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.

c) Experiment with different regularization options (e.g. L2 and dropout).You may need to make your network larger in case you dont find much benefits from applying regularization.

we recommend you to control your initialization parameters by means of a seed https://keras.io/api/layers/initializers/