kandi X-RAY | fastbook Summary
kandi X-RAY | fastbook Summary
These notebooks cover an introduction to deep learning, fastai, and PyTorch. fastai is a layered API for deep learning; for more information, see the fastai paper. Everything in this repo is copyright Jeremy Howard and Sylvain Gugger, 2020 onwards. These notebooks are used for a MOOC and form the basis of this book, which is currently available for purchase. It does not have the same GPL restrictions that are on this draft. The code in the notebooks and python .py files is covered by the GPL v3 license; see the LICENSE file for details. The remainder (including all markdown cells in the notebooks and other prose) is not licensed for any redistribution or change of format or medium, other than making copies of the notebooks or forking this repo for your own private use. No commercial or broadcast use is allowed. We are making these materials freely available to help you learn deep learning, so please respect our copyright and these restrictions. If you see someone hosting a copy of these materials somewhere else, please let them know that their actions are not allowed and may lead to legal action. Moreover, they would be hurting the community because we're not likely to release additional materials in this way if people ignore our copyright. This is an early draft. If you get stuck running notebooks, please search the fastai-dev forum for answers, and ask for help there if needed. Please don't use GitHub issues for problems running the notebooks. If you make any pull requests to this repo, then you are assigning copyright of that work to Jeremy Howard and Sylvain Gugger. (Additionally, if you are making small edits to spelling or text, please specify the name of the file and a very brief description of what you're fixing. It's difficult for reviewers to know which corrections have already been made. Thank you.).
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Trending Discussions on fastbook
I'm trying to implement the
Learner object and its steps and facing an issue with the
loss.backward() function as it raises and
AttributeError: 'NoneType' object has no attribute 'data'
The entire process works when I follow the Chapter 04 MNIST Basics. However, implementing within a class raises this error. Could anybody guide me on why this occurs and ways to fix this?
Here's the code below:...
ANSWERAnswered 2022-Feb-17 at 18:30
It seems like your optimizer and your trainer do not work on the same model.
model=simple_net, while the parameters for the optimizer are those of a different model
params=simple_net.parameters() -- that is, make sure the trainer's
params are those of
Fast Ai uses a very unconventional style of
from fastai import * etc.
I for one do not like it so was painstakingly identifying each import in the chapter 2 of the fastai book but ran into the error...
ANSWERAnswered 2020-Dec-08 at 13:25
I just faced the exact same issue. After looking at one of their tutorial I saw that the cnn learner is not imported from the expected package.
Normally statements like
from module import * are frowned upon by expert python programmers as they can lead to namespace clobbering. Yet they are frequent in Fast AI and the justification is that it makes life simpler for the student. Below is an excerpt from their book
This may be so and as long as it is simply a matter of importing everything that's fine I guess.
However, below we will see that it does more than a simple import and an instance of a cnn_learner that has no method called fine_tune ends up having one when we run from
fastai.vision.all import *.
ANSWERAnswered 2020-Dec-04 at 23:07
One alternate approach to
from fastai.vision.all import * is
I saw this on
ANSWERAnswered 2020-Oct-11 at 12:50
The option -q of pip give less output.
The Option is additive. In other words, you can use it up to 3 times (corresponding to WARNING, ERROR, and CRITICAL logging levels).
-qmeans display only the messages with WARNING,ERROR,CRITICAL log levels
-qqqmeans display only the messages with CRITICAL log level
No vulnerabilities reported
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