We increased our performance on the MNIST dataset and built a model that quickly achieves about 97% accuracy while training from scratch. Our first two layers are a combination of convolutions, ReLU, and Max Pooling. The second two layers are fully connected layers. We trained in batches of size 100, and looked at the accuracy and loss across the generations we trained. Finally, we also plotted six random digits and the predictions/actuals for each.
A CNN does very well with image recognition. Part of the reason for this is that the convolutional layer creates its own low-level features that are activated when they come across part of the image that is important. This type of model creates features on its own and uses them ...
