type_model, 'Classification_Layer', project_name + '.json'))
with path_hyperparameters_CL.open('r') as file_descriptor:
hyperparameters_cl = json.load(file_descriptor)
params = {
'dim': dim,
'path_instances': path_instances,
'batch_size': hyperparameters_cl['batch_size'],
'n_clases': 2,
'n_channels': n_channels,
'normalized': hyperparameters_cl['normalized'],
'shuffle': hyperparameters_cl['shuffle']
}
validation_ids_instances = read_instance_file_txt(path_ids_instances /
'test.txt')
validation_generator = DataGenerators_CrossingDetection_OrderPrediction.DataGeneratorCrossingDetectionOrderPrediction(
validation_ids_instances, **params)
path_model = Path(
join(
config['Performance_CrossingDetection_OrderPrediction']
['path_model'], dataset, 'CrossingDetection', data_sampling,
'Transfer_Learning', 'OrderPrediction', tuner_type, type_model,
project_name, 'model.h5'))
if type_model == 'SIAMESE':
model = load_model(str(path_model))
dataset = config['CrossingDetection_Shuffle']['dataset']
type_model = config['CrossingDetection_Shuffle']['type_model']
project_name = config['CrossingDetection_Shuffle']['project_name']
tuner_type = config['CrossingDetection_Shuffle']['tuner_type']
data_sampling = config['CrossingDetection_Shuffle']['data_sampling']
path_instances = Path(
join(config['CrossingDetection_Shuffle']['path_instances'], dataset,
'CrossingDetection',
str(n_frames) + '_frames', data_sampling))
path_id_instances = Path(
join(config['CrossingDetection_Shuffle']['path_id_instances'], dataset))
epochs = config['CrossingDetection_Shuffle']['epochs']
train_ids_instances = read_instance_file_txt(path_id_instances / 'train.txt')
validation_ids_instances = read_instance_file_txt(path_id_instances /
'validation.txt')
if config['CrossingDetection_Shuffle']['Transfer_Learning']:
tuner_type_pretext_task = config['CrossingDetection_Shuffle'][
'tuner_type_pretext_task']
project_name_pretext_task = config['CrossingDetection_Shuffle'][
'project_name_pretext_task']
tensorboard_logs = str(
Path(
join(config['CrossingDetection_Shuffle']['tensorboard_logs'],