Architecting a CNN for classification
Putting the three types of convolutional-related layers together, along with the fully connected layer(s), we can structure the CNN model for classification as follows:
In this example, the input images are first fed into a convolutional layer (with ReLU activation) composed of a bunch of filters. The coefficients of the convolutional filters are trainable. A well-trained initial convolutional layer is able to derive good low-level representations of the input images, which will be critical to downstream convolutional layers if there are any, and also downstream classification tasks. Each resulting feature map is then downsampled by the pooling layer.
Next, the aggregated feature maps are fed into the second convolutional layer. Similarly, the second pooling layer reduces the size of the output feature maps. You can chain as many pairs of convolutional and pooling layers as you want. The second (or...
