There was an interesting share in our SDC Nano Degree Bangalore Whatsapp group. I wanted to try out something similar based on the stuff I had with me. The intent is to map room using ultrasonic sensor and stepper motor
The project is forked from Param’s Project https://github.com/paramaggarwal/pingray that uses ultrasonic sensors and magnetic compass. It is a curious attempt to replicate the outcomes with a stepper motor and single ultrasonic sensor as I did not have a magnetic compass.
Out of curiosity I wanted to try Keras to do non linear fitting. The simplicity of Keras made it possible to quickly try out some neural network model without deep knowledge of Tensorflow.
The data for fitting was generated using a non linear continuous function. It has five inputs and one output. Both the training set and validation set have around 1000 data points.
Y = SIN(A) x EXP(B) + COS(C x C) + POWER(D,5) – TANH(E)
I realized that adding too many hidden layers worsened the fit. Looks like for continuous functions, one hidden layer with sufficient number of nodes and good choice of activation function is sufficient. I chose hyperbolic tangent (tanh) for activation function and adam for optimizer. The results were pretty good but required some good number of iterations.
I plan to compare this with other regression algorithms available in Azure Machine Learning.
I would like to share my experience with the new Udacity Self driving car Nanodegree. I am very excited about this technology due to my work experience in Process Control. There are similarities in engineering concepts but the control objective, sensors and final elements are very different for this technology. My end goal is to learn this technology and use it to work on potential innovations in the industry I work for (Oil and Gas).
Choosing to take this course was difficult. First the cost is high for Indian standards, second have to go through a selection process and third it is nine months long. I got a seat in the December 2016 Cohort which gave me some time to prepare the basics (Python, Github, Machine Learning, etc..)
The feeling I had when the course started was very different from other online courses. I got very excited like going to university again. I was very nervous as well. Though I was very familiar with algorithms, I never worked on image processing. The course started with Computer vision including a project in the very first week. I used the weekend to complete the project with full support form my family. It was a wonderful experience with a sense of achievement. I felt like tuning my very first PID controller. The support from the community (Whatsapp, Facebook, Slack, Udacity Mentor) helped get the confidence and support to complete the project.
Finding Lanes Project
The code is available on Github (https://github.com/shankarananth/CarND-LaneLines-P1)
I used the following sequence of steps to arrive at the solution
2) I used a debug folder to save all intermediate image. This helped me a lot in easy tuning of various parameters.
3) I did not attempt the Optional challenge to optimize available time with me. Test runs with current code was not successful.
4) In terms of improvement I could further smooth the lines across frames in video.
5) I did not have experience with Jupyter. It is very different for a coding environment. However after using it I could visibly see the advantage of such an environment.
6) Line Extrapolation – I used y=mx+c draw the extrapolated line (First identify slope with co-ordinates ((y2-y1)/(x2-x1)), Second calculate C and Third identify new co-ordinates based on given y-axis) (Note: I received feedback from reviewer that it is possible to achieve the results without the need for extrapolation just by tuning Hough Transform parameters)