@@ -1,4 +1,4 @@
-# Neural Network
+# Chapter. Neural Network
 
 In this chapter, we'll learn how to build a graph of neural network model. The key advantage of neural network compared to Linear Classifier is that it can separate data which it not linearly separable. We'll implement this model to classify hand-written digits images from the MNIST dataset.
 
 
@@ -9,6 +9,7 @@
 using NumSharp;
 using Tensorflow;
 using Tensorflow.Sessions;
+using TensorFlowNET.Examples.Text;
 using TensorFlowNET.Examples.Utility;
 using static Tensorflow.Python;
 
@@ -24,24 +25,27 @@ public class CnnTextClassification : IExample
         public int? DataLimit = null;
         public bool IsImportingGraph { get; set; } = false;
 
-        private const string dataDir = "word_cnn";
-        private string dataFileName = "dbpedia_csv.tar.gz";
+        const string dataDir = "cnn_text";
+        string dataFileName = "dbpedia_csv.tar.gz";
 
-        private const string TRAIN_PATH = "word_cnn/dbpedia_csv/train.csv";
-        private const string TEST_PATH = "word_cnn/dbpedia_csv/test.csv";
+        string TRAIN_PATH = $"{dataDir}/dbpedia_csv/train.csv";
+        string TEST_PATH = $"{dataDir}/dbpedia_csv/test.csv";
 
-        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;
+        int NUM_CLASS = 14;
+        int BATCH_SIZE = 64;
+        int NUM_EPOCHS = 10;
+        int WORD_MAX_LEN = 100;
+        int CHAR_MAX_LEN = 1014;
 
-        protected float loss_value = 0;
+        float loss_value = 0;
         double max_accuracy = 0;
 
-        int vocabulary_size = 50000;
+        int vocabulary_size = -1;
         NDArray train_x, valid_x, train_y, valid_y;
 
+        ITextModel textModel;
+        public string ModelName = "word_cnn"; // word_cnn | char_cnn | vd_cnn | word_rnn | att_rnn | rcnn
+
         public bool Run()
         {
             PrepareData();
@@ -68,7 +72,7 @@ public bool Run()
             return (train_x, valid_x, train_y, valid_y);
         }
 
-        private static void FillWithShuffledLabels(int[][] x, int[] y, int[][] shuffled_x, int[] shuffled_y, Random random, Dictionary<int, HashSet<int>> labels)
+        private void FillWithShuffledLabels(int[][] x, int[] y, int[][] shuffled_x, int[] shuffled_y, Random random, Dictionary<int, HashSet<int>> labels)
         {
             int i = 0;
             var label_keys = labels.Keys.ToArray();
@@ -114,10 +118,8 @@ public void PrepareData()
 
             Console.WriteLine("Building dataset...");
 
-            int alphabet_size = 0;
-
             var word_dict = DataHelpers.build_word_dict(TRAIN_PATH);
-            //vocabulary_size = len(word_dict);
+            vocabulary_size = len(word_dict);
             var (x, y) = DataHelpers.build_word_dataset(TRAIN_PATH, word_dict, WORD_MAX_LEN);
 
             Console.WriteLine("\tDONE ");
@@ -155,83 +157,19 @@ public Graph BuildGraph()
         {
             var graph = tf.Graph().as_default();
 
-            var embedding_size = 128;
-            var learning_rate = 0.001f;
-            var filter_sizes = new int[3, 4, 5];
-            var num_filters = 100;
-            var document_max_len = 100;
-
-            var x = tf.placeholder(tf.int32, new TensorShape(-1, document_max_len), name: "x");
-            var y = tf.placeholder(tf.int32, new TensorShape(-1), name: "y");
-            var is_training = tf.placeholder(tf.@bool, new TensorShape(), name: "is_training");
-            var global_step = tf.Variable(0, trainable: false);
-            var keep_prob = tf.where(is_training, 0.5f, 1.0f);
-            Tensor x_emb = null;
-
-            with(tf.name_scope("embedding"), scope =>
-            {
-                var init_embeddings = tf.random_uniform(new int[] { vocabulary_size, embedding_size });
-                var embeddings = tf.get_variable("embeddings", initializer: init_embeddings);
-                x_emb = tf.nn.embedding_lookup(embeddings, x);
-                x_emb = tf.expand_dims(x_emb, -1);
-            });
-
-            var pooled_outputs = new List<Tensor>();
-            for (int len = 0; len < filter_sizes.Rank; len++)
+            switch (ModelName)
             {
-                int filter_size = filter_sizes.GetLength(len);
-                var conv = tf.layers.conv2d(
-                    x_emb,
-                    filters: num_filters,
-                    kernel_size: new int[] { filter_size, embedding_size },
-                    strides: new int[] { 1, 1 },
-                    padding: "VALID",
-                    activation: tf.nn.relu());
-
-                var pool = tf.layers.max_pooling2d(
-                    conv,
-                    pool_size: new[] { document_max_len - filter_size + 1, 1 },
-                    strides: new[] { 1, 1 },
-                    padding: "VALID");
-
-                pooled_outputs.Add(pool);
+                case "word_cnn":
+                    textModel = new WordCnn(vocabulary_size, WORD_MAX_LEN, NUM_CLASS);
+                    break;
             }
 
