def setup_shooting_block(integrator=None,
in_features=20,
shooting_model='updown',
parameter_weight=1.0,
nr_of_particles=10,
inflation_factor=2,
nonlinearity='relu',
use_particle_rnn_mode=False,
use_particle_free_rnn_mode=False,
optimize_over_data_initial_conditions=False,
optimize_over_data_initial_conditions_type='linear'):
if shooting_model == 'updown':
smodel = shooting_models.AutoShootingIntegrandModelUpDown(
in_features=in_features,
nonlinearity=nonlinearity,
parameter_weight=parameter_weight,
inflation_factor=inflation_factor,
nr_of_particles=nr_of_particles,
particle_dimension=1,
particle_size=in_features,
use_analytic_solution=True,
use_rnn_mode=use_particle_rnn_mode,
optimize_over_data_initial_conditions=
optimize_over_data_initial_conditions,
optimize_over_data_initial_conditions_type=
optimize_over_data_initial_conditions_type)
elif shooting_model == 'updown_universal':
smodel = shooting_models.AutoShootingIntegrandModelUpDownUniversal(
in_features=in_features,
nonlinearity=nonlinearity,
parameter_weight=parameter_weight,
inflation_factor=inflation_factor,
nr_of_particles=nr_of_particles,
particle_dimension=1,
particle_size=in_features,
use_analytic_solution=True,
optional_weight=0.1,
use_rnn_mode=use_particle_rnn_mode,
optimize_over_data_initial_conditions=
optimize_over_data_initial_conditions,
optimize_over_data_initial_conditions_type=
optimize_over_data_initial_conditions_type)
elif shooting_model == 'periodic':
smodel = shooting_models.AutoShootingIntegrandModelUpdownPeriodic(
in_features=in_features,
nonlinearity=nonlinearity,
parameter_weight=parameter_weight,
inflation_factor=inflation_factor,
nr_of_particles=nr_of_particles,
particle_dimension=1,
particle_size=in_features,
use_analytic_solution=True,
use_rnn_mode=use_particle_rnn_mode,
optimize_over_data_initial_conditions=
optimize_over_data_initial_conditions,
optimize_over_data_initial_conditions_type=
optimize_over_data_initial_conditions_type)
elif shooting_model == 'simple':
smodel = shooting_models.AutoShootingIntegrandModelSimple(
in_features=in_features,
nonlinearity=nonlinearity,
parameter_weight=parameter_weight,
nr_of_particles=nr_of_particles,
particle_dimension=1,
particle_size=in_features,
use_analytic_solution=True,
use_rnn_mode=use_particle_rnn_mode)
print('Using shooting model {}'.format(shooting_model))
par_initializer = pi.VectorEvolutionParameterInitializer(
only_random_initialization=True, random_initialization_magnitude=0.5)
smodel.set_state_initializer(state_initializer=par_initializer)
shooting_block = shooting_blocks.ShootingBlockBase(
name='simple',
shooting_integrand=smodel,
use_particle_free_rnn_mode=use_particle_free_rnn_mode,
integrator=integrator)
return shooting_block
shootingintegrand_kwargs = {
'in_features':
1,
'nonlinearity':
args.nonlinearity,
'nr_of_particles':
args.nr_of_particles,
'parameter_weight':
args.pw,
'particle_dimension':
1,
'particle_size':
1,
'costate_initializer':
pi.VectorEvolutionParameterInitializer(
random_initialization_magnitude=0.1),
'optimize_over_data_initial_conditions':
args.optimize_over_data_initial_conditions,
'optimize_over_data_initial_conditions_type':
args.optimize_over_data_initial_conditions_type,
'optional_weight':
args.optional_weight
}
inflation_factor = args.inflation_factor # for the up-down models (i.e., how much larger is the internal state; default is 5)
use_particle_rnn_mode = args.use_particle_rnn_mode
use_particle_free_rnn_mode = args.use_particle_free_rnn_mode
use_analytic_solution = True # True is the proper setting here for models that have analytic solutions implemented
write_out_first_five_gradients = False # for debugging purposes; use jointly with check_gradient_over_iterations.py
use_fixed_sample_batch = write_out_first_five_gradients # has to be set to True if we want to compare autodiff and analytic gradients (as otherwise there will be different random initializations