机器学习笔记之反向传播算法
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Backpropagation Algorithm
"Backpropagation" is neural-network terminology for minimizing our cost function, just like what we were doing with gradient descent in logistic and linear regression. Our goal is to compute:
That is, we want to minimize our cost function J using an optimal set of parameters in theta. In this section we'll look at the equations we use to compute the partial derivative of J(Θ):
To do so, we use the following algorithm:
Back propagation Algorithm
Given training set
- Set
Δ(l)i,j := 0 for all (l,i,j), (hence you end up having a matrix full of zeros)
For training example t =1 to m:
- Set
a(1):=x(t) - Perform forward propagation to compute
a(l) for l=2,3,…,L
3. Using
Where L is our total number of layers and
4. Compute
The delta values of layer l are calculated by multiplying the delta values in the next layer with the theta matrix of layer l. We then element-wise multiply that with a function called g', or g-prime, which is the derivative of the activation function g evaluated with the input values given by
The g-prime derivative terms can also be written out as:
5.
Hence we update our new
D(l)i,j:=1m(Δ(l)i,j+λΘ(l)i,j) , if j≠0.D(l)i,j:=1mΔ(l)i,j If j=0
The capital-delta matrix D is used as an "accumulator" to add up our values as we go along and eventually compute our partial derivative. Thus we get
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