Testing different supervised machine learning architectures for the classification of liquid crystals

Different convolutional neural network (CNN) and inception network architectures were trained for the classification of isotropic, nematic, cholesteric and smectic liquid crystal phase textures to test the prediction accuracy for each one of these models. Varying the number of layers and inception blocks, as well as the regularisation, and application to different phase transitions and classification tasks, it is shown that in general the architecture of an inception network with two blocks leads to the best classification results. Regularisation, such as image flipping, and dropout layers additionally somewhat increase the classification accuracy. Even for simple tasks like the isotropic-nematic transition, which is of importance for applications in the automatic readout of sensors, convolutional neural networks need more than one layer. Care must be taken to not apply architectures of too large complexity, as this will again reduce the classification accuracy due to overfitting. Architecture complexity needs to be adjusted to the given classification task