How to Train a MNIST Classifier with Pytorch Lightning

How to Train a MNIST Classifier with Pytorch Lightning

In this blog post, we’ll show you how to train a MNIST classifier with Pytorch Lightning. We’ll go over the steps involved in training the classifier, including data preparation, model creation, and training.

Check out our video for more information:

Introduction: Why Pytorch Lightning?

Since its release in early 2018, Pytorch has gained immense popularity as a tool for deep learning. Its user-friendly API and customizable architectures make it a popular choice for developers and researchers alike. While Pytorch is relatively easy to use, there are still some pain points that can make working with it tedious and time-consuming. This is where Pytorch Lightning comes in.

Pytorch Lightning is a wrapper around the Pytorch library that makes deep learning research and development more streamlined and efficient. It does this by abstracting away many of the boilerplate code that is necessary for training deep learning models. In addition, it also includes a number of handy features out-of-the-box, such as early stopping and model checkpointing.

In this tutorial, we will be using Pytorch Lightning to train a simple MNIST classifier. We’ll start by briefly going over the basics of Pytorch Lightning, and then we’ll jump into the code!

Getting Started: Installing Pytorch Lightning

Pytorch Lightning is a framework that allows you to write very concise and readable code for training complex models. In this tutorial, we will show you how to install Pytorch Lightning and use it to train a simple MNIST classifier.

The first thing you need to do is install Pytorch Lightning. You can do this by running the following command:

pip install pytorch-lightning

Once Pytorch Lightning is installed, you can import it into your Python script like so:

import pytorch_lightning as pl

Now that Pytorch Lightning is installed, let’s write some code!

Data Preparation: Creating a Dataset for MNIST

In order to train a classifier on the MNIST dataset, we first need to prepare the data. Pytorch Lightning makes it easy to create a DataLoader from a Pytorch Dataset. In this tutorial, we’ll use the MNIST dataset which can be downloaded from Pytorch’s website.

First, we need to import the necessary packages.

import torch
from torch import nn
from torch import optim
from torchvision import datasets, transforms
import pytorch_lightning as pl

Then, we’ll define a function to download and transform the MNIST dataset into a Pytorch Dataset. The MNIST dataset consists of handwritten digits, each of which is 28×28 pixels. We’ll convert each image into a 1D vector of size 784. We also need to specify that our target labels are integers in the range [0, 9].
def mnist_transform(x): # x is an image # transform it into a 1D vector return x.view(784) # for each image in our dataset… def get_mnist_dataset(): mnist_trainset = datasets.MNIST(‘/tmp’, download=True, train=True, transform=mnist_transform) mnist_testset = datasets.MNIST(‘/tmp’, download=True, train=False, transform=mnist_transform) return mnist_trainset, mnist_testset“`

Model Definition: Defining a MNIST Classifier

In this section, we will define our MNIST classifier model. We will use Pytorch’s Lightning Module class to do this. The module class inherits from the nn.Module class, which is Pytorch’s standard way of defining models.

Our model will be a simple feed-forward neural network with three hidden layers, each with 128 neurons. We will use the ReLU activation function for all hidden layers, and a softmax activation function for the output layer. The softmax activation function is commonly used for classification tasks, as it gives us probability scores for each of the possible classes (i.e., it outputs a vector of length 10, where each element corresponds to the probability of the input image being that particular digit).

We will also add a dropout layer after each hidden layer, with a dropout rate of 0.2 (this means that 20% of the neurons in each hidden layer will be randomly dropped out at each training step). Dropout is a regularization technique that helps prevent overfitting by randomly dropping out neurons during training (the dropped-out neurons are ignored during training, but they are still present in the model).

Here is the code for our MNIST classifier model:

import torch
import torch.nn as nn
import torch.nn.functional as F

class MNISTClassifier(nn.Module):

def __init__(self):


self.fc1 = nn.Linear(28*28, 128) # inputlayer – 784 nodes (28*28 images) and output 128 nodes

self.fc2 = nn.Linear(128, 128) # 1st hiddelayer – 128 nodes and output again 128 nodes

self.fc3 = nn.Linear(128, 128) # 2nd hiddelayer – 128 nodes and output again 128 nodes

self.fc4 = nn

Training the Model: Using Pytorch Lightning to Train the MNIST Classifier

In this section, we will use Pytorch Lightning to train the MNIST classifier. First, we will need to define the model and prepare the data. Then, we will train the model and save it for later use.

1. Define the model class:

class MNISTClassifier(nn.Module):
def __init__(self):
self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
self.conv2_drop = nn.Dropout2d()
self.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)

def forward(self, x):

Evaluating the Model: Testing the MNIST Classifier

Now that we have our model trained, let’s see how it performs on some new data. We can evaluate the model on the test set that we left out previously. To do this, we’ll need to first transform the test data into the same form as our training data. Then, we can simply pass it through our trained model and see what class it predicts for each example.

1. Start by loading the test data from torchvision. This time, we’ll also convert the images to grayscale and normalize them to [0, 1].

2. Next, create a Pytorch DataLoader for the test set using your transformed data.

3. Now, we’ll use our trained model to predict the class of each example in the test set. For each image in the test set, get the model’s prediction by passing it through the model.

4. Finally, compute the accuracy of your model on the test set. That is, for each image in the test set, compare its predicted class to its true class and count how many times they match. The accuracy is simply this number divided by the size of the test set.

Conclusion: What Have We Learned?

Through this blog post, we learned how to train a MNIST classifier using Pytorch Lightning. We also saw how to improve our model’s performance by using data augmentation and transfer learning. Finally, we looked at how to deploy our model on AWS using Sagemaker.

Keyword: How to Train a MNIST Classifier with Pytorch Lightning

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