/CNN-Implementation-Numpy

Implementation of CNN (consisting of maxpool, relu, fully-connected and convolutional layers) using Numpy Vectorisation (from scratch without any third-party library), followed by analysis using hyperparameter tuning and different regularisation techniques

Primary LanguageJupyter Notebook

CNN Implementation Numpy

Includes:

  • Implementation of CNN (consisting of maxpool, relu, fully-connected and convolutional layers) using Numpy Vectorisation (from scratch without any third-party library)
  • Studying the effects of different hyperparameters using hyperparameter tuning in pytorch implementation of CNN:
    • learning rate (static or scheduled)
    • loss functions
    • Number of epochs
    • Batch-sizes
    • Adam and SGD optimisers
  • Achieving best accuracy on CIFAR-10 datasets using different techniques like Batch, Group Normalisations, Weight decays and Dropout layers

Use CIFAR-10 dataset from this link and refer to unpickling details for python given in the website.

Refer to Report.pdf for more implementation details and analysis part.