def get_grid_param_list(): dictlistprod = cartesian_dict_of_lists_product default_params = [{ 'save_folder': './data/models/sacred/paper/csp/online/remove-cz/', 'only_return_exp': False, }] subject_id_params = dictlistprod({ 'subject_id': ['elkh'] #'anla', 'hawe', 'lufi', 'sama', }) break_params = dictlistprod({ 'with_breaks': [True, False], }) preproc_params = dictlistprod({ 'min_freq': [1], }) grid_params = product_of_list_of_lists_of_dicts([ default_params, subject_id_params, break_params, preproc_params, ]) return grid_params
def get_grid_param_list(): dictlistprod = cartesian_dict_of_lists_product default_params = [{ 'save_folder': './data/models/sacred/paper/bcic-iv-2a-cross/start/', 'only_return_exp': False, 'n_chans': 22 }] subject_folder_params = dictlistprod({ 'subject_id': range(1,10), 'data_folder': ['/home/schirrmr/data/bci-competition-iv/2a/',] }) loss_params = dictlistprod({ 'loss_expression': ['$tied_loss']}) preproc_params = dictlistprod({ 'filt_order': [3,],#10 'clean_train': [False], 'low_cut_hz': [0,4], 'train_start_ms': [1500]}) model_params = dictlistprod({ 'network': ['shallow'] #deep }) eval_params = dictlistprod({ 'kappa_mode': ['max']}) grid_params = product_of_list_of_lists_of_dicts([ default_params, subject_folder_params, preproc_params, loss_params, model_params, eval_params, ]) return grid_params
def get_grid_param_list(): dictlistprod = cartesian_dict_of_lists_product default_params = [{ "save_folder": "/data/schirrmr/schirrmr/reversible/experiments/dropout-weight-decay", "only_return_exp": False, "debug": False, }] subject_id_params = dictlistprod({"subject_id": range(4, 5)}) data_params = dictlistprod({"n_sensors": [22], "final_hz": [256]}) preproc_params = dictlistprod({"half_before": [True]}) ival_params = [{"start_ms": 500, "stop_ms": 1500}] training_params = dictlistprod({"max_epochs": [600, 1200]}) implementation_params = dictlistprod({"constant_memory": [False]}) init_params = dictlistprod({"set_distribution_to_empirical": [True]}) optim_params = dictlistprod({ "uni_noise_factor": [ 0, 1e-2, 5e-2, ], # "gauss_noise_factor": [0.1], # "weight_decay": [ 1e-4, ], # }) network_params = dictlistprod({ "final_fft": [True, False], 'drop_p': [ 0, 0.1, ] }) # save_params = [{"save_model": True}] grid_params = product_of_list_of_lists_of_dicts([ default_params, subject_id_params, data_params, implementation_params, training_params, init_params, preproc_params, ival_params, optim_params, network_params, save_params, ]) return grid_params
def get_grid_param_list(): dictlistprod = cartesian_dict_of_lists_product save_params = [ { 'save_folder': '/home/schirrmr/data/exps/invertible-eeg/images/', }, ] data_params = [{ 'first_n': None, }] debug_params = [{ 'debug': False, }] train_params = dictlistprod({ 'n_epochs': [200], 'batch_size': [32], }) random_params= dictlistprod({ 'np_th_seed': range(0,2), }) optim_params = dictlistprod({ 'lr': [5e-4], 'weight_decay': [5e-5], 'cross_ent_weight': [0,],#1,10,100,1000 'scale_2_cross_ent': [False, True], 'mask_for_cross_ent': [False], 'nll_weight': [1], 'linear_classifier': [False], 'flow_gmm': [True], 'flow_coupling': ['affine'], }) model_params = dictlistprod({ 'n_mixes': [32], }) grid_params = product_of_list_of_lists_of_dicts([ save_params, train_params, debug_params, random_params, optim_params, model_params, data_params, ]) return grid_params
