Classification using CIFAR-10 dataset
In this experiment, we aim to classify the images from CIFAR-10 datasets into 10 different classes. The solution to this task was approached by implementing Neural Network with a hidden layer and investigated different tricks to improve the learning over time. The tricks were derived from the learning from back-propagation of error, which acts as the core of Neural Network. The accuracy improved with the addition of the tricks of trade like Momentum, Regularization and Activation’s. In addition, the network was tested with different topology to understand the behavior against different architectures.
Details on the underlying technical approaches and results can be found here
The following will run the program with default config.
$ python main.py You can make the necessary changes in config.yaml.