Answer Happy

You are welcome to use the CIFAR-10 data for this
exercise. You are welcome to use Python with
user-defined functions, Python with TensorFlow, and/or Python with
Keras. For example, you can conduct the following experiments on
the CIFAR-10 data. The goal is to compare DNN and CNN
architectures. In all the experiments, you may hold some
parameters constants - for example, the batch size to 100, the
number of epochs to 20, same optimizer, same loss function of
cross entropy, so that the comparisons are fair.
Experiment 1: DNN with 2 layers (no
regularization)
Experiment 2: DNN with 3 layers (no
regularization)
Experiment 3: CNN with 2 convolution/max
pooling layers (no regularization)
Experiment 4: CNN with 3 convolution/max
pooling layers (no regularization)
Experiment 5+ : You will conduct several
more experiments. (a) Redo all the 4 experiments with some
regularization technique. (b) Create more experiments on your own
by tweaking architectures and/or hyper parameters.
Result1: Create a table with the
accuracy and loss for train/test/validation & process time for
ALL the models.
Result2: Take Experiment 3 – Extract the
outputs from 2 filters from the 2 max pooling layers and visualize
them in a grid as images. See whether the ‘lighted’ up regions
correspond to some features in the original images.