At minute 49:27 in the video, I see a function S(x) = 1/(1-exp(x)). Is that an activation function? I seems to look like a Sigmoid, but that is 1/(1+exp(-x)).
Here are instructions for creating a keypair
You will want to replace
aws_fastai_gpu.pem with your keypair name which will be
git pull and
conda env update this evening and now lesson1.ipynb (for cell 29) gives
AttributeError: 'ConvLearner' object has no attribute 'data_path'.
What is the relationship between epoch and batch size? How to set batch size correctly?
At 1:19 the teacher is talking about epoch and batch size, at each epoch we take a batch size of 64…
I noticed I was unable to plot the learning rate learn.sched.plot() until I set the batch size to 6 for my 200 images (100 each of each type) with a setting of 75% training 15% valid. When I inspected the current batch size learn.data.bs it was already set to 64 before I changed it for my dataset.
Thanks for clarifications!
Thanks a bunch @duanev, i’d got the same issue, thanks for sharing the link to solution.
I get an error “selenium.common.exceptions.WebDriverException: Message: Process unexpectedly closed with status: 1” when i run the script. Could i be doing something wrong?
thanks for the terms and expressions !! It is well appreciated and keep doing it
Why do we always have to find the local minimum in Deep learning algorithms ? What purpose does it serve
The issue with np.mean() and the call to accuracy_np(probs, y) failing as it was getting passed a one-dim array:
AxisError: axis 1 is out of bounds for array of dimension 1
update: for some reason pip wasn’t loading the latest version of fastai - I replaced it with pulling directly from github and it all works now. So it was a false alarm.
Ideally we would want to find a global minimum of our loss function which should represent “how far away” we are from our desired values. But in practice we may end up with overfitting.
From this paper: https://arxiv.org/abs/1412.0233
We empirically verify several hypotheses regarding
learning with large-size networks:
• For large-size networks, most local minima are equivalent and yield similar performance on a test set.
• The probability of finding a “bad” (high value) local minimum is non-zero for small-size networks and decreases quickly with network size.
• Struggling to find the global minimum on the training set (as opposed to one of the many good local ones) is not useful in practice and may lead to overfitting.
I have written my first blog post on Dogs Vs Cats Classification, please have a look
give suggestion for further improvement
Hi just some feedback. I was following an older version of this course a while ago, and I found that much, much easier to follow than this version.
The old one had a few utility methods and stuff (“utils.py” and “vgg16.py”!), but this new one comes with thousands of lines of “helpful” code in the fastai library, way too much to casually understand without a lot of work.
Now I feel like I’m not learning how to use keras or theano or tensorflow or pytorch, I’m just investing a lot of time into learning your made-for-this-course framework.
I’m willing to work hard, but if I put in the work to understand the fastai library it’s not transferrable or useful. I’d much rather have to slowly build up over time all the code for image-loading, transforming, model creation, etc. Then at least that effort teaches me something that’s useful in the future.
As helpful as the fastai library is, it’s not likely to be used outside of this course. Rather than learn it, I’d like to learn how to do those things myself.
Can we please move the link to auto generate test data to a more prominent position in the wiki? I didn’t pay enough attention to this link until I actually spent considerable time to find web scrapers, download and arrange images into folders and be satisfied with my hours of work before realizing there was an easier way to do it.
In lesson 1 around the 29th minute, @jeremy says that you could download some pictures, change the path to point to those pictures and use the same lines of code to train the neural network to recognize those images too.
I wanted to train it to recognize minerals so I downloaded some pictures of 2 minerals and changed the path to point to the folder containing them but I’m getting some errors with the code.
I remember there was a link to a pdf where someone had made notes commenting in the Jupiter notebook itself and explaining the codes. Anyone know where I can find that?
This was just posted two days ago, May 15 2018, and it was very helpful in getting Fast.ai course work up and running in Sagemaker.
Did you make it work?
Check if you have good folder structure inside train and valid (in my example, I was compering chinese and windsor chairs)
Thanks - any thoughts as to Sagemaker vs Paperspace in terms of overall ease or use, setup, flexibility. I have managed to do most things locally to date but am thinking I need to move some stuff to the Cloud now and trying to decide which way to go.
I use AWS for other things and so I already had an account with all the billing things configured.
From a Sagemaker point of view I do not know enough to tell you if it’s better than Paperspace.
There are many “short-cuts” that AWS provides for you that may reduce the time spent in maintaining an ML network. Also with the new pricing reductions for a p2.xl is $0.90 an hour.
You might find that you’ll develop on Paperspace but then run production workloads on AWS.