-import sys
import pympi
-import random
import glob
import re
import os
# mfcc
-from python_speech_features import mfcc, fbank, logfbank
+from python_speech_features import mfcc, logfbank
import scipy.io.wavfile as wav
import numpy as np
-#keras
+# keras
from keras.models import Sequential
from keras.layers import Dense, Dropout # , Activation
+from keras import backend
# Testset ratio
testset = 0.10
verbose=verbosity)
print('{}\t{}\t{}\t{}\t{}\n'.format(
winlen, winstep, modelname, loss, acc))
+ return model
def simplemodel(d):
model = Sequential()
if __name__ == '__main__':
print('winlen\twinstep\tmodel\tloss\taccuracy\n')
- for winlen, winstep in ((0.025, 0.01), (0.1, 0.04), (0.2, 0.08)):
- for name, model in (('simple', simplemodel), ('bottle', bottlemodel)):
- run('mfcc', winlen, winstep, model, name)
+ with backend.get_session():
+ for winlen, winstep in ((0.025, 0.01), (0.1, 0.04), (0.2, 0.08)):
+ for name, model in (('simple', simplemodel), ('bottle', bottlemodel)):
+ m = run('mfcc', winlen, winstep, model, name)
+ fproot = 'model_{}_{}_{}.json'.format(winlen, winstep, name)
+ with open('{}.json'.format(fproot), 'w') as f:
+ f.write(m.to_json())
+ m.save_weights('{}.hdf5'.format(fproot))