param_dict=param_dict, n_permutations=24, kwarg_key_list=[ 'conv_filters1', 'kernel_sizes', 'maps_per_kernel', 'dropout1', 'dropout2', 'pool_size', 'conv_filters2', 'dense_units', 'lr' ]) elif ACTION == 'train_final_model': fncs.train_final_model() elif ACTION == 'cross_validation': fncs.run_cross_validation(nn_architechture='CNNPAR', in_kwargs={ 'conv_filters1': 75, 'conv_filters2': 300, 'dense_units': 120, 'dropout1': 0.0, 'dropout2': 0.0, 'kernel_sizes': [6, 8, 10], 'lr': 0.001, 'maps_per_kernel': 2, 'pool_size': 3 }) elif ACTION == 'test_final_model': importlib.reload(fncs) weights_path = ( "code_modules/nn_training/BAC_UNI_len2006/final_elmo_CNNPAR" "_BAC_UNI_len2006/final_elmo_CNNPAR_BAC_UNI_len2006" "_1565079880") fncs.train_final_model(just_test_model=True, weights_path=weights_path) else:
import importlib import code_modules.nn_training.functions as fncs importlib.reload(fncs) fncs.run_cross_validation(nn_architechture='CNN')
import importlib import code_modules.nn_training.functions as fncs importlib.reload(fncs) DO_GRIDSEARCH = False if DO_GRIDSEARCH: param_dict = { 'l1_units': [32, 64, 320], 'l2_units': [32, 64, 320], 'dropout1': [0.0, 0.2, 0.5], 'dropout2': [0.0, 0.2, 0.5], 'regularizer_vals': [0, 0.01, 0.001], 'epochs': [20, 50, 100], 'batch_size': [32, 32 * 4, 32 * 8] } results = fncs.hyperparameter_gridsearch(param_dict=param_dict) else: fncs.run_cross_validation(nn_architechture='DNN', in_kwargs=dict(dropout1=0.5, dropout2=0.5, l1_units=32, l2_units=64, regularizer_vals=0), epochs=50, batch_size=256)
import code_modules.nn_training.functions as fncs fncs.run_cross_validation(nn_architechture='BIDGRU', do_w2v=True, dataset_to_use='GLYLIP')
'dropout1': [0.2], 'dropout2': [0.0], 'epochs': [150], 'batch_size': [256] } results = fncs.hyperparameter_gridsearch(nn_architechture='RNNCNN', param_dict=param_dict, n_permutations=6, kwarg_key_list=[ 'lr', 'conv_filters', 'kernel_size', 'pool_size', 'lstm_units', 'dropout1', 'dropout2' ]) else: fncs.run_cross_validation(nn_architechture='RNNCNN', fold_i_to_skip=tuple(), **{ 'batch_size': 1024, 'epochs': 75, 'in_kwargs': { 'conv_filters': 640, 'dropout1': 0.2, 'dropout2': 0.0, 'kernel_size': 26, 'lr': 0.0001, 'pool_size': 26 } })
import code_modules.nn_training.functions as fncs fncs.run_cross_validation(nn_architechture='BIDGRU')