-            var h_pool = tf.concat(pooled_outputs, 3);
-            var h_pool_flat = tf.reshape(h_pool, new TensorShape(-1, num_filters * filter_sizes.Rank));
-            Tensor h_drop = null;
-            with(tf.name_scope("dropout"), delegate
-            {
-                h_drop = tf.nn.dropout(h_pool_flat, keep_prob);
-            });
-
-            Tensor logits = null;
-            Tensor predictions = null;
-            with(tf.name_scope("output"), delegate
-            {
-                logits = tf.layers.dense(h_drop, NUM_CLASS);
-                predictions = tf.argmax(logits, -1, output_type: tf.int32);
-            });
-
-            with(tf.name_scope("loss"), delegate
-            {
-                var sscel = tf.nn.sparse_softmax_cross_entropy_with_logits(logits: logits, labels: y);
-                var loss = tf.reduce_mean(sscel);
-                var adam = tf.train.AdamOptimizer(learning_rate);
-                var optimizer = adam.minimize(loss, global_step: global_step);
-            });
-
-            with(tf.name_scope("accuracy"), delegate
-            {
-                var correct_predictions = tf.equal(predictions, y);
-                var accuracy = tf.reduce_mean(tf.cast(correct_predictions, TF_DataType.TF_FLOAT), name: "accuracy");
-            });
-
             return graph;
         }
 
-        private bool Train(Session sess, Graph graph)
+        public void Train(Session sess)
         {
+            var graph = tf.get_default_graph();
             var stopwatch = Stopwatch.StartNew();
 
             sess.run(tf.global_variables_initializer());
@@ -263,10 +201,7 @@ private bool Train(Session sess, Graph graph)
                 loss_value = result[2];
                 var step = (int)result[1];
                 if (step % 10 == 0)
-                {
-                    var estimate = TimeSpan.FromSeconds((stopwatch.Elapsed.TotalSeconds / i) * total);
-                    Console.WriteLine($"Training on batch {i}/{total} loss: {loss_value}. Estimated training time: {estimate}");
-                }
+                    Console.WriteLine($"Training on batch {i}/{total} loss: {loss_value.ToString("0.0000")}.");
 
                 if (step % 100 == 0)
                 {
@@ -289,7 +224,7 @@ private bool Train(Session sess, Graph graph)
 
                     var valid_accuracy = sum_accuracy / cnt;
 
-                    print($"\nValidation Accuracy = {valid_accuracy}\n");
+                    print($"\nValidation Accuracy = {valid_accuracy.ToString("P")}\n");
 
                     // Save model
                     if (valid_accuracy > max_accuracy)
@@ -300,13 +235,6 @@ private bool Train(Session sess, Graph graph)
                     }
                 }
             }
-
-            return max_accuracy > 0.9;
-        }
-
-        public void Train(Session sess)
-        {
-            Train(sess, sess.graph);
         }
 
         public void Predict(Session sess)
Original file line number	Diff line number	Diff line change
@@ -1,4 +1,4 @@
1		- # Neural Network
	1	+ # Chapter. Neural Network
2	2
3	3	In this chapter, we'll learn how to build a graph of neural network model. The key advantage of neural network compared to Linear Classifier is that it can separate data which it not linearly separable. We'll implement this model to classify hand-written digits images from the MNIST dataset.
4	4