Training a neural network using PyTorch on MNIST Dataset In this project, We are using PyTorch to train a deep learning multi-class classifier on this dataset and test how the trained model performs on the test samples.

Neural Network Architecture:

In this project,

• we will create a convolutional neural network that contains:
  • convolutional,
  • linear,
  • max-pooling, and
  • dropout layers.
  • Log-Softmax is used for the final layer and
  • ReLU is used as the activation function for all the other layers. And
  • The model is trained using an Adadelta optimizer with a fixed learning rate of 0.5.

MNIST Dataset

For this exercise, we will be using the famous MNIST dataset [5],

• which is a sequence of images of handwritten postcode digits, zero through nine, with corresponding labels.

• The MNIST dataset consists of 60,000 training samples and 10,000 test samples,

• where each sample is a grayscale image with 28 x 28 pixels. PyTorch also provides the MNIST dataset under its Dataset module.

• MNIST, or the Modified National Institute of Standards and Technology database, is a collection of handwritten digits that's commonly used for training and testing image processing systems and machine learning:

  • What it is A large database of 70,000 grayscale images of handwritten digits, each 28x28 pixels in size
  • How it's used A standard benchmark for evaluating image classification algorithms. It's also used as a "hello world" example by data scientists.
  • How it was created A derivative work from the original NIST Special Database 1 and Special Database 3, which contain images of handwritten digits written by high school students and US Census Bureau employees, respectively
  • Who created it Yann LeCun of Courant Institute, NYU and Corinna Cortes of Google Labs, New York hold the copyright
  • Where to find it You can find the MNIST dataset on: Yann LeCun's website: Includes the train-images-idx3-ubyte.gz, train-labels-idx1-ubyte.gz, t10k-images-idx3-ubyte.gz, and t10k-labels-idx1-ubyte.gz files Hugging Face: Includes the ylecun/mnist dataset GitHub: Includes the cvdfoundation/mnist repository