Usage
mlp_teach_sgd(net, input, output, tol_level, max_epochs, learn_rate, l2reg = 0, minibatchsz = 100, lambda = 0, gamma = 0, momentum = 0, report_freq = 0)
Arguments
net
an object of mlp_net class
input
numeric matrix, each row corresponds to one input vector
number of columns must be equal to the number of neurons
in the network input layer
output
numeric matrix with rows corresponding to expected outputs,
number of columns must be equal to the number of neurons
in the network output layer, number of rows must be equal to the number
of input rows
tol_level
numeric value, error (MSE) tolerance level
max_epochs
integer value, maximal number of epochs (iterations)
learn_rate
numeric value, (initial) learning rate, depending
on the problem at hand, learning rates of 0.001 or 0.01 should
give satisfactory convergence
l2reg
numeric value, L2 regularization parameter (default 0)
minibatchsz
integer value, the size of the mini batch (default 100)
lambda
numeric value, rmsprop parameter controlling the update
of mean squared gradient, reasonable value is 0.1 (default 0)
gamma
numeric value, weight decay parameter (default 0)
momentum
numeric value, momentum parameter, reasonable values are
between 0.5 and 0.9 (default 0)
report_freq
integer value, progress report frequency, if set to 0
no information is printed on the console (this is the default)