9 Neural Networks with fastai (draft)

9.1 What is fastai?

As mentioned in the previous chapter, we will now look at a extremely popular deep learning framework called fastai. fastai is an open-source software library for machine learning, which provides high-level APIs for deep learning applications. It is built on top of PyTorch and is designed to be easy to use and flexible, allowing developers to quickly prototype and implement their ideas. fastai provides a wide range of pre-trained models, including state-of-the-art models for computer vision, natural language processing, and recommendation systems. Additionally, fastai offers a number of useful tools and utilities for data processing and model evaluation, making it a popular choice among researchers and practitioners alike.

The creators of fastai have also created accompanying educational resources to assist in learning deep learning through the use of their framework. Both the course and book are highly recommended.

Here is the link to the free course: Link
Here is the link to the book: Link

The creators also have a peer-review paper published explaining high-level functionality and layered approach to the fastai library- Link.

9.2 Creating a dataloader

from fastai.vision.all import *

dPath = Path("../data/mnist_png/")

dPath.ls()

(#3) [Path('../data/mnist_png/models'),Path('../data/mnist_png/testing'),Path('../data/mnist_png/training')]

get_image_files(dPath)

(#70000) [Path('../data/mnist_png/testing/0/10.png'),Path('../data/mnist_png/testing/0/1001.png'),Path('../data/mnist_png/testing/0/1009.png'),Path('../data/mnist_png/testing/0/101.png'),Path('../data/mnist_png/testing/0/1034.png'),Path('../data/mnist_png/testing/0/1047.png'),Path('../data/mnist_png/testing/0/1061.png'),Path('../data/mnist_png/testing/0/1084.png'),Path('../data/mnist_png/testing/0/1094.png'),Path('../data/mnist_png/testing/0/1121.png')...]

dataset = DataBlock(
                blocks = (ImageBlock(cls = PILImageBW), CategoryBlock),
                get_items = get_image_files,
                splitter = GrandparentSplitter(train_name='training', valid_name='testing'),
                get_y = parent_label,
                item_tfms = Resize(28),
                batch_tfms = None
            )

dls = dataset.dataloaders(dPath, bs=128)

print(dls.vocab) ## Prints class labels
print(dls.c) ## Prints number of classes
dls.show_batch(max_n=24,figsize=(10,6)) ## Show sample data

['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
10

dls.one_batch()[0].shape, dls.one_batch()[1].shape

(torch.Size([128, 1, 28, 28]), torch.Size([128]))

class MLP(nn.Module):
    def __init__(self, n_in, n_out):
        super().__init__()
        self.model = nn.Sequential(
            nn.Linear(n_in, 256),
            nn.ReLU(),
            nn.Linear(256, 128),
            nn.ReLU(),
            nn.Linear(128, n_out)
        )
    def forward(self, x):
        return self.model(x.view(-1,784))

## Defining the learner
model = MLP(784, 10)
mlp_learner = Learner(
    dls = dls, 
    model=model, 
    loss_func=F.cross_entropy, 
    model_dir=dPath/"models",
    metrics=accuracy)

## Finidng Ideal learning late
mlp_learner.lr_find()

SuggestedLRs(valley=0.0005754399462603033)

mlp_learner.fit_one_cycle(5,5e-2)

epoch	train_loss	valid_loss	accuracy	time
0	0.415048	0.640518	0.835600	00:37
1	0.299409	0.296634	0.935100	00:37
2	0.198500	0.212431	0.950100	00:38
3	0.119415	0.128112	0.967800	00:38
4	0.065490	0.106387	0.973300	00:38