def get_grid_param_list(): dictlistprod = cartesian_dict_of_lists_product default_params = [{ "save_folder": "/data/schirrmr/schirrmr/reversible/experiments/new-deep-invertible", "only_return_exp": False, "debug": False, }] subject_id_params = dictlistprod({"subject_id": range(4, 10)}) data_params = dictlistprod({"n_sensors": [22], "final_hz": [256]}) preproc_params = dictlistprod({"half_before": [True]}) ival_params = [{"start_ms": 500, "stop_ms": 1500}] training_params = dictlistprod({"max_epochs": [1000, 4000]}) model_params = dictlistprod({"model_name": ["deep_invertible"]}) implementation_params = dictlistprod({"constant_memory": [True]}) init_params = dictlistprod({ "data_zero_init": [False], "set_distribution_to_empirical": [True] }) network_params = dictlistprod({"final_fft": [False]}) # True clf_params = dictlistprod({"clf_loss": [None, 'sliced']}) # "likelihood", None dist_params = dictlistprod({ "ot_on_class_dims": [False], # , True "independent_class_dists": [True], }) save_params = [{"save_model": True}] grid_params = product_of_list_of_lists_of_dicts([ default_params, subject_id_params, data_params, implementation_params, training_params, init_params, model_params, preproc_params, dist_params, ival_params, network_params, clf_params, save_params, ]) return grid_params
def get_grid_param_list(): dictlistprod = cartesian_dict_of_lists_product debug_params = [{ 'debug': False, }] data_params = dictlistprod({ 'n_subjects': [None], #'#' 'n_seconds': [None], # 4 is one window 'subject_id': [None], }) train_params = dictlistprod({ 'n_epochs': [200], }) random_params = dictlistprod({ 'np_th_seed': range(0, 3), }) optim_params = dictlistprod({ 'lr': [5e-4], 'weight_decay': [5e-5], }) model_params = dictlistprod({ 'hidden_channels': [ 512, ], #512 'n_virtual_chans': [ 0, ], #1,2 'n_blocks_up': [4, 8], 'n_blocks_down': [4, 8], 'n_mixes': [128], 'splitter_last': ['haar', 'subsample'], 'init_perm_to_identity': [ True, ], #False#True }) grid_params = product_of_list_of_lists_of_dicts([ data_params, train_params, debug_params, random_params, optim_params, model_params, ]) return grid_params
def get_grid_param_list(): dictlistprod = cartesian_dict_of_lists_product default_params = [{ 'save_folder': './data/models/fbcsp/6-class/avg-cov/', }] filename_params = [ { 'filename': '/data/schirrmr/schirrmr/offline-6-class-cabin/AnTiCUO1_1-4_250Hz.BBCI.mat', }, { 'filename': '/data/schirrmr/schirrmr/offline-6-class-cabin/FeHeCUO1_1-8_250Hz.BBCI.mat', }, ] filterbank_params = [ { 'min_freq': 1, 'max_freq': 38, 'low_width': 6, 'high_width': 8, 'high_overlap': 4, 'last_low_freq': 10, 'low_overlap': 3, 'n_top_bottom_csp_filters': 5, 'n_selected_features': 20 }, { 'min_freq': 1, 'max_freq': 118, 'low_width': 6, 'high_width': 8, 'high_overlap': 4, 'last_low_freq': 10, 'low_overlap': 3, 'n_top_bottom_csp_filters': 5, 'n_selected_features': 20 }, ] sensor_params = [{ 'sensors': 'all', }, { 'sensors': 'C_sensors' }] grid_params = product_of_list_of_lists_of_dicts( [default_params, filename_params, filterbank_params, sensor_params]) return grid_params
def get_grid_param_list(): dictlistprod = cartesian_dict_of_lists_product default_params = [{ 'save_folder': './data/models/pytorch/online/niri-repl/', 'only_return_exp': False, }] stop_params = [{ 'max_epochs': 200, }] grid_params = product_of_list_of_lists_of_dicts([ default_params, stop_params, ]) return grid_params
