This summer, after completing Stanford University’s Machine Learning MOOC on Coursera, I sought hands-on experience through a side project. I chose to recreate Hamuchiwa’s AutoRCCar project and implemented my own neural network using the Python library Keras. Learning Keras was made significantly easier by reading Deep Learning with Python.

Overview

The Raspberry Pi is responsible for obtaining distance data from the sensors, and images from the PiCamera. The Raspberry Pi then sends this data to the laptop using TCP/IP. At the laptop, image processing is done and the trained deep learning model makes a prediction on what button to press in the form of a one-hot-encoded array (e.g. [0 0 1] to move right). The laptop then sends instructions to the Arduino which is interfaced with the RC controller to send the signal to move.

Deep Learning

Data Collection and Preprocessing

With the limitations of my RC car, training was a made a lot more difficult. The turning radius of the car prevented me from training on a circular track. Thus, I had to train on a linear track, resulting in having to manually pick up the car to bring it back to the staring line. By the end of the training, I had gathered about 1000 samples, yet it still wasn’t enough. So, I used a trick I learned from the Machine Learning MOOC to generate more data by flipping all the images and labels in the x direction.

Building The Model

The next step was to actually build the Keras model. We first split the data into training and testing sets. Our model consists of 3 Dense layers, but we add some Dropout between layers to prevent the model from picking up random patterns. The final activation is a softmax function which is necessary for our multi-label classification problem.

Fit The Model

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