To train a machine learning model to classify images based on the MNIST dataset.
The trained model should be able to classify incoming images into 10 categories (0 to 9) based on its learning from the MNIST dataset. Finally, the trained model must be able to correctly identify the digit represented in a newly created image (i.e. an image the model has never seen).
The MNIST dataset contains a large number of images of hand-written digits in the range 0 to 9, as well as the labels identifying the digit in each image. The dataset is split in three parts.
55,000 examples of training data
10,000 examples of test data
5,000 examples of validation data
The template model is a mixture of seven distinct layers.
2 x convolution
2 x max pooling
2 x dense (fully connected)
1 x dropout