So it is rather scaling.
Thank you for your prompt answer
Search found 14 matches
- Tue 9. Apr 2013, 13:46
- Forum: All about using MemBrain
- Topic: Normalization
- Replies: 2
- Views: 11216
- Sun 7. Apr 2013, 19:09
- Forum: All about using MemBrain
- Topic: Normalization
- Replies: 2
- Views: 11216
Normalization
I'd like to ask about the feature in Membrain called normalization.
Is it normalization or rather scaling ?
Over there exist many names, best answer would be its formula.
Is it normalization or rather scaling ?
Over there exist many names, best answer would be its formula.
- Sat 2. Feb 2013, 10:02
- Forum: All about using MemBrain
- Topic: Think on Lesson
- Replies: 8
- Views: 23160
Re: Think on Lesson
Thank you for your answer, it is clear now.
- Thu 31. Jan 2013, 10:18
- Forum: All about using MemBrain
- Topic: Think on Lesson
- Replies: 8
- Views: 23160
Re: Think on Lesson
What I meant was that there is a difference beetwen data when teaching is finished and then you perform ThinkOnLesson.
See attached file
See attached file
- Wed 30. Jan 2013, 16:07
- Forum: All about using MemBrain
- Topic: Think on Lesson
- Replies: 8
- Views: 23160
Re: Think on Lesson
But in case of time invariant networks with no loops, input -> hidden layer 1 -> hidden layer 2 -> output, results should be exactly the same ? After teaching (with reseting network after each lesson) when you ResetNet and ThinkOnLesson you don't get the same results. I checked also on Mackey Glass ...
- Sun 27. Jan 2013, 19:15
- Forum: All about using MemBrain
- Topic: Think on Lesson
- Replies: 8
- Views: 23160
Re: Think on Lesson
It is time variant network, so to obtain the same result I would have to do ThinkStep as many times as there are delay steps ? edit: I just realized it would not work During thinking the net remembers its last state, state after the last pattern has been applied during teaching. So how to validate a...
- Sun 27. Jan 2013, 15:17
- Forum: All about using MemBrain
- Topic: Think on Lesson
- Replies: 8
- Views: 23160
Think on Lesson
I have across a problem with thinking feature, when network is trained when I choose think from lesson editor or evaluate network error strange thing happens - results change (on the same data). I have checked and it happens in examples attached like Mackey series. Why is that ? Should it be exactly...
- Wed 12. Dec 2012, 20:18
- Forum: Project Support
- Topic: Neuron Output = Input
- Replies: 6
- Views: 18111
Re: Neuron Output = Input
Below I pasted part of the code I wrote, could you have a look and tell me if it is ok ? How the numbering of neurons is done ? 0 is the first neuron in Lesson Editor ? Edit: changed the code, now seems to work, but the result I get are not exactly the same, there is a small difference in fraction p...
- Tue 11. Dec 2012, 12:00
- Forum: Project Support
- Topic: Neuron Output = Input
- Replies: 6
- Views: 18111
Re: Neuron Output = Input
You don't have to perform this manually: MemBrain supports the so called 'Normalization' setting for input and output neurons. There you can specify a user defined value range for each neuron which is then automatically mapped by MemBrain to the applicable internal range of the neuron according to ...
- Mon 10. Dec 2012, 12:42
- Forum: Project Support
- Topic: Neuron Output = Input
- Replies: 6
- Views: 18111
Re: Neuron Output = Input
Thank you Thomas for the replay, also in the other post. I am aware of IDENTICAl activation function but it works only for inputs from -1 to 1 so it clips below and above values. I can work around by dividing inputs so they are in the range -1 .. 1. Now I have a trained net and I want it to employ o...