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Showing posts with label Machine learning. Show all posts
Showing posts with label Machine learning. Show all posts


Latest Update:

I have uploaded the complete code (Python and Jupyter notebook) on GitHub: https://github.com/javedsha/text-classification

Document/Text classification is one of the important and typical task in supervised machine learning (ML). Assigning categories to documents, which can be a web page, library book, media articles, gallery etc. has many applications like e.g. spam filtering, email routing, sentiment analysis etc. In this article, I would like to demonstrate how we can do text classification using python, scikit-learn and little bit of NLTK.

Disclaimer: I am new to machine learning and also to blogging (First). So, if there are any mistakes, please do let me know. All feedback appreciated.

Let’s divide the classification problem into below steps:

  1. Prerequisite and setting up the environment.

Step 1: Prerequisite and setting up the environment

The prerequisites to follow this example are python version 2.7.3 and jupyter notebook. You can just install anaconda and it will get everything for you. Also, little bit of python and ML basics including text classification is required. We will be using scikit-learn (python) libraries for our example.

Step 2: Loading the data set in jupyter.

The data set will be using for this example is the famous “20 Newsgoup” data set. About the data from the original website:

The 20 Newsgroups data set is a collection of approximately 20,000 newsgroup documents, partitioned (nearly) evenly across 20 different newsgroups. To the best of my knowledge, it was originally collected by Ken Lang, probably for his Newsweeder: Learning to filter netnews paper, though he does not explicitly mention this collection. The 20 newsgroups collection has become a popular data set for experiments in text applications of machine learning techniques, such as text classification and text clustering.

This data set is in-built in scikit, so we don’t need to download it explicitly.

i. Open command prompt in windows and type ‘jupyter notebook’. This will open the notebook in browser and start a session for you.

ii. Select New > Python 2. You can give a name to the notebook - Text Classification Demo 1

iii. Loading the data set: (this might take few minutes, so patience)

from sklearn.datasets import fetch_20newsgroups
twenty_train = fetch_20newsgroups(subset='train', shuffle=True)

 Note: Above, we are only loading the training data. We will load the test data separately later in the example.

iv. You can check the target names (categories) and some data files by following commands.

twenty_train.target_names #prints all the categories
print("\n".join(twenty_train.data[0].split("\n")[:3])) #prints first line of the first data file

Step 3: Extracting features from text files.

Text files are actually series of words (ordered). In order to run machine learning algorithms we need to convert the text files into numerical feature vectors. We will be using bag of words model for our example. Briefly, we segment each text file into words (for English splitting by space), and count # of times each word occurs in each document and finally assign each word an integer id. Each unique word in our dictionary will correspond to a feature (descriptive feature).

Scikit-learn has a high level component which will create feature vectors for us ‘CountVectorizer’. More about it here.

from sklearn.feature_extraction.text import CountVectorizer
count_vect = CountVectorizer()
X_train_counts = count_vect.fit_transform(twenty_train.data)

Here by doing ‘count_vect.fit_transform(twenty_train.data)’, we are learning the vocabulary dictionary and it returns a Document-Term matrix. [n_samples, n_features].

TF: Just counting the number of words in each document has 1 issue: it will give more weightage to longer documents than shorter documents. To avoid this, we can use frequency (TF - Term Frequencies) i.e. #count(word) / #Total words, in each document.

TF-IDF: Finally, we can even reduce the weightage of more common words like (the, is, an etc.) which occurs in all document. This is called as TF-IDF i.e Term Frequency times inverse document frequency.

We can achieve both using below line of code:

from sklearn.feature_extraction.text import TfidfTransformer
tfidf_transformer = TfidfTransformer()
X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)

The last line will output the dimension of the Document-Term matrix -> (11314, 130107).

Step 4. Running ML algorithms.

There are various algorithms which can be used for text classification. We will start with the most simplest one ‘Naive Bayes (NB)’ (don’t think it is too Naive! 😃)

You can easily build a NBclassifier in scikit using below 2 lines of code: (note - there are many variants of NB, but discussion about them is out of scope)

from sklearn.naive_bayes import MultinomialNB
clf = MultinomialNB().fit(X_train_tfidf, twenty_train.target)

This will train the NB classifier on the training data we provided.

Building a pipeline: We can write less code and do all of the above, by building a pipeline as follows:

>>> from sklearn.pipeline import Pipeline
>>> text_clf = Pipeline([('vect', CountVectorizer()),
... ('tfidf', TfidfTransformer()),
... ('clf', MultinomialNB()),
... ])
text_clf = text_clf.fit(twenty_train.data, twenty_train.target)

The names ‘vect’ , ‘tfidf’ and ‘clf’ are arbitrary but will be used later.

