Keras의 CNN 회귀 작업에서 기이 한 평균 제곱 오류 동작

Question

이미지 관련 회귀 작업에 대해 alexnet과 유사한 CNN을 사용하고 있습니다. 나는 손실 함수에 대해 rmse를 정의했다. 그러나 첫 번째 신기원에서 훈련 도중 손실은 큰 가치를 나타 냈습니다. 그러나 두 번째 시대 이후에는 의미있는 가치로 떨어졌습니다. 여기에 있습니다 :

1/51 [..............................] - ETA: 847s - loss: 104.1821 - acc: 0.2500 - root_mean_squared_error: 104.1821 2/51 [>.............................] - ETA: 470s - loss: 5277326.0910 - acc: 0.5938 - root_mean_squared_error: 5277326.0910 3/51 [>.............................] - ETA: 345s - loss: 3518246.7337 - acc: 0.5000 - root_mean_squared_error: 3518246.7337 4/51 [=>............................] - ETA: 281s - loss: 2640801.3379 - acc: 0.6094 - root_mean_squared_error: 2640801.3379 5/51 [=>............................] - ETA: 241s - loss: 2112661.3062 - acc: 0.5000 - root_mean_squared_error: 2112661.3062 6/51 [==>...........................] - ETA: 214s - loss: 1760566.4758 - acc: 0.4375 - root_mean_squared_error: 1760566.4758 7/51 [===>..........................] - ETA: 194s - loss: 1509067.6495 - acc: 0.4464 - root_mean_squared_error: 1509067.6495 8/51 [===>..........................] - ETA: 178s - loss: 1320442.6319 - acc: 0.4570 - root_mean_squared_error: 1320442.6319 9/51 [====>.........................] - ETA: 165s - loss: 1173734.9212 - acc: 0.4792 - root_mean_squared_error: 1173734.9212 10/51 [====>.........................] - ETA: 155s - loss: 1056369.3193 - acc: 0.4875 - root_mean_squared_error: 1056369.3193 11/51 [=====>........................] - ETA: 146s - loss: 960343.5998 - acc: 0.4943 - root_mean_squared_error: 960343.5998 12/51 [======>.......................] - ETA: 139s - loss: 880320.3762 - acc: 0.5052 - root_mean_squared_error: 880320.3762 13/51 [======>.......................] - ETA: 131s - loss: 812608.7112 - acc: 0.5216 - root_mean_squared_error: 812608.7112 14/51 [=======>......................] - ETA: 125s - loss: 754570.1939 - acc: 0.5402 - root_mean_squared_error: 754570.1939 15/51 [=======>......................] - ETA: 120s - loss: 704269.2443 - acc: 0.5479 - root_mean_squared_error: 704269.2443 16/51 [========>.....................] - ETA: 114s - loss: 660256.3035 - acc: 0.5508 - root_mean_squared_error: 660256.3035 17/51 [========>.....................] - ETA: 109s - loss: 621420.7248 - acc: 0.5607 - root_mean_squared_error: 621420.7248 18/51 [=========>....................] - ETA: 104s - loss: 586900.8398 - acc: 0.5712 - root_mean_squared_error: 586900.8398 19/51 [==========>...................] - ETA: 100s - loss: 556014.6719 - acc: 0.5806 - root_mean_squared_error: 556014.6719 20/51 [==========>...................] - ETA: 95s - loss: 528216.9077 - acc: 0.5875 - root_mean_squared_error: 528216.9077 21/51 [===========>..................] - ETA: 91s - loss: 503065.7743 - acc: 0.5967 - root_mean_squared_error: 503065.7743 22/51 [===========>..................] - ETA: 87s - loss: 480206.3521 - acc: 0.6094 - root_mean_squared_error: 480206.3521 23/51 [============>.................] - ETA: 83s - loss: 459331.8636 - acc: 0.6114 - root_mean_squared_error: 459331.8636 24/51 [=============>................] - ETA: 80s - loss: 440196.2991 - acc: 0.6159 - root_mean_squared_error: 440196.2991 25/51 [=============>................] - ETA: 76s - loss: 422590.8381 - acc: 0.6162 - root_mean_squared_error: 422590.8381 26/51 [==============>...............] - ETA: 73s - loss: 406339.5179 - acc: 0.6178 - root_mean_squared_error: 406339.5179 27/51 [==============>...............] - ETA: 69s - loss: 391292.6992 - acc: 0.6238 - root_mean_squared_error: 391292.6992 28/51 [===============>..............] - ETA: 66s - loss: 377319.9851 - acc: 0.6306 - root_mean_squared_error: 377319.9851 29/51 [===============>..............] - ETA: 63s - loss: 364310.7557 - acc: 0.6336 - root_mean_squared_error: 364310.7557 30/51 [================>.............] - ETA: 60s - loss: 352169.1059 - acc: 0.6385 - root_mean_squared_error: 352169.1059 31/51 [=================>............] - ETA: 57s - loss: 340810.8854 - acc: 0.6401 - root_mean_squared_error: 340810.8854 32/51 [=================>............] - ETA: 53s - loss: 330162.1334 - acc: 0.6455 - root_mean_squared_error: 330162.1334 33/51 [==================>...........] - ETA: 50s - loss: 320158.7622 - acc: 0.6553 - root_mean_squared_error: 320158.7622 34/51 [==================>...........] - ETA: 47s - loss: 310744.0080 - acc: 0.6645 - root_mean_squared_error: 310744.0080 35/51 [===================>..........] - ETA: 44s - loss: 301866.8259 - acc: 0.6714 - root_mean_squared_error: 301866.8259 36/51 [====================>.........] - ETA: 41s - loss: 293483.0129 - acc: 0.6762 - root_mean_squared_error: 293483.0129 37/51 [====================>.........] - ETA: 39s - loss: 285552.8197 - acc: 0.6757 - root_mean_squared_error: 285552.8197 38/51 [=====================>........] - ETA: 36s - loss: 278039.4488 - acc: 0.6752 - root_mean_squared_error: 278039.4488 39/51 [=====================>........] - ETA: 33s - loss: 270911.4670 - acc: 0.6795 - root_mean_squared_error: 270911.4670 40/51 [======================>.......] - ETA: 30s - loss: 264140.2391 - acc: 0.6820 - root_mean_squared_error: 264140.2391 41/51 [=======================>......] - ETA: 27s - loss: 257699.1895 - acc: 0.6852 - root_mean_squared_error: 257699.1895 42/51 [=======================>......] - ETA: 25s - loss: 251564.6846 - acc: 0.6890 - root_mean_squared_error: 251564.6846 43/51 [========================>.....] - ETA: 22s - loss: 245715.4124 - acc: 0.6933 - root_mean_squared_error: 245715.4124 44/51 [========================>.....] - ETA: 19s - loss: 240131.9916 - acc: 0.6960 - root_mean_squared_error: 240131.9916 45/51 [=========================>....] - ETA: 16s - loss: 234796.6948 - acc: 0.7007 - root_mean_squared_error: 234796.6948 46/51 [=========================>....] - ETA: 14s - loss: 229693.3717 - acc: 0.7045 - root_mean_squared_error: 229693.3717 47/51 [==========================>...] - ETA: 11s - loss: 224807.2748 - acc: 0.7055 - root_mean_squared_error: 224807.2748 48/51 [===========================>..] - ETA: 8s - loss: 220125.0731 - acc: 0.7077 - root_mean_squared_error: 220125.0731 49/51 [===========================>..] - ETA: 5s - loss: 215634.5638 - acc: 0.7117 - root_mean_squared_error: 215634.5638 50/51 [============================>.] - ETA: 3s - loss: 211323.1692 - acc: 0.7144 - root_mean_squared_error: 211323.1692 51/51 [============================>.] - ETA: 0s - loss: 207180.6328 - acc: 0.7151 - root_mean_squared_error: 207180.6328 52/51 [==============================] - 143s - loss: 203253.6237 - acc: 0.7157 - root_mean_squared_error: 203253.6237 - val_loss: 44.4203 - val_acc: 0.9878 - val_root_mean_squared_error: 44.4203 Epoch 2/128 1/51 [..............................] - ETA: 117s - loss: 52.6087 - acc: 0.7188 - root_mean_squared_error: 52.6087

