Machine Learning

mikael

@jmv38, if Pythonista crashes, google for ”dgelessus faulthandler” to get the ObjC exception. If it is not a Pythonista crash, what is the trace?

jmv38

it is a pythonista crash

jmv38

note that the images stored are very small (25x25) and only 3 SketchView objects are using them. So it cannot be memory overflow.
I am probably doing something very wrong somewhere, but what?
What is puzzling is that the very same code, executed 200 times works fine. I try to execute it only 3 times, and re-use the result, but then it does not work...
Note that it works the 1rst time. This code never crashes

  def run(self):
    global X, y, pts, NN
    n = len(self.vars)
    count = self.count
    if count<n:
      if count == 3: exit()

the crash occurs ramdomly during one of the next calls. Not always the same. I must be writing in the memory at a wrong place.

cvp

Crash, with faulthandler, gives an empty faultlog-temp.txt...strange

cvp

@jmv38 Not sure if that helps but no crash with

  def getImg(self):
    if self.sImage == None:
      pil_image = ui2pil(snapshot(self.subviews[0]))
      _,_,_,pil_image = pil_image.split()
      pil_image = pil_image.resize((100, 100),PILImage.BILINEAR)
      pil_image = pil_image.resize((50, 50),PILImage.BILINEAR)
      pil_image = pil_image.resize((25, 25),PILImage.BILINEAR)
      return pil_image.copy() <-------------------
      self.sImage = pil_image
    return self.sImage.copy()

jmv38

@cvp thank you, i feel less alone...
your last proposal is not a solution to the problem: the sImage is never updated, so it is recomputed at each cycle, that is what want to avoid.
I just tried some more changes (slow down, predefine self.sImage), but nothing works.
Must be something stupid (a bad local name, messing with a global?). Or are the some memory bugs in pythonista?
I think i must be degrading self.sImage, but how? i return a copy, not the image itself, and i dont modify sketch[] during learning...

cvp

@jmv38 this shows, on my iPad mini 4, that crash arrives after preparing 71/243

    if testBug:
      pil_image = v.getImg()
      time.sleep(0.05)

Preparing 74/243... .....always 74 even with time.sleep(0.5)

cvp

@jmv38 Not sure if this modification does destroy the algorithm

in updateLearninImage

  BWlearningSetImage = temp#.convert('RGB')

in showTraining

    global BWlearningSetImage
    BWlearningSetImage = BWlearningSetImage.convert('RGB')

jmv38

@cvp does it solve the bug?

jmv38

@cvp no crash, you are right!
Incredible that you found that.
Any insight of what is going on there?

jmv38

@cvp thank you so much for solving my problem!
I wish i understood what was wrong in my code, though...

cvp

@jmv38 I'm just happy to have been able to help.
Sincerely, I don't understand all your code but I've tried to follow it, step by step by skipping some process until I found this "solution". I agree that it does not explain the problem

Doing the conversion at end is less work, I think, because not converted at each iteration.
Perhaps, a problem of cpu consumption

jmv38

v12 https://gist.github.com/8fa3ac1516ef159284f3090ba9494390
big pef improvement with prior normalization of image size and position

jmv38

Huge update! Now you can inspect the states and weights of the internal layers and get a feeling of how the network decides!
https://gist.github.com/ef4439a8ca76d54a724a297680f98ede

Also I added a copyright because this is a lot of work. @mkeywood if you are uncomfortable with this let me know.

Here is a video showing the code in action
https://youtu.be/yBR80KwYtcE

jmv38

screenshot

jmv38

300 views and not a word...???
Hey guys (and gals), if you like the code i share, some encouragement is always welcome!
Thanks.

jmv38

v14 colors modified to better understand the network computation.
now it is much easier.
https://gist.github.com/94a8d1474a6ef6e49972518baa730f1b

JonB

Can you explain what is happening in the bottom set of plots?

jmv38

@JonB hello.
the bottom plot shows:

during training: the training set and the color of each element corresponds to the recongnition result of this element.

during guessing (what you can se above):
the states of input layer0, then weights 0>1, then states of layer1, then weights 1>2, then states of layer2, then weights 2>3, then states of layer2, then output layer3 = 3 neurons.

First i display the weights in black and white (white=positive, black=negative), then i display in color and in sequence the signal propagation through the network:

the neuron states: green means active (1) and red means inactive (0).
neurons x weights values: Green means positive influence on the neuron (excitation) and red negative influence (damping). The brighter the stronger. I.ll call this wxn for weights x neuron.

The colors of the 3 neurons of layer 3 (red,red,green) are the same under the sketches: they correspond to the same data.
the group of 3 blocs on the left of these neurons (weights2>3) are the input weights of each neuron x the input of previous layer.

Let’s analyse this picture

Here you can see that a single wxn is mainly responsible for the 3 neuron states: its the weight (1/2, 0/2) ((0,0) is top left of each bloc), excited by neuron (1/2, 0/2) (green). Other neurons are desactivated (red) so the wxn are insignificant (black). This neuron is damping the 2 first neurons (red wxn) and exciting the last one (green wxn).

Now let’s see why neuron (1/2, 0/2) of layer2 is excited. Look in wxn1>2 at the bloc (1/2, 0/2). Several wxn are green, they are responsible for the response. There is no opposite response (red).

Let’s look at the stonger response wxn (2/4,3/4). The previous layer neuron corresponding is green too (active). look at the corresponding wxn0>1: you can see the the top left part of the ‘o’ drawing is green = detected.

so we can say the ‘o’ has been detected because of its top left part, which is not present in the 2 other drawings. That makes sense.
And the 2 other choices have been rejected for the same reason (it might not be the case).

I hope this explaination is what you were expecting.

jmv38

here is a video that is the same explanation
https://youtu.be/L-6ZwbXjG48