10-11-2024, 05:31 PM
(translated by Google translator)
Hi all.
I have posted in my OneDrive a screenshot "DemoTrainingProgram screenshot.jpg" of the current version of the demo training program for SB-Neurons.
https://1drv.ms/f/s!AnoSlTzMqlL6jNx6H4Qi...w?e=8D73FE
In this screenshot you can see two graphs.
The left graph shows the real training data for a SB-Neuron that has 4 logic inputs. The inputs of this neuron will be fed with "0" or "1", which will correspond to the color of a pixel on the image "feature" matrix, measuring 2 by 2 pixels.
Given this input, our trained SB-Neuron will determine the TYPE of the image "feature".
As you can see, the real training data is quite scattered across the graph.
This is clearly visible on the right graph of the screenshot, where the real data is already shown as red dots.
When real data is so widely scattered, it is very difficult for a classical neuron to learn to give accurate answers.
On the same right graph, you will see a blue line that depicts the ideal shape of the neuron activation function for such a task.
But to work successfully with such an activation function, our SB-Neuron training program will have to select new weights for the neuron inputs.
Once the demo program is created, we will be able to see on the right graph how the red dots of the neuron's responses will gradually approach the blue target line.
I hope the experiment will be a success. And for the first time in the history of Humanity, we will perform a SIMULTANEOUS setup of both the activation functions and the weights at the inputs of our proprietary SB-Neuron.
Right?..
Hi all.
I have posted in my OneDrive a screenshot "DemoTrainingProgram screenshot.jpg" of the current version of the demo training program for SB-Neurons.
https://1drv.ms/f/s!AnoSlTzMqlL6jNx6H4Qi...w?e=8D73FE
In this screenshot you can see two graphs.
The left graph shows the real training data for a SB-Neuron that has 4 logic inputs. The inputs of this neuron will be fed with "0" or "1", which will correspond to the color of a pixel on the image "feature" matrix, measuring 2 by 2 pixels.
Given this input, our trained SB-Neuron will determine the TYPE of the image "feature".
As you can see, the real training data is quite scattered across the graph.
This is clearly visible on the right graph of the screenshot, where the real data is already shown as red dots.
When real data is so widely scattered, it is very difficult for a classical neuron to learn to give accurate answers.
On the same right graph, you will see a blue line that depicts the ideal shape of the neuron activation function for such a task.
But to work successfully with such an activation function, our SB-Neuron training program will have to select new weights for the neuron inputs.
Once the demo program is created, we will be able to see on the right graph how the red dots of the neuron's responses will gradually approach the blue target line.
I hope the experiment will be a success. And for the first time in the history of Humanity, we will perform a SIMULTANEOUS setup of both the activation functions and the weights at the inputs of our proprietary SB-Neuron.
Right?..