Asked Oct 4th, 2018 9:27 AM 152 0 2
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Deep Learning

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Chuyện là mình có tập dữ liệu âm thanh. Mình đã rút trích đặc trưng theo MFCC và cho huấn luyện mô hình.
Mình huấn luyện mô hình


import IPython.display as ipd
from scipy.io import wavfile
import os
import pandas as pd
import librosa
import glob
import librosa.display
import matplotlib.pyplot as plt
import random
import numpy as np
import time
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.optimizers import Adam
from sklearn import metrics
from sklearn.preprocessing import LabelEncoder
from keras.utils import np_utils

#doc du lieu
temp1 = pd.read_csv('detached1.csv')
y1 = np.array(temp1.label.tolist())
X1 =  np.array(temp1.feature.tolist())
lb = LabelEncoder()
y1 = np_utils.to_categorical(lb.fit_transform(y1))


#huan luyen mo hinh
num_labels = y1.shape[1]

model = Sequential()

model.add(Dense(256, input_shape=(40,)))
model.add(Activation('relu'))
model.add(Dropout(0.5))

model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dropout(0.5))

model.add(Dense(num_labels))
model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam')

model.fit(X1, y1, batch_size=32, epochs=200, validation_split=0.1, shuffle=True)

Thì xảy ra lỗi:

Using TensorFlow backend.
Traceback (most recent call last):
  File ".\train.py", line 119, in <module>
    model.fit(X1, y1, batch_size=32, epochs=200, validation_split=0.1, shuffle=True)
  File "C:\Python36\lib\site-packages\keras\engine\training.py", line 950, in fit
    batch_size=batch_size)
  File "C:\Python36\lib\site-packages\keras\engine\training.py", line 749, in _standardize_user_data
    exception_prefix='input')
  File "C:\Python36\lib\site-packages\keras\engine\training_utils.py", line 137, in standardize_input_data
    str(data_shape))
ValueError: Error when checking input: expected dense_1_input to have shape (40,) but got array with shape (1,)

Đây là tập dữ liệu mà mình đã rút trích MFCC theo từng nhãn:

