config = join_dicts( PRESETS_TRAIN['numu_v3'], { # Config: 'batch_size' : 1024, #'vars_input_slice', #'vars_input_png2d', #'vars_input_png3d', #'vars_target_total', #'vars_target_primary', 'dataset' : 'numu/prod4/fd_fhc/dataset_lstm_ee_fd_fhc_nonswap_std_cut.csv.xz', 'early_stop' : { 'name' : 'standard', 'kwargs' : { 'monitor' : 'val_loss', 'min_delta' : 0, 'patience' : 40, }, }, 'epochs' : 200, 'loss' : 'mean_absolute_percentage_error', 'max_prongs' : None, 'model' : { 'name' : 'lstm_v3_stack', 'kwargs' : { 'batchnorm' : True, 'layers_pre' : [128, 128, 128], 'layers_post' : [128, 128, 128], 'lstm3d_spec' : [ (32, 'forward') ], 'lstm2d_spec' : [ (32, 'forward') ], 'n_resblocks' : 0, }, }, 'noise' : None, 'optimizer' : { 'name' : 'RMSprop', 'kwargs' : { 'lr' : 0.001 }, }, 'prong_sorters' : None, 'regularizer' : { 'name' : 'l1', 'kwargs' : { 'l' : 0.001 }, }, 'schedule' : { 'name' : 'standard', 'kwargs' : { 'monitor' : 'val_loss', 'factor' : 0.5, 'patience' : 5, 'cooldown' : 0 }, }, 'seed' : 1337, 'steps_per_epoch' : 250, 'test_size' : 200000, 'weights' : { 'name' : 'flat', 'kwargs' : { 'bins' : 50, 'range' : (0, 5) }, }, # Args: 'vars_mod_png2d' : None, 'vars_mod_png3d' : None, 'vars_mod_slice' : None, 'outdir' : \ 'numu/prod4/final_model_perturbations/stack_of_3dlstms/', } )
config = join_dicts( PRESETS_TRAIN['nue_v3'], { # Config: 'batch_size' : 1024, #'vars_input_slice', #'vars_input_png2d', #'vars_input_png3d', #'vars_target_total', #'vars_target_primary', 'dataset' : 'nue/prod4/fd_fhc/dataset_lstm_ee_fd_fhc_fluxswap_std_cut.csv.xz', 'early_stop' : { 'name' : 'standard', 'kwargs' : { 'monitor' : 'val_loss', 'min_delta' : 0, 'patience' : 40, }, }, 'epochs' : 200, 'loss' : 'mean_absolute_percentage_error', 'max_prongs' : None, 'model' : { 'name' : 'lstm_v3', 'kwargs' : { 'batchnorm' : True, 'layers_pre' : [ 128, 128, 128 ], 'layers_post' : [ 128, 128, 128 ], 'lstm_units2d' : 32, 'lstm_units3d' : 32, 'n_resblocks' : 0, }, }, 'noise' : { 'affected_vars_slice' : [ 'calE', 'orphCalE', 'remPngCalE' ], 'affected_vars_png2d' : [ 'png2d.calE', 'png2d.weightedCalE' ], 'affected_vars_png3d' : [ 'png.calE', 'png.weightedCalE', 'png.bpf[0].overlapE', 'png.bpf[1].overlapE', 'png.bpf[2].overlapE', ], }, 'optimizer' : { 'name' : 'RMSprop', 'kwargs' : { 'lr' : 0.001 }, }, 'prong_sorters' : None, 'regularizer' : { 'name' : 'l1', 'kwargs' : { 'l' : 0.001 }, }, 'schedule' : { 'name' : 'standard', 'kwargs' : { 'monitor' : 'val_loss', 'factor' : 0.5, 'patience' : 5, 'cooldown' : 0 }, }, 'seed' : 0, 'steps_per_epoch' : 250, 'test_size' : 200000, 'weights' : { 'name' : 'flat', 'kwargs' : { 'bins' : 25, 'range' : (0, 5), 'clip' : 50 }, }, # Args: 'vars_mod_png2d' : None, 'vars_mod_png3d' : None, 'vars_mod_slice' : None, 'outdir' : 'nue/prod4/01_initial_studies/03_lstm_v3_final_wclip_noise/', } )
config = join_dicts( PRESETS_TRAIN['dune_numu_v1'], { # Config: 'batch_size': 1024, #'vars_input_slice', #'vars_input_png2d', #'vars_input_png3d', #'vars_target_total', #'vars_target_primary', 'dataset': 'dune/numu/dataset_rnne_dune_numu.csv.xz', 'early_stop': { 'name': 'standard', 'kwargs': { 'monitor': 'val_loss', 'min_delta': 0, 'patience': 40, }, }, 'epochs': 200, 'loss': 'mean_absolute_percentage_error', 'max_prongs': None, 'model': { 'name': 'lstm_v2', 'kwargs': { 'batchnorm': True, 'layers_pre': [128, 128, 128], 'layers_post': [128, 128, 128], 'lstm_units': 32, 'n_resblocks': 0, }, }, 'noise': None, 'optimizer': { 'name': 'RMSprop', 'kwargs': { 'lr': 0.001 }, }, 'prong_sorters': None, 'regularizer': { 'name': 'l1', 'kwargs': { 'l': 0.001 }, }, 'schedule': { 'name': 'standard', 'kwargs': { 'monitor': 'val_loss', 'factor': 0.5, 'patience': 5, 'cooldown': 0 }, }, 'seed': 0, 'steps_per_epoch': 250, 'test_size': 0.2, 'weights': None, # Args: 'vars_mod_png2d': None, 'vars_mod_png3d': None, 'vars_mod_slice': None, 'outdir': 'dune/numu/01_rnne_v1/', })
