thanks ;-) 2017-04-25 15:09 GMT+02:00 Oleks <olk.zayt...@gmail.com>:
> Hello, > > There isn't one yet. But I will try to create it today. I will let you know > > Cheers, > Oleks > > On Apr 25, 2017 16:10, "francescoagati [via Smalltalk]" <[hidden email] > <http:///user/SendEmail.jtp?type=node&node=4944027&i=0>> wrote: > >> Hi Oleks, >> there is a mode for install neural network from metacello? >> >> 2017-04-25 13:00 GMT+02:00 Alexandre Bergel <[hidden email] >> <http:///user/SendEmail.jtp?type=node&node=4944025&i=0>>: >> >>> Continue to push that topic Oleks. You are on the right track! >>> >>> Alexandre >>> >>> > On Apr 24, 2017, at 1:43 AM, Oleks <[hidden email] >>> <http:///user/SendEmail.jtp?type=node&node=4944025&i=1>> wrote: >>> > >>> > Hello, >>> > >>> > Thanks a lot for your advice! It was very helpful and educating (for >>> > example, I thought that we store biases in the weight matrix and >>> prepend 1 >>> > to input to make it faster, but now I see why it's actually slower that >>> > way). >>> > >>> > I've implemented a multi-layer neural network as a linked list of >>> layers >>> > that propagate the input and error from one to another, similar to the >>> Chain >>> > of Responsibility pattern. Also, now I represent biases as separate >>> vectors. >>> > The LearningAlgorithm is a separate class with Backpropagation as its >>> > subclass (though at this point the network can only learn through >>> > backpropagation, but I'm planning to change that). I'm trying to >>> figure out >>> > how the activation and cost functions should be connected. For example, >>> > cross-entropy works best with logistic sigmoid activation etc. I would >>> like >>> > to give the user a freedom to use whatever he wants (plug in whatever >>> you >>> > like and see what happens), but it can be very inefficient (because >>> some >>> > time-consuming parts of activation and cost derivatives cancel out each >>> > other). >>> > >>> > Also, there is an interface for setting the learning rate for the whole >>> > network, which can be used to choose the learning rate prior to >>> learning, as >>> > well as to change the learning rate after each iteration. I am >>> planning to >>> > implement some optimization algorithms that would automize the process >>> of >>> > choosing a learning rate (adagrad for example), but this would require >>> a bit >>> > different design (maybe I will implement the Optimizer, as you >>> suggested). >>> > >>> > I'm attaching two images with UML diagrams, describing my current >>> > implementation. Could you please tell me what you think about this >>> design? >>> > The first image is a class diagram that shows the whole architecture, >>> and >>> > the second one is a sequence diagram of backpropagation. >>> > >>> > mlnn.png <http://forum.world.st/file/n4943698/mlnn.png> >>> > backprop.png <http://forum.world.st/file/n4943698/backprop.png> >>> > >>> > Sincerely yours, >>> > Oleksandr >>> > >>> > >>> > >>> > -- >>> > View this message in context: http://forum.world.st/Neural-N >>> etworks-in-Pharo-tp4941271p4943698.html >>> > Sent from the Pharo Smalltalk Users mailing list archive at Nabble.com. >>> > >>> >>> -- >>> _,.;:~^~:;._,.;:~^~:;._,.;:~^~:;._,.;:~^~:;._,.;: >>> Alexandre Bergel http://www.bergel.eu >>> ^~:;._,.;:~^~:;._,.;:~^~:;._,.;:~^~:;._,.;:~^~:;. >>> >>> >>> >>> >>> >> >> >> ------------------------------ >> If you reply to this email, your message will be added to the discussion >> below: >> http://forum.world.st/Neural-Networks-in-Pharo-tp4941271p4944025.html >> To unsubscribe from Neural Networks in Pharo, click here. >> NAML >> <http://forum.world.st/template/NamlServlet.jtp?macro=macro_viewer&id=instant_html%21nabble%3Aemail.naml&base=nabble.naml.namespaces.BasicNamespace-nabble.view.web.template.NabbleNamespace-nabble.view.web.template.NodeNamespace&breadcrumbs=notify_subscribers%21nabble%3Aemail.naml-instant_emails%21nabble%3Aemail.naml-send_instant_email%21nabble%3Aemail.naml> >> > > ------------------------------ > View this message in context: Re: Neural Networks in Pharo > <http://forum.world.st/Neural-Networks-in-Pharo-tp4941271p4944027.html> > > Sent from the Pharo Smalltalk Users mailing list archive > <http://forum.world.st/Pharo-Smalltalk-Users-f1310670.html> at Nabble.com. >