def get_grid_param_list(): dictlistprod = cartesian_dict_of_lists_product default_params = [{ 'save_folder': './data/models/sacred/paper/bcic-iv-2b/deepshallow/', 'only_return_exp': False, 'n_chans': 3, 'run_after_early_stop': True, }] subject_folder_params = dictlistprod({ 'subject_id': range(1, 10), 'data_folder': ['/home/schirrmr/data/bci-competition-iv/2b/'], 'train_inds': [[1, 2, 3]], 'test_inds': [[4, 5]], 'sets_like_fbcsp_paper': [True], }) model_params = dictlistprod({ 'network': ['deep', 'shallow'], #'deep'#'shallow' }) stop_params = dictlistprod({'stop_chan': ['misclass']}) #'misclass', loss_params = dictlistprod({'loss_expression': ['$tied_loss']}) #'misclass', preproc_params = dictlistprod({ 'clean_train': [ False, ], #False 'filt_order': [ 3, ], #10 'low_cut_hz': [0, 4] }) grid_params = product_of_list_of_lists_of_dicts([ default_params, subject_folder_params, model_params, stop_params, preproc_params, loss_params, ]) return grid_params
def get_grid_param_list(): dictlistprod = cartesian_dict_of_lists_product default_params = [{ 'save_folder': './data/models/sacred/paper/bcic-iv-2a/repl/', 'only_return_exp': False, 'n_chans': 22 }] subject_folder_params = dictlistprod({ 'subject_id': range(1, 10), 'data_folder': [ '/home/schirrmr/data/bci-competition-iv/2a/', ] }) grid_params = product_of_list_of_lists_of_dicts([ default_params, subject_folder_params, ]) return grid_params
def get_grid_param_list(): dictlistprod = cartesian_dict_of_lists_product default_params = [{ 'save_folder': './data/models/sacred/paper/bcic-iv-2b/cv-proper-sets/', 'only_return_exp': False, 'n_chans': 3, }] subject_folder_params = dictlistprod({ 'subject_id': range(1, 10), 'data_folder': ['/home/schirrmr/data/bci-competition-iv/2b/'], }) exp_params = dictlistprod({ 'run_after_early_stop': [ True, ], }) stop_params = dictlistprod({'stop_chan': ['misclass']}) #, loss_params = dictlistprod({'loss_expression': ['$tied_loss']}) #'misclass', preproc_params = dictlistprod({ 'filt_order': [ 3, ], #10 'low_cut_hz': [4], 'sets_like_fbcsp_paper': [False, True] }) grid_params = product_of_list_of_lists_of_dicts([ default_params, exp_params, subject_folder_params, stop_params, preproc_params, loss_params, ]) return grid_params
def get_grid_param_list(): dictlistprod = cartesian_dict_of_lists_product save_params = [ { 'save_folder': '/home/schirrmr/data/exps/invertible-eeg/hgd-21-ch-32-hz/', }, ] data_params = dictlistprod({ 'subject_id': list(range(1, 15)), 'dataset_name': ['hgd'], # 4 is one window }) model_params = dictlistprod({ 'hidden_channels': [ 128, ], #512 'n_virtual_chans': [ 0, ], #1,2 'n_blocks_up': [8], 'n_blocks_down': [8], 'n_mixes': [128], 'splitter_last': [ 'haar', ], 'init_perm_to_identity': [ True, ], #False#True }) grid_params = product_of_list_of_lists_of_dicts([ save_params, data_params, model_params, ]) return grid_params
