How to transfer weights from a pre-trained CNN regressive neurale network to a new one (Transfer learning)

Hi everyone!

I would like to know how to transfer the weights from a pre-trained CNN for regression to a new one. It is a sort of Transfer Learning. I have seen how to perform this problem with a CNN for classification, but I have many doubts about how to do it on a CNN for regression. Moreover, is it important to have the same dimension of the images in the imageInputLayer for both the regressive CNN? Thank you in adavance.

Answer 1

[Kartik]

Hi,

Transfer learning works in the similar manner in both regression and classification.

The process is similar to transfer learning for classification, but with some differences in the output layer and loss function.

To transfer the weights from a pre-trained CNN for regression to a new one, you need to follow these steps:

1. Load the pre-trained CNN model. In MATLAB, you can use the alexnet, googlenet, or resnet50 functions to load pre-trained models.
2. Modify the last layers of the pre-trained CNN. In transfer learning, the last layers of the pre-trained CNN are removed and replaced with new layers that are specific to your problem. For a regression problem, you can replace the last layer with a fully connected layer and a regression layer.
3. Load your data and prepare it for training. In MATLAB, you can use imageDatastore to load and preprocess your images.
4. Fine-tune the pre-trained CNN using your data. In MATLAB, you can use the trainNetwork function to train the modified CNN. You will need to specify the training options, such as the number of epochs, mini-batch size, and learning rate.

It is important to note that the input size of your data should match the input size of the pre-trained CNN. The augmentedImageDatastore function is used to resize and augment the input images to match the input size of the model.

In summary, to transfer the weights from a pre-trained CNN for regression to a new one in MATLAB, you need to modify the last layers of the pre-trained CNN to fit your problem, load your data and prepare it for training, and fine-tune the CNN using your data.