andreasgeorgopoulos / cnn-image-recogition

Convolutional Neural Networks for single-digit image recognition

Convolutional Neural Networks for single-digit image recognition

At this project three different Convolutional Neural Netwroks for the hand-written single-digit image recognition problem of the MNIST dataset are developed (the MNIST dataset can be found here). Two different implementations take place in order to discern the effect of data augmentation on each model’s classification power: (1) training on pre-processed training data set, (2) training on pre-processed and augmented training data set. The newly created training dataset of “augmented” images has an increased sample size of 84,000 images. With a stochastic gradient descent optimization method and a categorical cross entropy loss function each model is compiled and then trained with the number of training epochs and the size of batches set to 10 and 200 respectively.

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