]
optimizers = [
SgdOptimizer(1),
SgdOptimizer(0.1),
SgdOptimizer(0.05),
AdamOptimizer(len(psi.parameters), 0.1, 0.9),
AdamOptimizer(len(psi.parameters), 0.1, 0.8),
]
E = []
for opt in optimizers:
# psi.parameters = org_params
psi = RBMWavefunction(N * D, 2)
# psi = SimpleGaussian(0.8)
sampler = ImportanceSampler(system, psi, 0.1)
sampler.thermalize(10000)
E_training = EnergyCallback(samples=1000000, verbose=True)
train(
psi,
H,
sampler,
iters=500,
samples=1000,
gamma=0.0,
optimizer=opt,
call_backs=[E_training],
call_back_resolution=50,
)
E.append(np.asarray(E_training))
if master_rank():
fig, ax = plt.subplots()