# set patch parameters
config['xInput'] = config['n_frames']
config['yInput'] = config['audio_rep']['n_mels']
# load audio representation paths
file_index = config_file.DATA_FOLDER + config[
'audio_representation_folder'] + 'index.tsv'
[audio_repr_paths,
id2audio_repr_path] = shared.load_id2audioReprPath(file_index)
# load training ground truth
file_ground_truth_train = config_file.DATA_FOLDER + config['gt_train']
[all_ids_train, id2gt_train] = shared.load_id2gt(file_ground_truth_train)
[_, id2label_train] = shared.load_id2label(file_ground_truth_train)
label2ids_train = shared.load_label2ids(id2label_train)
# load test ground truth
file_ground_truth_test = config_file.DATA_FOLDER + config['gt_test']
[all_ids_test, id2gt_test] = shared.load_id2gt(file_ground_truth_test)
[_, id2label_test] = shared.load_id2label(file_ground_truth_test)
label2ids_test = shared.load_label2ids(id2label_test)
# set output according to the experimental setup
classes_vector = list(range(config['num_classes_dataset']))
# tensorflow: define the model
with tf.name_scope('model'):
# support for training [classes, support, time, freq, channel]
x = tf.placeholder(tf.float32,
# set patch parameters
config['xInput'] = config['n_frames']
config['yInput'] = config['audio_rep']['n_mels']
# load audio representation paths
file_index = config_file.DATA_FOLDER + config[
'audio_representation_folder'] + 'index.tsv'
[audio_repr_paths,
id2audio_repr_path] = shared.load_id2audioReprPath(file_index)
# load training ground truth
file_ground_truth_train = config_file.DATA_FOLDER + config['gt_train']
[all_ids_train, id2gt_train] = shared.load_id2gt(file_ground_truth_train)
[_, id2label_train] = shared.load_id2label(file_ground_truth_train)
label2ids_train = shared.load_label2ids(id2label_train)
# load validation ground truth
file_ground_truth_val = config_file.DATA_FOLDER + config['gt_val']
[all_ids_val, id2gt_val] = shared.load_id2gt(file_ground_truth_val)
[_, id2label_val] = shared.load_id2label(file_ground_truth_val)
label2ids_val = shared.load_label2ids(id2label_val)
# set output according to the experimental setup
config['classes_vector'] = list(range(config['num_classes_dataset']))
# save experimental settings
experiment_id = 'fold_' + str(config_file.FOLD) + '_' + str(
shared.get_epoch_time())
experiment_folder = config_file.DATA_FOLDER + 'experiments/' + str(
experiment_id) + '/'