feature,label
"[-4.27537437e+02  1.84112623e+01 -5.08398999e+01  5.31637161e+00
 -2.10733792e+01 -1.12434670e+01 -1.80090488e+01 -5.75498212e+00
 -6.93904306e-01 -3.86425538e-01 -1.34887390e+01 -4.86926960e+00
  7.08283909e+00  2.14888911e+01  4.27220821e+00  2.68281675e+00
 -7.48988856e+00 -4.83437475e+00 -4.97837031e+00  4.75456743e+00
  3.78830787e+00  2.42193371e+00 -5.37803284e-01  1.91941476e+00
 -1.30461991e+00 -5.38917506e+00  2.95554388e+00  7.73115650e+00
  7.86978456e+00  6.78713944e+00  4.96348127e+00 -5.65352412e+00
 -8.27031422e+00 -2.86780917e-01  2.48317310e+00 -2.81628395e+00
 -7.71405737e+00 -2.17011549e+00  1.39035042e+00  5.37204073e+00]",Hi-hat
"[-3.38268402e+02  9.04193385e+01 -6.23142458e+01  1.51335214e+01
 -1.79282013e+01 -1.14170883e+01 -1.44048668e+01 -7.91434938e+00
 -2.60386278e+01 -1.33425042e+01 -1.95374448e+01 -7.54015238e+00
 -9.25282725e+00 -1.42761031e+01 -8.95404860e+00 -1.19382583e+01
 -1.71928449e+00 -5.69528325e+00 -3.24685922e+00 -5.55717358e-01
 -2.90710922e+00 -3.16496626e-01  2.31073633e+00  7.50894389e+00
  8.43927660e+00  1.55867110e+00 -2.81710750e-01  7.12674402e-01
  5.41010774e+00  8.03800790e+00  8.17677948e+00  5.93762669e+00
  4.77439264e+00  9.63560502e+00  1.16040133e+01  1.08942222e+01
  8.61317843e+00  5.05467430e+00  5.23978546e-01 -5.62048446e+00]",Saxophone
"[-6.24420361e+02  1.05825675e+02  1.33219059e+00  8.52950436e+00
 -7.24284650e+00  9.61647224e+00 -1.01478913e+01  1.54396490e+01
 -5.98731125e+00  1.84971132e+01  8.43774569e+00  1.68807995e+01
  6.56443439e+00  1.19779162e+01 -6.25981335e+00  9.84080965e+00
 -5.64382929e+00  2.58264885e+00 -2.00159095e+00  7.13945232e+00
  6.38080413e+00  2.79037469e+00  4.53217316e+00  3.97428187e+00
  1.43712071e+00  3.41697916e+00 -1.55604338e+00 -1.93967453e+00
 -5.66032483e+00 -4.18978478e+00 -1.28271250e+00 -1.14015339e+00
 -1.31668160e+00  5.41810899e-01 -2.46240243e+00  1.39396516e+00
 -9.86597523e-02 -4.03033817e+00 -4.41016969e+00 -1.98476223e+00]",Trumpet
"[-764.52938314    8.71873263   -6.43843907  -15.01001293  -11.87936678
    4.85713407   14.52481245   16.67583444    0.828503    -13.2422332
   -8.63103285   -2.09927411   12.74072178   17.04830133    3.82904079
   -4.54364402  -12.89317423   -2.72160362    4.6501101    13.58426343
   12.71656223   -3.82231561   -9.36074789   -4.471898      0.80438819
   11.11886213    9.47849708    5.23002199   -6.7693081    -9.3544149
    1.98950657    4.5134517    11.00089496    5.36468628   -2.9164897
   -4.27201551   -3.70712954    4.4849342     6.91849163    5.93127321]",Glockenspiel
"[-317.20211467   94.64278976  -29.87401214   24.54868907   -6.09035615
   -0.59327219   -6.94212706   20.30568222   -7.34163477   13.18906205
    1.63050593    4.11504883   -8.83981319   -9.26372591   -4.08433343
    0.7372643    -3.76954889    0.47261775   -6.12578635   -2.8851219
    5.83740187   -2.93667644  -10.85349448  -17.17998129   -9.75256509
    2.82462774    9.73483052   35.35451003   43.11077942   24.04688464
    1.16382936  -15.37443287  -13.61991705   -0.33005041   17.23820257
    6.06095399   -3.37540842  -12.25133004   -1.37950356   11.89579574]",Cello
"[-3.85405043e+02  1.82486473e+02 -2.33892432e+00  3.33028570e+01
