Title: How to Do Lesson 2 of Fast.ai in Google Colab: A Step-by-Step Guide
Fast.ai’s deep learning course offers an opportunity for beginners and experts alike to delve into the world of artificial intelligence and machine learning. Lesson 2 of the course covers essential concepts such as image classification, data cleaning, and working with relational data. In this article, we will guide you through the process of completing Lesson 2 in Google Colab, a free cloud-based platform that provides access to GPUs and TPUs, making it an ideal choice for running deep learning experiments.
Step 1: Setting Up Google Colab
Begin by signing into your Google account and navigating to Google Colab. Once you have accessed the platform, you can create a new notebook for Lesson 2. Select “File” from the menu and click on “New Notebook” to open a blank notebook where you can write and execute code.
Step 2: Importing the Fast.ai Library
Fast.ai is an open-source library for deep learning that is built on top of PyTorch. To access the library in Google Colab, you need to install it using the following command:
“`python
!pip install fastai
“`
Once the installation is complete, import the library into your notebook using the following code:
“`python
from fastai.vision.all import *
“`
Step 3: Loading and Preparing the Dataset
For Lesson 2, you will work with a dataset of images to build an image classification model. Fast.ai provides convenient ways to load and prepare datasets for training. To load the dataset, you can use the `untar_data` function to download and extract the necessary files, as shown in the following example using the Oxford-IIIT Pet Dataset:
“`python
path = untar_data(URLs.PETS)/’images’
“`
Step 4: Data Cleaning and Preprocessing
Before training a model, data cleaning and preprocessing are essential steps to ensure that the dataset is ready for use. Fast.ai simplifies this process by providing high-level functions for data augmentation, normalization, and batch creation. For example, you can use the `DataBlock` class to define how the data should be processed and organized for training:
“`python
pets = DataBlock(blocks = (ImageBlock, CategoryBlock),
get_items=get_image_files,
splitter=RandomSplitter(seed=42),
get_y=using_attr(RegexLabeller(r'(.+)_\d+.jpg$’), ‘name’))
“`
Step 5: Training and Evaluating the Model
With the data ready, you can now proceed to train a model using Fast.ai’s high-level training API. This involves creating a `Learner` object and selecting an appropriate architecture such as ResNet or VGG for the model. Here’s an example of how to train a model using transfer learning:
“`python
dls = pets.dataloaders(path)
learn = cnn_learner(dls, resnet34, metrics=error_rate)
learn.fine_tune(2)
“`
Step 6: Deploying and Testing the Model
Once the model is trained, you can deploy it to make predictions on new images. Fast.ai provides methods for visualizing model predictions and evaluating performance. Use the following code to test the model on a sample image:
“`python
img = PILImage.create(‘sample_image.jpg’)
img.to_thumb(192)
is_cat,_,probs = learn.predict(img)
print(f”Is this a cat?: {is_cat}.”)
print(f”Probability it’s a cat: {probs[1].item()}.”)
“`
In conclusion, completing Lesson 2 of Fast.ai in Google Colab is an excellent way to gain hands-on experience with deep learning concepts and techniques. By following the step-by-step guide provided in this article, you can effectively work through the lesson and develop a strong foundation in image classification and data processing. As you continue your journey in deep learning, remember that Google Colab offers a powerful and accessible platform for running your experiments and building cutting-edge models.
