Читать книгу Machine Learning for Tomographic Imaging - Professor Ge Wang - Страница 61

CNN consists of input and output layers and a number of hidden layers. The hidden layers can be categorized by convolution, pooling, activation, and full connection. The input layer is generally a vector, matrix, or tensor. A convolutional layer is used to convolve an input layer and extract features at a higher level, while a pooling layer is for a sample to reduce the amount of data while maintaining critical information. An activation layer introduces nonlinear features. A fully connected layer integrates features obtained by convolution and pooling. Finally, the output layer produces the final output.