  1.81593151e+01 -9.75835474e-01 -6.42208460e+00  3.98433223e-02
  2.95680088e+00 -1.12593921e+00 -2.26398369e+00 -2.32506312e+00
 -2.47300684e+01 -9.01468486e-01 -7.79015057e+00 -4.05737105e+00
 -1.14309429e+01 -1.89083919e+01 -1.25810084e+01  7.34797983e-01
  2.68597806e+00  1.48029000e+00 -4.17226495e+00 -9.31283741e+00
 -1.85426023e+00 -6.49108933e+00  1.14746636e+00 -2.65551034e+00
 -8.37654899e+00 -3.26042608e+00 -1.94110840e+00 -8.57032086e+00
 -2.65129743e+00 -1.32203655e+01 -1.84632703e+01 -8.26353278e+00
 -1.88503566e+00 -8.35524375e+00 -1.62149390e+01 -1.82253732e+00]",Cello
"[-5.24920427e+02  5.94717202e+01  5.38309772e+00  1.26111679e+01
  2.67003759e+00  8.02280542e+00  1.61448988e+00  6.37815119e+00
  9.59449536e-01  6.12097763e+00 -2.49745585e-01  3.34996805e+00
 -7.38250253e-01  1.50706689e+00 -2.10623382e-01  1.81277624e+00
 -8.95322039e-01  3.65742001e+00 -6.42121196e-01  2.33773064e-01
  1.05758933e+00  1.10403540e+00  9.32153897e-01  1.19968231e-01
  2.08067929e+00  9.60565473e-02  7.88911438e-01  2.47309143e+00
  3.21383146e+00  9.77452626e-02 -9.99862741e-02  5.02418510e-01
 -3.06560278e-01 -4.77943679e-01 -1.30247224e+00 -1.57172712e+00
 -3.50584169e+00 -3.74525384e+00 -1.26045936e+00 -2.23543676e+00]",Knock
"[-280.47011806   87.04624775    1.51867678   28.7544023     8.51132611
   18.14832172    0.52928007   11.7828036     3.75681467    5.85570039
   -3.73229661   -9.33423393   -9.80057998   -1.44813319   -9.81222806
   -2.71234692   -7.2027108    -1.04184486   -4.2185896    -0.66948758
   -5.41804403   -5.20026059   -4.9516565    -2.83758151   -3.87877157
   -0.90021028    1.58843783   -0.96575431   -2.81714288   -1.31403267
   -3.04378508   -4.39345396   -4.46993251   -4.4497167    -3.17821052
   -4.55557494   -6.55964337   -3.58372651   -3.26926145   -3.99807356]",Gunshot_or_gunfire
"[-455.31825766   24.64795147  -59.7479186     6.57805275   -7.34159473
   -0.75412168   -9.78392258   -6.14321207  -22.97267742  -11.33437337
  -10.24463331   13.61014451   22.3439737    38.85788626   45.62329334
   32.3592885     6.6244562   -13.46206869   -4.15417543   -0.56052938
    4.28166291  -19.09338248  -32.89559066  -11.32265161   25.509636
   22.46449093    3.71999159   18.30566019   39.33050818   27.53211081
  -13.85470948  -16.00923342    8.10740382    7.70651575   -3.56247767
    2.3622675    -3.03281754  -24.13676426   -2.25025972   31.24887203]",Clarinet
"[-4.16342246e+02  2.39453186e+01  8.25088021e+00  1.77851446e+01
 -1.66942222e+01  2.56676681e+01 -1.37727697e+01  1.78291707e+01
 -8.90931038e+00  1.61025820e+01 -9.29215844e+00  7.44784387e+00
 -1.52496814e+01  8.44466390e+00 -1.13882392e+01  4.84605317e+00
 -8.73099370e+00  6.03583024e+00 -8.63527381e+00  4.14996525e+00
 -9.21384412e+00 -2.13100241e+00 -1.00770288e+01 -1.97386608e+00
 -7.85499989e+00 -2.73425950e-01 -4.74793935e+00 -2.76770417e-01
 -7.36556228e+00 -3.21462230e+00 -5.16336007e+00 -1.72343303e+00
 -4.85442690e+00 -2.04850630e+00 -2.97923671e+00  5.15375491e-01
 -3.80343525e+00 -1.62268470e+00 -4.35805247e+00 -1.16887440e+00]",Computer_keyboard
"[-5.19274594e+02 -2.71449943e+01 -2.83462511e+01  1.66401010e+01
 -6.06738251e+00  1.47208344e+01 -1.07641763e+01  3.56846923e+00
