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lib/AI/NNEasy.hploo  view on Meta::CPAN

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What is hard in a NN is to find this I<weights>. By default L<AI::NNEasy> uses
I<backprop> as learning algorithm. With I<backprop> it pastes the inputs through
the Neural Network and adjust the I<weights> using random numbers until we find
a set of I<weights> that give to us the right output.
 
The secret of a NN is the number of hidden layers and nodes/neurons for each layer.
Basically the best way to define the hidden layers is 1 layer of (INPUT_NODES+OUTPUT_NODES).
So, a layer of 2 input nodes and 1 output node, should have 3 nodes in the hidden layer.
This definition exists because the number of inputs define the maximal variability of
the inputs (N**2 for bollean inputs), and the output defines if the variability is reduced by some logic restriction, like
int the XOR example, where we have 2 inputs and 1 output, so, hidden is 3. And as we can see in the
logic we have 3 groups of inputs:
 
  0 0 => 0 # false
  0 1 => 1 # or
  1 0 => 1 # or
  1 1 => 1 # true
 
Actually this is not the real explanation, but is the easiest way to understand that
you need to have a number of nodes/neuros in the hidden layer that can give the
right output for your problem.

lib/AI/NNEasy.pm  view on Meta::CPAN

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What is hard in a NN is to find this I<weights>. By default L<AI::NNEasy> uses
I<backprop> as learning algorithm. With I<backprop> it pastes the inputs through
the Neural Network and adjust the I<weights> using random numbers until we find
a set of I<weights> that give to us the right output.
 
The secret of a NN is the number of hidden layers and nodes/neurons for each layer.
Basically the best way to define the hidden layers is 1 layer of (INPUT_NODES+OUTPUT_NODES).
So, a layer of 2 input nodes and 1 output node, should have 3 nodes in the hidden layer.
This definition exists because the number of inputs define the maximal variability of
the inputs (N**2 for bollean inputs), and the output defines if the variability is reduced by some logic restriction, like
int the XOR example, where we have 2 inputs and 1 output, so, hidden is 3. And as we can see in the
logic we have 3 groups of inputs:
 
  0 0 => 0 # false
  0 1 => 1 # or
  1 0 => 1 # or
  1 1 => 1 # true
 
Actually this is not the real explanation, but is the easiest way to understand that
you need to have a number of nodes/neuros in the hidden layer that can give the
right output for your problem.