42 lines
1.4 KiB
Mathematica
42 lines
1.4 KiB
Mathematica
|
function v = roughNN( w , x )
|
||
|
|
%
|
||
|
|
% v = roughNN( w , x )
|
||
|
|
%
|
||
|
|
% returns the falue of the function v = f( x ) as currently estimated by
|
||
|
|
% a small NN with 1 input, 1 output, 3 hidden layers of 5 nodes each, and
|
||
|
|
% tanh activation function.
|
||
|
|
%
|
||
|
|
% Input:
|
||
|
|
%
|
||
|
|
% - w is the [ 76 x 1 ] real vector containing the weights of the NN,
|
||
|
|
% i.e., w is made as follows:
|
||
|
|
% [ 1 .. 5 ] are the [ 5 x 1 ] weigths of the first layer
|
||
|
|
% [ 6 .. 10 ] are the [ 5 x 1 ] biases of the first layer
|
||
|
|
% [ 11 .. 35 ] are the [ 5 x 5 ] weigths of the second layer
|
||
|
|
% [ 36 .. 40 ] are the [ 5 x 1 ] biases of the second layer
|
||
|
|
% [ 41 .. 65 ] are the [ 5 x 5 ] weigths of the third layer
|
||
|
|
% [ 66 .. 70 ] are the [ 5 x 1 ] biases of the third layer
|
||
|
|
% [ 71 .. 75 ] are the [ 5 x 1 ] weigths of the fourth (output) layer
|
||
|
|
% [ 76 ] is the [ 1 x 1 ] bias of the fourth (output) layer
|
||
|
|
%
|
||
|
|
% - x is the real scalar containing the input of f()
|
||
|
|
%
|
||
|
|
% Output:
|
||
|
|
%
|
||
|
|
% - v (real, scalar): v = f( x ) as estimated by the NN with weights w
|
||
|
|
%
|
||
|
|
%{
|
||
|
|
% =======================================
|
||
|
|
% Author: Antonio Frangioni
|
||
|
|
% Date: 28-08-22
|
||
|
|
% Version 1.00
|
||
|
|
% Copyright Antonio Frangioni
|
||
|
|
% =======================================
|
||
|
|
%}
|
||
|
|
|
||
|
|
g = tanh( ( ones( 5 , 1 ) * x ) .* w( 1 : 5 ) + w( 6 : 10 ) );
|
||
|
|
g = tanh( reshape( w( 11 : 35 ) , [ 5 5 ] ) * g + w( 36 : 40 ) );
|
||
|
|
g = tanh( reshape( w( 41 : 65 ) , [ 5 5 ] ) * g + w( 66 : 70 ) );
|
||
|
|
v = g' * w( 71 : 75 ) + w( 76 );
|
||
|
|
|
||
|
|
end
|