 -1.62394525e+01  2.58537802e+00 -4.56741501e+00  2.34174948e+00
 -1.59728309e+01  7.78185987e-01 -1.29608242e+01 -3.86139441e+00
 -6.90194615e+00  5.39548086e+00 -2.40081456e+00  3.20240751e+00
 -3.26320455e+00  2.60146420e+00  1.02619756e+00  5.97824882e+00
 -1.17367641e+00  1.66920391e+00  1.77947348e+00  7.43221466e+00
  2.11138313e+00  5.34002900e+00  2.16695357e-01  1.53387821e-01
 -3.44682113e+00 -1.31276163e+00 -2.47808711e+00  2.25855008e+00
 -7.96137488e-01 -4.80598658e+00 -6.78338005e+00 -3.44156603e-01]",Hi-hat
"[-5.11254565e+02  5.10915827e+00 -3.91603563e+01  3.72906926e+01
 -5.41286280e+00  2.45443313e+01 -1.01455891e+01  1.54803725e+01
  3.11070726e-01  1.12446725e+01 -9.53414668e+00  1.25973173e+01
 -6.58191956e+00  7.64917415e+00 -6.31176949e+00  5.63290461e+00
 -2.15468728e+00  3.24888960e+00  3.58324877e-01  3.32424837e+00
 -3.28164742e+00  5.00128487e+00 -2.21771250e+00  4.21021373e+00
 -2.17697709e+00  6.51156093e+00 -2.44081838e+00  5.74518198e+00
 -1.71610276e+00  5.09435083e+00 -1.42065432e+00  2.99195342e+00
 -2.19029010e-01  2.91630452e+00  3.12646255e-01  2.46836080e+00
  9.40062335e-01  2.88474875e+00  7.29112169e-01  1.91076338e+00]",Keys_jangling
"[-2.70970613e+02  4.41825471e+01 -2.71657128e+01  8.34881285e+00
 -1.69852725e+01  4.41621599e-01 -1.36084170e+01  3.30848527e+00
 -1.16992683e+01  2.50560480e+00 -1.27283351e+01  2.17467185e+00
 -1.40992865e+01 -4.51612206e+00 -1.56652726e+01 -5.17631284e+00
 -1.21148181e+01 -4.30362719e+00 -1.05397332e+01 -2.37359875e+00
 -7.50444779e+00 -4.63788263e-01 -2.79868337e+00  4.91808954e-02
 -6.10196649e+00 -9.46571464e-01 -4.50134621e+00  4.45210813e-01
 -1.57192046e+00 -1.37422032e+00 -3.75160744e+00 -4.97624069e-01
 -2.39860514e+00  2.13355452e-02 -3.14399860e+00 -3.41856172e+00
 -3.71535928e+00 -1.67329311e+00 -2.28692094e+00  9.12734995e-01]",Snare_drum
"[-5.32791881e+02  5.53392756e+01 -4.23844965e+00  4.32248864e+01
 -6.68890584e+00  2.10324813e+01 -6.68072132e+00  1.93206158e+01
 -5.93869390e+00  1.00728757e+01 -1.20915412e+01  8.35914716e+00
 -6.83334787e+00  1.04005723e+01 -1.00428315e+01  3.63580785e+00
 -7.01152778e+00  5.48456909e+00 -4.12633407e+00  2.93005015e+00
 -1.10928915e+00  4.44012759e+00  1.01764271e+00 -1.38631256e+00
  2.33545777e+00  1.66915380e+00 -6.74666616e-01 -3.49031360e+00
  1.15497161e+00  4.98827185e-01 -2.92391846e+00  1.82407029e-03
  1.12726490e+00 -3.68309456e+00 -9.90012043e-01  1.02524714e+00
 -2.01671215e+00 -1.18087267e+00 -6.58942356e-01 -1.04852341e-01]",Writing
"[-3.88631500e+02  1.36379931e+02 -1.77521786e+01  9.47604936e+00
  1.07755710e+01  1.23910543e+01 -2.61411621e+00  1.80973210e+00
 -5.35874886e+00  3.42813978e+00 -3.19641142e+00 -5.10204444e+00
 -1.52943596e+01 -1.13649575e+01 -2.02562810e+00 -5.18617727e+00
 -5.17221832e+00 -3.08956149e+00 -3.61940867e+00 -6.03197103e+00
 -5.21686540e+00  1.77924719e+00 -3.71307196e-01  1.05386576e+00
 -1.75084515e+00 -4.09861376e+00 -9.37304723e+00 -3.73861911e+00
 -3.07228109e+00 -1.97830041e+00 -9.26318664e+00 -3.24813016e+00
 -6.89011953e+00 -3.70117311e+00  5.48948537e-01 -1.97276564e-01
 -4.81475418e+00 -9.96419591e+00 -8.37488110e+00 -6.38731101e+00]",Cello

