Fast.ai is a popular deep learning library that aims to make deep learning more accessible to everyone. One of the key components of Fast.ai is its flexible and powerful functionality for working with Jupyter notebooks. Jupyter notebooks are interactive documents that allow you to write and execute code, visualize the output, and document your work all in one place. In this article, we will explore how to use notebooks in Fast.ai to create and train deep learning models.
Getting Started with Fast.ai Notebooks:
To get started with Fast.ai notebooks, you will first need to install Fastai library and its dependencies. Fastai can be installed using pip by running the command “pip install fastai”. Additionally, you will need to have Jupyter notebooks installed on your system.
Once you have Fastai and Jupyter notebooks installed, you can start a new notebook by opening a terminal and running the command “jupyter notebook”. This will open a new browser window with the Jupyter notebook interface, where you can create a new notebook.
Creating a New Notebook:
To create a new notebook, simply click on the “New” button in the Jupyter notebook interface and select “Python 3” under the “Notebooks” section. This will create a new notebook with the file extension “.ipynb” where you can start writing and executing code.
Importing Fastai Libraries:
Once you have created a new notebook, the first step is to import the necessary Fastai libraries. You can do this by running the following command in a code cell:
“`python
from fastai.vision.all import *
“`
This will import all the necessary libraries and classes required for working with deep learning models in Fastai.
Loading and Preprocessing Data:
One of the key steps in training a deep learning model is to load and preprocess the data. Fastai provides a convenient way to do this using the `DataBlock` and `DataLoaders` classes. These classes allow you to define the data source, apply transformations, and create data loaders for training and validation sets.
For example, to load and preprocess an image dataset, you can use the following code:
“`python
dls = ImageDataLoaders.from_folder(path, train=”train”, valid=”valid”)
“`
In this example, `path` is the path to the dataset, and `train` and `valid` are the names of the training and validation folders within the dataset.
Defining and Training a Model:
Once the data is loaded and preprocessed, you can define a deep learning model using the `cnn_learner` class. This class allows you to specify the architecture of the model, the loss function, and the optimizer.
For example, you can define a simple convolutional neural network (CNN) model using the following code:
“`python
learn = cnn_learner(dls, resnet18, metrics=accuracy)
“`
In this example, `dls` is the data loader, `resnet18` is the architecture of the model, and `accuracy` is the metric used to evaluate the model.
You can then train the model using the `fit_one_cycle` method:
“`python
learn.fit_one_cycle(5)
“`
This will train the model for 5 epochs using the “one-cycle” learning rate policy.
Visualizing Model Performance:
After training the model, you can visualize its performance using the `Learner` object’s `show_results` method. This will display a random selection of images from the validation set with their predicted and actual labels.
For example, you can visualize the model’s performance using the following code:
“`python
learn.show_results()
“`
This will display a grid of images with their predicted labels and confidence scores.
Saving and Exporting the Model:
Finally, once you are satisfied with the model’s performance, you can save it to disk using the `export` method of the `Learner` object:
“`python
learn.export(“model.pkl”)
“`
This will save the model in a file named “model.pkl” in the current working directory.
In conclusion, Fastai notebooks provide a powerful and flexible environment for creating and training deep learning models. By following the steps outlined in this article, you can leverage the full potential of Fastai to create and train state-of-the-art deep learning models with ease. Whether you are a beginner or an experienced deep learning practitioner, Fastai notebooks offer a rich set of tools and functionalities to help you realize your deep learning goals.
