Transfer learning based identification model for macerals of exinite in coal

To improve the identification accuracy of exinite macerals in coal and avoid the manual intervention in the feature extraction stage of classifier construction, the deep learning method was employed for the automatic identification of macerals of exinite in coal. Owing to the limiting of sample number of exinite maceral in coal, the classifier constructed with the conventional convolutional neural network tends to be over-fitting and the poor generalization ability. To solve this problem, an identification model (classifier), which is based on transfer learning, was proposed. Based on conventional convolution neural network model, this method shared the weight parameters of convolution layer and pooling layer with the pre-training network model by transfer learning, optimized the network model structure and full connection layer parameters with samples of exinite macerals, and constructed a new deep learning network model for the maceral identification of exinite of coal. The experimental results show that, compared with that of the conventional neural network, the transfer learning based identification model proposed in this paper is more effective, and the classifier with VGG16 as pre-training network is of best performance on this dataset, with identification accuracy of 98.10% for test samples; owing to the reduction of parameter number, the training time is obviously shortened, and the convergence is achieved in a short training period with a stable identification accuracy, indicating that the classifier based on VGG16 as the pre training model has better performance in the identification of macerals of exinite in coal.