Mình biết có thể lỗi chỗ X1. Nhưng mình sửa hoài không ra.
Output X1:

['[-4.27537437e+02  1.84112623e+01 -5.08398999e+01  5.31637161e+00\r\n -2.10733792e+01 -1.12434670e+01 -1.80090488e+01 -5.75498212e+00\r\n -6.93904306e-01 -3.86425538e-01 -1.34887390e+01 -4.86926960e+00\r\n  7.08283909e+00  2.14888911e+01  4.27220821e+00  2.68281675e+00\r\n -7.48988856e+00 -4.83437475e+00 -4.97837031e+00  4.75456743e+00\r\n  3.78830787e+00  2.42193371e+00 -5.37803284e-01  1.91941476e+00\r\n -1.30461991e+00 -5.38917506e+00  2.95554388e+00  7.73115650e+00\r\n  7.86978456e+00  6.78713944e+00  4.96348127e+00 -5.65352412e+00\r\n -8.27031422e+00 -2.86780917e-01  2.48317310e+00 -2.81628395e+00\r\n -7.71405737e+00 -2.17011549e+00  1.39035042e+00  5.37204073e+00]'
 '[-3.38268402e+02  9.04193385e+01 -6.23142458e+01  1.51335214e+01\r\n -1.79282013e+01 -1.14170883e+01 -1.44048668e+01 -7.91434938e+00\r\n -2.60386278e+01 -1.33425042e+01 -1.95374448e+01 -7.54015238e+00\r\n -9.25282725e+00 -1.42761031e+01 -8.95404860e+00 -1.19382583e+01\r\n -1.71928449e+00 -5.69528325e+00 -3.24685922e+00 -5.55717358e-01\r\n -2.90710922e+00 -3.16496626e-01  2.31073633e+00  7.50894389e+00\r\n  8.43927660e+00  1.55867110e+00 -2.81710750e-01  7.12674402e-01\r\n  5.41010774e+00  8.03800790e+00  8.17677948e+00  5.93762669e+00\r\n  4.77439264e+00  9.63560502e+00  1.16040133e+01  1.08942222e+01\r\n  8.61317843e+00  5.05467430e+00  5.23978546e-01 -5.62048446e+00]'
 '[-6.24420361e+02  1.05825675e+02  1.33219059e+00  8.52950436e+00\r\n -7.24284650e+00  9.61647224e+00 -1.01478913e+01  1.54396490e+01\r\n -5.98731125e+00  1.84971132e+01  8.43774569e+00  1.68807995e+01\r\n  6.56443439e+00  1.19779162e+01 -6.25981335e+00  9.84080965e+00\r\n -5.64382929e+00  2.58264885e+00 -2.00159095e+00  7.13945232e+00\r\n  6.38080413e+00  2.79037469e+00  4.53217316e+00  3.97428187e+00\r\n  1.43712071e+00  3.41697916e+00 -1.55604338e+00 -1.93967453e+00\r\n -5.66032483e+00 -4.18978478e+00 -1.28271250e+00 -1.14015339e+00\r\n -1.31668160e+00  5.41810899e-01 -2.46240243e+00  1.39396516e+00\r\n -9.86597523e-02 -4.03033817e+00 -4.41016969e+00 -1.98476223e+00]'
 '[-764.52938314    8.71873263   -6.43843907  -15.01001293  -11.87936678\r\n    4.85713407   14.52481245   16.67583444    0.828503    -13.2422332\r\n   -8.63103285   -2.09927411   12.74072178   17.04830133    3.82904079\r\n   -4.54364402  -12.89317423   -2.72160362    4.6501101    13.58426343\r\n   12.71656223   -3.82231561   -9.36074789   -4.471898      0.80438819\r\n   11.11886213    9.47849708    5.23002199   -6.7693081    -9.3544149\r\n    1.98950657    4.5134517    11.00089496    5.36468628   -2.9164897\r\n   -4.27201551   -3.70712954    4.4849342     6.91849163    5.93127321]'
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Mong mọi người giúp đỡ.

