Algorithm 1. Learned sensing based network for optimized microscope illumination.

1:	Input: {Ij,n} with n ∈ {1, …, N} uniquely illuminated images of the jth object, object labels {yj}. Number of iterations T. An image classification model F parametrized by θ.
2:	Randomly initialize the LED weights $\mathit{w}={\left\{w_n\right\}}_{n=1}^N$.
3:	for iteration t = 1 … , T do
4:	Sample a minibatch of $({\{I_{j,n}\}}_{n=1}^N,y_j)$,
5:	Generate each pattern-illuminated image $I_j^{\prime }$ via the weighted sum $I_j^{\prime }={\sum }_{n=1}^N{w}_n{I}_{j,n}$
6:	Take a gradient descent step on CrossEntropy($F(I_j^{\prime }),y_j$ ) with respect to w and θ.
7:	end for
8:	Output: the optimized LED weights w and the model parameters θ.