Develop CNN model in keras to classify CIFAER-10 dataset
The CIFAR-10 dataset consists of 60000 32x32 colour images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
The dataset is divided into five training batches and one test batch, each with 10000 images. The test batch contains exactly 1000 randomly-selected images from each class. The training batches contain the remaining images in random order, but some training batches may contain more images from one class than another. Between them, the training batches contain exactly 5000 images from each class.
CIFAR-10 dataset contains images from 10 classes: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck
This project consists of three files:
- CIFAR-10.ipynb -- train CNN from scratch
- CIFAR-10_optimizer.ipynb -- Apply various optimizers
- CIFAR-10_learning_rate_methods.ipynb -- Methods to apply and modify learning rate at each epoch