Figure 2.

Representation of a Convolutional Neural Network. (a) The input layer consists of the SNP matrix. The convolution filters are the same through all different SNPs; we slide these filters horizontally with a stride of “s” SNPs, i.e., the number of SNPs that the filter is moved to compute the next output. (b) Neuron outputs of convolutional layer with K dimensions (outlined as blue and green squares) are computed from inputs of input layer, which fall within their receptive field (here consecutive sets of three SNPs) in the layer below (shown as blue- and green-colored rectangles). (c) Convolutional networks usually include pooling layers, combining the output of the previous layer at certain locations into a single neuron (here, a 1 × 2 pooling is outlined in yellow). (d) Fully connected layers connect every neuron in one layer to every neuron in another layer. It is the same as traditional MLPs, finally obtaining an estimated output (e). Partly redrawn using code in http://www.texample.net/tikz/examples/neural-network/.