It is not possible to directly add a Convolutional Neural Network (CNN) layer to a Backpropagation Neural Network (BPNN) in MATLAB, as the two types of networks have different architectures and training algorithms.
However, you can use a technique called transfer learning to adapt a pre-trained CNN for use in a BPNN. Transfer learning involves taking a CNN that has already been trained on a large dataset, and using the features learned by the CNN to train a new network on a smaller, related dataset.
Here's a high-level overview of the process:
- Load the pre-trained CNN using MATLAB's alexnet (https://www.mathworks.com/help/deeplearning/ref/alexnet.html) or vgg16 (https://www.mathworks.com/help/deeplearning/ref/vgg16.html) functions, which will give you access to the CNN's layers and weights.
- Extract the activations of the CNN's final layer for your training data. These activations represent the "features" learned by the CNN that are most relevant to your problem.
- Use the extracted activations as input to your BPNN, along with any additional input features that may be relevant to your problem.
- Train the BPNN using the extracted activations as input and the desired outputs as targets.
Note that this is just a general outline of the process, and the specific details will depend on the specific problem you are trying to solve. Additionally, there are many variations and optimizations to transfer learning that you may want to consider, such as fine-tuning the pre-trained CNN on your specific dataset, or using multiple pre-trained CNNs to extract features at different levels of abstraction.
MATLAB provides several functions that can help with transfer learning, including activations (https://www.mathworks.com/help/deeplearning/ref/seriesnetwork.activations.html), classify (https://www.mathworks.com/help/stats/classify.html) and trainNetwork (https://www.mathworks.com/help/deeplearning/ref/trainnetwork.html).