Performance of NB Classifier: Now we will test the performance of the NB classifier on test set.

import numpy as np
twenty_test = fetch_20newsgroups(subset='test', shuffle=True)
predicted = text_clf.predict(twenty_test.data)
np.mean(predicted == twenty_test.target)

The accuracy we get is ~77.38%, which is not bad for start and for a naive classifier. Also, congrats!!! you have now written successfully a text classification algorithm 👍

Support Vector Machines (SVM): Let’s try using a different algorithm SVM, and see if we can get any better performance. More about it here.

>>> from sklearn.linear_model import SGDClassifier>>> text_clf_svm = Pipeline([('vect', CountVectorizer()),
... ('tfidf', TfidfTransformer()),
... ('clf-svm', SGDClassifier(loss='hinge', penalty='l2',
... alpha=1e-3, n_iter=5, random_state=42)),
... ])
>>> _ = text_clf_svm.fit(twenty_train.data, twenty_train.target)>>> predicted_svm = text_clf_svm.predict(twenty_test.data)
>>> np.mean(predicted_svm == twenty_test.target)

The accuracy we get is~82.38%. Yipee, a little better 👌

Step 5. Grid Search

Almost all the classifiers will have various parameters which can be tuned to obtain optimal performance. Scikit gives an extremely useful tool ‘GridSearchCV’.

>>> from sklearn.model_selection import GridSearchCV
>>> parameters = {'vect__ngram_range': [(1, 1), (1, 2)],
... 'tfidf__use_idf': (True, False),
... 'clf__alpha': (1e-2, 1e-3),
... }

Here, we are creating a list of parameters for which we would like to do performance tuning. All the parameters name start with the classifier name (remember the arbitrary name we gave). E.g. vect__ngram_range; here we are telling to use unigram and bigrams and choose the one which is optimal.

Next, we create an instance of the grid search by passing the classifier, parameters and n_jobs=-1 which tells to use multiple cores from user machine.

gs_clf = GridSearchCV(text_clf, parameters, n_jobs=-1)
gs_clf = gs_clf.fit(twenty_train.data, twenty_train.target)

This might take few minutes to run depending on the machine configuration.

Lastly, to see the best mean score and the params, run the following code:


The accuracy has now increased to ~90.6% for the NB classifier (not so naive anymore! 😄) and the corresponding parameters are {‘clf__alpha’: 0.01, ‘tfidf__use_idf’: True, ‘vect__ngram_range’: (1, 2)}.

Similarly, we get improved accuracy ~89.79% for SVM classifier with below code. Note: You can further optimize the SVM classifier by tuning other parameters. This is left up to you to explore more.

>>> from sklearn.model_selection import GridSearchCV
>>> parameters_svm = {'vect__ngram_range': [(1, 1), (1, 2)],
... 'tfidf__use_idf': (True, False),
... 'clf-svm__alpha': (1e-2, 1e-3),
... }
gs_clf_svm = GridSearchCV(text_clf_svm, parameters_svm, n_jobs=-1)
gs_clf_svm = gs_clf_svm.fit(twenty_train.data, twenty_train.target)

Step 6: Useful tips and a touch of NLTK.

  1. Removing stop words: (the, then etc) from the data. You should do this only when stop words are not useful for the underlying problem. In most of the text classification problems, this is indeed not useful. Let’s see if removing stop words increases the accuracy. Update the code for creating object of CountVectorizer as follows:
>>> from sklearn.pipeline import Pipeline
>>> text_clf = Pipeline([('vect', CountVectorizer(stop_words='english')),
... ('tfidf', TfidfTransformer()),
... ('clf', MultinomialNB()),
... ])

This is the pipeline we build for NB classifier. Run the remaining steps like before. This improves the accuracy from 77.38% to 81.69% (that is too good). You can try the same for SVM and also while doing grid search.

2. FitPrior=False: When set to false for MultinomialNB, a uniform prior will be used. This doesn’t helps that much, but increases the accuracy from 81.69% to 82.14% (not much gain). Try and see if this works for your data set.

3. Stemming: From Wikipedia, stemming is the process of reducing inflected (or sometimes derived) words to their word stem, base or root form. E.g. A stemming algorithm reduces the words “fishing”, “fished”, and “fisher” to the root word, “fish”.

We need NLTK which can be installed from here. NLTK comes with various stemmers (details on how stemmers work are out of scope for this article) which can help reducing the words to their root form. Again use this, if it make sense for your problem.

