def fid_err_grad_wrapper(self, *args):
"""
Get the gradient of the fidelity error with respect to all of the
variables, i.e. the ctrl amplidutes in each timeslot
This is called by generic optimisation algorithm as the gradients of
func to the minimised wrt the variables. The argument is the current
variable values, i.e. control amplitudes, passed as
a flat array. Hence these are reshaped as [nTimeslots, n_ctrls]
and then used to update the stored ctrl values (if they have changed)
Although the optimisation algorithms have a check within them for
function convergence, i.e. local minima, the sum of the squares
of the normalised gradient is checked explicitly, and the
optimisation is terminated if this is below the min_gradient_norm
condition
"""
# *** update stats ***
if self.stats is not None:
self.stats.num_grad_func_calls += 1
if self.log_level <= logging.DEBUG:
logger.debug("gradient call {}".format(
self.stats.num_grad_func_calls))
amps = args[0].copy().reshape(self.dynamics.ctrl_amps.shape)
self.dynamics.update_ctrl_amps(amps)
fid_comp = self.dynamics.fid_computer
# gradient_norm_func is a pointer to the function set in the config
# that returns the normalised gradients
grad = fid_comp.get_fid_err_gradient()
if self._grad_norm_fpath is not None:
fh = open(self._grad_norm_fpath, 'a')
fh.write("{:<10n}{:14.6g}\n".format(
self.stats.num_grad_func_calls, fid_comp.grad_norm))
fh.close()
if self.config.test_out_grad:
# save gradients to file
dyn = self.dynamics
fname = "grad_{}_{}_{}_{}_call{}{}".format(
self.id_text,
dyn.id_text,
dyn.prop_computer.id_text,
dyn.fid_computer.id_text,
self.stats.num_grad_func_calls,
self.config.test_out_f_ext)
fpath = os.path.join(self.config.test_out_dir, fname)
np.savetxt(fpath, grad, fmt='%11.4g')
tc = self.termination_conditions
if fid_comp.grad_norm < tc.min_gradient_norm:
raise errors.GradMinReachedTerminate(fid_comp.grad_norm)
return grad.flatten()
def fid_err_grad_wrapper(self, *args):
"""
Get the gradient of the fidelity error with respect to all of the
variables, i.e. the ctrl amplidutes in each timeslot
This is called by generic optimisation algorithm as the gradients of
func to the minimised wrt the variables. The argument is the current
variable values, i.e. control amplitudes, passed as
a flat array. Hence these are reshaped as [nTimeslots, n_ctrls]
and then used to update the stored ctrl values (if they have changed)
Although the optimisation algorithms have a check within them for
function convergence, i.e. local minima, the sum of the squares
of the normalised gradient is checked explicitly, and the
optimisation is terminated if this is below the min_gradient_norm
condition
"""
# *** update stats ***
self.num_grad_func_calls += 1
if self.stats is not None:
self.stats.num_grad_func_calls = self.num_grad_func_calls
if self.log_level <= logging.DEBUG:
logger.debug("gradient call {}".format(
self.stats.num_grad_func_calls))
amps = self._get_ctrl_amps(args[0].copy())
self.dynamics.update_ctrl_amps(amps)
fid_comp = self.dynamics.fid_computer
# gradient_norm_func is a pointer to the function set in the config
# that returns the normalised gradients
grad = fid_comp.get_fid_err_gradient()
if self.iter_summary:
self.iter_summary.grad_func_call_num = self.num_grad_func_calls
self.iter_summary.grad_norm = fid_comp.grad_norm
if self.dump:
if self.dump.dump_grad_norm:
self.dump.update_grad_norm_log(fid_comp.grad_norm)
if self.dump.dump_grad:
self.dump.update_grad_log(grad)
tc = self.termination_conditions
if fid_comp.grad_norm < tc.min_gradient_norm:
raise errors.GradMinReachedTerminate(fid_comp.grad_norm)
return grad.flatten()
