for unit in unit_arr[:]:
savedir = join(recorddir, "optim_cmp", "%s_%s_%d" % (unit[0], unit[1], unit[2]))
os.makedirs(savedir, exist_ok=True)
for Optim_str in Optim_arr:
for triali in range(10):
t0 = time()
if Optim_str == "Genetic":
optim = Genetic(population_size, mutation_rate, mutation_size, kT_multiplier, recorddir,
parental_skew=0.5, n_conserve=0)
elif Optim_str == "CholCMA":
optim = CholeskyCMAES(recorddir=recorddir, space_dimen=code_length, init_sigma=init_sigma,
Aupdate_freq=Aupdate_freq, init_code=initcode) # np.zeros([1, code_length])
experiment = ExperimentEvolve(unit, max_step=100, optimizer=optim)
experiment.run()
fig0 = experiment.visualize_best(show=False)
fig0.savefig(join(savedir, "%s_BestImgTrial%03d.png" % (Optim_str, triali,)))
fig = experiment.visualize_trajectory(show=False)
fig.savefig(join(savedir, "%s_ScoreTrajTrial%03d.png" % (Optim_str, triali,)))
# fig2 = experiment.visualize_exp(show=False)
# fig2.savefig(join(savedir, "EvolveTrial%03d.png" % (triali)))
plt.close('all')
np.savez(join(savedir, "%s_scores_trial%03d.npz" % (Optim_str, triali)),
generations=experiment.generations,
scores_all=experiment.scores_all)
print("Optimization with %s took %.1f s" % (Optim_str, time() - t0))
# lastgen_max = [experiment.scores_all[experiment.generations == geni].max() for geni in
# range(experiment.generations.max() - 10, experiment.generations.max() + 1)]
# best_scores_col.append(lastgen_max)
rankbasis=True,
sphere_norm=9)
optimizerZH.lr_schedule(
100, lim=(10, 6),
mode="lin") # the space is much tighter than the fc6 space, so step size
# should be tuned for that
experiment = ExperimentEvolve(("alexnet", "fc8", 1),
max_step=100,
backend="torch",
optimizer=optimizerZH,
GAN="BigBiGAN",
verbose=True)
experiment.run()
experiment.visualize_trajectory(True)
experiment.visualize_exp(True)
experiment.visualize_best(True)
#%%
mode_dict = ["lin", "exp"] #"inv",
def optim_result(param):
pop_size = int(param[0, 0])
select_rate = param[0, 1]
select_size = int(pop_size * select_rate)
lr = param[0, 2]
mu_init = param[0, 3]
mu_rate = param[0, 4]
mode = int(param[0, 5])
sphere_norm = int(param[0, 6])
n_gen = 3000 // pop_size # 4000 is the total evaluation budget
optimizer = ZOHA_Sphere_lr_euclid(120,