Regarding the instruction to read and understand the #Click me cell of Chapter 1, these are my thoughts and questions. As will be obvious, Iām a novice programmer.
from fastai2.vision.all import *
Import everything (classes, libraries, etc.) from the fastai vision library
path = untar_data(URLs.PETS)/'images'
I had a misunderstanding about this one. I thought untar_data(URLs.PETS) was downloading the URLs of the pet images, possibly because Iām predisposed to think of downloading URLs for the classifier for Lesson 1 from v3, but also because itās URLs plural, not URL. So I checked the docs, and it turns out thereās a URLs class weāre using, and PETS is one of its methods. There are similar URLs methods for other datasets, but only the fastai ones. This approach doesnāt generalize to non-fastai datasets (but weāll be learning other approaches that do generalize!).
So the dataset is extracted, and the location of the extracted dataset is returned to path. But what does the /'images' at the end do? I searched the forum and found the notes from Lesson 3 of v3, and if Iām extrapolating correctly, I think the pets dataset has a folder named āimagesā, and weāre telling the path to point specifically to that folder, rather than to the dataset folder as a whole. Is that right?
def is_cat(x): return x[0].isupper()
Define a function is_cat to which we pass x, the filename of each pet image. A characteristic of this particular dataset is that the first character of the filename is uppercase if the file is an image of a cat, so is_cat returns True if the first character of x is uppercase, and False otherwise.
dls = ImageDataLoaders.from_name_func(
path, get_image_files(path), valid_pct=0.2, seed=42,
label_func=is_cat, item_tfms=Resize(224))
I have questions about this one, too. The book says:
" The fourth line tells fastai what kind of dataset we have, and how it is structured. There are various different classes for different kinds of deep learning dataset and problemāhere weāre using ImageDataLoaders . The first part of the class name will generally be the type of data you have, such as image, or text. The second part will generally be the type of problem you are solving, such as classification, or regression."
What is āthe second part of the class nameā that is āthe type of problem you are solvingā¦ā? Weāre doing classification, but itās not obvious to me where thatās declared in the class name.
Then weāre using the from_name_func method of the ImageDataLoaders class, which creates our DataLoaders (dls as weāre calling them here), setting aside 20% of our data as the validation set, setting the optional seed value to 42, setting the labelling function to be our is_cat function defined above, and selecting the Resize(224) as the transformation to be applied to the images, resizing them all to 224x244 pixels for historical reasons.
But why is the seed set to 42? The book and the docs say itās for reproducibility, and I understand that getting the same validation set every time is what gives us reproducible results, but what is a seed, and how does it achieve a reproducible validation set? I Googled āreproducibility seedā and found this post helpful:
āThe āseedā is a starting point for the sequence and the guarantee is that if you start from the same seed you will get the same sequence of numbers.ā
But if the elements of the validation set are chosen randomly, how does starting from the same point help? And why 42? Is there a practical consideration at work, or is it just Douglas Adams?
learn = cnn_learner(dls, resnet34, metrics=error_rate)
Use the cnn (convolution neural network) learner, telling it to use the dls we established above, the ResNet34 architecture, and the error rate as a metric. Pretty straightforward for me.
learn.fine_tune(1)
Since weāre using a pretrained model, we donāt want to start fitting the model from scratch, as we would if we used learn.fit. Instead, weāll fine-tune the model for our particular dataset for one epoch (a complete pass through the dataset) to create the head of our model, which is unique to this dataset. The book says:
āAfter calling fit , the results after each epoch are printed, showing the epoch number, the training and validation set losses (the āmeasure of performanceā used for training the model), and any metrics youāve requested (error rate, in this case).ā
But it must mean āAfter calling fine_tune.ā
I did have another hiccup, trying to use ?? to see the docs for methods, e.g. ??cnn_learner; I keep getting an error āObject cnn_learner not found.ā Other shortcuts such as b to create a new cell are working for me, so Iām not sure what Iām doing wrong with this one.
And thatās the lot! Thanks for reading all of this, and please let me know if you can answer any of my questions, or if Iāve mischaracterized anything.
I have done a so-so job on this so far myself, but still 4 more days (assuming the next class is on Tuesday 
) so there is hope!
.
