Scilab Function
Last update : 11/5/2006
ANN_JACOB - Function to calculate the jacobian performance vector of a feed forward artifical neural
network
Calling Sequence
-
[JE,JJ,normJE,[JX]] = ANN_JACOB(IN,W,IN_W,HID_OUT,ERR_OUT)
Parameters
-
IN
: Input data (Matrix [PxN] where P is the number of input neurons and N the number of input patterns)
-
W
: Initial weight and bias values (2 dimensions Matrix [max(Nhid,M) x max(P+1,Nhid+1) x 2]).
Wini(1:Nhid,1,1) are the bias for the hidden neurons Wini(1:Nhid,2:P+1,1) are the weights for the hidden neurons (P weights for each hidden neuron) Wini(1:M,1,2) are the bias for the ouput neurons Wini(1:M,2:Nhid+1,2) are the weights for the ouput neurons (Nhid weights for each output neuron)
-
IN_W
: Weighted inputs
-
HID_OUT
: Ouputs from the hidden layer
-
ERR_OUT
: Errors (target-network ouput)
-
JE
: Jacobian times errors (Matrix [N_PARxNB_OUT] where NB_PAR is the number of parameters in the network
-
JJ
: Transposed jacobian times jacobian (Matrix [N_PARxNB_PAR])
-
normJE
: Transposed JE matrix times JE
-
JX
: Full Jacobian matrix
Description
-
This function is valid only for feed forward network with one hidden layer.
The activation function of the hidden layer is the hyperbolic tangent and the identity function for the output layer.
This function is used in the function 'LMBR' for the training of feed-forward neural network with Levenberg-Marquadt algorithm under bayesian regulation.
Examples
// Calibration of a network with 6 input nodes, 4 nodes in the hidden layer and 1 output node
IN = rand(6,100);
TARG = rand(1,100);
W = rand(4,7,2);
[OUT,IN_W,HID_OUT] = ANN_SIM(IN,4,1,W);
ERR_OUT = TARG-OUT;
[JE,JJ,NORMGX] = ANN_JACOB(IN,W,IN_W,HID_OUT,ERR_OUT);
See Also
ANN_CONV_W
,
ANN_LMBR
,
ANN_NORM
,
ANN_SIM
,
Authors
-
Julien Lerat
CEMAGREF Antony, HBAN Unit, julien.lerat@cemagref.fr
Bibliography
MacKay, Neural Computation, vol. 4, no. 3, 1992, pp. 415-447.
Foresee and Hagan, Proceedings of the International Joint Conference on Neural Networks, June, 1997.