def get_grid_param_list(): dictlistprod = cartesian_dict_of_lists_product default_params = [ { "save_folder": "/data/schirrmr/schirrmr/reversible/experiments/deepshallow", "only_return_exp": False, "debug": False, } ] subject_id_params = dictlistprod({"subject_id": range(4, 10)}) data_params = dictlistprod({"n_sensors": [22], "final_hz": [256]}) preproc_params = dictlistprod({"half_before": [True]}) ival_params = [{"start_ms": 500, "stop_ms": 1500}] training_params = dictlistprod({"max_epochs": [100]}) model_params = dictlistprod({"model": ["deep_invertible",], "final_fft": [True], "add_bnorm": [False],}) # , True optim_params = dictlistprod({"weight_decay": [0.5 * 0.001, 0.5*0.01], "act_norm": [True, False]}) save_params = [{"save_model": False}] grid_params = product_of_list_of_lists_of_dicts( [ default_params, subject_id_params, data_params, training_params, preproc_params, ival_params, training_params, model_params, save_params, optim_params, ] ) return grid_params
def get_grid_param_list(): dictlistprod = cartesian_dict_of_lists_product save_params = [ { 'save_folder': '/home/schirrmr/data/exps/invertible-eeg/tuh-all-chans/', }, ] data_params = dictlistprod({ 'n_subjects': [2076], #'#' 'n_seconds': [15 * 4], # 4 is one window 'dataset_name': ['tuh'], # 4 is one window }) grid_params = product_of_list_of_lists_of_dicts([ save_params, data_params, ]) return grid_params
def get_grid_param_list(): dictlistprod = cartesian_dict_of_lists_product default_params = [{ 'save_folder': './data/models/pytorch/auto-diag/smac-test/', 'only_return_exp': False, }] load_params = [{ 'max_recording_mins': 35, 'n_recordings': 5, }] clean_defaults = { 'max_min_threshold': None, 'shrink_val': None, 'max_min_expected': None, 'max_abs_val': None, 'batch_set_zero_val': None, 'batch_set_zero_test': None, 'max_min_remove': None, } clean_variants = [ #{}, #{'batch_set_zero_val': 500, 'batch_set_zero_test': True}, { 'max_abs_val': 800 }, #{'max_abs_val' : 500}, #{'shrink_val': 200}, #{'shrink_val': 500}, # {'shrink_val': 800}, ] clean_params = product_of_list_of_lists_of_dicts([[clean_defaults], clean_variants]) preproc_params = dictlistprod({ 'sec_to_cut': [60], 'duration_recording_mins': [3], 'sampling_freq': [100], 'low_cut_hz': [ None, ], 'high_cut_hz': [ None, ], 'divisor': [10], }) standardizing_defaults = { 'exp_demean': False, 'exp_standardize': False, 'moving_demean': False, 'moving_standardize': False, 'channel_demean': False, 'channel_standardize': False, } standardizing_variants = [ {}, ] standardizing_params = product_of_list_of_lists_of_dicts( [[standardizing_defaults], standardizing_variants]) split_params = dictlistprod({ 'n_folds': [5], 'i_test_fold': [0], }) model_params = [ { 'input_time_length': 1200, 'final_conv_length': 40, 'model_name': 'shallow', }, ] model_constraint_params = dictlistprod({ 'model_constraint': ['defaultnorm', None], }) iterator_params = [{'batch_size': 64}] stop_params = [{ 'max_epochs': 3, }] grid_params = product_of_list_of_lists_of_dicts([ default_params, load_params, clean_params, preproc_params, split_params, model_params, iterator_params, standardizing_params, stop_params, model_constraint_params ]) return grid_params
