I’m currently working on a problem that involves approximating a partial differential equation using neural networks. I’ve created a script that generates a set of neural networks, computes their outputs for a batch of samples, and calculates both the first and second-order partial derivatives for each sample with respect to their respective inputs.
The stripped-down version of my script is below.
I’m looking for ways to optimize the part of the script that computes the gradient and hessian. Currently, I’m iterating over each sample and calculating these values individually. While this works, I’m worried that it may not be the most efficient approach, especially when dealing with a large number of samples. I’d appreciate any advice on how to make these calculations more efficient.
Thank you in advance for your help!
using Flux using Flux: params, hessian, gradient using Random # Set the seed Random.seed!(123) # Define the constants used in the model M = 2 # Number of models BS = 100 # Batch size # Define neural networks for each unknown function model_F = [Chain( Dense(M, 64, tanh), Dense(64, 64, tanh), Dense(64, 1) ) |> f64 for _ in 1:M] Y = rand(0.01:100, M, BS) # Generate new samples for id in 1:M model = model_F[id] output = model(Y) # Compute the first-order partial derivatives and second-order partial derivatives grads = zeros(M, BS) hess = zeros(M, M, BS) for i in 1:size(Y, 2) y = Y[:, i] g = gradient(x -> sum(model(x)), y) grads[:, i] = g g2 = hessian(x -> sum(model(x)), y) hess[:, :, i] = g2 end end