T.A. de Lorm, C. Horswill, D. Rabaiotti, R.M. Ewers, R. J. Groom, J. Watermeyer, R. Woodroffe
Table S1 The structure of a Convolutional Neural Net that classifies images of African Wild Dogs into “standing” or “not standing” (S1.A), and a Convolutional Neural Net that classifies images of African Wild Dogs into “left flank” or “right flank” (S1.B). The layers are given in the order at which they occur in the model. The models were optimised using RMSprop, an algorithm which guides how the model improves itself (Tieleman & Hinton, 2012). The activation column refers to which activation function was used in each layer, which determines how the nodes within layers convert its input to an output-value. ReLu was used as activator function, as this has been found to improve multi-layer networks (Glorot, Bordes & Bengio, 2011). The last layer is activated with a Sigmoid function, which turns the input into a single, binary prediction (“standing” or “not standing”, “left” or “right”).