D | |
| deriv_error [Ann_func] |
Type of the derivative of the error function
|
| deriv_out [Ann_func] |
Type of the derivative of the transfer function of the output layer
|
G | |
| gradient_learning [Ann_config] |
Type allowing to choose the number of patterns used to compute the
gradient of the error, before making a step in the descent direction
(in the weights space).
|
I | |
| input [Annet] | |
M | |
| mode [Ann_config] |
The program can be runned in several modes
|
N | |
| nn_func [Ann_func] |
Error function, tranfer functions for the hidden and output units, and
their derivatives.
|
| nn_topology [Ann_topology] |
Description of the networks units, layers, and connections.
|
O | |
| opti [Ann_config] |
Type of optimization method used to minimize the error.
|
| out_choice [Ann_func] |
The derivatives of the activation function of the output layer
and of the error fuction are used to compute the deltas of the
output layer (see Ann_backprop, and [Bishop96], section 4.8).
|
V | |
| vector [Ann_func] |