为什么我们的CNN不能按预期预测标签? [英] Why does my CNN not predict labels as expected?
问题描述
我对相似性学习的概念是陌生的.我目前正在使用Siamese神经网络对野生数据集中的带标签人脸进行人脸识别模型.
I am new to the concept of Similarity Learning. I am currently doing a face recognition model using Siamese Neural Network for the Labelled Faces in the Wild Dataset.
暹罗网络模型的代码(考虑每个代码段为Colab中的一个单元):
Code for Siamese Network Model (Consider each code snippets to be a cell in Colab):
from keras.applications.inception_v3 import InceptionV3
from keras.applications.mobilenet_v2 import MobileNetV2
from keras.models import Model
from keras.layers import Input,Flatten
def return_inception_model():
input_vector=Input((224,224,3))
subnet=InceptionV3(include_top=False,weights="imagenet",input_tensor=input_vector)
out=subnet.output
out=Flatten()(out)
model=Model(subnet.input,out,name="SubConvNet")
return model
import keras.backend as K
def euclidean_distance(vect):
x,y=vect
return K.sqrt(K.maximum(K.sum(K.square(x - y), axis=1, keepdims=True), K.epsilon()))
def contrastive_loss(y_true, y_pred):
margin = 1
return K.mean(y_true * K.square(y_pred) + (1 - y_true) * K.square(K.maximum(margin - y_pred, 0)))
def accuracy(y_true, y_pred):
return K.mean(K.equal(y_true, K.cast(y_pred < 0.5, y_true.dtype)))
from keras.layers import Lambda
base_model=return_inception_model()
left_input=Input((224,224,3))
right_input=Input((224,224,3))
feature_1=base_model(left_input)
feature_2=base_model(right_input)
lambda_layer= Lambda(euclidean_distance)([feature_1,feature_2])
output=Dense(1,activation='sigmoid')(lambda_layer)
model=Model([left_input,right_input],output)
model.summary()
Model: "model"
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_2 (InputLayer) [(None, 224, 224, 3) 0
__________________________________________________________________________________________________
input_3 (InputLayer) [(None, 224, 224, 3) 0
__________________________________________________________________________________________________
SubConvNet (Functional) (None, 51200) 21802784 input_2[0][0]
input_3[0][0]
__________________________________________________________________________________________________
lambda (Lambda) (None, 1) 0 SubConvNet[0][0]
SubConvNet[1][0]
__________________________________________________________________________________________________
dense (Dense) (None, 1) 2 lambda[0][0]
==================================================================================================
Total params: 21,802,786
Trainable params: 21,768,354
Non-trainable params: 34,432
from keras.utils.vis_utils import plot_model
plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)
from keras.optimizers import SGD,RMSprop,Adam
optimizer=Adam(lr=0.00001)
model.compile(loss=contrastive_loss,metrics=[accuracy],optimizer=optimizer)
model.fit(x=[[train_nparr_pairs[:, 0], train_nparr_pairs[:, 1]]], y=train_labels[:],
validation_data=([[test_nparr_pairs[:, 0], test_nparr_pairs[:, 1]]], test_labels[:]), epochs=64,use_multiprocessing=True)
Epoch 56/64
69/69 [==============================] - 8s 118ms/step - loss: 0.5132 - accuracy: 0.4868 - val_loss: 0.5000 - val_accuracy: 0.4883
Epoch 57/64
69/69 [==============================] - 8s 118ms/step - loss: 0.5044 - accuracy: 0.4956 - val_loss: 0.5000 - val_accuracy: 0.4883
Epoch 58/64
69/69 [==============================] - 8s 118ms/step - loss: 0.5064 - accuracy: 0.4936 - val_loss: 0.5000 - val_accuracy: 0.4883
Epoch 59/64
69/69 [==============================] - 8s 118ms/step - loss: 0.4806 - accuracy: 0.5194 - val_loss: 0.5000 - val_accuracy: 0.4883
Epoch 60/64
69/69 [==============================] - 8s 118ms/step - loss: 0.4843 - accuracy: 0.5157 - val_loss: 0.5000 - val_accuracy: 0.4883
Epoch 61/64
69/69 [==============================] - 8s 117ms/step - loss: 0.5060 - accuracy: 0.4940 - val_loss: 0.5000 - val_accuracy: 0.4883
Epoch 62/64
69/69 [==============================] - 8s 119ms/step - loss: 0.5048 - accuracy: 0.4952 - val_loss: 0.5000 - val_accuracy: 0.4883
Epoch 63/64
69/69 [==============================] - 8s 119ms/step - loss: 0.5110 - accuracy: 0.4890 - val_loss: 0.5000 - val_accuracy: 0.4883
Epoch 64/64
69/69 [==============================] - 8s 119ms/step - loss: 0.5118 - accuracy: 0.4882 - val_loss: 0.5000 - val_accuracy: 0.4883
在输出中，您可以注意到整个会话期间的损失和准确性是相同的. val_loss的值正好是0.5.此外，在整个会话中，val_accuracy都将保持恒定值.我已经对图像进行了归一化，但是仍然会发生这种情况.此输出背后有什么原因吗?
In the output, one can notice that the loss and accuracy are the same throughout the session. The value of val_loss is exactly 0.5. Also the val_accuracy remains as a constant value throughout the session. I have normalized the images yet still this happens. Is there any reason behind this output?
推荐答案
首先，您可以轻松删除精度作为度量标准，因为它与您的情况无关(至少以Keras计算准确性).
First of all, you can easily remove the accuracy as a metric, since it is not relevant for your case (at least in the way Keras calculates the accuracy).
在暹罗网络中，计算精度的方法是根据任务选择阈值，例如T，如果两个图像相同且similarity index >= T，则认为良好的预测=> + = 1，否则请勿增加计数.
In Siamese Networks, the way in which accuracy should be calculated, is that you select a threshold, say T, according to your task, and if two images are the same and similarity index >= T, then you consider that a good prediction => +=1, otherwise do not increase the count.
但这与Keras的操作不同，对于accuracy > 0.5，它被认为是正肯定"，您可以将其完全删除，因为Keras中内置的精度度量标准仅适用于典型的分类问题.
But this is different from what Keras does, with accuracy > 0.5 is considered a True Positive, you can remove it altogether, because the built-in accuracy metric in Keras is suitable only for typical classification problems.
那是第一部分.
第二部分是您的权重不会相应更新.这是由于您这样声明了优化器:optimizer=Adam(lr=0.1).在这种情况下，学习率太高，特别是如果我们正在谈论转移学习您在预训练的InceptionV3中应用了什么.
The second part is that your weights are not updated accordingly. This is due to the fact that you declared the optizimer like this: optimizer=Adam(lr=0.1). The learning rate is too high in this case, particularly if we are talking about transfer learning what you applied with the pre-trained InceptionV3.
总结:
- 删除精度作为度量标准.
- 实例化
optimizer=Adam(lr=0.00001)
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