def get_grid_param_list(): dictlistprod = cartesian_dict_of_lists_product default_params = [{ 'save_folder': './data/models/pytorch/auto-diag/threepath-10fold/', 'only_return_exp': False, }] load_params = [{ 'max_recording_mins': 35, 'n_recordings': 1500, }] clean_defaults = { 'max_min_threshold': None, 'shrink_val': None, 'max_min_expected': None, 'max_abs_val': None, 'batch_set_zero_val': None, 'batch_set_zero_test': None, 'max_min_remove': None, } clean_variants = [ {'max_abs_val' : 800}, ] clean_params = product_of_list_of_lists_of_dicts( [[clean_defaults], clean_variants]) preproc_params = dictlistprod({ 'sec_to_cut': [60], 'duration_recording_mins': [3], 'sampling_freq': [100], 'low_cut_hz': [None,], 'high_cut_hz': [None,], 'divisor': [10], }) standardizing_defaults = { 'exp_demean': False, 'exp_standardize': False, 'moving_demean': False, 'moving_standardize': False, 'channel_demean': False, 'channel_standardize': False, } standardizing_variants = [ {}, ] standardizing_params = product_of_list_of_lists_of_dicts( [[standardizing_defaults], standardizing_variants]) split_params = dictlistprod({ 'n_folds': [10], 'i_test_fold': [0,1,2,3,4,5,6,7,8,9], }) model_params = dictlistprod({ 'virtual_chan_1x1_conv': [True], 'mean_across_features': [False], 'n_classifier_filters': [100,], 'n_start_filters': [8,10], 'drop_prob': [0.5], 'early_bnorm': [False], 'extra_conv_stride': [2,4,], 'later_kernel_len': [5,9], }) model_params = product_of_list_of_lists_of_dicts( [model_params, [ { 'n_preds_per_input': 3000, 'input_time_length': 6000, }, ] ] ) final_layer_params = dictlistprod({ 'sigmoid': [False ], }) model_constraint_params = dictlistprod({ 'model_constraint': ['defaultnorm', ], }) iterator_params = [{ 'batch_size': 64 }] stop_params = [{ 'max_epochs': 35, }] grid_params = product_of_list_of_lists_of_dicts([ default_params, load_params, clean_params, preproc_params, split_params, model_params, final_layer_params, iterator_params, standardizing_params, stop_params, model_constraint_params ]) return grid_params
def get_grid_param_list(): dictlistprod = cartesian_dict_of_lists_product default_params = [{ 'save_folder': './data/models/pytorch/auto-diag/more-layers/', 'only_return_exp': False, }] load_params = [{ 'max_recording_mins': 35, 'n_recordings': 1500, }] clean_defaults = { 'max_min_threshold': None, 'shrink_val': None, 'max_min_expected': None, 'max_abs_val': None, 'batch_set_zero_val': None, 'batch_set_zero_test': None, 'max_min_remove': None, } clean_variants = [ { 'max_abs_val': 800 }, ] clean_params = product_of_list_of_lists_of_dicts([[clean_defaults], clean_variants]) preproc_params = dictlistprod({ 'sec_to_cut': [60], 'duration_recording_mins': [3], 'sampling_freq': [100], 'low_cut_hz': [ None, ], 'high_cut_hz': [ None, ], 'divisor': [10], }) standardizing_defaults = { 'exp_demean': False, 'exp_standardize': False, 'moving_demean': False, 'moving_standardize': False, 'channel_demean': False, 'channel_standardize': False, } standardizing_variants = [ {}, ] standardizing_params = product_of_list_of_lists_of_dicts( [[standardizing_defaults], standardizing_variants]) split_params = dictlistprod({ 'n_folds': [10], 'i_test_fold': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], }) model_params = [ { 'input_time_length': 18000, 'final_conv_length': 1, }, ] model_params = product_of_list_of_lists_of_dicts([ model_params, dictlistprod({ 'pool_stride': [3], 'n_blocks_to_add': [0, 1, 2] }) + dictlistprod({ 'pool_stride': [4], 'n_blocks_to_add': [0, 1] }) ]) final_layer_params = dictlistprod({ 'sigmoid': [False], }) model_constraint_params = dictlistprod({ 'model_constraint': [ 'defaultnorm', ], }) iterator_params = [{'batch_size': 32}] stop_params = [{ 'max_epochs': 35, }] grid_params = product_of_list_of_lists_of_dicts([ default_params, load_params, clean_params, preproc_params, split_params, model_params, final_layer_params, iterator_params, standardizing_params, stop_params, model_constraint_params ]) return grid_params
