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    Sam Hodge
    @samhodge
    let me show you what I mean
    this commit kind of spells it out: samhodge/incubator-mxnet@1d72d60
    Anirudh Subramanian
    @anirudh2290
    looking
    Sam Hodge
    @samhodge
    I think I may know the solution to my problem
    I need to save parts of the model and put the pieces together in C++
    Anirudh Subramanian
    @anirudh2290
    i know that for backend symbolic api one uses mx.sym.variable and then binds it to the model
    Sam Hodge
    @samhodge
    that way I can have a resolution independent model
    Anirudh Subramanian
    @anirudh2290
    i have to see how to do that in gluon
    Sam Hodge
    @samhodge
    I can just reshape the appropriate symbole
    Anirudh Subramanian
    @anirudh2290
    yes, but you need something equivalent to mx.sym.variable in gluon correct ?
    Sam Hodge
    @samhodge
    I think so
    it feels like I am trying to rough out an idea at this stage
    Sam Hodge
    @samhodge
    Actually dont worry about that, I think I need to continue on with a fixed resolution model first and simply try that out with the feedforward in C++ and then build it up from there, I am a little confused about if it is possible to build the gram matrix and the inspiration network in C++, but as a start it might be worth just loading up the model and params that I have for a decent trained model.
    I just worry that I am delaying a decision
    Anirudh Subramanian
    @anirudh2290
    so the link that you pointed to https://github.com/samhodge/incubator-mxnet/blob/master/cpp-package/include/mxnet-cpp/symbol.hpp is just the cpp package, its just another frontend like python.
    Sam Hodge
    @samhodge
    Yup, but C++ is easier to deploy than Python
    Anirudh Subramanian
    @anirudh2290
    i am just wondering why you can not keep weight and height as symbol variables and bind it at runtime.
    width*
    Sam Hodge
    @samhodge
    Sounds great
    but I am not sure if I understand how to do that
    how can you load on the params if the width and height are not known
    so when you do x = mx.sym.var('data')
    y = style_model(x)
    Sam Hodge
    @samhodge
    can you also do some sort of magic like a= mx.sym.var('width'), b=mx.sym.var('height') y=style_model(x,height=a,width=b)
    is that what you mean?
    Anirudh Subramanian
    @anirudh2290
    yes
    Sam Hodge
    @samhodge
    I am honestly a bit of a noob
    Anirudh Subramanian
    @anirudh2290
    i am also a noob with gluon
    Sam Hodge
    @samhodge
    its all good you learn by making mistakes
    let me try this out.
    Anirudh Subramanian
    @anirudh2290
    but with symbolic api you just use placeholders for data, and then bind it to the data at the end, i think this should be possible with gluon too
    Anirudh Subramanian
    @anirudh2290
    apache/incubator-mxnet#6087 you probably need something like this
    Sam Hodge
    @samhodge
    I am hoping I can work out how to get the width and height as symbols to the network
    Sam Hodge
    @samhodge
    I think this relates to my issue
    Sam Hodge
    @samhodge
    @
    Anirudh Subramanian
    @anirudh2290
    @samhodge this should help you
    apache/incubator-mxnet#9893
    Sam Hodge
    @samhodge
    thanks I need to go a few steps back at the moment.
    it seems that the training the model that I am trying to serialise as a symbolic network doesnt work without any modifications
    I will put in a ticket now about this issue
    Sam Hodge
    @samhodge
    apache/incubator-mxnet#9989
    So I would be happy with a fixed resolution for now, but I cannot even get that working.
    Sam Hodge
    @samhodge
    @zhanghang1989 do you have an opinion?
    Lutz Roeder
    @lutzroeder

    Screen Shot 2018-03-07 at 8.23.49 PM.png

    Netron now supports MXNet -symbol.json models. Feedback is welcome.

    Anirudh Subramanian
    @anirudh2290
    @lutzroeder awesome thanks a lot ! i am not sure there are enough people from the community here. i will post this in the slack channel. i know that some people have asked for this in the community.
    ThomasDelteil
    @ThomasDelteil
    Thanks @lutzroeder, really cool, I'll use netron in my next talk to show the model architecture ! Just tried it out with the Crepe model, I am just wondering what makes a convolution show as dilates=(1,) rather than just hiding the dilation factor? (I am assuming dilates=(1,) is the same as no dilation?)