concept - Deep Learning¶
Convolutional Neural Network¶
CONV layer is a set of filters that spatially slide across input volume and output dot product during each step.
- why: much less parameters compared to a fully connected layer, parameter sharing, sparsity of connection
- convolve: the action of sliding over width and height
- local connectivity: each filter only look at input volume within the filter window, NOT fully connected
- filter: matrix of size (f,f,c), f is mostly odd, c is the same as input volume depth (n,n,c)
- depth: hyperparameter, number of filters, depth column, or fiber
- stride: Step size when convolve the filter, mostly 1 or 2
- pooling: reduce size of representation, speeds up computation, more robust features.
- hyperparameter: (f,f,s) s is step size
- output: n+2p-f+1 / s,
- Max Pooling: max(spatial filter)
- Average Pooling: less common, used to collapse
- structure: input -> layer 1 (conv1, pool1) -> layer 2(conv2, pool2) -> layer 3(fully connected), layer 4(FC) -> softmax