np.mean, np.std 로 직접 정규화를 해주는 함수에서
scikitlearn.preprocessing 의 StandardScaler 를 사용해
data의 평균을 1로, 분산을 0으로 만들어 정규화하였습니다.
| 0730 |
0729 |
def dB( magnitude ):
decibel = 20*np.log10( np.abs(magnitude) + np.finfo(float).eps )
return decibel
def scaler( L, R):
LR = np.concatenate( (L,R), axis=0 )
"""normalization"""
#z = MinMaxScaler().fit_transform(LR[:])
z = StandardScaler().fit_transform(LR[:])
#z = RobustScaler().fit_transform(LR[:])
#z = MaxAbsScaler().fit_transform(LR[:])
z = z.reshape(2, 257, 382)
return z[0], z[1] |
def dB( magnitude ):
return 20*np.log10( np.abs(magnitude) + np.finfo(np.float32).eps )
def Mag_normalization( L, R ):
Mag = np.asarray( [ L, R ] ) #(2, 257, 382)
mu = np.mean( Mag )
sigma = np.std( Mag )
z = ( Mag - mu ) / sigma
return z[0], z[1]
def Phase_normalization( phase ):
mu = np.mean( phase )
sigma = np.std( phase )
z = ( phase - mu ) / sigma
return z |
epoch=100
batch_size=20
lr=0.00002
0729 (np.mean, np.std) |
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training accuracy: 82% validation accuracy: 11% |
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0730 (sklearn.preprocessing StandardScaler) |
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training accuracy: 91.250% validation accuracy: 44.5% |
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