b . Using Nadam optimization and early stopping, train the network on the CIFAR 1 0 dataset.
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b Using Nadam optimization and early stopping, train the network on the CIFAR dataset. You can load it with keras.datasets.cifarloaddata The dataset is composed of times pixel color images for training, for testing with classes, so you'll need a softmax output layer with neurons. Remember to search for the right learning rate each time you change the model's architecture or hyperparameters.
c Now try adding Batch Normalization and compare the learning curves: Is it converging faster than before? Does it produce a better model? How does it affect training speed?
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