如何使用 Keras 的深度学习模型解决不适合 imagenet 数据集的问题？

Question

I followed a blog on how to implement a vgg16-model from scratch and want to do the same with the pretrained model from Keras. I looked up some other blogs but can't find a fitting solution I think. My task is to classify integrated circuit images into defect or non defects.

I have seen on a paper that they used pretrained imagenet model of vgg16 for fabric defect detection, where they freezed the first seven layers and fine tuned the last nine for their own problem. (Source: https://journals.sagepub.com/doi/full/10.1177/1558925019897396 )

I have already seen examples on how to freeze all layers except the fully connected layers, but how can I try the example with freezing first x layers and fine tune the others for my problem?

The VGG16 is fairly easy to implement from scratch but for models like resnet or xception it is getting a little trickier.

Answer 1

It is not necessary to implement a model from scratch to freeze a few layers. You can do this on pre-trained models as well. In keras, you'd use trainable = False .

For example, let's say you want to use the pre-trained Xception model from keras and want to freeze the first x layers:

#In your includes
from keras.applications import Xception

#Since you're using the model for a different task, you'd want to remove the top
base_model = Xception(weights='imagenet', include_top=False)

#Freeze layers 0 to x
for layer in base_model.layers[0:x]:
    layer.trainable = False

#To see all the layers in detail and to check trainable parameters
base_model.summary()

Ideally you'd want to add another layer on top of this model with the output as your classes. For more details, you can check this keras guide: https://keras.io/guides/transfer_learning/

A lot of times the pre-trained weights can be very useful in other classification tasks but in case you want to train a model from scratch on your dataset, you can load the model without the imagenet weights. Or better, load the weights but don't freeze any layers. This will retrain every layer taking imagenet weights as an initialization.

I hope I've answered your question.