z_indices=z_indices,
unexp_z_indices=unexp_z_indices,
parameters=cstrs_plant_parameters,
exp_dist_indices=exp_dist_indices,
seed=50,
tsteps_steady=5)
cstrs_offline_simulator = _get_cstrs_offline_simulator(
cstrs_mpc_controller,
cstrs_plant_parameters,
z_indices,
unexp_z_indices,
exp_dist_indices,
Nsim=150000,
num_data_gen_task=1,
num_process_per_task=1,
conservative_factor=1.02,
seed=1)
# Create a dictionary with the required data.
cstrs_parameters = dict(plant=cstrs,
mpc=cstrs_mpc_controller,
us=cstrs_us_controller,
satdlqr=cstrs_satdlqr_controller,
short_horizon=cstrs_sh_controller,
offline_simulator=cstrs_offline_simulator,
online_test_scenarios=cstrs_online_test_scenarios,
cstrs_plant_parameters=cstrs_plant_parameters)
#cstrs_parameters = dict(online_test_scenarios=cstrs_online_test_scenarios,
# cstrs_plant_parameters=cstrs_plant_parameters)
# Save data.
PickleTool.save(data_object=cstrs_parameters,
filename='cstrs_parameters.pickle')
scale=True)
# Get a NN controller.
regulator_dims = [72, 224, 224, 224, 6]
nn_controller = create_nn_controller(regulator_dims=regulator_dims)
# Train the NN controller.
(nn_controller, training_time) = train_nn_controller(nn_controller,
training_data,
stdout_filename='cstrs_train_unstd.txt',
ckpt_path='cstrs_train_unstd.ckpt')
# Get the best weights.
nn_controller.load_weights('cstrs_train_unstd.ckpt')
trained_regulator_weights = nn_controller.get_weights()
# Get the NN memory footprints.
memory_footprint = PickleTool.get_nn_memory_footprint(regulator_weights=
trained_regulator_weights,
filename='cstrs_trained_weights_unstd.pickle')
# Save the weights.
cstrs_training_data = dict(trained_regulator_weights=trained_regulator_weights,
training_time=training_time,
memory_footprint=memory_footprint,
xscale=xscale,
regulator_dims=regulator_dims)
# Save data.
PickleTool.save(data_object=cstrs_training_data,
filename='cstrs_train_unstd.pickle')
nn_controller,
training_data,
stdout_filename=str(train_architecture) + '-cstrs_train.txt',
ckpt_path=str(train_architecture) + '-cstrs_train.ckpt')
# Load and save the best weights.
nn_controller.load_weights(str(train_architecture) + '-cstrs_train.ckpt')
trained_regulator_weights.append(nn_controller.get_weights())
# Get the overall NN design time.
data_generation_time = num_sample / num_samples[-1]
data_generation_time = data_generation_time * raw_data['data_gen_time']
data_generation_times.append(data_generation_time)
training_times.append(training_time)
# Get the NN memory footprints.
memory_footprint = PickleTool.get_nn_memory_footprint(
regulator_weights=trained_regulator_weights[-1],
filename=str(train_architecture) + '-cstrs_trained_weights.pickle')
memory_footprints.append(memory_footprint)
# Save the weights.
cstrs_training_data = dict(trained_regulator_weights=trained_regulator_weights,
num_architectures=len(regulator_dims),
num_samples=num_samples,
data_generation_times=data_generation_times,
training_times=training_times,
memory_footprints=memory_footprints,
xscale=xscale,
regulator_dims=regulator_dims)
# Save data.
PickleTool.save(data_object=cstrs_training_data,
filename=str(train_architecture) + '-cstrs_train.pickle')