Scilab Function
Last update : 5/9/2006
ANN_CONV_W - Function to convert the weight and bias stored in a matrix or vector form in the other form (vector or matrix form respectively)
Calling Sequence
-
Wtr = ANN_CONV_W(Wini,Nin,Nhid,Nout,Type)
Parameters
-
Wini
: Initial weight and bias values in vector form (Matrix [Px1] where P = {Nb Input+1}*Nb hidden nodes + {Nb hidden nodes+1}*Nb ouput) or in matrix form(2 dimensions Matrix [max(Nhid,Nout) x max(Nin+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)
-
Nin
: Number of input nodes
-
Nhid
: Number of hidden nodes
-
Nout
: Number of output nodes
-
Type
: Type of Wini ('matrix' or 'vector')
-
Wtr
: Resulting conversion (if Wini is of 'matrix' type, Wtr is of 'vector' type and vice and versa)
Description
-
This function offers a convenient way to store network bias and weight values.
The size of the vector form gives immediately the number of parameters required by a network.
Examples
// Ouput from a network with 6 input nodes, 4 nodes in the hidden layer and 1 output node
IN = rand(6,100);
W = rand(4,7,2);
[OUT,IN_W,HID_OUT] = ANN_SIM(IN,4,1,W);
Wvect = ANN_CONV_W(W,6,4,1,'matrix');
See Also
ANN_LMBR
,
ANN_JACOB
,
ANN_NORM
,
ANN_SIM
,
Authors
-
Julien Lerat
CEMAGREF Antony, HBAN Unit, julien.lerat@cemagref.fr