lesson 9/11

11-09/TestFunctions Matlab/roughNN.m

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+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