Updated examples to refer to the correct file names.

examples/dnn_imagenet_ex.cpp

@@ -10,8 +10,8 @@
     about 2 weeks.  This pretrained model has a top5 error of 7.572% on the 2012
     imagenet validation dataset.
 
-    For an introduction to dlib's DNN module read the dnn_mnist_ex.cpp and
+    For an introduction to dlib's DNN module read the dnn_introduction_ex.cpp and
-    dnn_mnist_advanced_ex.cpp example programs.
+    dnn_introduction2_ex.cpp example programs.
 
     
     Finally, these tools will use CUDA and cuDNN to drastically accelerate

examples/dnn_imagenet_train_ex.cpp

@@ -4,7 +4,7 @@
     network used by the dnn_imagenet_ex.cpp example program.  
 
     You should be familiar with dlib's DNN module before reading this example
-    program.  So read dnn_mnist_ex.cpp and dnn_mnist_advanced_ex.cpp first.  
+    program.  So read dnn_inception_ex.cpp and dnn_introduction2_ex.cpp first.  
 */
 
 

examples/dnn_inception_ex.cpp

@@ -2,8 +2,8 @@
 /*
     This is an example illustrating the use of the deep learning tools from the
     dlib C++ Library.  I'm assuming you have already read the introductory
-    dnn_mnist_ex.cpp and dnn_mnist_advanced_ex.cpp examples.  In this example we
+    dnn_introduction_ex.cpp and dnn_introduction2_ex.cpp examples.  In this
-    are going to show how to create inception networks. 
+    example we are going to show how to create inception networks. 
 
     An inception network is composed of inception blocks of the form:
 

examples/dnn_introduction2_ex.cpp

@@ -1,7 +1,7 @@
 // The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
 /*
     This is an example illustrating the use of the deep learning tools from the
-    dlib C++ Library.  I'm assuming you have already read the dnn_mnist_ex.cpp
+    dlib C++ Library.  I'm assuming you have already read the dnn_inception_ex.cpp 
     example.  So in this example program I'm going to go over a number of more
     advanced parts of the API, including:
         - Using multiple GPUs