Hokie, I think crestle.ai team might be able to answer your questions re: the visibility of your notebooks to others. I presume you are trying to share your progress with someone by sharing a link to it?
AFAICT, since you have to log in to your account in Crestle, you’d be the only one who has direct access to your jupyter notebook.
Also, Jupyter automatically saves your work, at least on my local machine it does. I’m not sure what the behavior is for the hosted jupyter notebooks at crestle, I would assume that they also do this.
I would suggest you put these questions to Crestle support to get definitive answers.
One thing that Jeremy recommends in his first lecture is to make a copy of the notebook and make changes in that notebook (because jupyter saves your changes every few seconds/minutes)
I haven’t played with the new crestle.ai notebooks so I’m not sure how they behave. I’m assuming they behave like regular jupyter notebooks, except that they are hosted for you on a machine in the cloud and you can login to your area on their servers and run the notebook on their servers.
Is Jeremy’s guide to creating our own dataset up-to-date? I read through it and saw a lot of error/warning messages underneath the code chunks. Is that a problem? I don’t know whether to ignore that.
Hey, I’ve been trying to import a kaggle dataset onto the kaggle kernel.
The folder set up is as follow:
/input
/train_images
/train.csv
the images are in folder train_images and labels are present in train.csv.
i tried to use ImageList.from_csv(path, ‘train.csv’, cols =2) but it looks for the label filename in input folder and not in train_images folder. However if i try to use ImageDataBunch.from_csv(path,ds_tfms=tfms, size=28) then the function looks for only ‘labels.csv’ specifically and returns an error saying no folder called ‘…/input/labels.csv’ because the file is called ‘train.csv’.
And i cannot rename the file either cause kaggle doesn’t allow writes onto data and hence I’m unable to rename.
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-17-a7fe6964f6f7> in <module>
----> 1 doc(interp.plot_top_losses)
~/miniconda3/envs/p37cu10FastAI/lib/python3.7/site-packages/fastai/gen_doc/nbdoc.py in doc(elt)
129 use_relative_links = False
130 elt = getattr(elt, '__func__', elt)
--> 131 md = show_doc(elt, markdown=False)
132 if is_fastai_class(elt):
133 md += f'\n\n<a href="{get_fn_link(elt)}" target="_blank" rel="noreferrer noopener">Show in docs</a>'
~/miniconda3/envs/p37cu10FastAI/lib/python3.7/site-packages/fastai/gen_doc/nbdoc.py in show_doc(elt, doc_string, full_name, arg_comments, title_level, alt_doc_string, ignore_warn, markdown, show_tests)
114 else: raise Exception(f'doc definition not supported for {full_name}')
115 source_link = get_function_source(elt) if is_fastai_class(elt) else ""
--> 116 test_link, test_modal = get_pytest_html(elt, anchor_id=anchor_id) if show_tests else ('', '')
117 title_level = ifnone(title_level, 2 if inspect.isclass(elt) else 4)
118 doc = f'<h{title_level} id="{anchor_id}" class="doc_header">{name}{source_link}{test_link}</h{title_level}>'
~/miniconda3/envs/p37cu10FastAI/lib/python3.7/site-packages/fastai/gen_doc/nbtest.py in get_pytest_html(elt, anchor_id)
55 def get_pytest_html(elt, anchor_id:str)->Tuple[str,str]:
56 md = build_tests_markdown(elt)
---> 57 html = HTMLExporter().markdown2html(md).replace('\n','') # nbconverter fails to parse markdown if it has both html and '\n'
58 anchor_id = anchor_id.replace('.', '-') + '-pytest'
59 link, body = get_pytest_card(html, anchor_id)
TypeError: markdown2html() missing 1 required positional argument: 'source'
running most recent fastAI installed into a python 3.7.3 conda environment and with jupyter notebook.
However the next cell displays the confusion matrix ok.
suggestion for resolution?
Can someone help me with understanding how to choose the value from LR Finder. From what I understood, select the max learning rate as the LR right before the reporter plot starts showing gain in loss as we keep on increasing the LR. The minimum can then be chosen as 10x or 100x smaller than max.
I understand the LR slice we chose for resnet 34 but I don’t get the slice we chose for resnet 50. Since the LR decreases until after 1e-2, shouldn’t the slice be 1e-2 to 1e-4? We have however chosen 1e-6 to 1e-4.
doc(interp.plot_top_losses)
---------------------------------------------------------------------------
NameError Traceback (most recent call last)
<ipython-input-19-a7fe6964f6f7> in <module>
----> 1 doc(interp.plot_top_losses)
NameError: name 'doc' is not defined
I have a problem, the link from 21:52 minute of video: https://s3.amazonaws.com/fast-ai-imageclas/oxford-iiit-pet
Doesn’t seem to work. I receive an error and when i paste it to the browser I get error: NoSuchKey and they key is oxford-iiit-pet
show_batch for me takes a long time to run as well, i am using google colab. are you using google colab and did you ever figure out how to speed it up?
Need help here for getting predictions. I join a kaggle competition on image analysis and went through all the steps from lesson 1. Now I have a problem of getting predictions of the test set and exporting into a csv file to submit to kaggle,
My ImageDataBunch current has 3 datasets. A training dataset, a validation set (20% of training), and a test set provided by kaggle.
As I understand from the lecture - it is a matter of experimentation. You are correct that the possible rule is
select the max learning rate as the LR right before the reporter plot starts showing gain in loss as we keep on increasing the LR. The minimum can then be chosen as 10x or 100x smaller than max.
But you also should try different slices near that one and check whether they lead to a better result.
It’s in the 'magic **kwargs** argument, that is then being passed as another **kwargs to show_xys, which accepts the figsize. The fastai library uses these **kwargs quite a lot. Here’s more info: http://book.pythontips.com/en/latest/args_and_kwargs.html
you can either continue on the course, where we deal with predictions, or you can check inside kaggle competition current kernels that were submitted. search ones with fast keyword and you will have dozen submitted with fast.ai library. Fork the notebooks and learn from them.
I responded here with a reasonable way to do it. If you already have a test set within your data bunch skip the steps that add a test set to it and run the predictions. You will have to take an argmax from the logits.
Hi everyone, im new here. i have some background on on DL but all on tf, keras i see the first lesson and im really interesting on complete all the course. wich material u recommend to read to be up to date with fastai lib and Pytorch? only docs will be ok?
In another hand, any chance to be online assistant of part 2 of the course?
Hi,
I am reading train and test imagelists from pandas.dataframe and loading it into a databunch.
But, I want to get the dataset object for train and test sets. How can I do that?