2 ANSWERS


Answered Oct 4th, 2018 10:43 AM
Accepted
+3

Mình sửa 1 số chỗ trong phần đầu code của bạn, bạn chạy lại thứ nhá.

import IPython.display as ipd
from scipy.io import wavfile
import os
import pandas as pd
import matplotlib.pyplot as plt
import random
import numpy as np
import time
import ast
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.optimizers import Adam
from sklearn import metrics
from sklearn.preprocessing import LabelEncoder
from keras.utils import np_utils
import re

#doc du lieu
temp1 = pd.read_csv('test.csv')
y1 = np.array(temp1.label.tolist())
X1 =  np.array([ast.literal_eval(re.sub('\s+', ',', x)) for x in temp1.feature.tolist()])
lb = LabelEncoder()
y1 = np_utils.to_categorical(lb.fit_transform(y1))

#huan luyen mo hinh
num_labels = y1.shape[1]

model = Sequential()

model.add(Dense(256, input_shape=(40,)))
model.add(Activation('relu'))
model.add(Dropout(0.5))

model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dropout(0.5))

model.add(Dense(num_labels))
model.add(Activation('softmax'))

model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='adam')

model.fit(X1, y1, batch_size=32, epochs=200, validation_split=0.1, shuffle=True)
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Oct 5th, 2018 1:42 AM

Ok bạn. Cảm ơn bạn nha.

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Oct 5th, 2018 2:47 AM

Mình đã test trên tập dữ liệu thì ok. Nhưng khi mình test tập dữ liệu lớn hơn thì xảy ra lỗiUntitled.png

Đây là tập dữ liệu lớn hơn mình test: https://drive.google.com/file/d/1nNz7og0AMYx9A6vrf5M1Nq9woAsz_10r/view?usp=sharing Hy vọng bạn có thể giúp mình.

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Oct 5th, 2018 3:10 AM

Để sửa lỗi trên, bạn thay dòng này:

X1 =  np.array([ast.literal_eval(re.sub('\s+', ',', x)) for x in temp1.feature.tolist()])

Bằng dòng này là được:

X1 =  np.array([ast.literal_eval(re.sub('(\d)\s+', r'\1,', x)) for x in temp1.feature.tolist()])

Vấn đề ở đây chính là cách bạn lưu trữ dữ liệu tiền xử lý ở dạng string nên mình phải xử lý theo kiểu string, nhưng cách lưu trữ dữ liệu lại không thống nhất dẫn đến việc code chạy trên tập dữ liệu nhỏ thì đúng là sang 1 tập lớn hơn lại sai(Vì trong tập dữ liệu lớn có 1 sample được lưu khác với các sample còn lại và hôm qua mình không biết có điều đó). Bạn để ý dòng 2024 trong dữ liệu sẽ nhận thấy điều này.

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Oct 5th, 2018 4:04 AM

Ok bạn. Lỗi đó có thể lúc mình rút trích mfcc mình dùng to_csv để lưu trữ. Nên ma trận được lưu theo dạng string dẫn đến 1 số sample bị lỗi. Mình đã thử lưu ma trận bằng numpy vào 1 file .npy theo cách của bạn @hoangdinhthoi thì kết quả cho ra cũng chính xác. Cảm ơn @hoangdinhthoi @QuangPH nhiều nha.

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Answered Oct 4th, 2018 9:37 AM
+1

Bạn cho mình xin file .csv đc không? Không thì bạn cho mình xin shape của X1 và y1 với. Tại keras nó báo lỗi về shape của data bạn truyền vào mạng.

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Oct 4th, 2018 9:46 AM
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Oct 4th, 2018 9:53 AM

shape X (15,) , shape Y (15, 12)

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Oct 4th, 2018 9:58 AM

Untitled.png

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Hoang Dinh Thoi @hoangdinhthoi
Oct 4th, 2018 10:34 AM

@Phuoc Lỗi này của bạn là do data lưu trong file .csv đang có kiểu là string. Lần sau lúc bạn làm dữ liệu thì có thể dùng numpy để lưu luôn ma trận lại chứ k nên convert nó sang string rồi lưu vào csv làm gì. Theo mình thấy trong file của bạn thì có 15 mẫu dữ liệu, mỗi 1 mẫu là 1 vector 40 chiều (number of features=40), như vậy thì có thể lưu luôn ma trận 15x40 bằng numpy vào 1 file .npy cho nhanh. Còn về phần label, mình thấy bạn đang để là ma trận 15x12, làm như vậy cũng đc, nhưng bạn có thể bỏ qua bước convert sang one-hot và lúc compile model thì thay "categorical_crossentropy" bằng "sparse_categorical_crossentropy".

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Oct 5th, 2018 2:53 AM

@hoangdinhthoi ok bạn. Để mình test thử cách của bạn.

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