  In this paper we theoretically investigate application of a bistable
Fabry-P\'{e}rot semiconductor laser under optical-injection as all-optical
activation unit for multilayer perceptron optical neural networks. The proposed
device is programmed to provide reconfigurable sigmoid-like activation
functions with adjustable thresholds and saturation points and benchmarked on
machine learning image recognition problems. Due to the reconfigurability of
the activation unit, the accuracy can be increased by up to 2% simply by
adjusting the control parameter of the activation unit to suit the specific
problem. For a simple two-layer perceptron neural network, we achieve inference
accuracies of up to 95% and 85%, for the MNIST and Fashion-MNIST datasets,
respectively.
