From 0484aab0f973169d808305441c8aed1ec35dd4b6 Mon Sep 17 00:00:00 2001
From: Oceania2018 <haiping008@gmail.com>
Date: Mon, 22 Apr 2019 21:34:09 -0500
Subject: [PATCH] optimize image dataset loading

---
 test/TensorFlowNET.Examples/LogisticRegression.cs   | 11 ++++++++++-
 test/TensorFlowNET.Examples/NeuralNetXor.cs         |  2 +-
 test/TensorFlowNET.Examples/Utility/DataSet.cs      |  4 ++--
 test/TensorFlowNET.Examples/Utility/MnistDataSet.cs |  2 +-
 4 files changed, 14 insertions(+), 5 deletions(-)

diff --git a/test/TensorFlowNET.Examples/LogisticRegression.cs b/test/TensorFlowNET.Examples/LogisticRegression.cs
index 1646f149..71238bf6 100644
--- a/test/TensorFlowNET.Examples/LogisticRegression.cs
+++ b/test/TensorFlowNET.Examples/LogisticRegression.cs
@@ -1,6 +1,7 @@
 using NumSharp;
 using System;
 using System.Collections.Generic;
+using System.Diagnostics;
 using System.IO;
 using System.Linq;
 using System.Text;
@@ -55,6 +56,8 @@ namespace TensorFlowNET.Examples
             // Initialize the variables (i.e. assign their default value)
             var init = tf.global_variables_initializer();
 
+            var sw = new Stopwatch();
+
             return with(tf.Session(), sess =>
             {
                 // Run the initializer
@@ -63,6 +66,8 @@ namespace TensorFlowNET.Examples
                 // Training cycle
                 foreach (var epoch in range(training_epochs))
                 {
+                    sw.Start();
+
                     var avg_cost = 0.0f;
                     var total_batch = mnist.train.num_examples / batch_size;
                     // Loop over all batches
@@ -79,9 +84,13 @@ namespace TensorFlowNET.Examples
                         avg_cost += c / total_batch;
                     }
 
+                    sw.Stop();
+
                     // Display logs per epoch step
                     if ((epoch + 1) % display_step == 0)
-                        print($"Epoch: {(epoch + 1).ToString("D4")} cost= {avg_cost.ToString("G9")}");
+                        print($"Epoch: {(epoch + 1).ToString("D4")} cost= {avg_cost.ToString("G9")} elapse= {sw.ElapsedMilliseconds}ms");
+
+                    sw.Reset();
                 }
 
                 print("Optimization Finished!");
diff --git a/test/TensorFlowNET.Examples/NeuralNetXor.cs b/test/TensorFlowNET.Examples/NeuralNetXor.cs
index a52fa3dd..92984af3 100644
--- a/test/TensorFlowNET.Examples/NeuralNetXor.cs
+++ b/test/TensorFlowNET.Examples/NeuralNetXor.cs
@@ -12,7 +12,7 @@ namespace TensorFlowNET.Examples
     /// </summary>
     public class NeuralNetXor : Python, IExample
     {
-        public int Priority => 2;
+        public int Priority => 10;
         public bool Enabled { get; set; } = true;
         public string Name => "NN XOR";
 
diff --git a/test/TensorFlowNET.Examples/Utility/DataSet.cs b/test/TensorFlowNET.Examples/Utility/DataSet.cs
index 10e68fa7..6b6b8769 100644
--- a/test/TensorFlowNET.Examples/Utility/DataSet.cs
+++ b/test/TensorFlowNET.Examples/Utility/DataSet.cs
@@ -54,8 +54,8 @@ namespace TensorFlowNET.Examples.Utility
 
                 // Get the rest examples in this epoch
                 var rest_num_examples = _num_examples - start;
-                var images_rest_part = _images[np.arange(start, _num_examples)];
-                var labels_rest_part = _labels[np.arange(start, _num_examples)];
+                //var images_rest_part = _images[np.arange(start, _num_examples)];
+                //var labels_rest_part = _labels[np.arange(start, _num_examples)];
                 // Shuffle the data
                 if (shuffle)
                 {
diff --git a/test/TensorFlowNET.Examples/Utility/MnistDataSet.cs b/test/TensorFlowNET.Examples/Utility/MnistDataSet.cs
index d4ee0824..41728813 100644
--- a/test/TensorFlowNET.Examples/Utility/MnistDataSet.cs
+++ b/test/TensorFlowNET.Examples/Utility/MnistDataSet.cs
@@ -102,7 +102,7 @@ namespace TensorFlowNET.Examples.Utility
             for(int row = 0; row < num_labels; row++)
             {
                 var col = labels_dense.Data<byte>(row);
-                labels_one_hot.SetData(1, row, col);
+                labels_one_hot.SetData(1.0, row, col);
             }
 
             return labels_one_hot;