Re: [Dclib-devel] [dclib-devel] Implementing a GAN with dlib

[Davis K. <dav...@gm...>]

Yes: http://dlib.net/dlib/dnn/layers_abstract.h.html#extract_

On Sun, Oct 22, 2017 at 8:06 PM, Eloi Du Bois <elo...@gm...>
wrote:

> Ok, thank you for your help. Is there any reshape layer that I could use?
>
>
> 2017-10-22 19:04 GMT-05:00 Davis King <dav...@gm...>:
>
>> Well, I don't know what paper you are following.  Maybe it's the most
>> convenient way.  But you probably want to use the dnn_trainer to train
>> multiple steps at a time when optimizing each of the subnets.  But maybe
>> not.
>>
>> Anyway, the fc layer outputs are 1 row and 1 columns but k channels.
>> That's why you get only 16 outputs.  This is all in the documentation for
>> the layers.
>>
>> On Sun, Oct 22, 2017 at 7:58 PM, Eloi Du Bois <elo...@gm...>
>> wrote:
>>
>>> I'm sorry, I'm not sure I see how I can do using another way.
>>> I think I'm almost done training a gaussian function, only problem I
>>> have right now is that this network:
>>>
>>> // A 1 filter; 2x1 transposed conv layer that does 2x upsampling
>>> template<class SUBNET>
>>> using cont2 = dlib::cont<1, 1, 2, 1, 2, SUBNET>;
>>> template<class SUBNET>
>>> using GeneratorT = cont2<cont2<cont2<cont2<dlib::fc<8, SUBNET>>>>>; //
>>> This will take an input of size 32 and generate an output of 128
>>>
>>> Is generating vectors of 16, I'm surprised as I put a fully connected
>>> layer on the input that outputs 8 values. Then, everything is multiplied by
>>> 2 going thru cont layers. It should be 128, no? How can this happen? Is
>>> there something I'm missing or is there something else?
>>>
>>> Thanks,
>>>
>>>
>>> 2017-10-22 7:47 GMT-05:00 Davis King <dav...@gm...>:
>>>
>>>> It does it for you.  Really though, I don't see why you would need to
>>>> call the backward function yourself considering the task you have outlined.
>>>>
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