def get_grid_param_list(): dictlistprod = cartesian_dict_of_lists_product train_test_filenames = [ { 'train_filename': 'BhNoMoSc1S001R01_ds10_1-12.BBCI.mat', 'test_filename': 'BhNoMoSc1S001R13_ds10_1-2BBCI.mat', }, { 'train_filename': 'FaMaMoSc1S001R01_ds10_1-14.BBCI.mat', 'test_filename': 'FaMaMoSc1S001R15_ds10_1-2BBCI.mat', }, { 'train_filename': 'FrThMoSc1S001R01_ds10_1-11.BBCI.mat', 'test_filename': 'FrThMoSc1S001R12_ds10_1-2BBCI.mat', }, { 'train_filename': 'GuJoMoSc01S001R01_ds10_1-11.BBCI.mat', 'test_filename': 'GuJoMoSc01S001R12_ds10_1-2BBCI.mat' }, { 'train_filename': 'KaUsMoSc1S001R01_ds10_1-11.BBCI.mat', 'test_filename': 'KaUsMoSc1S001R12_ds10_1-2BBCI.mat' }, { 'train_filename': 'LaKaMoSc1S001R01_ds10_1-9.BBCI.mat', 'test_filename': 'LaKaMoSc1S001R10_ds10_1-2BBCI.mat' }, { 'train_filename': 'LuFiMoSc3S001R01_ds10_1-11.BBCI.mat', 'test_filename': 'LuFiMoSc3S001R12_ds10_1-2BBCI.mat' }, { 'train_filename': 'MaJaMoSc1S001R01_ds10_1-11.BBCI.mat', 'test_filename': 'MaJaMoSc1S001R12_ds10_1-2BBCI.mat' }, { 'train_filename': 'MaKiMoSC01S001R01_ds10_1-4.BBCI.mat', 'test_filename': 'MaKiMoSC01S001R05_ds10_1-2BBCI.mat' }, { 'train_filename': 'MaVoMoSc1S001R01_ds10_1-11.BBCI.mat', 'test_filename': 'MaVoMoSc1S001R12_ds10_1-2BBCI.mat' }, # { # 'train_filename': 'PiWiMoSc1S001R01_ds10_1-11.BBCI.mat', # 'test_filename': 'PiWiMoSc1S001R12_ds10_1-2BBCI.mat' # }, # { # 'train_filename': 'RoBeMoSc03S001R01_ds10_1-9.BBCI.mat', # 'test_filename': 'RoBeMoSc03S001R10_ds10_1-2BBCI.mat' # }, # { # 'train_filename': 'RoScMoSc1S001R01_ds10_1-11.BBCI.mat', # 'test_filename': 'RoScMoSc1S001R12_ds10_1-2BBCI.mat' # }, # { # 'train_filename': 'StHeMoSc01S001R01_ds10_1-10.BBCI.mat', # 'test_filename': 'StHeMoSc01S001R11_ds10_1-2BBCI.mat' # }, ] data_split_train_params = dictlistprod({ 'n_folds': [10], 'i_test_fold': list(range(9, 10)), 'test_on_eval_set': [False], }) # data_split_test_params = [ # { # 'n_folds': None, # 'i_test_fold': None, # 'test_on_eval_set': True, # }] no_early_stop_params = [{ 'valid_set_fraction': None, 'use_validation_set': False, 'max_increase_epochs': None, }] adamw_adam_comparison_params = [{ 'use_norm_constraint': False, 'optimizer_name': 'adam', 'schedule_weight_decay': False, }, { 'use_norm_constraint': False, 'optimizer_name': 'adamw', 'schedule_weight_decay': True, }] scheduler_params = dictlistprod({ 'scheduler_name': [ 'cosine', ], 'restarts': [None], }) lr_weight_decay_params = dictlistprod({ 'model_name': ['deep'], 'init_lr': np.array([1 / 8.0, 4.0, 8.0]) * 0.01, 'weight_decay': np.array([ 0, 1 / 64.0, 1 / 32.0, 1 / 16.0, 1 / 8.0, 1 / 4.0, 1 / 2.0, 1.0, 2.0, 4.0, 8.0 ]) * 0.001, }) + dictlistprod({ 'model_name': ['deep'], 'init_lr': np.array([1 / 8.0, 1 / 4.0, 1 / 2.0, 1.0, 2.0, 4.0, 8.0]) * 0.01, 'weight_decay': np.array([1 / 64.0, 8.0]) * 0.001, #8.0 possibly removable }) + dictlistprod({ 'model_name': ['shallow'], 'init_lr': np.array([ 1 / 128.0, 1 / 64.0, 1 / 2.0, ]) * 0.01, 'weight_decay': np.array([ 0, 1 / 128.0, 1 / 64.0, 1 / 32.0, 1 / 16.0, 1 / 8.0, 1 / 4.0, 1 / 2.0, 1.0, 2.0, 4.0 ]) * 0.001, }) + dictlistprod({ 'model_name': ['shallow'], 