Building a CNN in MATLAB
In this section, we will see how to train a CNN for image classification, starting from the images as input layers. The overall architecture of a CNN typically comprises a sequence of convolutional layers, interspersed with ReLU layers and, when appropriate, standardization and pooling layers. Ultimately, the network concludes with a series of FC layers leading to the output layer.
CNNs consist of two primary types of neurons:
- Processing neurons: These neurons undertake the responsibility of processing specific sections of the input image through convolution functions. Their primary role involves extracting distinctive features from the input data.
- Aggregation or pooling neurons: These neurons aggregate the input data and reduce its dimensions through subsampling, enhancing efficiency for subsequent layers’ processing.
By assembling the output values from a given layer, it becomes possible to reconstruct an intermediate image that...
