I was reading this interesting article on convolutional neural networks. It showed this image, explaining that for every receptive field of 5x5 pixels/neurons, a value for a hidden value is calculated.
We can think of max-pooling as a way for the network to ask whether a given feature is found anywhere in a region of the image. It then throws away the exact positional information.
So max-pooling is applied.
With multiple convolutional layers, it looks something like this:
But my question is, this whole architecture could be build with perceptrons, right?
For every convolutional layer, one perceptron is needed, with layers:
input_size = 5x5; hidden_size = 10; e.g. output_size = 1;
Then for every receptive field in the original image, the 5x5 area is inputted into a perceptron to output the value of a neuron in the hidden layer. So basically doing this for every receptive field:
So the same perceptron is used 24x24 amount of times to construct the hidden layer, because:
is that we're going to use the same weights and bias for each of the 24×24 hidden neurons.
And this works for the hidden layer to the pooling layer as well,
input_size = 2x2;
output_size = 1;. And in the case of a max-pool layer, it's just a
max() function on an array.
and then finally:
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.
which is a perceptron again.
So my final architecture looks like this:
-> 1 perceptron for every convolutional layer/feature map -> run this perceptron for every receptive field to create feature map -> 1 perceptron for every pooling layer -> run this perceptron for every field in the feature map to create a pooling layer -> finally input the values of the pooling layer in a regular ALL to ALL perceptron
Or am I overseeing something? Or is this already how they are programmed?