'init_lr': np.array([ 1 / 128.0, 1 / 64.0, 1 / 32.0, 1 / 16.0, 1 / 8.0, 1 / 4.0, 1 / 2.0, ]) * 0.01, 'weight_decay': np.array([1 / 128.0, 1 / 64.0, 2.0, 4.0]) * 0.001, }) + dictlistprod({ 'model_name': ['resnet-xavier-uniform'], 'init_lr': np.array([1 / 128.0, 1 / 64.0, 1 / 2.0, 1]) * 0.01, 'weight_decay': np.array([ 0, 1 / 32.0, 1 / 16.0, 1 / 8.0, 1 / 4.0, 1 / 2.0, 1.0, 2.0, 4.0, 8.0, 16 ]) * 0.001, }) + dictlistprod({ 'model_name': ['resnet-xavier-uniform'], 'init_lr': np.array([ 1 / 128.0, 1 / 64.0, 1 / 32.0, 1 / 16.0, 1 / 8.0, 1 / 4.0, 1 / 2.0, 1 ]) * 0.01, 'weight_decay': np.array([16.0]) * 0.001, }) # final_settings_params = [{ # 'max_epochs': 320, # 'save_folder': '/home/schirrmr/data/models/adameegeval/final-no-restart/', # 'use_norm_constraint': False, # 'restarts': None, # }] # final_settings_variants = [ # { # 'model_name': 'deep', # 'optimizer_name': 'adam', # 'scheduler_name': None, # 'schedule_weight_decay': False, # 'init_lr': 0.5 * 0.01, # 'weight_decay': (1/32.0) * 0.001, # }, # { # 'model_name': 'deep', # 'optimizer_name': 'adam', # 'scheduler_name': 'cosine', # 'schedule_weight_decay': False, # 'init_lr': 2.0 * 0.01, # 'weight_decay': (1/8.0) * 0.001, # }, # { # 'model_name': 'deep', # 'optimizer_name': 'adamw', # 'scheduler_name': None, # 'schedule_weight_decay': True, # 'init_lr': 2.0 * 0.01, # 'weight_decay': 0 * 0.001, # }, # { # 'model_name': 'deep', # 'optimizer_name': 'adamw', # 'scheduler_name': 'cosine', # 'schedule_weight_decay': True, # 'init_lr': 1.0 * 0.01, # 'weight_decay': 0.5 * 0.001, # }, # { # 'model_name': 'shallow', # 'optimizer_name': 'adam', # 'scheduler_name': None, # 'schedule_weight_decay': False, # 'init_lr': (1/32.0) * 0.01, # 'weight_decay': (1/32.0) * 0.001, # }, # { # 'model_name': 'shallow', # 'optimizer_name': 'adam', # 'scheduler_name': 'cosine', # 'schedule_weight_decay': False, # 'init_lr': (1/8.0) * 0.01, # 'weight_decay': 1.0 * 0.001, # }, # { # 'model_name': 'shallow', # 'optimizer_name': 'adamw', # 'scheduler_name': None, # 'schedule_weight_decay': True, # 'init_lr': (1/32.0) * 0.01, # 'weight_decay': (1/8.0) * 0.001, # }, # { # 'model_name': 'shallow', # 'optimizer_name': 'adamw', # 'scheduler_name': 'cosine', # 'schedule_weight_decay': True, # 'init_lr': (1/16.0) * 0.01, # 'weight_decay': 0 * 0.001, # }, # { # 'model_name': 'resnet-xavier-uniform', # 'optimizer_name': 'adam', # 'scheduler_name': None, # 'schedule_weight_decay': False, # 'init_lr': (1/32.0) * 0.01, # 'weight_decay': 0 * 0.001, # }, # { # 'model_name': 'resnet-xavier-uniform', # 'optimizer_name': 'adam', # 'scheduler_name': 'cosine', # 'schedule_weight_decay': False, # 'init_lr': (1/8.0) * 0.01, # 'weight_decay': 2.0 * 0.001, # }, # { # 'model_name': 'resnet-xavier-uniform', # 'optimizer_name': 'adamw', # 'scheduler_name': None, # 'schedule_weight_decay': True, # 'init_lr': (1/16.0) * 0.01, # 'weight_decay': (1/32.0) * 0.001, # }, # { # 'model_name': 'resnet-xavier-uniform', # 'optimizer_name': 'adamw', # 'scheduler_name': 'cosine', # 'schedule_weight_decay': True, # 'init_lr': (1/32.0) * 0.01, # 'weight_decay': 2.0 * 0.001, # }, # ] seed_params = dictlistprod({ 'np_th_seed': [0] #0,1,2,3,4 }) preproc_params = dictlistprod({ 'low_cut_hz': [4] #0 }) stop_params = [ # # {'max_epochs': None}, { 'max_epochs': 20, 