Create the datasets by running the sprites.py code. Create a CNN that predicts the whether the car can safely remain on the current position without crashing into non-drivable terrain.

Project

Task 1: Train a CNN to predict a clear road ahead 15 points
The python program sprites.py creates a training and test set of “minirace” scenes, trainingpix.csv (1024 examples) and testingpix.csv (256 examples). Each row represents a 16 × 16 screenshot (flattened in row-major order), plus an extra value of either 0 or 1 that indicates if the car can safely drive straight without going off-road in the immediate next step (i.e., there are 257 columns).

Steps
1. Create the datasets by running the sprites.py code.
2. Create a CNN that predicts the whether the car can safely remain on the current position (i.e., drive straight) without crashing into non-drivable terrain.
(a) Describe (no programming): what is a good loss function for this problem?
(b) Implement and train the CNN on the training set.
(c) Compute the accuracy of your model on the test data set.

• Your are free to choose the architecture of your network, but there should be at least one convolutional layer.
• You can normalise/standardise the data if it helps improve the training.

What to submit:
• A description of your CNN and the training. Calculate the size of each layer, and include it in the description.
• Include the explanation for the loss function in your description.
• For how long did you train your model (number of epochs, time taken)? What is the performance on the test set?
• Submit the python code for your solution (either as .py or .ipynb).