def e_007(): a = ProjectVariable() a.experiment_name = '007. prid450, cosine, no CLR, rank=225' a.ranking_number = 225 a.iterations = 10 a.datasets = ['prid450'] a.cost_module_type = 'cosine' a.use_cyclical_learning_rate = False a.log_file = 'thesis_experiment_log.txt' scn.super_main(a)
def e_075(): a = ProjectVariable() a.experiment_name = '075. prid450, euclidean, no CLR, rank=100' a.ranking_number = 100 a.iterations = 30 a.datasets = ['prid450'] a.cost_module_type = 'euclidean' a.use_cyclical_learning_rate = False a.log_file = 'thesis_experiment_log.txt' scn.super_main(a)
def e_004(): a = ProjectVariable() a.experiment_name = '004. caviar, euclidean, no CLR, rank=36' a.ranking_number = 36 a.iterations = 10 a.datasets = ['caviar'] a.cost_module_type = 'euclidean' a.use_cyclical_learning_rate = False a.log_file = 'thesis_experiment_log.txt' scn.super_main(a)
def e_073(): a = ProjectVariable() a.experiment_name = '073. priming on viper. no CLR. LR = 0.00000001' a.log_file = 'thesis_experiment_log.txt' a.priming = True a.ranking_number = 316 a.load_weights_name = 'viper_weigths_0.h5' a.datasets = ['viper'] a.prime_epochs = 5 a.use_cyclical_learning_rate = False a.learning_rate = 0.00000001 a.iterations = 10 prime.super_main(a)
def ex_2_1_2(): a = ProjectVariable() a.experiment_name = 'experiment 2_1_2: prid450, cost_module_type=cosine, lr=0.00001' a.use_gpu = '1' a.log_file = 'log_%s.txt' % a.use_gpu a.epochs = 100 a.iterations = 20 a.dataset_test = 'prid450' a.ranking_number_test = 100 a.cost_module_type = 'cosine' a.use_cyclical_learning_rate = False a.optimizer = 'rms' scn.super_main(a)
def ex_2_0_1(): a = ProjectVariable() a.experiment_name = 'experiment 2_0_1: grid, cost_module_type=euclidean, lr=0.00001' a.use_gpu = '1' a.log_file = 'log_%s.txt' % a.use_gpu a.epochs = 100 a.iterations = 20 a.dataset_test = 'grid' a.ranking_number_test = 100 a.cost_module_type = 'euclidean' a.use_cyclical_learning_rate = False a.optimizer = 'rms' scn.super_main(a)
def e_093(): a = ProjectVariable() a.experiment_name = '093. viper > grid, no CLR' a.epochs = 100 # a.save_inbetween = True # a.name_indication = 'dataset_name' # a.save_points = [100] a.ranking_number = 125 a.iterations = 10 a.batch_size = 32 a.load_weights_name = 'viper_weigths_base.h5' a.activation_function = 'elu' a.use_cyclical_learning_rate = False # a.cl_min = 0.00005 # a.cl_max = 0.001 a.neural_distance = 'absolute' a.datasets = ['grid'] a.log_file = 'thesis_experiment_log.txt' scn.super_main(a)