потеря тройной грани с сеткой
Я пытаюсь внедрить facenet в Keras с помощью Thensorflow, и у меня возникла проблема с потерей триплета.
Я вызываю функцию подгонки с 3 * n количеством изображений, а затем определяю свою пользовательскую функцию потерь следующим образом:
def triplet_loss(self, y_true, y_pred):
embeddings = K.reshape(y_pred, (-1, 3, output_dim))
positive_distance = K.mean(K.square(embeddings[:,0] - embeddings[:,1]),axis=-1)
negative_distance = K.mean(K.square(embeddings[:,0] - embeddings[:,2]),axis=-1)
return K.mean(K.maximum(0.0, positive_distance - negative_distance + _alpha))
self._model.compile(loss=triplet_loss, optimizer="sgd")
self._model.fit(x=x,y=y,nb_epoch=1, batch_size=len(x))
где у - просто фиктивный массив, заполненный нулями
Проблема заключается в том, что даже после первой итерации с размером партии 20 модель начинает предсказывать одинаковое вложение для всех изображений. Поэтому, когда я впервые делаю прогноз для партии, каждое вложение отличается. Затем я делаю подбор и снова предсказываю, и внезапно все вложения становятся почти одинаковыми для всех изображений в пакете.
Также обратите внимание, что в конце модели есть лямбда-слой. Он нормализует вывод сети, поэтому все вложения имеют единичную длину, как это было предложено в исследовании фазовой сети.
Кто-нибудь может помочь мне здесь?
Краткое изложение модели
Layer (type) Output Shape Param # Connected to
====================================================================================================
input_1 (InputLayer) (None, 224, 224, 3) 0
____________________________________________________________________________________________________
convolution2d_1 (Convolution2D) (None, 112, 112, 64) 9472 input_1[0][0]
____________________________________________________________________________________________________
batchnormalization_1 (BatchNormal(None, 112, 112, 64) 128 convolution2d_1[0][0]
____________________________________________________________________________________________________
maxpooling2d_1 (MaxPooling2D) (None, 56, 56, 64) 0 batchnormalization_1[0][0]
____________________________________________________________________________________________________
convolution2d_2 (Convolution2D) (None, 56, 56, 64) 4160 maxpooling2d_1[0][0]
____________________________________________________________________________________________________
batchnormalization_2 (BatchNormal(None, 56, 56, 64) 128 convolution2d_2[0][0]
____________________________________________________________________________________________________
convolution2d_3 (Convolution2D) (None, 56, 56, 192) 110784 batchnormalization_2[0][0]
____________________________________________________________________________________________________
batchnormalization_3 (BatchNormal(None, 56, 56, 192) 384 convolution2d_3[0][0]
____________________________________________________________________________________________________
maxpooling2d_2 (MaxPooling2D) (None, 28, 28, 192) 0 batchnormalization_3[0][0]
____________________________________________________________________________________________________
convolution2d_5 (Convolution2D) (None, 28, 28, 96) 18528 maxpooling2d_2[0][0]
____________________________________________________________________________________________________
convolution2d_7 (Convolution2D) (None, 28, 28, 16) 3088 maxpooling2d_2[0][0]
____________________________________________________________________________________________________
maxpooling2d_3 (MaxPooling2D) (None, 28, 28, 192) 0 maxpooling2d_2[0][0]
____________________________________________________________________________________________________
convolution2d_4 (Convolution2D) (None, 28, 28, 64) 12352 maxpooling2d_2[0][0]
____________________________________________________________________________________________________
convolution2d_6 (Convolution2D) (None, 28, 28, 128) 110720 convolution2d_5[0][0]
____________________________________________________________________________________________________
convolution2d_8 (Convolution2D) (None, 28, 28, 32) 12832 convolution2d_7[0][0]
____________________________________________________________________________________________________
convolution2d_9 (Convolution2D) (None, 28, 28, 32) 6176 maxpooling2d_3[0][0]
____________________________________________________________________________________________________
merge_1 (Merge) (None, 28, 28, 256) 0 convolution2d_4[0][0]
convolution2d_6[0][0]
convolution2d_8[0][0]
convolution2d_9[0][0]
____________________________________________________________________________________________________
convolution2d_11 (Convolution2D) (None, 28, 28, 96) 24672 merge_1[0][0]
____________________________________________________________________________________________________
convolution2d_13 (Convolution2D) (None, 28, 28, 32) 8224 merge_1[0][0]
____________________________________________________________________________________________________
maxpooling2d_4 (MaxPooling2D) (None, 28, 28, 256) 0 merge_1[0][0]
____________________________________________________________________________________________________
convolution2d_10 (Convolution2D) (None, 28, 28, 64) 16448 merge_1[0][0]
____________________________________________________________________________________________________
convolution2d_12 (Convolution2D) (None, 28, 28, 128) 110720 convolution2d_11[0][0]
____________________________________________________________________________________________________
convolution2d_14 (Convolution2D) (None, 28, 28, 64) 51264 convolution2d_13[0][0]
____________________________________________________________________________________________________
convolution2d_15 (Convolution2D) (None, 28, 28, 64) 16448 maxpooling2d_4[0][0]
____________________________________________________________________________________________________
merge_2 (Merge) (None, 28, 28, 320) 0 convolution2d_10[0][0]
convolution2d_12[0][0]
convolution2d_14[0][0]
