A Single Function to Streamline Image Classification with Keras - KDnuggets

Plenty has been written about deep learning frameworks such as Keras and PyTorch, and how powerful yet simple to use they are for constructing and playing with wonderful deep learning models. There are so many tutorials/articles already written about model architecture and optimizers-- the concept of convolution, max pooling, optimizers such as ADAM or RMSprop. What if, all you wanted, is a single function to pull automatically images from a specified directory on your disk, and give you back a fully trained neural net model, ready to be used for prediction? Therefore, in this article, we focus on how to use a couple of utility methods from the Keras (TensorFlow) API to streamline the training of such models (specifically for a classification task) with a proper data pre-processing. In the end, we aim to write a single utility function, which can take just the name of your folder where training images are stored, and give you back a fully trained CNN model.