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?

Getting the LeNet model working with Face Recognition

In my last post, I talked about how the LeNet Convolutional Neural Network model is capable of handling much more complex data than the intended MNIST dataset. We saw how it got ~99% accuracy when it learned to identify 10 faces from the raw pixel intensities.

So, let’s see the code I used to get it working.

First of all, I needed a training dataset. For that, I created a set of face images of 10 subjects with around 500 images each.

The training dataset (yep, that's my face)

I use a file naming convention as <subject_label>-<subject_name>-<unique_number>.jpg (e.g. 0-Thimira-1475137898.65.jpg) for the training images to make it easier to read in and get the metadata of the images in one go. (I will do a separate post on how to easily create training datasets of face images like this).

We'll mainly be using Keras to build the model, and scikit-learn for some utility functions. We’ll need to import the following packages,

 from sklearn.cross_validation import train_test_split  
 from keras.optimizers import SGD  
 from keras.utils import np_utils  
 import numpy as np  
 import argparse  
 import cv2  
 import os  
 import sys  
 from PIL import Image