이 동작을 이해하는 방법? 여기 내 구현입니다. 모델에 대한 다음

from keras import backend as K 
def root_mean_squared_error(y_true, y_pred): 
    return K.sqrt(K.mean(K.square(y_pred - y_true), axis=-1)) 

: 먼저 RMSE 함수를 정의

이 가

estimator = alexmodel() 
datagen = ImageDataGenerator() 
datagen.fit(x_train) 
start = time.time() 
history = estimator.fit_generator(datagen.flow(x_train, x_train,batch_size=batch_size, shuffle=True), 
      epochs=epochs, 
      steps_per_epoch=x_train.shape[0]/batch_size, 
      validation_data=(x_test, y_test)) 
end = time.time() 

가

사람이 이유는 말해 줄 수 :

는

model.compile(optimizer="rmsprop", loss=root_mean_squared_error, metrics=['accuracy', root_mean_squared_error]) 

그런 다음 모델에 맞게? 잠재력이 잘못된 것은 무엇입니까?

Answer 1

데이터를 표준화하는 것이 중요합니다. 목표를 정규화하지 않은 것으로 보입니다. 네트워크가 일반적으로 처음부터 작은 값을 생성하는 방식으로 초기화되므로 첫 번째 신기원에서 손실이 너무 커졌습니다. 따라서 높은 수치를 생성해야 할 필요가 있기 때문에 네트워크의 가중치가 훨씬 높은 절대 값을 가지게되어 네트워크를 막아야하기 때문에 대상을 정규화하는 것이 좋습니다 (StandardScaler 또는 MinMaxScaller).