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)
