Lesson 1 Discussion ✅


Hello! Does anyone know whether learn.fit_one_cycle is addititive?
Does it hold that
is the same as
number crunching seems this to be sort off the case, but then the error rates do not seem to go down monotonously… there seems to be a little jump at the beginning of each
?? anyone knows how this works?


The data is at https://s3.amazonaws.com/fast-ai-imageclas/oxford-iiit-pet.tgz, their interface is just a bit weird (the .tgz was added in the function itself…)

The network connection part in kaggle kernel also trips me over though, even though I turned on the internet connection it still didn’t work (ended up using colab instead)

(Piotr Olchawa) #1312

Yeah, it was the same for me, I also switched to Colab. Thanks for help!

(Vikrant Behal) #1313

How can I untar data into my google collab?

How do I change?

path = untar_data(URLs.PETS); path

Per documenation I’ve done following:

from google.colab import drive
drive.mount(’/content/gdrive’, force_remount=True)
root_dir = “/content/gdrive/My Drive/”
base_dir = root_dir + ‘fastai-v3/’

and made directory using:

path = Path(base_dir + ‘data’)
folder = ‘lesson1_pets’
dest = path/folder
path.mkdir(parents=True, exist_ok=True)


i just watched the lecture and tried the code simultaniously on my local pc. apart from some minor hick ups it worked great. while doing the training parts, i was looking at my cpu and gpu load and it was actually pretty much idle for most of the time, with minor spikes for the cpu (up to 100%) and gpu (highest about 20%, although the memory got utilized about 50%), is that normal or do i have something configured wrong.

i was expecting the gpu to do most of the work and also why is there so much downtime?
i havent timed that but from my feeling; after hitting shirt+enter to execute the line until it was done, the cpu/gpu was more idle than doing something.

greetings from austria


Simple question:

After building a convnet and applying learn.fit_one_cycle(4), prior to any fine-tuning, what’s the learning rate used by default? Is it a default constant or does it try to pick an appropriate value?

(kelvin chan) #1316

I have yet to read the code in details. But I used to see this in Keras if you “recompiled” your model after each .fit. If I transfer the knowledge from there, I speculate it maybe due to optimizer statefulness. Optimizers such as Adams and batchnorm layers track histories (e.g. exp avg of the gradients). If these aren’t kept inbetween .fit, then u will see that behavior.

Again, I ain’t fastai codebase expert and I wait for better folks to give Insights.

(Taher Ali) #1317

hi raimanu-ds have you find solution for this .


No I haven’t. I would be interested to know how though ^^

The only ways I was able to load other datasets so far were through uploads using Colab’s GUI, CLI commands in Colab or Kaggle’s API and feed it through ImageDataBunch.from_folder().

(Taher Ali) #1319

Please write it here . if you find solution for this


I found on this forum you should change the regular expression pattern to something else if you are on windows:
pat = re.compile(r’\([^\]+)_\d+.jpg$’)
this works for me!

(David) #1321

The fit_one_cycle function uses a max learning rate: max_lr(): the default value is 0.003 (or 3e-03).
Check the docs for more info.

(Agatha) #1322

I’m actually not really sure what this error is or if the output below the warning(?)/error(?) message is the one-preloaded with the notebook, or the actual one I’m running right now.

I don’t really get why the ‘lower’ attribute had an error, that just lowercases the what I assume is a string right for ‘front_end’? Has anybody reported on this bug before?

*As an added note, I’m running on my own machine running Ubuntu 18.04, and I followed the installation instructions for AWS.

(Osam) #1323

I unfroze the model and “trained more layers” but used 4 epochs instead of 1. The model came out more accurate. The time lost in training was about 15 secs.


Hi all,

I am running the jupyter notebook on my own computer. After running the learn.fit_one_cycle(4) part of the code, the next cell next (learn.load(‘stage-1’) does not run properly. It kind of gets stuck. I can not interrupt my kernel anymore, the only solution is to restart it, but then I also lose all of my data. Does anyone have an idea how I can fix this?

I am using Jupyter notebook 5.7.4, python version 3.7.1, fastai 1.0.38 and pytorch 1.0.0.

~ Sophie

(chandan ) #1325

check if you have imported the fastai library properly

(Parth ) #1327

I do not know if this is the right place to ask this question. After implementing this lecture, I wonder how would I go on to train a classifier with multiple classes which can detect all of those classes if given in a single image for prediction? I have seen somewhere those bounding boxes which detect multiple classes in a single image. Is it a completely different task or this tutorial can be extended to do something like that?


Hi :wave:t3:,
You will learn more about this in Lesson 3 :wink:

(Parth ) #1329

Thanks for reply. I have actually seen that lecture. There Jeremy discusses about multi-label classification. I was asking - for example, if I have trained my model to classify 10 celebrities and if I give a group photo or something like that does model can identify different celebrities in it(with where the person is)? How would that generally work?


I have just started with the first lesson of DL part 1. I would be interested in DL applications for de novo design.