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train.py
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train.py
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import os
import numpy as np
from config.config import get_config
from utils.feature_extractor import parse_metadata
from utils.kmeans import KMEANS
from utils.gmm import GMM
from utils.hmm import HMM
from utils.dnn import DNN
from utils.cnn import CNN
from utils.rnn import LSTM
config = get_config()
if __name__ == '__main__':
# Read data
DATA_PATH = config.load_path
MODEL_TYPE = config.model
metadata = parse_metadata(DATA_PATH + "/UrbanSound8K.csv")
if MODEL_TYPE == 'kmeans':
model = KMEANS()
elif MODEL_TYPE == 'gmm':
model = GMM()
elif MODEL_TYPE == 'hmm':
model = HMM()
elif MODEL_TYPE == 'dnn':
model = DNN()
elif MODEL_TYPE == 'cnn':
model = CNN()
elif MODEL_TYPE == 'rnn':
model = LSTM()
else:
raise ValueError
os.makedirs(model.model_path, exist_ok=True)
if config.isTrain: # Train
print('==================================================')
print('Training Start!')
for fold in range(config.n_fold):
print('# %d-Fold' % (fold + 1), end='\t')
# Train/Test(Validation) split
select_valid = metadata.pop(0)
select_train = metadata.copy()
metadata.insert(10, select_valid)
model.train(fold, select_train, select_valid)
print('Training Finish!')
print()
else:
Acc = []
for fold in range(config.n_fold):
# Train/Test(Validation) split
select_test = metadata.pop(0)
select_train = metadata.copy()
metadata.insert(10, select_test)
Acc.append(model.test(fold, select_test))
print('{0:s}\tAccuracy:{1:.3f}'.format(config.model.upper(), np.mean(Acc)))