Machine UI : An IDE for Machine Learning, currently in Alpha

Machine UI, or just "Machine" as it's commonly referred, is an IDE for Machine Learning, which is currently in its Alpha stage. It has been designed to work with TensorFlow, and aims at simplifying setting up machine Learning experiments so that you spend more time experimenting, and less time configuring.

The interface of Machine UI (Note: This is a screenshot from their announcement video)

As per their announcement video, the machine learning experiments are set up visually. The input data, convolutions, and the outputs are placed as nodes on a graph. You can think of it as a more interactive version of the Tensor Board which comes with TensorFlow.

Wink Detection using Dlib and OpenCV

A couple of weeks ago, I was going through a tutorial for eye blink detection by Adrian at PyImageSearch. It was an excellent tutorial, which explained the use of Eye Aspect Ratio (EAR) in order to detect when an eye gets closed. Then, few weeks back, I was having a chat with Shirish Ranade, a reader of this blog and a fellow computer vision and machine learning enthusiast, on whether we can perform an action by winking at the computer. So, I decided to try out a code to detect winking.

Wink Detection Running with Dlib and OpenCV

It's an interesting idea to perform an action or a task just by winking at your computer. It can be thought as a form of gesture detection or facial expression detection as well. So, here's how you can build your own 'wink' detector for it.

We start by importing all the necessary packages,

 import numpy as np  
 import cv2  
 import dlib  
 from scipy.spatial import distance as dist  

OpenCV 3.2 and Dlib 19.4 Packages Now Available from Conda-Forge

Anaconda is an asset for us Machine Learning enthusiasts. Not only does it have the ability to create fully isolated Python environments, its pre-built packages for many operating systems and architectures helps you to spend less time setting up, and more time doing actual machine learning stuff.

In some scenarios, to get some Python packages to work in certain environments, getting them from Anaconda was the only way that worked. If you tried installing Dlib or OpenCV 3 on Windows 64-Bit, then you know the effort it takes to get them setup, if it wasn't for Anaconda.

OpenCV and Dlib working perfectly together, thanks to Conda

If you check my posts on Installing Dlib on Anaconda Python on Windows, and Installing OpenCV 3 on Anaconda Python 3.5 on Windows, you know how easy it is to use conda to install them on Python 3.5 64-Bit on Windows.

But there was a catch: The Anaconda registry only had OpenCV 3.1 and Dlib 19.0 - not the latest versions.

What is Deep Learning? - Updated

What is Deep Learning? And, how does it relates to Machine Learning, and Artificial Intelligence?

I did an article to answer these questions some time back.

Now, thanks to the feedback I got from you all, I was able to updated it, with more clarifications, improved examples, and answers to more questions in Deep Learning.

Check out the updated article here,

What is Deep Learning?

Your feedback are always welcome.

Build Deeper: Deep Learning Beginners' Guide is the ultimate guide for anyone taking their first step into Deep Learning.

Get your copy now!

image_data_format vs. image_dim_ordering in Keras v2

If you have been using Keras for some time, then you would probably know the image_dim_ordering parameter of Keras. Specially, if you switch between TensorFlow and Theano backends frequently when using Keras.

When I first started using Keras for image classification, most of my experiments failed because I have set the image_dim_ordering incorrectly. Learning from my mistakes, last year I did a post on what image_dim_ordering is and why is it important.

The keras.json file houses the configuration options for Keras

In short, image_dim_ordering instructed Keras to properly rearrange the image data structure when passing to the backend:
Both TensorFlow and Theano expects 4D tensors of image data as input. But, while TensorFlow expects its structure/shape to be (samples, rows, cols, channels), Theano expects it to be (samples, channels, rows, cols). So, setting the image_dim_ordering to 'tf' made Keras use the TensorFlow ordering, while setting it to 'th' made it Theano ordering.

At least, that's how it used to work.

But recently, if you have updated to the latest version of Keras, you might have run into issues with the dimension ordering, even if you're sure that you set the image_dim_ordering correctly.

You may have gotten errors like,

 ValueError: The shape of the input to "Flatten" is not fully defined (got (0, 7,  
  50). Make sure to pass a complete "input_shape" or "batch_input_shape" argument  
  to the first layer in your model.  

It may seem to you that Keras has started to ignore your image_dim_ordering setting.

And you're right.

Visualizing Keras Models - Updated

About 2 months back, I did a post on how you can visualize the structure of a Keras model. As I mentioned, when the machine learning (or deep learning) model you're building is complex, then it may be easier to understand it if you can see a visual representation of it.

I showed you how to use the Visualization utility in Keras in order to draw the structure of a model in Keras, such as this visualization of the LeNet model,

Visualizing the LeNet model

But a few days back, several people had got some errors when following the steps I explained. I digged a bit to find why the errors are happening, and found that with the latest version of Keras (v 2.*) they have changed the API of the visualization utility.

The following are the main changes,

• The module has been renamed, from visualize_util to vis_utils.
• The function name for plotting has been renamed, from plot to plot_model.

So, here's the updated guide on how to visualize a Keras model.

Energy Threshold Calibration in Speech Recognition

In my last post on Speech Recognition, I showed how to setup the Python SpeechRecognition package with PyAudio, and pocketsphinx to recognize speech with just a few lines of code. And, as you can remember, we ran into issues where the speech recognition just hangs there unable to recognize our speaking.

Speech Recognition just hanging there, not recognizing that you're speaking

We found out that this was happening due to ambient noise.

Although we humans are able to distinguish speech from noise naturally, for a computer program they are just audio levels. It needs to know which levels should be considered speech (which it needs to process in order to recognize what's being said), and which levels should be considered silence or background noise. So, libraries like the SpeechRecognition has an energy threshold set which defines what audio level and above should be considered speech.

Now, this default energy threshold works most of the time. If your environment is sufficiently quiet, it will be able to recognize you talking without problems. But, if your environment is noisy - e.g. an office environment with many people talking, or there's machinery around - then the program will have issues distinguishing speech from noise, which will cause the issue we observed.

So, in a situation like that, we should adjust the energy threshold to properly distinguish the speech from noise. The SpeechRecognition package has a couple of parameters that helps you with this.