Below I have used Snowball stemmer which works very well for English language.

import nltk
from nltk.stem.snowball import SnowballStemmer
stemmer = SnowballStemmer("english", ignore_stopwords=True)
class StemmedCountVectorizer(CountVectorizer):
def build_analyzer(self):
analyzer = super(StemmedCountVectorizer, self).build_analyzer()
return lambda doc: ([stemmer.stem(w) for w in analyzer(doc)])
stemmed_count_vect = StemmedCountVectorizer(stop_words='english')text_mnb_stemmed = Pipeline([('vect', stemmed_count_vect),
... ('tfidf', TfidfTransformer()),
... ('mnb', MultinomialNB(fit_prior=False)),
... ])
text_mnb_stemmed = text_mnb_stemmed.fit(twenty_train.data, twenty_train.target)predicted_mnb_stemmed = text_mnb_stemmed.predict(twenty_test.data)np.mean(predicted_mnb_stemmed == twenty_test.target)

The accuracy with stemming we get is ~81.67%. Marginal improvement in our case with NB classifier. You can also try out with SVM and other algorithms.

Conclusion: We have learned the classic problem in NLP, text classification. We learned about important concepts like bag of words, TF-IDF and 2 important algorithms NB and SVM. We saw that for our data set, both the algorithms were almost equally matched when optimized. Sometimes, if we have enough data set, choice of algorithm can make hardly any difference. We also saw, how to perform grid search for performance tuning and used NLTK stemming approach. You can use this code on your data set and see which algorithms works best for you.

Update: If anyone tries a different algorithm, please share the results in the comment section, it will be useful for everyone.

Please let me know if there were any mistakes and feedback is welcome ✌️

Recommend, comment, share if you liked this article.


http://scikit-learn.org/ (code)

http://qwone.com/~jason/20Newsgroups/ (data set)

Machine Learning, NLP: Text Classification using scikit-learn, python and NLTK

 For robotics applications, many consider Robot Operating System (ROS) as the default go-to solution. The version of ROS that runs on the NVIDIA Jetson Nano Developer Kit is ROS Melodic. Installing ROS on the Jetson Nano is simple. Looky here:


ROS was originally developed at Stanford University as a platform to integrate methods drawn from all areas of artificial intelligence, including machine learning, vision, navigation, planning, reasoning, and speech/natural language processing.

From 2008 until 2013, development on ROS was performed primarily at the robotics research company Willow Garage who open-sourced the code. During that time, researchers at over 20 different institutions collaborated with Willow Garage and contributed to the codebase. In 2013, ROS stewardship transitioned to the Open Source Robotics Foundation.

From the ROS website:

The Robot Operating System (ROS) is a flexible framework for writing robot software. It is a collection of tools, libraries, and conventions that aim to simplify the task of creating complex and robust robot behavior across a wide variety of robotic platforms.

Why? Because creating truly robust, general-purpose robot software is hard. From the robot’s perspective, problems that seem trivial to humans often vary wildly between instances of tasks and environments. Dealing with these variations is so hard that no single individual, laboratory, or institution can hope to do it on their own.

Core Components

At the lowest level, ROS offers a message passing interface that provides inter-process communication. Like most message-passing systems, ROS has a publish/subscribe mechanism along with request/response procedure calls. An important thing to remember about ROS, and one of the reasons that it is so powerful, is that you can run the system on a heterogeneous group of computers. This allows you to distribute tasks across different systems easily.

For example, you may want to have the Jetson running as the main node, and controlling other processors as control subsystems. A concrete example is to have the Jetson doing a high-level task like path planning, and instructing microcontrollers to perform lower-level tasks like controlling motors to drive the robot to a goal.

At a higher level, ROS provides facilities and tools for a Robot Description Language, diagnostics, pose estimation, localization, navigation, and visualization. 

You can read more about the Core Components here.


The installROS repository on the JetsonHacksNano account on Github contains convenience scripts to help us install ROS. The main script, installROS.sh, is a straightforward implementation of the install instructions taken from the ROS Wiki. The instructions install ROS Melodic on the Jetson Nano.

You can clone the repository on to the Jetson:

$ git clone https://github.com/JetsonHacksNano/installROS.git
$ cd installROS


The install script installROS.sh will install the prerequisites and ROS packages you specify. Usage:

Usage: ./installROS.sh  [[-p package] | [-h]]
 -p | --package <packagename>  ROS package to install
                               Multiple Usage allowed
                               The first package should be a base package. One of the following:

Default is ros-melodic-ros-base if do not specify any packages. Typically people will install ros-base if they are not running any desktop applications on the robot.

Example Usage:

$ ./installROS.sh -p ros-melodic-desktop -p ros-melodic-rgbd-launch

This script installs a baseline ROS environment. There are several tasks:

  • Enable repositories universe, multiverse, and restricted
  • Adds the ROS sources list
  • Sets the needed keys
  • Loads specified ROS packages (defaults to ros-melodic-base-ros if none specified)
  • Initializes rosdep

You can edit this file to add the ROS packages for your application.


setupCatkinWorkspace.sh builds a Catkin Workspace.


