Reference: https://www.cs.swarthmore.edu/~meeden/cs81/s10/BackPropDeriv.pdf
I spent nearly one hour to deduce the vector form of the back propagation. Just in case that I may forget, but need to utilize them, I will write down all the formula here to make a backup.
Structure:
Standard BP Network with $\displaystyle \lambda$ hidden layers, one input layer and one output layer.
Activation function: sigmoid.
Notations:
$\displaystyle W^{i+1,i}$, denotes the weight matrix connecting from $i$th layer to $i+1$th layer.
$\displaystyle N^i$, denotes the net input of the $i$th layer.
$\displaystyle A^i$, denotes the activation input of the $i$th layer.
$\displaystyle \delta ^i$, denotes the error of the $i$th layer.
$\displaystyle \epsilon$, denotes the learning rate.
*, stands for element by element multiplication.
(omit), stands for matrix multiplication.
Specifically,
$\displaystyle X$, denotes the input layer, while equals $\displaystyle A^0$.
$\displaystyle A^{\lambda + 1}$, denotes the output layer.
$\displaystyle Y$, denotes the expected output.
Propagations:
Forward:
$\displaystyle N^i = W^{i,i-1}A^{i-1}$.
$\displaystyle A^i = \frac{1}{1+e^{-N^i}}$.
Backward:
$\displaystyle \Delta W^{i+1,i} = \epsilon \delta^{i+1}(A^{i})^{T}$.
$\displaystyle \delta ^i = ((\delta^{i+1})^{T}W^{i+1,i})^{T}$.
$\displaystyle \delta ^{\lambda + 1} = (Y - A^{\lambda + 1})*A^{\lambda + 1}*(1-A^{\lambda + 1})$.
Deduction:
I am not capable of taking the partial derivative of vector or matrix over vector or matrix, so I derive these formulas by observing the formula for each element in the matrix and extend it to the vector form.
$\displaystyle \Delta W^{\lambda+1,\lambda}_{i,j} = \epsilon (Y_i - A^{\lambda+1}_i)A^{\lambda+1}_i(1-A^{\lambda +1}_i)A^{\lambda}_j$.
Let's assume $\displaystyle \delta ^{\lambda+1}_{i} := (Y_i - A^{\lambda+1}_i)A^{\lambda+1}_i(1-A^{\lambda +1}_i)$.
$\displaystyle \Delta W^{\lambda,\lambda-1}_{i,j}=\epsilon (\delta^{\lambda+1})^{T}W^{\lambda+1,\lambda}_{col(i)}A_j^{\lambda-1}$.
Let's assume $\displaystyle \delta ^{\lambda}_{i} := (\delta^{\lambda+1})^{T}W^{\lambda+1,\lambda}_{col(i)}$.
The left are reserved for the readers to complete.
来源：https://www.cnblogs.com/raymondjiang/p/12258145.html