  Principles of machine learning are applied to models that support skyrmion
phases in two dimensions. Successful feature predictions on various phases of
the skyrmion model were possible with several layers of convolutional neural
network inserted together with several neural network layers. A new training
scheme based on features of the input configuration such as magnetization and
spin chirality is introduced. It proved possible to further train external
parameters such as the magnetic field and temperature and make reliable
predictions on them. Algorithms trained on only the z-component or the
xy-components of the spin gave equally reliable predictions. The predictive
capacity of the algorithm extended to configurations not generated by the
original model, but related ones. A procedure for integrating the machine
learning algorithm into the interpretation of experimental data is given.
