How to solve Scikit-learn Deprecation Warning on cross_validation

When using the Scikit-learn library and trying out various examples found over the web, have you come across a DeprecationWarning for the cross_validation module?

The DeprecationWarning on cross_validation

This most commonly happens when the code you're trying to run utilizes the train_test_split() function - a handy function used to quickly split the training and test datasets from a main dataset. The full warning message is something like this,

 C:\Users\Thimira\Anaconda3\envs\tensorflow12\lib\site-packages\sklearn\cross_val  
 idation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in  
  favor of the model_selection module into which all the refactored classes and f  
 unctions are moved. Also note that the interface of the new CV iterators are dif  
 ferent from that of this module. This module will be removed in 0.20.  
  "This module will be removed in 0.20.", DeprecationWarning)  

So, how to solve this?

TensorFlow 1.0 announced with better Mobile support and Java API

TensorFlow, with its r0.12 version gave the (much awaited) compatibility to Windows (check How to setup TensorFlow on Windows), and an Experimental API for the GO language. Now, they have announced the first major version - version r1.0 - of TensorFlow, with several exciting features.

While Python would still be the primary API - and would be the most complete API for TensorFlow - version r1.0 will introduce a new, experimental Java API. While it could be far from complete, and may take a few releases to stabilize, having the Java API may open TensorFlow to new possibilities. Also note that in the current release candidates of r1.0, you will need to compile TensorFlow from source in order to get the Java interface (instructions from TensorFlow GitHub page), and it's still only available for Linux and Mac OS.

Installing OpenCV 3 on Anaconda Python 3.5 on Windows

If you checked my last post, you know that TensorFlow now officially works on Windows. This was really exciting to me, since I was using a Linux machine for my TensorFlow experiments, I had to switch between the Linux and Windows for my other tasks. Now, I should be able to perform some of those experiments on Windows as well. (I would still switch to Linux for more processor intensive deep learning models). So, I decided to set everything up on Windows again.

I've been using OpenCV 3 on Anaconda Python 3.5 on Linux. (Check out my guide on Installing OpenCV from source on Anaconda Python on Ubuntu 16.10 if you are trying to install on Linux). But on Windows, I've previously only tried OpenCV on Python 2.7. Since the OpenCV Downloads page has pre-built binaries for OpenCV 3 (version 3.2 being latest), I thought the installation would be straightforward.

I downloaded and ran the OpenCV 3.2 installer (which just extracts OpenCV to a directory of your choosing), set the OPENCV_DIR environment variable, add the bin dir in OpenCV to the PATH, and finally copy the cv2.pyd file to the site-packages directory in my Anaconda environment. I fired up the Anaconda environment, and tried loading cv2 in Python. But I got the following error,

 (tensorflow) C:\>python  
 Python 3.5.2 |Continuum Analytics, Inc.| (default, Jul 5 2016, 11:41:13) [MSC v  
 .1900 64 bit (AMD64)] on win32  
 Type "help", "copyright", "credits" or "license" for more information.  
 >>> import cv2  
 Traceback (most recent call last):  
  File "<stdin>", line 1, in <module>  
 ImportError: DLL load failed: The specified module could not be found.  

Setting up TensorFlow on Windows

TensorFlow, since it has been released just over an year ago, has gained a huge popularity with its capabilities. But the platforms it worked on was limited - there were only pre-packaged Python packages for Linux initially, while it gained a Mac OS compatible version along the way. Windows users were left wondering when (or if) there will be a Windows version of TensorFlow. There has been few unofficial builds for Windows, and some have attempted to build from source for Windows with various degrees of success. But, there was nothing official.

Now, I'm happy to learn that TensorFlow now officially supports Windows. The release notes suggests that Windows compatibility was present from TensorFlow v0.12.0 RC0, and it has been tested on Windows 7, Windows 10, and Windows Server 2016. It only supports 64-Bit Python 3.5. Both the standard Python distribution (from Python.org) and Anaconda Python are supported.

