In general, an FFNN consists of an input layer with activation a (0) := emo s (w i ) ∈ R |s| followed by multiple hidden layers with activation a (l+1) := σ(W (l+1) a (l) + b (l+1) ) where W (l+1) and b (l+1) are the weights and biases for layer l + 1 and σ is a nonlinear activation function.
since the emotion formats under scrutiny capture affective states as real-valued vectors, the activation on the output layer a out (where out is the number of non-input layers in the network) is computed as the affine transformation our model differs from the other approaches presented in this section by sharing model parameters (weights and biases of the hidden layers) across the different dimensions/categories of the target format with only the last layer having parameters which are uniquely associated to one of the outputs (see Equation 4).