与keras的facenet三胞胎损失
我正在尝试使用Thensorflow后端在Keras中实现facenet，我对三元组丢失有一些问题。
我用3 * n个图像调用fit函数，然后按如下方式定义自定义丢失函数：
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))
其中y只是一个填充0s的虚拟数组
问题在于即使在批量大小为20的第一次迭代之后，模型也开始为所有图像预测相同的嵌入。因此，当我第一次对批处理进行预测时，每次嵌入都是不同的。然后我做了拟合并再次预测，突然所有嵌入对于批处理中的所有图像变得几乎相同
另请注意，模型末尾有一个Lambda图层。它规范了网络的输出，因此所有嵌入都具有单面长度，如面网研究中所建议的那样。
有人可以帮我吗？
模型摘要
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
4 个答案:
答案 0 :(得分：7)
除了学习率太高之外，可能发生的事情是，有效地使用了不稳定的三元组选择策略。例如，如果你只使用'hard triplets'（距离小于ap距离的三元组），你的网络权重可能会将所有嵌入都折叠到一个点（使得损失始终相等）到边距（你的_alpha），因为所有嵌入距离都是零。）
这也可以通过使用其他类型的三元组来修复（例如'半硬三元组'，其中ap小于a，但ap和an之间的距离仍然小于margin） 。所以，如果你总是检查这个......可以在这篇博文中详细解释：https://omoindrot.github.io/triplet-loss
答案 1 :(得分：5)
您是否正在限制嵌入到＆＃34;在d维超球面上？＃34;？尝试在嵌入式CNN出现后立即运行tf.nn.l2_normalize。
问题可能在于嵌入有点像智能用户。减少损失的一种简单方法是将所有内容设置为零。 l2_normalize强制它们为单位长度。
看起来你想要在最后一次平均游泳池之后添加规范化。
答案 2 :(得分：2)
我遇到了同样的问题，我做了一些研究工作。我认为这是因为三重态丢失需要多个输入，这可能会导致网络生成这样的输出。我还没有解决问题，但您可以查看keras的问题页面了解更多详情https://github.com/keras-team/keras/issues/9498。
在问题页面中，我实现了假数据集和假三元组丢失以重新减少问题，在我改变了网络的输入结构后，损失变得正常
答案 3 :(得分：0)
张量流中的损失函数需要标签列表，即整数列表。我认为您正在传递2D矩阵，即一种热编码。
尝试
import keras.backend as K
from tf.contrib.losses.metric_learning import triplet_semihard_loss
def loss(y_true, y_pred):
y_true = K.argmax(y_true, axis = -1)
return triplet_semihard_loss(labels=y_true, embeddings=y_pred, margin=1.)
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