choconlangthang
November 23rd, 2005, 04:03 AM
Hello there, plz help me with this.
I have a 3-layer feed-forward backpropagation neural network. I use it to recognize letter (printed uppcase only). The features extracted are 256 subregions of a 64x64pixel image (which means a region is 16x16 pixel). So the input vector contains 256 members.
However, when I try with 3 letters (A,B,C). It seems the product is one letter only, it is B. I am sure that my code is in correct implementation (the theory can be found here http://www.speech.sri.com/people/anand/771/html/node37.html ). I try to modify some parameters like learning rate, loop for each letter, but it seems there is no change.
Any idea is welcomed, plz help, thanks.
I have a 3-layer feed-forward backpropagation neural network. I use it to recognize letter (printed uppcase only). The features extracted are 256 subregions of a 64x64pixel image (which means a region is 16x16 pixel). So the input vector contains 256 members.
However, when I try with 3 letters (A,B,C). It seems the product is one letter only, it is B. I am sure that my code is in correct implementation (the theory can be found here http://www.speech.sri.com/people/anand/771/html/node37.html ). I try to modify some parameters like learning rate, loop for each letter, but it seems there is no change.
Any idea is welcomed, plz help, thanks.