def CAVIKee_sphere_dataset(datapath, seed=4, iterate_seed=False):
# Construct a dataset using collision avoidance position tracking data only to train collision avoidance
(_, rawdatanames, datasetnames, _, rawdatapaths, datasetpaths, _, _, _) = CAI_args(datapath)
(random, randomstate, seed) = CAI_random(seed, iterate_seed=iterate_seed)
args_per_selection_method = ((0.55,),)
selection_methods = (select_random_proportion,)
split_proportion = (0.7, 0.15, 0.15)
max_obstacles = 5
(dataset, filenames) = construct(random, (rawdatapaths[1],), max_obstacles, CAVIKee_sphere_IO_from_rawdata, selection_methods, args_per_selection_method, *split_proportion)
save_numpy(datasetpaths[3], datasetnames[3], dataset)
save_numpy(datasetpaths[3], datasetnames[3] + '_filenames', filenames)
def VIK_dataset(datapath, seed=2, iterate_seed=False):
# Construct a dataset using all data to train velocity inverse kinematic for tracking assuming zero obstacles
(_, rawdatanames, datasetnames, _, rawdatapaths, datasetpaths, _, _, _) = CAI_args(datapath)
(random, randomstate, seed) = CAI_random(seed, iterate_seed=iterate_seed)
args_per_selection_method = ((1,),)
selection_methods = (select_random_proportion,)
split_proportion = (0.7, 0.15, 0.15)
max_obstacles = 0
(dataset, filenames) = construct(random, (rawdatapaths[0],), max_obstacles, VIK_IO_from_rawdata, selection_methods, args_per_selection_method, *split_proportion)
save_numpy(datasetpaths[0], datasetnames[0], dataset)
save_numpy(datasetpaths[0], datasetnames[0] + '_filenames', filenames)
def set_normalization_params(self):
(_, _, datasetnames, _, _, datasetpaths, _, _,
_) = CAI_args(self.datapath)
datasetpath = ''
datasetname = ''
datasettype = ''
if self.name[0:10] == 'CAVIKAUGee' or self.name[0:3] == 'VIK':
datasetpath = datasetpaths[2]
datasetname = datasetnames[2]
else:
print('Inference_Model not yet supported')
exit(1)
found_params_json = False
for filename in os.listdir(datasetpath):
if filename.find('_training_normalization_params') > -1:
params = np.load(datasetpath + '/' + datasetname +
'_training_normalization_params.npy')
self.input_normalization_params = params[0]
self.output_normalization_params = params[1]
found_params_json = True
break
if not found_params_json:
print(
'Could not find normalization parameters, loading, calculating, and storing them for later expedience.'
)
input_nor_params = normalize_parameters(
np.load(datasetpath + datasetname + '_training_inputs.npy'))
output_nor_params = normalize_parameters(
np.load(datasetpath + datasetname + '_training_outputs.npy'))
np.save(
datasetpath + '/' + datasetname +
'_training_normalization_params',
(input_nor_params, output_nor_params))
self.input_normalization_params = input_nor_params
self.output_normalization_params = output_nor_params
def CAVIKAUGee_slot_dataset_test(datapath, seed=5, iterate_seed=False):
# Construct a dataset using collision avoidance position tracking data only to train collision avoidance
(_, rawdatanames, datasetnames, _, rawdatapaths, datasetpaths, _, _, _) = CAI_args(datapath)
(random, randomstate, seed) = CAI_random(seed, iterate_seed=iterate_seed)
args_per_selection_method = ((0.20,),)
selection_methods = (select_random_proportion,)
split_proportion = (0, 0, 1)
max_obstacles = 5
((training_inputs, training_outputs, validation_inputs, validation_outputs, test_inputs, test_outputs), filenames) = construct(random, (rawdatapaths[1],), max_obstacles, CAVIKAUGee_slots_IO_from_rawdata, selection_methods, args_per_selection_method, *split_proportion)
path = datasetpaths[2] + '_test'
name = datasetnames[2]
make_path(path)
numpy.save(path + '/' + name + '_filenames', filenames)
numpy.save(path + '/' + name + '_test_inputs', test_inputs)
numpy.save(path + '/' + name + '_test_outputs', test_outputs)
def make_plots():
seed = 100
datapath = os.getcwd() + '/data'
(random, randomstate, seed) = CAI_random(seed)
(_, _, _, _, _, _, checkpointpath, modelspath, _) = CAI_args(datapath)
plotpath = datapath + '/sessions/CAI/plots/training_history_plots'
plot_all_most_recent_checkpoints(random,
checkpointpath,
performance_threshold_to_show=0.0,
containing=None,
save=True,
plotpath=plotpath)
modelnames = [
'CAVIKAUGee_sphere_correct_ReLU_SGDcustom_clipping_no_batchnormalization_simulated_annealing_greater_step_bigger_model_well_connected', #'CAVIKAUGee_sphere_correct_activation12_ReLU_SGDcustom_clipping_no_batchnormalization_simulated_annealing_greater_step_bigger_model_well_connected',
'CAVIKAUGee_slot_ReLU_SGDcustom_clipping_no_batchnormalization_simulated_annealing_greater_step_bigger_model_well_connected',
'CAVIKAUGee_no_obst_input_control_experiment_ReLU_SGDcustom_clipping_no_batchnormalization_simulated_annealing_greater_step_bigger_model_well_connected'
]
plot_histories_of_models_valfix(modelnames, 'training_history_comparison')
plot_histories_of_models(('VIK_pyramid', ), 'training_history_trials')