Deep learning in pythonista .?
Does pythonista currently supports any deep learning module .(via stash ) I love pythonista for what all it can do but having just basic deep learning would be so awesome.. just to learn
I tried a deep learning module npdl in pythonista it inported wothout error but always seems to get stuck at model.fit()
dgelessus last edited by
Most Python machine learning libraries are written in native code (C, C++, Assembly, Fortran, etc.), so you cannot install them in Pythonista (because of iOS restrictions).
npdlis a bit different, it uses only pure Python and
numpy, so it should be usable in Pythonista.
If your code hangs on the
model.fitcall, maybe it just takes a while to run? Depending on what your device is, your code will run much slower than on a normal computer, especially for code that requires a lot of computation. Try letting it run for a few minutes and see if anything happens. (If your device gets warm, you can tell that it's still doing something.
Note: I know nothing about machine learning, so I can't help very much, sorry.
Yeah I tried it on my iPhone 5s and let it run for 3-4 minutes .. i just wanted to know if it was actually running and working on more powerful devices ir not .?
Post some script (+ what to install), so, I can run it on iPad Pro and will let you know for how long it runs.
Well i just pip installed npdl
Then git cloned this repo:
And ran the lstm-character.py in examples folder
But it hung on train model
I figured there were too many characters in tiny shakespeare.txt
So i reduced them but it still didnt went past the train model..
However its working fine in my pc .. still took a lot of time though
Here're results from the iPad Pro 2nd gen 12.9". I had to change max iterations to 10, it took much longer with 100.
Started: 2017-09-18 10:50:13.803954 data has 1115389 charactres, 65nique. Building model ... Train model ... iter 1, train-[loss 4.1734, acc 0.0362]; iter 2, train-[loss 4.1724, acc 0.0362]; iter 3, train-[loss 4.1714, acc 0.0375]; iter 4, train-[loss 4.1704, acc 0.0375]; iter 5, train-[loss 4.1695, acc 0.0400]; iter 6, train-[loss 4.1685, acc 0.0413]; iter 7, train-[loss 4.1675, acc 0.0437]; iter 8, train-[loss 4.1665, acc 0.0437]; iter 9, train-[loss 4.1655, acc 0.0462]; iter 10, train-[loss 4.1646, acc 0.0475]; Started: 2017-09-18 10:50:13.803954, Ended: 2017-09-18 11:21:58.137529 Duration: 1904.333575
Approximately 32 minutes. You should run these tasks on a computer as @dgelessus already mentioned. These pure Python implementations will be always slow.
Kush last edited by Kush
Oh wow thanks .. decreasing the max iter definitely helped and i also found out that decreasing the n_out to 30(from 300) ie reducing the no of nodes made it a lot faster
Just run it on my desktop (MacBook Pro (15-inch, Late 2016), 2,9 GHz Intel Core i7, 16 GB 2133 MHz LPDDR3) for fun ...
/Users/robertvojta/anaconda/bin/python3 /Users/robertvojta/Work/purposefly/NumpyDL/examples/lstm-character-lm.py Started: 2017-09-18 22:14:10.355194 data has 1115389 charactres, 65nique. Building model ... Train model ... iter 1, train-[loss 4.1730, acc 0.0262]; iter 2, train-[loss 4.1716, acc 0.0262]; iter 3, train-[loss 4.1702, acc 0.0262]; iter 4, train-[loss 4.1688, acc 0.0262]; iter 5, train-[loss 4.1674, acc 0.0250]; iter 6, train-[loss 4.1660, acc 0.0262]; iter 7, train-[loss 4.1646, acc 0.0262]; iter 8, train-[loss 4.1632, acc 0.0275]; iter 9, train-[loss 4.1619, acc 0.0288]; iter 10, train-[loss 4.1605, acc 0.0300]; Started: 2017-09-18 22:14:10.355194, Ended: 2017-09-18 22:15:04.876571 Duration: 54.521377
... and the result is less than one minute, 32x faster :) Okay, it's not just about desktop vs iPad, but also pure Python vs optimized NumPy libs, ...
Kush last edited by Kush
Haha yeah pure python libraries are indeed slower
ellie_ff1493 last edited by
iOS 11 has a deep learning api in it
chibill last edited by
For pretrained models.