool Run()
@@ -61,8 +62,21 @@ namespace TensorFlowNET.Examples.CnnTextClassification
         {
             var stopwatch = Stopwatch.StartNew();
             Console.WriteLine("Building dataset...");
-            var path = UseSubset ? SUBSET_PATH : TRAIN_PATH;           
-            var (x, y, alphabet_size) = DataHelpers.build_char_dataset(path, model_name, CHAR_MAX_LEN, DataLimit = null, shuffle:!UseSubset);
+            var path = UseSubset ? SUBSET_PATH : TRAIN_PATH;
+            int[][] x = null;
+            int[] y = null;
+            int alphabet_size = 0;
+            int vocabulary_size = 0;
+
+            if (model_name == "vd_cnn")
+                (x, y, alphabet_size) = DataHelpers.build_char_dataset(path, model_name, CHAR_MAX_LEN, DataLimit = null, shuffle:!UseSubset);
+            else
+            {
+                var word_dict = DataHelpers.build_word_dict(TRAIN_PATH);
+                vocabulary_size = len(word_dict);
+                (x, y) = DataHelpers.build_word_dataset(TRAIN_PATH, word_dict, WORD_MAX_LEN);
+            }
+
             Console.WriteLine("\tDONE ");
 
             var (train_x, valid_x, train_y, valid_y) = train_test_split(x, y, test_size: 0.15f);
@@ -75,18 +89,19 @@ namespace TensorFlowNET.Examples.CnnTextClassification
             Console.WriteLine("\tDONE " + stopwatch.Elapsed);
 
             sess.run(tf.global_variables_initializer());
+            var saver = tf.train.Saver(tf.global_variables());
 
             var train_batches = batch_iter(train_x, train_y, BATCH_SIZE, NUM_EPOCHS);
             var num_batches_per_epoch = (len(train_x) - 1) / BATCH_SIZE + 1;
             double max_accuracy = 0;
 
-            Tensor is_training = graph.get_tensor_by_name("is_training:0");
-            Tensor model_x = graph.get_tensor_by_name("x:0");
-            Tensor model_y = graph.get_tensor_by_name("y:0");
-            Tensor loss = graph.get_tensor_by_name("loss/value:0");
-            Tensor optimizer = graph.get_tensor_by_name("loss/optimizer:0");
-            Tensor global_step = graph.get_tensor_by_name("global_step:0");
-            Tensor accuracy = graph.get_tensor_by_name("accuracy/value:0");
+            Tensor is_training = graph.OperationByName("is_training");
+            Tensor model_x = graph.OperationByName("x");
+            Tensor model_y = graph.OperationByName("y");
+            Tensor loss = graph.OperationByName("loss/Mean"); // word_cnn
+            Operation optimizer = graph.OperationByName("loss/Adam"); // word_cnn
+            Tensor global_step = graph.OperationByName("Variable");
+            Tensor accuracy = graph.OperationByName("accuracy/accuracy");
             stopwatch = Stopwatch.StartNew();
             int i = 0;
             foreach (var (x_batch, y_batch, total) in train_batches)
@@ -105,11 +120,10 @@ namespace TensorFlowNET.Examples.CnnTextClassification
                 var result = sess.run(new ITensorOrOperation[] { optimizer, global_step, loss }, train_feed_dict);
                 loss_value = result[2];
                 var step = (int)result[1];
-                if (step % 10 == 0 || step < 10)
+                if (step % 10 == 0)
                 {
                     var estimate = TimeSpan.FromSeconds((stopwatch.Elapsed.TotalSeconds / i) * total);
-                    Console.WriteLine($"Training on batch {i}/{total}. Estimated training time: {estimate}");
-                    Console.WriteLine($"Step {step} loss: {loss_value}");
+                    Console.WriteLine($"Training on batch {i}/{total} loss: {loss_value}. Estimated training time: {estimate}");
                 }
 
                 if (step % 100 == 0)
@@ -133,13 +147,15 @@ namespace TensorFlowNET.Examples.CnnTextClassification
 
                     var valid_accuracy = sum_accuracy / cnt;
 
-                    print($"\nValidation Accuracy = {valid_accuracy}\n");
-
-                    //    # Save model
-                    //        if valid_accuracy > max_accuracy:
-                    //        max_accuracy = valid_accuracy
-                    //        saver.save(sess, "{0}/{1}.ckpt".format(args.model, args.model), global_step = step)
-                    //        print("Model is saved.\n")
+                    print($"\nValidation Accuracy = {valid_accuracy}\n");
+
+                    //    # Save model
+                    if (valid_accuracy > max_accuracy)
+                    {
+                        max_accuracy = valid_accuracy;
+                        // saver.save(sess, $"{dataDir}/{model_name}.ckpt", global_step: step.ToString());
+                        print("Model is saved.\n");
+                    }
                 }
             }
 
@@ -180,9 +196,9 @@ namespace TensorFlowNET.Examples.CnnTextClassification
             //int samples = len / classes;
             int train_size = (int)Math.Round(len * (1 - test_size));
             var train_x = x[new Slice(stop: train_size), new Slice()];
-            var valid_x = x[new Slice(start: train_size + 1), new Slice()];
+            var valid_x = x[new Slice(start: train_size), new Slice()];
             var train_y = y[new Slice(stop: train_size)];
-            var valid_y = y[new Slice(start: train_size + 1)];
+            var valid_y = y[new Slice(start: train_size)];
             Console.WriteLine("\tDONE");
             return (train_x, valid_x, train_y, valid_y);
         }