@corey
I’ve been keeping a list of the fastai terms. Whenever I can’t remember what something stands for, I go to the top of my repo fastai_deeplearn_part1 and do a search.
In this case, ps means p’s (plural, I believe) to represent the probability of dropout.
I’ve been trying to recreate jeremy 's code on the dogsbreed example in lesson 2. I have managed to get up to 1:31:08 of the youtube video but get an error when I run:
Hi
May I ask the tip been mentioned in “01:32:45 Undocumented Pro-Tip from Jeremy: train on a small size, then use ‘learn.set_data()’ with a larger data set (like 299 over 224 pixels)” is kind of data augmentation or not ?
And why the neural network can adapt to different image size dynamically / automatically ?
Thank you.
I recreated the Jeremy`s notebook Dogbreeds and got in top16% of competition. So I think changing image size works perfect))
As I understood correctly, the images with bigger size (299) like are a new images for the model. And learns again without overfitting.
About size-changing Jeremy talk in detail in Lesson3 (short answer: model changes size of original images to 224 or 299 every time they are loaded into model). So if original size of your images is very large it better to previously change size.
Hey guys ! So I just finished with Lesson 2 and I have a few doubts.
1: What are the precomputed activations Jeremy talks about in the lesson ? For example, some activations are activated when there are eye balls in the picture, some are activated when there are dogs and so on. I just want to understand them fundamentally.
2: I think this is related but what do you mean by freezing and unfreezing layers ?
I was redoing the Lesson 2 and currently facing a challenge at the learn.sched.plot. My learn.lr_find() works well but when I plot it I am not able to infer anything from it. What should I be doing to make the learning rate visible for me to infer?
Hi @GregFet, your response does answer my question
Since you mentioned “like … new images”, I would consider this tip is kind of “data augmentation”.
Many thanks.
OK @jeremy,
Indeed, as all parts of implementation are already in the lecture of lesson 2, I thought that the notebook had been made available.
So, I will rewrite them from the lecture, thank you.
@jeremy
Hi Jeremy,
Any way to get the images for dogbreeds competition? Git or download from Kaggle directly? It would be good if there’s a instruction on getting the competition images?