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4. Tuning generalization In this question you will construct a neural network to classify a large set of low resolution images. Differently from Q2, in this case we suggest you a neural network to start experimenting with, but we would like you to describe the behavior of the network as you modify certain parameters. You will be reproducing some concepts mentioned during the lectures, such as the one shown on slide 8, of the lecture on "Ensembles, regularization and feature selection from Week 4. a) Use the CIFAR-100 dataset (available from Keras) fron keras.datasets import cifar10 (x_train_original, y_train_original), (x_test_original, y_test_original) - cifar189.load_data(label_mode="fine) to train a neural network with two hidden layers using the logistic activation function, with 500 and 200 hidden nodes, respectively. The output layer should be defined according to the nature of the targets. a) Generate a plot that shows average precision for training and test sets as a function of the number of epochs Indicate what a reasonable number of epochs should be. b) Generate a plot that shows average precision for training and test sets as a function of the number of weights/parameters (# hidden nodes). For this question part, you will be modifying the architecture that was given to you as a starting point c) Generate a plot that shows average precision for training and test sets as a function of the number of instances in the training set. For this question part, you will be modifying your training set. For instance, you can run 10 experiments where you first use a random 10% of the training data, a second experiment where you use a random 20% of the training data, and so on until you use the entire training set. Keep the network hyperparameters constant during your experiments d) Based on all your experiments above, define a network architecture and report accuracy and average precision for all classes. e) Can you improve test prediction performance by using an ensemble of neural networks