Perl AI Deep Learning Tutorial

Find the sum of squares error-loss function

Let's write a function to find the sum of squares error in Perl. The sum of squares error is one of the loss functions used to calculate the error between the output result and the expected output result (correct answer).

use strict;
use warnings;

#Square sum error
sub sum_of_square_error {
  my ($outputs, $desired_outputs) = @_;
  
  if (@$outputs! = @$desired_outputs) {
    die "Outputs length is different from Desired length";
  }
  
  my $total_pow2 = 0;
  for (my $i = 0; $i <@$outputs; $i ++) {
    $total_pow2 + = ($outputs->[$i]-$desired_outputs->[$i]) ** 2;
  }
  my $sum_of_square_error = 0.5 * $total_pow2;
  
  return $sum_of_square_error;
}

my $outputs = [0.7, 0.2, 0.1];
my $desired_outputs = [1, 0, 0];
my $sum_of_square_error = sum_of_square_error ($outputs, $desired_outputs);

In deep learning, the weight and bias parameters are adjusted so that the error obtained by the loss function is small.

As a loss function in the problem of pattern recognition, it is better to use cross entropy error than the square sum error because the form of partial differential is difficult and the calculation is complicated. Seems desirable.