# create training folder
model_id = 'model_fold' + str(config['fold']) + '_' + str(
shared.get_epoch_time())
model_folder = experiment_folder + model_id + '/'
if not os.path.exists(model_folder):
os.makedirs(model_folder)
# listing all models trained for this experiment
fall = open(experiment_folder + 'models.list', 'a')
fall.write(str(model_id) + '\n')
fall.close()
# few-shot learning: data selection/preparation
tmp_data = shared.few_shot_data_preparation(all_ids_train, all_ids_val,
config['classes_vector'],
label2ids_train,
label2ids_val, config)
[ids_train, ids_val, _] = tmp_data
# pescador train: define streamer
train_pack = [
config, config['train_sampling'], config['param_train_sampling']
]
train_streams = [
pescador.Streamer(data_gen, id, id2audio_repr_path[id],
id2gt_train[id], train_pack) for id in ids_train
]
train_mux_stream = pescador.StochasticMux(
train_streams,
n_active=config['batch_size'] * 2,
rate=None,
'Update\tEpoch\tloss\tmean_distance_protos\tmean_gradients\taccuracy\talpha\tbeta\n'
)
else:
fy.write(
'Update\tEpoch\tloss\tmean_distance_protos\tmean_gradients\taccuracy\n'
)
fy.close()
# saving a list of all the models trained for this experiment
fall = open(experiment_folder + 'models.list', 'a')
fall.write(str(model_id) + '\n')
fall.close()
# few-shot learning: ids selection
tmp_ids = shared.few_shot_data_preparation(all_ids_train, all_ids_test,
classes_vector,
label2ids_train,
label2ids_test, config)
[ids_train, ids_test, label2selectedIDs] = tmp_ids
# few-shot learning: fetch data
tmp_data = fetch_data(classes_vector, label2selectedIDs,
id2audio_repr_path, id2gt_train, config)
[
train_set_dic, train_gt_dic, train_id_dic,
minimum_number_of_patches, total_number_of_patches
] = tmp_data
# tensorflow: create a session to run the tensorflow graph
sess.run(tf.global_variables_initializer())
# training
elif config[
'num_classes_dataset'] == 10: # dummy dictionary for US8k dataset (to easily share this function)
dummy_dic = {
0: [],
1: [],
2: [],
3: [],
4: [],
5: [],
6: [],
7: [],
8: [],
9: []
}
tmp_data = shared.few_shot_data_preparation(all_ids_train, dummy_list,
config['classes_vector'],
label2ids_train, dummy_dic,
config)
[ids_train, _, _] = tmp_data
# pescador train: define streamer
train_pack = [
config, config['train_sampling'], config['param_train_sampling']
]
train_streams = [
pescador.Streamer(data_gen, id, id2audio_repr_path[id],
id2gt_train[id], train_pack) for id in ids_train
]
train_mux_stream = pescador.StochasticMux(
train_streams,
n_active=config['batch_size'] * 2,
rate=None,