This was great news for me, since I was switching between Linux and Windows for my machine learning experiments. So, I gave a try to setup TensorFlow on Windows.

I've been using Anaconda Python for most of my experiments, so I created an Anaconda environment - named 'tensorflow' - with all the basic packages I need.

 conda create --name tensorflow numpy scipy scikit-learn pillow h5py mingw libpython  

Then I activated the environment I just created,

 activate tensorflow  

Now for the big step, installing TensorFlow from pip.

New Page - What is Deep Learning?

Deep Learning is the latest trend in Machine Learning. We hear about new innovations in Deep Learning every day, to a point that it has started to become a household name. Big companies such as Google, Apple, Amazon, Microsoft, IBM, and many others seems to be pushing for Deep Learning.

But, do you know what Deep Learning really is?

Since it's one of the most frequent questions that comes up, I created a new page "What is Deep Learning?" which talks about what Deep Learning is, how it relates to Machine Learning and Artificial Intelligence, and their history. Have a look at it from the below link,

What is Deep Learning?

Let me know what you think of Deep Learning.

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

Get your copy now!

Stepping into audio classification - Getting started with PyAudio

I've had an idea to attempt to train a deep learning model that can classify different audio noises. Having a system that can accurately identify different sounds would have implications in many fields, from medical diagnostics to echo locations systems, among others. I wouldn't expect building such a system would be an easy task. But, let's give it a try.

I first had the idea about 8 years back. But back then, libraries that enable audio processing were scarce, and what was available required a massive effort to get installed. Now it seems that things have improved a lot. I myself was searching online for audio processing libraries for Python, and I came across this excellent post - Realtime Audio Visualization in Python - by Scott Harden of University of Florida.

Realtime Audio Visualization with PyAudio

The tutorial uses the PyAudio Python package (PyAudio homepage), which is the Python bindings for PortAudio (PortAudio homepage) - a cross-platform audio I/O library - allowing PyAudio to give a consistent interface to process audio across platforms. So, I decided to give PyAudio a try. First of all, I needed to install PyAudio.

Installing PyAudio

At the time of this writing, the latest version of PyAudio is v0.2.9, and the PyAudio team has made the installation as simple as possible.

Installing OpenCV from source on Anaconda Python on Ubuntu 16.10

I recently switched to Linux for my Machine Learning experiments, and I did a post on How to install Keras and Anaconda Python on Ubuntu 16.10.

Now, I wanted to install OpenCV on Ubuntu also. Since OpenCV does not have a pre-built package for Linux, it meant I had to compile OpenCV from source.

OpenCV 3.1 running on Lubuntu 16.10

Adrian of PyImageSearch has recently done a post about how to compile OpenCV on Ubuntu 16.04 using virtualenv. I followed his steps as a base, but had to make numerous adjustments to some of the packages which gets installed (e.g. libpng-dev, libhdf5-serial-dev) and the build commands due to the changes from Ubuntu 16.04 to 16.10, and because I'm using Anaconda environments rather than virtualenv.

I'll be installing OpenCV 3.1, and will be using the Lubuntu 16.10 virtual machine which I used in my earlier post. But the steps and commands will be exactly the same for any flavor of Ubuntu 16.10.

First, as a habit, get and install the latest updates for Ubuntu,

 sudo apt-get update   
 sudo apt-get upgrade   

Then (if you have not done already) install the necessary build tools,

 sudo apt-get install build-essential cmake git unzip pkg-config  

Then, we install the following packages which allows OpenCV to interact with various image and video formats,

 sudo apt-get install libjpeg8-dev libtiff5-dev libjasper-dev libpng-dev  
 sudo apt-get install libavcodec-dev libavformat-dev libswscale-dev libv4l-dev  
 sudo apt-get install libxvidcore-dev libx264-dev  

Note: on Ubuntu 16.04, the package name for libpng was libpng12-dev. But on 16.10, it should be libpng-dev.