Why does the loss remain constant for the perceptual losses method (green line)?
Why does the x-axis say L_BFGS iteration when they are using Adam?
In supplementary material, in case of super-resolution, for the convolutional layers in res_blocks , they didn’t use any padding because it causes artifacts.Because of that the output after 2 conv layers will be of different shape compared to input to res_block.To avoid this they center cropped the input to match the size of output of 2 conv layers.I can understand cropping raw images but at the res block stage they are features(whatever that means) right?, what is the intuition behind cropping features.
How long is training expected to take when training the super-resolution network? I’m using a GTX 1080 and it looks like training will take 30 min, which is longer than I would have expected:
This is much slower than when training previous CNNs. (I made sure to set VGG layers to not trainable, so that isn’t the problem.) I also checked my GPU usage while training using the nvidia-smi command, and GPU seems to have jumped since starting the training, suggesting that the GPU is indeed being used:
When I run arr_lr = bcolz.open(‘trn_resized_72_r.bc’)[:]
I get error : FileNotFoundError: [Errno 2] No such file or directory: ‘trn_resized_72_r.bc\meta\sizes’
When I run arr_lr = bcolz.open(‘trn_resized_72_r.bc’)[:]
I get error : FileNotFoundError: [Errno 2] No such file or directory: ‘trn_resized_72_r.bc\meta\sizes’
I don’t know this problem, and so by default I’d redownload the data. If that didn’t work, I’d explore this mysterious “\meta\sizes” thing, by studying the nature of bcolz arrays and how they’re stored (via bcolz documentation). Maybe the bcolz package updated and became out of sync with these bcolz arrays, which may have been created with an older version of bcolz.
Thanks for reply.
I have downgrade the bcolz to 1.0.0 but still the same problem.
To download the files again, it says I don’t have access. Can I get the access or it is just for people registered in the course?
Fast.ai moves fast, and so information about them can go out of date fast. This applies to many posts on this forum. I didn’t have access to these files either with respect to the original link. I found the new link by going to course.fast.ai and pretending I was new. I soon ended up at the lesson 1 page and saw this:
“Important note: All files in the course are now available from files.fast.ai, rather than platform.ai, as shown in the videos.”
The following is the assumption that led me to this solution: “Fast.ai cares deeply about being inclusive. Restricting access to learning materials is the opposite of being inclusive. Fast.ai would never do that. There must be another link.”
Why is AWS p2 slower than my MacBook Pro? I am running a small sample of images through the fast style transfer main algorithm. This takes 9 seconds per iteration on my MacBook Pro and 16 seconds per iteration on p2.xlarge. I thought Tensorflow was supposed to take advantage of a GPU automatically, resulting in a significant speed up!? Am I missing some configuration setting?
Have you ever experienced an issue like this before?
Run the following command in a tmux frame:
watch -n 1 nvidia-smi
then run the training and let us know if you see the GPU Util. % increase.
Lastly, what versions of keras and tf are you using? Is this the AWS instance set up using the fast.ai script? Or did you download tensorflow yourself? If the latter, you may have downloaded regular tensorflow instead of tensorflow-gpu?
Hi, thanks for replying to my question. I am running the Jupyter notebook in a Python 3.6 environment.
I tried the watch command and confirmed that the GPU Uilt %age rose to 100% when I started training.
I am using keras version 1.2.2 and tf.__version__1.1.0
I tried the Logging Device placement scrip on https://www.tensorflow.org/tutorials/using_gpu and confirmed that all operations were running on the GPU.
So as far as I can see the code is running on the GPU, just painfully slowly. To add to my worries I an now getting a ResourceExhaustedError when exactly the same code ran without producing this error last week. I can work around this by reducing the batch size from 18 to 8.
I am very confused. My p2.xlarge instance provides me slower run times and a smaller batch size limit that my MacBook. The only positive is that I got some very good results after running my MacBook for 18 hours. Yet I still have to question what I am paying for on AWS. Should I try setting up the whole p2.xlarge instance again from scratch?
Any other suggestions gratefully accepted.
Yours cheerfully, Gavin
I’ve got to admit that I’m stumped. You could certainly try redoing your AWS, but man what a pain in the ass…
Hmmm… It’s obviously using the GPU like you said. Just doesn’t seem to be doing anything, which… just doesn’t make any sense… Man, sorry I can’t be of more help.
Do you know how to implement the fast style transfer model in xcode using CoreML?
I have transferred the model to coreml model, but cannot predict its output and show it in Xcode.