Instructions: This is an individual assignment; collaboration is not allowed. Submit the solutions using Canvas’ submission system. Please follow the submission instructions specific for each question.
Notes on the Academic Integrity Policy (https://usgc.ontariotechu.ca/policy/policy- library/policies/academic/academic-integrity-policy.php):
a) If you reuse code written by someone else: (a) Make sure you credit the source/author of the reused code by clearly stating the origin of that code using comments as appropriate. (b) Beware that you can only receive credits for your own code. (c) Failing to credit the source/author of the reused code is considered academic misconduct. Please refer to articles 16 through 19.
b) It is prohibited to disseminate/post this assignment or parts of it. Please refer to Article 20.
1. [Marks 60%] Use the UCI Abalone dataset abalone.data available at https://archive.ics.uci.edu/ml/datasets/Abalone
a) [Marks: 0%] Split the data into 50% training and 50% validation sets.
b) [Marks: 20%] Train a poor deep neural network (DNN) model using keras.models.Sequential to classify abalones into “male”, “female”, or “infant”, in such a way that the model intentionally overfits the training data. Use 100 epochs. Generate a graph that depicts loss, accuracy, val_loss, and val_accuracy.
c) [Marks: 20%] Improve the model from Part b) by changing some, or all of the following hyperparameters: kernel initializer, activation function, normalization, regularization, optimizer, and learning rate schedule. Use 100 epochs. Generate a graph that depicts loss, accuracy, val_loss, and val_accuracy. You may want to use a utility called Keras Tuner.
d) [Marks: 20%] Your code should display each model from b) and c) using
keras.utils.plot_model.
Submit:
[1] your code as a Jupyter notebook named abalone.ipynb
[2] the graphs in separate files called abalone-c.png, and abalone-d.png, and
[3] both models from Part d) as files called abalone-db.png and abalone-dc.png
2. [Marks: 40%] Use the Auto MPG dataset auto-mpg.data, available at
https://archive.ics.uci.edu/ml/datasets/Auto+MPG
a) [Marks: 0%] Drop the name and origin attributes.
b) [Marks: 0%] Attribute horsepower has 6 missing values. Fill these up with the average horsepower for the respective number of cylinders.
c) [Marks: 5%] The year attribute has two digits. Add 1900 so that the year has four digits. For example, in the dataset “70” really means “1970”, so you need to change the value from 70 to 1970.
d) [Marks: 5%] Split the data into 50% training and 50% validation sets.
e) [Marks: 15%] Train a keras.models.Sequential DNN that predicts the mpg of vehicles.
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