convolution2d_15[0][0]
____________________________________________________________________________________________________
convolution2d_16 (Convolution2D) (None, 28, 28, 128) 41088 merge_2[0][0]
____________________________________________________________________________________________________
convolution2d_18 (Convolution2D) (None, 28, 28, 32) 10272 merge_2[0][0]
____________________________________________________________________________________________________
convolution2d_17 (Convolution2D) (None, 14, 14, 256) 295168 convolution2d_16[0][0]
____________________________________________________________________________________________________
convolution2d_19 (Convolution2D) (None, 14, 14, 64) 51264 convolution2d_18[0][0]
____________________________________________________________________________________________________
maxpooling2d_5 (MaxPooling2D) (None, 14, 14, 320) 0 merge_2[0][0]
____________________________________________________________________________________________________
merge_3 (Merge) (None, 14, 14, 640) 0 convolution2d_17[0][0]
convolution2d_19[0][0]
maxpooling2d_5[0][0]
____________________________________________________________________________________________________
convolution2d_21 (Convolution2D) (None, 14, 14, 96) 61536 merge_3[0][0]
____________________________________________________________________________________________________
convolution2d_23 (Convolution2D) (None, 14, 14, 32) 20512 merge_3[0][0]
____________________________________________________________________________________________________
maxpooling2d_6 (MaxPooling2D) (None, 14, 14, 640) 0 merge_3[0][0]
____________________________________________________________________________________________________
convolution2d_20 (Convolution2D) (None, 14, 14, 256) 164096 merge_3[0][0]
____________________________________________________________________________________________________
convolution2d_22 (Convolution2D) (None, 14, 14, 192) 166080 convolution2d_21[0][0]
____________________________________________________________________________________________________
convolution2d_24 (Convolution2D) (None, 14, 14, 64) 51264 convolution2d_23[0][0]
____________________________________________________________________________________________________
convolution2d_25 (Convolution2D) (None, 14, 14, 128) 82048 maxpooling2d_6[0][0]
____________________________________________________________________________________________________
merge_4 (Merge) (None, 14, 14, 640) 0 convolution2d_20[0][0]
convolution2d_22[0][0]
convolution2d_24[0][0]
convolution2d_25[0][0]
____________________________________________________________________________________________________
convolution2d_27 (Convolution2D) (None, 14, 14, 112) 71792 merge_4[0][0]
____________________________________________________________________________________________________
convolution2d_29 (Convolution2D) (None, 14, 14, 32) 20512 merge_4[0][0]
____________________________________________________________________________________________________
maxpooling2d_7 (MaxPooling2D) (None, 14, 14, 640) 0 merge_4[0][0]
____________________________________________________________________________________________________
convolution2d_26 (Convolution2D) (None, 14, 14, 224) 143584 merge_4[0][0]
____________________________________________________________________________________________________
convolution2d_28 (Convolution2D) (None, 14, 14, 224) 226016 convolution2d_27[0][0]
____________________________________________________________________________________________________
convolution2d_30 (Convolution2D) (None, 14, 14, 64) 51264 convolution2d_29[0][0]
____________________________________________________________________________________________________
convolution2d_31 (Convolution2D) (None, 14, 14, 128) 82048 maxpooling2d_7[0][0]
____________________________________________________________________________________________________
merge_5 (Merge) (None, 14, 14, 640) 0 convolution2d_26[0][0]
convolution2d_28[0][0]
convolution2d_30[0][0]
convolution2d_31[0][0]
____________________________________________________________________________________________________
convolution2d_33 (Convolution2D) (None, 14, 14, 128) 82048 merge_5[0][0]
____________________________________________________________________________________________________
convolution2d_35 (Convolution2D) (None, 14, 14, 32) 20512 merge_5[0][0]
____________________________________________________________________________________________________
maxpooling2d_8 (MaxPooling2D) (None, 14, 14, 640) 0 merge_5[0][0]
____________________________________________________________________________________________________
convolution2d_32 (Convolution2D) (None, 14, 14, 192) 123072 merge_5[0][0]
____________________________________________________________________________________________________
convolution2d_34 (Convolution2D) (None, 14, 14, 256) 295168 convolution2d_33[0][0]
____________________________________________________________________________________________________
convolution2d_36 (Convolution2D) (None, 14, 14, 64) 51264 convolution2d_35[0][0]
____________________________________________________________________________________________________
convolution2d_37 (Convolution2D) (None, 14, 14, 128) 82048 maxpooling2d_8[0][0]
____________________________________________________________________________________________________
merge_6 (Merge) (None, 14, 14, 640) 0 convolution2d_32[0][0]
convolution2d_34[0][0]
convolution2d_36[0][0]
convolution2d_37[0][0]
____________________________________________________________________________________________________
convolution2d_39 (Convolution2D) (None, 14, 14, 144) 92304 merge_6[0][0]
____________________________________________________________________________________________________
convolution2d_41 (Convolution2D) (None, 14, 14, 32) 20512 merge_6[0][0]