'save_folder': '/home/schirrmr/data/models/adameegeval/4sec-cv-lr-wd-20-epoch-2/', }, { 'max_epochs': 40, 'save_folder': '/home/schirrmr/data/models/adameegeval/4sec-cv-lr-wd-40-epoch-2/', }, { 'max_epochs': 80, 'save_folder': '/home/schirrmr/data/models/adameegeval/4sec-cv-lr-wd-80-epoch/', }, { 'max_epochs': 160, 'save_folder': '/home/schirrmr/data/models/adameegeval/4sec-cv-lr-wd-160-epoch/', }, { 'max_epochs': 320, 'save_folder': '/home/schirrmr/data/models/adameegeval/4sec-cv-lr-wd-320-epoch/', }, ] debug_params = [{ 'debug': False, }] grid_params = product_of_list_of_lists_of_dicts([ train_test_filenames, data_split_train_params, preproc_params, no_early_stop_params, adamw_adam_comparison_params, scheduler_params, lr_weight_decay_params, stop_params, seed_params, debug_params, ]) return grid_params
def get_grid_param_list(): dictlistprod = cartesian_dict_of_lists_product default_params = [{ 'save_folder': '/data/schirrmr/schirrmr/models/auto-diag/final-eval-from-smalldata-config/', 'only_return_exp': False, }] seed_params = dictlistprod({ 'np_th_seed': list(range(5, 10)) #[0,1,2,3,4] }) save_params = [{ 'save_predictions': False, 'save_crop_predictions': False, }] load_params = [{ 'max_recording_mins': 35, 'n_recordings': None, }] clean_params = [{ 'max_abs_val': 800, }] sensor_params = [ { 'n_chans': 21, 'sensor_types': ['EEG'], }, ] preproc_params = dictlistprod({ 'sec_to_cut_at_start': [60], 'sec_to_cut_at_end': [0], 'duration_recording_mins': [20], 'test_recording_mins': [None], 'sampling_freq': [100], 'divisor': [ None, ], # 10 before 'clip_before_resample': [False], #False, }) # this differentiates train/test also. split_params = dictlistprod({ 'test_on_eval': [True], 'n_folds': [5], 'i_test_fold': [4], 'shuffle': [False], }) model_params = [ # { # 'input_time_length': 6000, # 'final_conv_length': 35, # 'model_name': 'shallow', # 'n_start_chans': 40, # 'n_chan_factor': None, # 'model_constraint': 'defaultnorm', # 'stride_before_pool': None, # 'scheduler': None, # 'optimizer': 'adam', # 'learning_rate': 1e-3, # 'weight_decay': 0, # 'merge_train_valid': False, # }, # { # 'input_time_length': 6000, # 'final_conv_length': 1, # 'model_name': 'deep', # 'n_start_chans': 25, # 'n_chan_factor': 2, # 'model_constraint': 'defaultnorm', # 'stride_before_pool': True, # 'scheduler': None, # 'optimizer': 'adam', # 'learning_rate': 1e-3, # 'weight_decay': 0, # 'merge_train_valid': False, # }, # { # 'input_time_length': 6000, # 'final_conv_length': 35, # 'model_name': 'shallow', # 'n_start_chans': 40, # 'n_chan_factor': None, # 'model_constraint': None, # 'stride_before_pool': None, # 'scheduler': 'cosine', # 'optimizer': 'adamw', # 'learning_rate': 0.0625 * 0.01, # 'weight_decay': 0, # 'merge_train_valid': True, # }, { 'input_time_length': 6000, 'final_conv_length': 1, 'model_name': 'deep', 'n_start_chans': 25, 'n_chan_factor': 2, 'model_constraint': None, 'stride_before_pool': True, 'scheduler': 'cosine', 'optimizer': 'adamw', 'learning_rate': 1 * 0.01, 'weight_decay': 0.5 * 0.001, 'merge_train_valid': True, }, ] iterator_params = [{'batch_size': 64}] stop_params = [{ 'max_epochs': 35, }] grid_params = product_of_list_of_lists_of_dicts([ default_params, seed_params, save_params, load_params, clean_params, preproc_params, sensor_params, split_params, model_params, iterator_params, stop_params, ]) return grid_params