]
myBopt = BayesianOptimization(
f=Hess_all_reg_BigGAN_optim, # Objective function
domain=hess_all_reg_domain, # Box-constraints of the problem
initial_design_numdata=0, # Number data initial design
initial_design_type="random",
acquisition_optimizer_type='lbfgs',
acquisition_type='EI', # Expected Improvement ‘EI’
exact_feval=False, # True evaluations, no sample noise
maximize=False)
#%%
myBopt.X = np.array([[-1.3, -0.5, -2.34, -4, -3, 0.7],
[-1.3, -0.5, -2.34, -4, -3, 1.0],
[-1.3, -0.5, -2.34, -4, -3, 1.3],
[-1, -0.5, -2.34, -4, -3, 0.7],
[-1.3, -1, -2.34, -4, -3, 0.7],
[-1.3, -0.5, -2.34, -3, -2, 0.7],
[-1.8, -0.5, -2.34, -4, -3, 0.7]])
myBopt.Y = np.array([[dsim], [dsim2], [dsim3], [dsim4], [dsim5], [dsim6],
[dsim7]])
#%% 40 mins for 48 iterations
max_iter = 1000 ## maximum number of iterations
max_time = 36000 ## maximum allowed time
eps = 1e-4 ## tolerance, max distance between consicutive evaluations.
myBopt.run_optimization(max_iter=max_iter,
max_time=max_time,
eps=eps,
verbosity=True)
np.savez(join(savedir, "BigGAN_Hess_Adam_optim_BO_tune600.npz"),
{'name': 'wd2', 'type': 'continuous', 'domain': (-4, -1.5),'dimensionality': 1},
{'name': 'momentum1', 'type': 'continuous', 'domain': (0, 0.9),'dimensionality': 1},
{'name': 'momentum2', 'type': 'continuous', 'domain': (0, 0.9),'dimensionality': 1},]
savedir = r"E:\OneDrive - Washington University in St. Louis\BigGAN_invert"
#%%
%%time
myBopt = BayesianOptimization(f=optim_BigGAN, # Objective function
domain=mixed_domain, # Box-constraints of the problem
initial_design_numdata = 0, # Number data initial design
initial_design_type="random",
acquisition_optimizer_type='lbfgs',
acquisition_type='LCB', # Expected Improvement ‘EI’
exact_feval = False, # True evaluations, no sample noise
maximize=False)
#%%
myBopt.X = np.array([[-1.5, -2.5, -3, -3, 0.8, 0.5], [-1.5, -2.5, -3.5, -3.5, 0.8, 0.5]])
myBopt.Y = np.array([[1.666],[1.790]])
#%% 40 mins for 48 iterations
max_iter = 900 ## maximum number of iterations
max_time = 30000 ## maximum allowed time
eps = 1e-4 ## tolerance, max distance between consicutive evaluations.
myBopt.run_optimization(max_iter=max_iter, max_time=max_time, eps=eps, verbosity=True)
np.savez(join(savedir, "BigGAN_SGD_optim_BO_tune300.npz"), X=myBopt.X, Y=myBopt.Y, Y_best=myBopt.Y_best, domain=mixed_domain)
scores_short_tab = pd.DataFrame(np.append(myBopt.X, myBopt.Y, 1), columns=["lr1","lr2","wd1","wd2","mom1","mom2","scores"])
scores_short_tab.to_csv(join(savedir, "BigGAN_SGD_optim_BO_tune300.csv"))
# %%
#%% Using Hessian to Pre-condition the latent space
#%%
