11
Tôi đang cố gắng triển khai facenet trong Keras với phần phụ trợ Thensorflow và tôi gặp một số vấn đề với tổn thất ba lần. 
lỗ ba mặt facenet với keras
tôi gọi là chức năng phù hợp với 3 * n số hình ảnh và sau đó tôi xác định chức năng mất tùy chỉnh của tôi như sau:
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))
nơi y chỉ là một mảng giả đầy 0s
Các vấn đề là ngay cả sau lần lặp đầu tiên với kích thước lô 20, mô hình bắt đầu dự đoán cùng một nhúng cho tất cả các hình ảnh. Vì vậy, khi tôi lần đầu tiên thực hiện dự đoán trên mỗi lần nhúng thì khác nhau. Sau đó, tôi làm phù hợp và dự đoán một lần nữa và đột nhiên tất cả các embeddings trở nên gần như giống nhau cho tất cả các hình ảnh trong lô
Cũng lưu ý rằng có một lớp Lambda ở cuối mô hình. Nó bình thường hóa đầu ra của mạng để tất cả các embeddings có chiều dài đơn vị như nó đã được đề xuất trong nghiên cứu net mặt.
Ai có thể giúp tôi ở đây không?
tóm tắt mẫu
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
tốc độ học tập của bạn là gì? Có lẽ nó quá lớn. – weitang114
Tôi nghĩ về điều đó vì vậy tôi đã cố gắng rất thấp tỷ lệ học tập liek 1e-10 mà mô hình chỉ nên thay đổi trọng lượng bằng một số lượng rất nhỏ và nó vẫn học để sản xuất cùng một đầu ra cho mỗi hình ảnh trong lô trong một lần lặp rất kỳ quặc. – DalekSupreme
Trong mã của bạn, "embeddings [0] - embeddings [1]", có phải là "embeddings [:, 0] - embeddings [:, 1]" không? – weitang114