
  uuppddaatteeWWeeiigghhttss -- uuppddaatteess tthhee wweeiigghhttss uussiinngg tthhee ccuurrrreenntt lliinnkk ddeerriivvaattiivveess

  UUSSAAGGEE

        updateWeights [-algorithm <algorithm> | -noreset | -report]

  DDEESSCCRRIIPPTTIIOONN

  This performs a single weight update using the currently accumulated link
  derivatives. This is useful in conjunction with _d_o_E_x_a_m_p_l_e for designing
  custom training patterns other than the standard fixed batch size.

  If an algorithm, such as steepest, momentum, or dougsMomentum, is
  specified, it will be used to update the weights, but the network's default
  algorithm will not change. If no algorithm is specified, the network
  default will be used.

  By default the link and unit derivatives will be reset after the update,
  but this can be prevented with the -noreset option.

  -report will cause a report to be printed.

  EEXXAAMMPPLLEESS

  Here's a way to train using random batch sizes:

        repeat 1000 {
          repeat [randInt 100] {doExample -train};
          updateWeights
        }

  SSEEEE AALLSSOO

  _t_r_a_i_n, _d_o_E_x_a_m_p_l_e, _s_t_e_e_p_e_s_t, _m_o_m_e_n_t_u_m, _d_e_l_t_a_B_a_r_D_e_l_t_a

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    Last modified: Fri Nov 17 12:39:05 EST 2000

