text classification train

test/TensorFlowNET.Examples/TextProcess/TextClassificationTrain.cs

@@ -1,6 +1,7 @@
 using System;
 using System.Collections;
 using System.Collections.Generic;
+using System.Diagnostics;
 using System.IO;
 using System.Linq;
 using System.Text;
@@ -52,7 +53,7 @@ namespace TensorFlowNET.Examples.CnnTextClassification
         protected virtual bool RunWithImportedGraph(Session sess, Graph graph)
         {
             Console.WriteLine("Building dataset...");
-            var (x, y, alphabet_size) = DataHelpers.build_char_dataset("train", model_name, CHAR_MAX_LEN, DataLimit);
+            var (x, y, alphabet_size) = DataHelpers.build_char_dataset("train", model_name, CHAR_MAX_LEN, DataLimit=null);
             Console.WriteLine("\tDONE");
 
             var (train_x, valid_x, train_y, valid_y) = train_test_split(x, y, test_size: 0.15f);
@@ -76,12 +77,13 @@ namespace TensorFlowNET.Examples.CnnTextClassification
             Tensor optimizer = graph.get_operation_by_name("loss/optimizer");
             Tensor global_step = graph.get_operation_by_name("global_step");
             Tensor accuracy = graph.get_operation_by_name("accuracy/accuracy");
-
+            var stopwatch = Stopwatch.StartNew();
             int i = 0;
-            foreach (var (x_batch, y_batch) in train_batches)
+            foreach (var (x_batch, y_batch, total) in train_batches)
             {
                 i++;
-                Console.WriteLine("Training on batch " + i);
+                var estimate = TimeSpan.FromSeconds((stopwatch.Elapsed.TotalSeconds / i) * total);
+                Console.WriteLine($"Training on batch {i}/{total}. Estimated training time: {estimate}");
                 var train_feed_dict = new Hashtable
                 {
                     [model_x] = x_batch,
@@ -90,8 +92,7 @@ namespace TensorFlowNET.Examples.CnnTextClassification
                 };
                 // original python:
                 //_, step, loss = sess.run([model.optimizer, model.global_step, model.loss], feed_dict = train_feed_dict)
-                var result = sess.run(new Tensor[] { optimizer, global_step, loss }, train_feed_dict);
-                // exception here, loss value seems like a float[]
+                var result = sess.run(new ITensorOrOperation[] { optimizer, global_step, loss }, train_feed_dict);
                 //loss_value = result[2];
                 var step = result[1];
                 if (step % 10 == 0)
@@ -102,7 +103,7 @@ namespace TensorFlowNET.Examples.CnnTextClassification
                     // # Test accuracy with validation data for each epoch.
                     var valid_batches = batch_iter(valid_x, valid_y, BATCH_SIZE, 1);
                     var (sum_accuracy, cnt) = (0, 0);
-                    foreach (var (valid_x_batch, valid_y_batch) in valid_batches)
+                    foreach (var (valid_x_batch, valid_y_batch, total_validation_batches) in valid_batches)
                     {
                         //        valid_feed_dict = {
                         //            model.x: valid_x_batch,
@@ -170,16 +171,19 @@ namespace TensorFlowNET.Examples.CnnTextClassification
             return (train_x, valid_x, train_y, valid_y);
         }
 
-        private IEnumerable<(NDArray, NDArray)> batch_iter(NDArray inputs, NDArray outputs, int batch_size, int num_epochs)
+        private IEnumerable<(NDArray, NDArray, int)> batch_iter(NDArray inputs, NDArray outputs, int batch_size, int num_epochs)
         {
-            var num_batches_per_epoch = (len(inputs) - 1); // batch_size + 1
+            var num_batches_per_epoch = (len(inputs) - 1) / batch_size;
+            var total_batches = num_batches_per_epoch * num_epochs;
             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[new Slice(start_index, end_index)], outputs[new Slice(start_index,end_index)]);
+                    if (end_index <= start_index)
+                        break;
+                    yield return (inputs[new Slice(start_index, end_index)], outputs[new Slice(start_index,end_index)], total_batches);
                 }
             }
         }