____________________________________________________________________________________________________
maxpooling2d_9 (MaxPooling2D) (None, 14, 14, 640) 0 merge_6[0][0]
____________________________________________________________________________________________________
convolution2d_38 (Convolution2D) (None, 14, 14, 160) 102560 merge_6[0][0]
____________________________________________________________________________________________________
convolution2d_40 (Convolution2D) (None, 14, 14, 288) 373536 convolution2d_39[0][0]
____________________________________________________________________________________________________
convolution2d_42 (Convolution2D) (None, 14, 14, 64) 51264 convolution2d_41[0][0]
____________________________________________________________________________________________________
convolution2d_43 (Convolution2D) (None, 14, 14, 128) 82048 maxpooling2d_9[0][0]
____________________________________________________________________________________________________
merge_7 (Merge) (None, 14, 14, 640) 0 convolution2d_38[0][0]
convolution2d_40[0][0]
convolution2d_42[0][0]
convolution2d_43[0][0]
____________________________________________________________________________________________________
convolution2d_44 (Convolution2D) (None, 14, 14, 160) 102560 merge_7[0][0]
____________________________________________________________________________________________________
convolution2d_46 (Convolution2D) (None, 14, 14, 64) 41024 merge_7[0][0]
____________________________________________________________________________________________________
convolution2d_45 (Convolution2D) (None, 7, 7, 256) 368896 convolution2d_44[0][0]
____________________________________________________________________________________________________
convolution2d_47 (Convolution2D) (None, 7, 7, 128) 204928 convolution2d_46[0][0]
____________________________________________________________________________________________________
maxpooling2d_10 (MaxPooling2D) (None, 7, 7, 640) 0 merge_7[0][0]
____________________________________________________________________________________________________
merge_8 (Merge) (None, 7, 7, 1024) 0 convolution2d_45[0][0]
convolution2d_47[0][0]
maxpooling2d_10[0][0]
____________________________________________________________________________________________________
convolution2d_49 (Convolution2D) (None, 7, 7, 192) 196800 merge_8[0][0]
____________________________________________________________________________________________________
convolution2d_51 (Convolution2D) (None, 7, 7, 48) 49200 merge_8[0][0]
____________________________________________________________________________________________________
maxpooling2d_11 (MaxPooling2D) (None, 7, 7, 1024) 0 merge_8[0][0]
____________________________________________________________________________________________________
convolution2d_48 (Convolution2D) (None, 7, 7, 384) 393600 merge_8[0][0]
____________________________________________________________________________________________________
convolution2d_50 (Convolution2D) (None, 7, 7, 384) 663936 convolution2d_49[0][0]
____________________________________________________________________________________________________
convolution2d_52 (Convolution2D) (None, 7, 7, 128) 153728 convolution2d_51[0][0]
____________________________________________________________________________________________________
convolution2d_53 (Convolution2D) (None, 7, 7, 128) 131200 maxpooling2d_11[0][0]
____________________________________________________________________________________________________
merge_9 (Merge) (None, 7, 7, 1024) 0 convolution2d_48[0][0]
convolution2d_50[0][0]
convolution2d_52[0][0]
convolution2d_53[0][0]
____________________________________________________________________________________________________
convolution2d_55 (Convolution2D) (None, 7, 7, 192) 196800 merge_9[0][0]
____________________________________________________________________________________________________
convolution2d_57 (Convolution2D) (None, 7, 7, 48) 49200 merge_9[0][0]
____________________________________________________________________________________________________
maxpooling2d_12 (MaxPooling2D) (None, 7, 7, 1024) 0 merge_9[0][0]
____________________________________________________________________________________________________
convolution2d_54 (Convolution2D) (None, 7, 7, 384) 393600 merge_9[0][0]
____________________________________________________________________________________________________
convolution2d_56 (Convolution2D) (None, 7, 7, 384) 663936 convolution2d_55[0][0]
____________________________________________________________________________________________________
convolution2d_58 (Convolution2D) (None, 7, 7, 128) 153728 convolution2d_57[0][0]
____________________________________________________________________________________________________
convolution2d_59 (Convolution2D) (None, 7, 7, 128) 131200 maxpooling2d_12[0][0]
____________________________________________________________________________________________________
merge_10 (Merge) (None, 7, 7, 1024) 0 convolution2d_54[0][0]
convolution2d_56[0][0]
convolution2d_58[0][0]
convolution2d_59[0][0]
____________________________________________________________________________________________________
averagepooling2d_1 (AveragePoolin(None, 1, 1, 1024) 0 merge_10[0][0]
____________________________________________________________________________________________________
flatten_1 (Flatten) (None, 1024) 0 averagepooling2d_1[0][0]
____________________________________________________________________________________________________
dense_1 (Dense) (None, 128) 131200 flatten_1[0][0]
____________________________________________________________________________________________________
lambda_1 (Lambda) (None, 128) 0 dense_1[0][0]
====================================================================================================
Total params: 7456944
____________________________________________________________________________________________________
None