TextClassification: implemented batching using NumSharp slicing

test/TensorFlowNET.Examples/Text/TextClassificationTrain.cs

@@ -1,8 +1,10 @@
 using System;
+using System.Collections;
 using System.Collections.Generic;
 using System.IO;
 using System.Linq;
 using System.Text;
+using NumSharp;
 using Tensorflow;
 using Tensorflow.Keras.Engine;
 using TensorFlowNET.Examples.Text.cnn_models;
@@ -29,6 +31,8 @@ namespace TensorFlowNET.Examples.CnnTextClassification
 
         private const int CHAR_MAX_LEN = 1014;
         private const int NUM_CLASS = 2;
+        private const int BATCH_SIZE = 64;
+        private const int NUM_EPOCHS = 10;
         protected float loss_value = 0;
 
         public bool Run()
@@ -54,13 +58,30 @@ namespace TensorFlowNET.Examples.CnnTextClassification
             var meta_file = model_name + "_untrained.meta";
             tf.train.import_meta_graph(Path.Join("graph", meta_file));
 
-            //sess.run(tf.global_variables_initializer());
+            //sess.run(tf.global_variables_initializer()); // not necessary here, has already been done before meta graph export
+
+            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_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");
+            Tensor loss = graph.get_operation_by_name("Variable");
+            Tensor accuracy = graph.get_operation_by_name("accuracy/accuracy");
+
+            foreach (var (x_batch, y_batch) in train_batches)
+            {
+                var train_feed_dict = new Hashtable
+                {
+                    [model_x] = x_batch,
+                    [model_y] = y_batch,
+                    [is_training] = true,
+                };
+
+                //_, step, loss = sess.run([model.optimizer, model.global_step, model.loss], feed_dict = train_feed_dict)
+            }
+
             return false;
         }
 
@@ -122,6 +143,23 @@ namespace TensorFlowNET.Examples.CnnTextClassification
             return (train_x.ToArray(), valid_x.ToArray(), train_y.ToArray(), valid_y.ToArray());
         }
 
+        private IEnumerable<(NDArray, NDArray)> batch_iter(int[][] raw_inputs, int[] raw_outputs, int batch_size, int num_epochs)
+        {
+            var inputs = np.array(raw_inputs);
+            var outputs = np.array(raw_outputs);
+
+            var num_batches_per_epoch = (len(inputs) - 1); // batch_size + 1
+            foreach (var epoch in range(num_epochs))
+            {
+                foreach (var batch_num in range(num_batches_per_epoch))
+                {
+                    var start_index = batch_num * batch_size;
+                    var end_index = Math.Min((batch_num + 1) * batch_size, len(inputs));
+                    yield return (inputs[$"{start_index}:{end_index}"], outputs[$"{start_index}:{end_index}"]);
+                }
+            }
+        }
+
         public void PrepareData()
         {
             string url = "https://github.com/le-scientifique/torchDatasets/raw/master/dbpedia_csv.tar.gz";