From 47a7ac8017b1dfe5c832d7957f69b030e5daca40 Mon Sep 17 00:00:00 2001
From: Meinrad Recheis <meinrad.recheis@gmail.com>
Date: Sat, 11 May 2019 15:19:43 +0200
Subject: [PATCH] TextClassification: delete old cached meta graph which has a
 bug

---
 .../TextProcess/TextClassificationTrain.cs      | 17 ++++++++++++-----
 1 file changed, 12 insertions(+), 5 deletions(-)

diff --git a/test/TensorFlowNET.Examples/TextProcess/TextClassificationTrain.cs b/test/TensorFlowNET.Examples/TextProcess/TextClassificationTrain.cs
index 891ad81f..9d24e3c6 100644
--- a/test/TensorFlowNET.Examples/TextProcess/TextClassificationTrain.cs
+++ b/test/TensorFlowNET.Examples/TextProcess/TextClassificationTrain.cs
@@ -32,6 +32,7 @@ namespace TensorFlowNET.Examples.CnnTextClassification
         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 WORD_MAX_LEN = 1014;
         private const int NUM_CLASS = 2;
         private const int BATCH_SIZE = 64;
         private const int NUM_EPOCHS = 10;
@@ -58,6 +59,8 @@ namespace TensorFlowNET.Examples.CnnTextClassification
             Console.WriteLine("\tDONE ");
 
             var (train_x, valid_x, train_y, valid_y) = train_test_split(x, y, test_size: 0.15f);
+            Console.WriteLine("Training set size: " + train_x.shape[0]);
+            Console.WriteLine("Test set size: " + valid_x.shape[0]);
 
             Console.WriteLine("Import graph...");
             var meta_file = model_name + ".meta";
@@ -74,7 +77,6 @@ namespace TensorFlowNET.Examples.CnnTextClassification
             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/value");
-            //var optimizer_nodes = graph._nodes_by_name.Keys.Where(key => key.Contains("optimizer")).ToArray();
             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/value");
@@ -92,7 +94,7 @@ 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 ITensorOrOperation[] { optimizer, global_step, loss }, train_feed_dict);
-                //loss_value = result[2];
+                loss_value = result[2];
                 var step = result[1];
                 if (step % 10 == 0)
                 {
@@ -177,7 +179,7 @@ namespace TensorFlowNET.Examples.CnnTextClassification
 
         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;
+            var num_batches_per_epoch = (len(inputs) - 1) / batch_size +1;
             var total_batches = num_batches_per_epoch * num_epochs;
             foreach (var epoch in range(num_epochs))
             {
@@ -202,8 +204,13 @@ namespace TensorFlowNET.Examples.CnnTextClassification
             {
                 // download graph meta data
                 var meta_file = model_name + ".meta";
-                if (File.GetLastWriteTime(meta_file) < new DateTime(2019,05,11)) // delete old cached file which contains errors
-                    File.Delete(meta_file);
+                var meta_path = Path.Combine("graph", meta_file);
+                if (File.GetLastWriteTime(meta_path) < new DateTime(2019, 05, 11))
+                {
+                    // delete old cached file which contains errors
+                    Console.WriteLine("Discarding cached file: " + meta_path);
+                    File.Delete(meta_path);
+                }
                 url = "https://raw.githubusercontent.com/SciSharp/TensorFlow.NET/master/graph/" + meta_file;
                 Web.Download(url, "graph", meta_file);
             }