Artificial Neuron Model
(1)
(2)We should learn some denotion first to understand the above second model.
For the activation units ,we have the below expression :
Finally , we can get the conclusion that the layer J's theta is a
matrix.
stands for the number of the layer J's units .
stands for the number of the layer (J+1)'s units
Vetorization rise implementation
In order to make our calcus more simple, we have the following new denotions and formulars.
is a transition variable .
Finally ,
Pay attention : When programing , it's necessary to add the bias unit manually in the process of calculating new alpha.
One vs all algorithm in neuron model
Assume that we have n classes to be classified , our model has m layers then our output layer should look like :
And it can be
... ...
Each one represents one situation of output ,like the following example
