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When you have finished and are happy with your code, press the Submit Assignment button at the top of this notebook. How to submit ¶Ĭomplete all the tasks you are asked for in the worksheet. Inside these graded cells, you can use any functions or classes that are imported below, but make sure you don't use any variables that are outside the scope of the function. Don't edit the function name or signature provided in these cells, otherwise the automatic grader might not function properly. These cells require you to write your own code to complete them, and are automatically graded when you submit the notebook. Some cells begin with the line:ĭon't move or edit this first line - this is what the automatic grader looks for to recognise graded cells.

You should avoid editing provided code, and make sure to execute the cells in order to avoid unexpected errors. Some code cells are provided you in the notebook. You will then use the map and filter functions of the Dataset class with the CIFAR-100 dataset to train a network to classify a processed subset of the images. You will use the ImageDataGenerator class in the tf.keras module to feed a network with training and test images from a local directory containing a subset of the LSUN dataset, and train the model both with and without data augmentation. In this notebook, you will implement a data processing pipeline using tools from both Keras and the tf.data module.

Create a new data generator with data augmentation ¶.Display sample images and labels from the training set ¶.Create a data generator using the ImageDataGenerator class ¶.