Fig. 1
From: Deep phenotyping: deep learning for temporal phenotype/genotype classification
The schematic of Alexnet. A CNN often consists of convolutional layers, max-pooling layers and fully connected layers. The output of each convolutional layer is a block of 2D images (a.k.a. feature maps), which are computed by convolving previous feature maps with a small filter. The filter parameters are learned during the training process. The last few layers of CNN are densely connected to each other, and the class scores are obtained from the final layer