I am trying to learn about convolutional neural networks, but i am having trouble understanding what happens to neural networks after the pooling step.
So starting from the left we have our 28x28 matrix representing our picture. We apply a three 5x5 filters to it to get three 24x24 feature maps. We then apply max pooling to each 2x2 square feature map to get three 12x12 pooled layers. I understand everything up to this step.
But what happens now? The document I am reading says:
"The final layer of connections in the network is a fully-connected layer. That is, this layer connects every neuron from the max-pooled layer to every one of the 10 output neurons. "
The text did not go further into describing what happens beyond that and it left me with a few questions.
How are the three pooled layers mapped to the 10 output neurons? By fully connected, does it mean each neuron in every one of the three layers of the 12x12 pooled layers has a weight connecting it to the output layer? So there are 3x12x12x10 weights linking from the pooled layer to the output layer? Is an activation function still taken at the output neuron?
Pictures and extract taken from this online resource: http://neuralnetworksanddeeplearning.com/chap6.html