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diff --git a/ml/dlib/examples/dnn_introduction_ex.cpp b/ml/dlib/examples/dnn_introduction_ex.cpp deleted file mode 100644 index 6ae3ddf73..000000000 --- a/ml/dlib/examples/dnn_introduction_ex.cpp +++ /dev/null @@ -1,170 +0,0 @@ -// 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. In it, we will train the venerable LeNet convolutional - neural network to recognize hand written digits. The network will take as - input a small image and classify it as one of the 10 numeric digits between - 0 and 9. - - The specific network we will run is from the paper - LeCun, Yann, et al. "Gradient-based learning applied to document recognition." - Proceedings of the IEEE 86.11 (1998): 2278-2324. - except that we replace the sigmoid non-linearities with rectified linear units. - - These tools will use CUDA and cuDNN to drastically accelerate network - training and testing. CMake should automatically find them if they are - installed and configure things appropriately. If not, the program will - still run but will be much slower to execute. -*/ - - -#include <dlib/dnn.h> -#include <iostream> -#include <dlib/data_io.h> - -using namespace std; -using namespace dlib; - -int main(int argc, char** argv) try -{ - // This example is going to run on the MNIST dataset. - if (argc != 2) - { - cout << "This example needs the MNIST dataset to run!" << endl; - cout << "You can get MNIST from http://yann.lecun.com/exdb/mnist/" << endl; - cout << "Download the 4 files that comprise the dataset, decompress them, and" << endl; - cout << "put them in a folder. Then give that folder as input to this program." << endl; - return 1; - } - - - // MNIST is broken into two parts, a training set of 60000 images and a test set of - // 10000 images. Each image is labeled so that we know what hand written digit is - // depicted. These next statements load the dataset into memory. - std::vector<matrix<unsigned char>> training_images; - std::vector<unsigned long> training_labels; - std::vector<matrix<unsigned char>> testing_images; - std::vector<unsigned long> testing_labels; - load_mnist_dataset(argv[1], training_images, training_labels, testing_images, testing_labels); - - - // Now let's define the LeNet. Broadly speaking, there are 3 parts to a network - // definition. The loss layer, a bunch of computational layers, and then an input - // layer. You can see these components in the network definition below. - // - // The input layer here says the network expects to be given matrix<unsigned char> - // objects as input. In general, you can use any dlib image or matrix type here, or - // even define your own types by creating custom input layers. - // - // Then the middle layers define the computation the network will do to transform the - // input into whatever we want. Here we run the image through multiple convolutions, - // ReLU units, max pooling operations, and then finally a fully connected layer that - // converts the whole thing into just 10 numbers. - // - // Finally, the loss layer defines the relationship between the network outputs, our 10 - // numbers, and the labels in our dataset. Since we selected loss_multiclass_log it - // means we want to do multiclass classification with our network. Moreover, the - // number of network outputs (i.e. 10) is the number of possible labels. Whichever - // network output is largest is the predicted label. So for example, if the first - // network output is largest then the predicted digit is 0, if the last network output - // is largest then the predicted digit is 9. - using net_type = loss_multiclass_log< - fc<10, - relu<fc<84, - relu<fc<120, - max_pool<2,2,2,2,relu<con<16,5,5,1,1, - max_pool<2,2,2,2,relu<con<6,5,5,1,1, - input<matrix<unsigned char>> - >>>>>>>>>>>>; - // This net_type defines the entire network architecture. For example, the block - // relu<fc<84,SUBNET>> means we take the output from the subnetwork, pass it through a - // fully connected layer with 84 outputs, then apply ReLU. Similarly, a block of - // max_pool<2,2,2,2,relu<con<16,5,5,1,1,SUBNET>>> means we apply 16 convolutions with a - // 5x5 filter size and 1x1 stride to the output of a subnetwork, then apply ReLU, then - // perform max pooling with a 2x2 window and 2x2 stride. - - - - // So with that out of the way, we can make a network instance. - net_type net; - // And then train it using the MNIST data. The code below uses mini-batch stochastic - // gradient descent with an initial learning rate of 0.01 to accomplish this. - dnn_trainer<net_type> trainer(net); - trainer.set_learning_rate(0.01); - trainer.set_min_learning_rate(0.00001); - trainer.set_mini_batch_size(128); - trainer.be_verbose(); - // Since DNN training can take a long time, we can ask the trainer to save its state to - // a file named "mnist_sync" every 20 seconds. This way, if we kill this program and - // start it again it will begin where it left off rather than restarting the training - // from scratch. This is because, when the program restarts, this call to - // set_synchronization_file() will automatically reload the settings from mnist_sync if - // the file exists. - trainer.set_synchronization_file("mnist_sync", std::chrono::seconds(20)); - // Finally, this line begins training. By default, it runs SGD with our specified - // learning rate until the loss stops decreasing. Then it reduces the learning rate by - // a factor of 10 and continues running until the loss stops decreasing again. It will - // keep doing this until the learning rate has dropped below the min learning rate - // defined above or the maximum number of epochs as been executed (defaulted to 10000). - trainer.train(training_images, training_labels); - - // At this point our net object should have learned how to classify MNIST images. But - // before we try it out let's save it to disk. Note that, since the trainer has been - // running images through the network, net will have a bunch of state in it related to - // the last batch of images it processed (e.g. outputs from each layer). Since we - // don't care about saving that kind of stuff to disk we can tell the network to forget - // about that kind of transient data so that our file will be smaller. We do this by - // "cleaning" the network before saving it. - net.clean(); - serialize("mnist_network.dat") << net; - // Now if we later wanted to recall the network from disk we can simply say: - // deserialize("mnist_network.dat") >> net; - - - // Now let's run the training images through the network. This statement runs all the - // images through it and asks the loss layer to convert the network's raw output into - // labels. In our case, these labels are the numbers between 0 and 9. - std::vector<unsigned long> predicted_labels = net(training_images); - int num_right = 0; - int num_wrong = 0; - // And then let's see if it classified them correctly. - for (size_t i = 0; i < training_images.size(); ++i) - { - if (predicted_labels[i] == training_labels[i]) - ++num_right; - else - ++num_wrong; - - } - cout << "training num_right: " << num_right << endl; - cout << "training num_wrong: " << num_wrong << endl; - cout << "training accuracy: " << num_right/(double)(num_right+num_wrong) << endl; - - // Let's also see if the network can correctly classify the testing images. Since - // MNIST is an easy dataset, we should see at least 99% accuracy. - predicted_labels = net(testing_images); - num_right = 0; - num_wrong = 0; - for (size_t i = 0; i < testing_images.size(); ++i) - { - if (predicted_labels[i] == testing_labels[i]) - ++num_right; - else - ++num_wrong; - - } - cout << "testing num_right: " << num_right << endl; - cout << "testing num_wrong: " << num_wrong << endl; - cout << "testing accuracy: " << num_right/(double)(num_right+num_wrong) << endl; - - - // Finally, you can also save network parameters to XML files if you want to do - // something with the network in another tool. For example, you could use dlib's - // tools/convert_dlib_nets_to_caffe to convert the network to a caffe model. - net_to_xml(net, "lenet.xml"); -} -catch(std::exception& e) -{ - cout << e.what() << endl; -} - |