Thank you for your response.  The command that I posted previously was for
fine tuning, so I was hoping to figure out what was causing the changes in
accuracy when changes to the parameters should have no effect at all.j

I'm not sure if we will move forward with replacing the layers, but I will
post about it if we do.

Thanks again!

On Tue, Aug 21, 2018 at 6:08 PM, Shree Devi Kumar <[email protected]>
wrote:

> >lstmtraining --model_output ./layer_from_deva/layer --continue_from
> ./layer_from_deva/Devanagari.lstm --append_index 5 --net_spec '[Lfx192
> O1c1]' --traineddata ./sanplustrain/san/san.traineddata --train_listfile
> ./sanplustrain/san.training_files.txt --eval_listfile
> ./sanpluseval/san.training_files.txt --debug_interval 0 --max_image_MB
> 6000 --max_iterations 500
> Loaded file ./layer_from_deva/Devanagari.lstm, unpacking...
> Warning: LSTMTrainer deserialized an LSTMRecognizer!
> Continuing from ./layer_from_deva/Devanagari.lstm
> Appending a new network to an old one!!Warning: given outputs 1 not equal
> to unicharset of 149.
> Num outputs,weights in Series:
>   Lfx192:192, 197376
>   Fc149:149, 28757
> Total weights = 226133
> Built network:[1,48,0,1[C3,3Ft16]Mp3,3Lfys64Lfx64Lrx64Lfx192Fc149] from
> request [Lfx192 O1c1]
> Training parameters:
>   Debug interval = 0, weights = 0.1, learning rate = 0.001, momentum=0.5
>
>
> I  have not tried changing the parameters even with replace layers. Do
> provide feedback on your experience,
>
>
> On Tue, Aug 21, 2018 at 12:00 PM Jacob Biros <[email protected]>
> wrote:
>
>> Thank you for the prompt response!
>>
>> On Tue, Aug 21, 2018 at 1:14 PM, Shree Devi Kumar <[email protected]>
>> wrote:
>>
>>> If you want to change parameters, please look at the replace layers
>>> option. With fine tuning you cannot change them.
>>>
>>> On Tue 21 Aug, 2018, 7:27 AM , <[email protected]> wrote:
>>>
>>>> Is it possible to change the parameters when Fine Tuning?
>>>>
>>>> The documentation says "Fine tuning is the process of training an
>>>> existing model on new data without changing any part of the network", but
>>>> does that mean that parameters like momentum, learning rate, etc.  cannot
>>>> be changed?
>>>>
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> ____________________________________________________________
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