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Added some more comments

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This commit is contained in:
Davis King 2016-06-25 18:31:21 -04:00
parent 034e0e1996
commit 5e550a261e
1 changed files with 7 additions and 2 deletions
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9
examples/dnn_imagenet_ex.cpp
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@ -121,7 +121,11 @@ int main(int argc, char** argv) try
anet_type net;
deserialize("resnet34_1000_imagenet_classifier.dnn") >> net >> labels;
// Make a network with softmax as the final layer. We don't have to do this
// if we just want to output the single best prediction, since the anet_type
// already does this. But if we instead want to get the probability of each
// class as output we need to replace the last layer of the network with a
// softmax layer, which we do as follows:
softmax<anet_type::subnet_type> snet;
snet.subnet() = net.subnet();
@ -131,7 +135,7 @@ int main(int argc, char** argv) try
dlib::rand rnd;
image_window win;
// read images from the command prompt and print the top 5 best labels for each.
// Read images from the command prompt and print the top 5 best labels for each.
for (int i = 1; i < argc; ++i)
{
load_image(img, argv[i]);
@ -143,6 +147,7 @@ int main(int argc, char** argv) try
matrix<float,1,1000> p = sum_rows(mat(snet(images.begin(), images.end())))/num_crops;
win.set_image(img);
// Print the 5 most probable labels
for (int k = 0; k < 5; ++k)
{
unsigned long predicted_label = index_of_max(p);