In this question you will experiment with a neural network in the context of text classification, where a document can belong to one out of several possible categories. The main goal for you is to try different hyperparameters in a systematic manner so that you can propose a network configuration that is properly justified. You will experiment with the IMDB movie review, which can be loaded directly from Keras: from keras.datasets import imdb (train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000) a) Experiment with different hyper-parameters and report your best accuracy found. The most important hyperparameters that you need to experiment with in this question part are: number of layers nodes per hidden layer learning rate number of epochs b) Describe how your convergence changes when you vary the size of your mini-batch. A plot showing cost in terms of number of epochs would be enough. Discuss the reasons for this