Thursday

For today's class you loaded and preprocessed the tf.dataset the tf_flowers, and then trained a CNN in order to predict whether a particular image was of one of five different types of flowers. For the first part of this exercise, instead of using the tf.keras.utils.get_file() to load your data, use tfds.load() to load a tensorflow dataset as illustrated in the section Using TensorFlow Datasets. Using the relevant parts from the entirety of the example script as a guide, fit a CNN model to your training data and validate using the beans dataset from Tensorflow datasets and then again train and validate using the eurosat dataset. Present your results and discuss the accuracy of each of model.

Additionally, go to the image augmentation exercise and read through and become familiar with the many individual examples presented. Towards the end of this exercise is the Apply augmentation to a dataset example, that illustrates a resize and rescale image augmentation implementation to the tf_flowers dataset. Apply this same method to both the beans and eurosat datasets. Did your model performance improve? How many epochs were you able to run and how much time did it take? Present your results and discuss the accuracy of your augmented output for tomorrow's class.