gradient descent for custom function
I have four equations:
1) e = m – y
2) y = W_3 * h
3) h = z + W_2 * z + f
4) f = W_1 * x
I want to update W_1, W_2 and W_3 in order to minimize a cost function J = (e^T e ) by using gradient descent.
x is an input, y is the output and m is the desired value for each sample in the dataset
I would like to do W_1 = W_1 – eta* grad(J)_w_1
W_2 = W_2 – eta* grad(J)_w_2
W_3 = W_3 – eta* grad(J)_w_3
Going through documentation I found out that you can train standard neural networks. But notice that I have some custom functions, so I guess it would be more of an optimization built in function to use.
Any ideas?I have four equations:
1) e = m – y
2) y = W_3 * h
3) h = z + W_2 * z + f
4) f = W_1 * x
I want to update W_1, W_2 and W_3 in order to minimize a cost function J = (e^T e ) by using gradient descent.
x is an input, y is the output and m is the desired value for each sample in the dataset
I would like to do W_1 = W_1 – eta* grad(J)_w_1
W_2 = W_2 – eta* grad(J)_w_2
W_3 = W_3 – eta* grad(J)_w_3
Going through documentation I found out that you can train standard neural networks. But notice that I have some custom functions, so I guess it would be more of an optimization built in function to use.
Any ideas? I have four equations:
1) e = m – y
2) y = W_3 * h
3) h = z + W_2 * z + f
4) f = W_1 * x
I want to update W_1, W_2 and W_3 in order to minimize a cost function J = (e^T e ) by using gradient descent.
x is an input, y is the output and m is the desired value for each sample in the dataset
I would like to do W_1 = W_1 – eta* grad(J)_w_1
W_2 = W_2 – eta* grad(J)_w_2
W_3 = W_3 – eta* grad(J)_w_3
Going through documentation I found out that you can train standard neural networks. But notice that I have some custom functions, so I guess it would be more of an optimization built in function to use.
Any ideas? gradient descent, neural network, optimization MATLAB Answers — New Questions