now work all good with last version of polymath thanks :-) 2017-04-27 17:29 GMT+02:00 Oleks <olk.zayt...@gmail.com>:
> Hello, > > I have finally added a configuration to the NeuralNetwork project. Now you > can use this Metacello script to load it into your Pharo image: > > Metacello new > repository: 'http://smalltalkhub.com/mc/Oleks/NeuralNetwork/main'; > configuration: 'MLNeuralNetwork'; > version: #development; > load. > > Sorry for the delay > > Oleks > > On Tue, Apr 25, 2017 at 4:13 PM, francescoagati [via Smalltalk] <[hidden > email] <http:///user/SendEmail.jtp?type=node&node=4944473&i=0>> wrote: > >> thanks ;-) >> >> 2017-04-25 15:09 GMT+02:00 Oleks <[hidden email] >> <http:///user/SendEmail.jtp?type=node&node=4944028&i=0>>: >> >>> 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. >>> >> >> >> >> ------------------------------ >> If you reply to this email, your message will be added to the discussion >> below: >> http://forum.world.st/Neural-Networks-in-Pharo-tp4941271p4944028.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-tp4941271p4944473.html> > Sent from the Pharo Smalltalk Users mailing list archive > <http://forum.world.st/Pharo-Smalltalk-Users-f1310670.html> at Nabble.com. >