config = join_dicts( PRESETS_TRAIN['numu_v2'], { # Config: 'batch_size': 1024, #'vars_input_slice', #'vars_input_png2d', #'vars_input_png3d', #'vars_target_total', #'vars_target_primary', 'dataset': 'numu/prod4/fd_fhc/dataset_lstm_ee_fd_fhc_nonswap_std_cut.csv.xz', 'early_stop': { 'name': 'standard', 'kwargs': { 'monitor': 'val_loss', 'min_delta': 0, 'patience': 40, }, }, 'epochs': 200, 'loss': 'mean_absolute_percentage_error', 'max_prongs': None, 'model': { 'name': 'lstm_v2', 'kwargs': { 'batchnorm': True, 'layers_pre': [128, 128, 128], 'layers_post': [128, 128, 128], 'lstm_units': 32, 'n_resblocks': 0, }, }, 'noise': None, 'optimizer': { 'name': 'RMSprop', 'kwargs': { 'lr': 0.001 }, }, 'prong_sorters': None, 'regularizer': { 'name': 'l1', 'kwargs': { 'l': 0.001 }, }, 'schedule': { 'name': 'standard', 'kwargs': { 'monitor': 'val_loss', 'factor': 0.5, 'patience': 5, 'cooldown': 0 }, }, 'seed': 1337, 'steps_per_epoch': 250, 'test_size': 200000, 'weights': None, # Args: 'vars_mod_png2d': None, 'vars_mod_png3d': [ '-png.cvnpart.neutronid', '-png.cvnpart.pizeroid', '-png.bpf[2].pid', ], 'vars_mod_slice': None, 'outdir': 'numu/prod4/initial_studies/lstm_v2_final/', })
config = join_dicts( PRESETS_TRAIN['numu_v3'], { # Config: 'batch_size' : 1024, #'vars_input_slice', #'vars_input_png2d', #'vars_input_png3d', #'vars_target_total', #'vars_target_primary', 'dataset' : ( 'numu/prod4/nd_fhc' '/dataset_lstm_ee_nd_fhc_nonswap_loose_cut.csv.xz' ), 'early_stop' : { 'name' : 'standard', 'kwargs' : { 'monitor' : 'val_loss', 'min_delta' : 0, 'patience' : 40, }, }, 'epochs' : 200, 'loss' : 'mean_absolute_percentage_error', 'max_prongs' : None, 'model' : { 'name' : 'lstm_v3', 'kwargs' : { 'batchnorm' : True, 'layers_pre' : [ 128, 128, 128 ], 'layers_post' : [ 128, 128, 128 ], 'lstm_units2d' : 32, 'lstm_units3d' : 32, 'n_resblocks' : 0, }, }, 'noise' : None, 'optimizer' : { 'name' : 'RMSprop', 'kwargs' : { 'lr' : 0.001 }, }, 'prong_sorters' : None, 'regularizer' : { 'name' : 'l1', 'kwargs' : { 'l' : 0.001 }, }, 'schedule' : { 'name' : 'standard', 'kwargs' : { 'monitor' : 'val_loss', 'factor' : 0.5, 'patience' : 5, 'cooldown' : 0 }, }, 'seed' : 1337, 'steps_per_epoch' : 250, 'test_size' : 200000, 'weights' : None, # Args: 'vars_mod_png2d' : None, 'vars_mod_png3d' : None, 'vars_mod_slice' : None, 'outdir' : ( 'numu/prod4/04_nd_weights_fine_tune/02_weights_fine_tune_nd/' ), } )
config = join_dicts( PRESETS_TRAIN['nue_v3'], { # Config: 'batch_size': 1024, #'vars_input_slice', #'vars_input_png3d', #'vars_target_total', #'vars_target_primary', 'dataset': 'nue/prod4/fd_fhc/dataset_lstm_ee_fd_fhc_fluxswap_std_cut.csv.xz', 'early_stop': { 'name': 'standard', 'kwargs': { 'monitor': 'val_loss', 'min_delta': 0, 'patience': 40, }, }, 'epochs': 200, 'loss': 'mean_absolute_percentage_error', 'max_prongs': 5, 'model': { 'name': 'lstm_v1', 'kwargs': { 'batchnorm': False, 'lstm_units': 32, }, }, 'noise': None, 'optimizer': { 'name': 'RMSprop', 'kwargs': { 'lr': 0.001 }, }, 'prong_sorters': None, 'regularizer': None, 'schedule': { 'name': 'standard', 'kwargs': { 'monitor': 'val_loss', 'factor': 0.5, 'patience': 5, 'cooldown': 0 }, }, 'seed': 0, 'steps_per_epoch': 250, 'test_size': 200000, #'test_size' : 0.1, 'vars_input_png2d': None, 'weights': None, # Args: 'vars_mod_png2d': None, 'vars_mod_png3d': None, 'vars_mod_slice': None, 'outdir': 'nue/prod4/01_initial_studies/01_original/', })