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pytorch-L-BFGS-example
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import torch | |
import torch.optim as optim | |
import matplotlib.pyplot as plt | |
# 2d Rosenbrock function | |
def f(x): | |
return (1 - x[0])**2 + 100 * (x[1] - x[0]**2)**2 | |
# Gradient descent | |
x_gd = 10*torch.ones(2, 1) | |
x_gd.requires_grad = True | |
gd = optim.SGD([x_gd], lr=1e-5) | |
history_gd = [] | |
for i in range(100): | |
gd.zero_grad() | |
objective = f(x_gd) | |
objective.backward() | |
gd.step() | |
history_gd.append(objective.item()) | |
# L-BFGS | |
def closure(): | |
lbfgs.zero_grad() | |
objective = f(x_lbfgs) | |
objective.backward() | |
return objective | |
x_lbfgs = 10*torch.ones(2, 1) | |
x_lbfgs.requires_grad = True | |
lbfgs = optim.LBFGS([x_lbfgs], | |
history_size=10, | |
max_iter=4, | |
line_search_fn="strong_wolfe") | |
history_lbfgs = [] | |
for i in range(100): | |
history_lbfgs.append(f(x_lbfgs).item()) | |
lbfgs.step(closure) | |
# Plotting | |
plt.semilogy(history_gd, label='GD') | |
plt.semilogy(history_lbfgs, label='L-BFGS') | |
plt.legend() | |
plt.show() |
Thanks for this gist
Thanks for sharing this. The docs are a bit unclear but I think if we are doing full dataset training (as opposed to minibatching) we can just set
max_iter
sufficiently high and then the for loop isn't needed (i.e., just calllbfgs.step
once).
I think so, but maybe lbfgs.step might need to be called twice to work. I'm using it on full dataset training, and running it just once doesn't seem to update anything. There's a couple if state['n_iter'] == 1:
sections in lbfgs.step that initialize variables that are later computed when n_iter is greater than one.
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Thanks for sharing this. The docs are a bit unclear but I think if we are doing full dataset training (as opposed to minibatching) we can just set
max_iter
sufficiently high and then the for loop isn't needed (i.e., just calllbfgs.step
once).