Scaled Exponential Linear Unit activation function is a modified ELU.
x: an input data point
In general and .
# Scaled Exponential Linear Unit def s_elu(x, a=1.6733, t=1.0507): result =  for i in x: if i<0: i = t*(a*(np.exp(i)-1)) result.append(i) return result y = s_elu(x, a=1) plot_graph(x, y, 'Scaled Exponential Linear Unit')
Used in hidden layers and can be used as an alternative to ReLU.
SELU induces self-normalizing property to the neural networks. That is, the neuron activations converge towards zero mean and unit variance.
It isn’t affected by vanishing and exploding gradient problems.
Need more computation power while training the network.