A quick introduction to tiny-dnn

Include tiny_dnn.h:

    #include "tiny_dnn/tiny_dnn.h"
    using namespace tiny_dnn;
    using namespace tiny_dnn::layers;
    using namespace tiny_dnn::activation;

Declare the model as network. There are 2 types of network: network<sequential> and network<graph>. The sequential model is easier to construct.

    network<sequential> net;

Stack layers:

    net << conv<tan_h>(32, 32, 5, 1, 6, padding::same)  // in:32x32x1, 5x5conv, 6fmaps
        << max_pool<tan_h>(32, 32, 6, 2)                // in:32x32x6, 2x2pooling
        << conv<tan_h>(16, 16, 5, 6, 16, padding::same) // in:16x16x6, 5x5conv, 16fmaps
        << max_pool<tan_h>(16, 16, 16, 2)               // in:16x16x16, 2x2pooling
        << fc<tan_h>(8*8*16, 100)                       // in:8x8x16, out:100
        << fc<softmax>(100, 10);                        // in:100 out:10

Some layer takes an activation as a template parameter : max_pool<relu> means “apply a relu activation after the pooling”. if the layer has no successive activation, use max_pool<identity> instead.

Declare the optimizer:

    adagrad opt;

In addition to gradient descent, you can use modern optimizers such as adagrad, adadelta, adam.

Now you can start the training:

    int epochs = 50;
    int batch = 20;
    net.fit<cross_entropy>(opt, x_data, y_data, batch, epochs);

If you don’t have the target vector but have the class-id, you can alternatively use train.

    net.train<cross_entropy>(opt, x_data, y_label, batch, epochs);

Validate the training result:

    auto test_result = net.test(x_data, y_label);
    auto loss = net.get_loss<cross_entropy>(x_data, y_data);

Generate prediction on the new data:

    auto y_vector = net.predict(x_data);
    auto y_label = net.predict_max_label(x_data);

Save the trained parameter:

    ofstream ofs("data", std::ios::binary);
    ofs << net;

For a more in-depth about tiny-dnn, check out MNIST classification where you can see the end-to-end example. You will find tiny-dnn’s API in How-to.