$ ./setupCatkinWorkspace.sh [optionalWorkspaceName]

where optionalWorkspaceName is the name and path of the workspace to be used. The default workspace name is catkin_ws. If a path is not specified, the default path is the current home directory. This script also sets up some ROS environment variables.

The script sets placeholders for some ROS environment variables in the file ~/.bashrc

The script .bashrc is located in the home directory. The preceding period indicates that the file is “hidden”. The names of the ROS variables that the script adds are (they should be towards the bottom of the .bashrc file):

  • ROS_IP

The script sets ROS_MASTER_URI to the local host, and basically lists the network interfaces after the ROS_IP entry. You will need to configure these variables for your robots network configuration and how you desire your network topology.


  • In the video, the Jetson Nano is freshly prepared with L4T 32.2.1 / JetPack 4.2.2
  • In the video, the Jetson Nano is running from a micro-SD card.

Install ROS on Jetson Nano




  • Python3
  • TensorFlow
  • pip install -r requirements.txt


$ python train.py
$ python train.py -h
usage: train.py [-h] [--embedding_size EMBEDDING_SIZE]
                [--num_layers NUM_LAYERS] [--num_hidden NUM_HIDDEN]
                [--keep_prob KEEP_PROB] [--learning_rate LEARNING_RATE]
                [--batch_size BATCH_SIZE] [--num_epochs NUM_EPOCHS]
                [--max_document_len MAX_DOCUMENT_LEN]

optional arguments:
  -h, --help            show this help message and exit
  --embedding_size EMBEDDING_SIZE
                        embedding size.
  --num_layers NUM_LAYERS
                        RNN network depth.
  --num_hidden NUM_HIDDEN
                        RNN network size.
  --keep_prob KEEP_PROB
                        dropout keep prob.
  --learning_rate LEARNING_RATE
                        learning rate.
  --batch_size BATCH_SIZE
                        batch size.
  --num_epochs NUM_EPOCHS
                        number of epochs.
  --max_document_len MAX_DOCUMENT_LEN
                        max document length.

Experimental Results

Language Model Training Loss

Text Classification Training Loss

Thanks & Cheers
Stack Exchanges

Multi-task Learning with TensorFlow

 The Intel RealSense T265 Tracking Camera solves a fundamental problem in interfacing with the real world by helpfully answering “Where am I?” Looky here:


One of the most important tasks in interfacing with the real world from a computer is to calculate your position in relationship to a map of the surrounding environment. When you do this dynamically, this is known as Simultaneous Localization And Mapping, or SLAM.

If you’ve been around the mobile robotics world at all (rovers, drones, cars), you probably have heard of this term. There are other applications too, such as Augmented Reality (AR) where a computing system must place the user precisely in the surrounding environment. Suffice it to say, it’s a foundational problem.

SLAM is a computational problem. How does a device construct or update a map of an unknown environment while simultaneously keeping track of its own location within that environment? People do this naturally in small places such as a house. At a larger scale, people have been clever enough to use visual navigational aids, such as the stars, to help build their maps.

This  V-SLAM solution does something very similar. Two fisheye cameras combine with the information from an  Inertial  Measurement  Unit (IMU) to navigate using visual features to track its way around even unknown environments with accuracy. 

Let’s just say that this is a non-trivial problem. If you have tried to implement this yourself, you know that it can be expensive and time consuming. The Intel RealSense T265 Tracking Camera provides precise and robust tracking that has been extensively tested in a variety of conditions and environments.

The T265 is a self-contained tracking system that plugs into a USB port. Install the librealsense SDK, and you can start streaming pose data right away.

Tech Stuffs

Here’s some tech specs:


  • OV9282
  • Global Shutter, Fisheye Field of View = 163 degrees
  • Fixed Focus, Infrared Cut Filter
  • 848 x 800 resolution
  • 30 frames per second

Inertial Measurement Unit (IMU)

  • 6 Degrees of Freedom (6 DoF)
  • Accelerometer 
  • Gyroscope

Visual Processing Unit (VPU)

  • Movidius MA215x ASIC (Application Specific Integrated Circuit)

The Power Requirement is 300 mA at 5V (!!!). The package is 108mm Wide x 24.5mm High x 12.50mm Deep. The camera weighs 60 grams.


To interface with the camera,  Intel provides the open source library librealsense. On the JetsonHacksNano account on Github, there is a repository named installLibrealsense. The repository contains convenience scripts to install librealsense.

Note: Starting with L4T 32.2.1/JetPack 4.2.2 a swap file is now part of the default install. You do not need to create a swap file if you are using this release or later. Skip the following step if using 32.2.1 or above.

In order to use the install script, you will either need to create a swapfile to ease an out of memory issue, or modify the install script to run less jobs during the make process. In the video, we chose the swapfile route. To install the swapfile:

$ git clone https://github.com/jetsonhacksnano/installSwapfile
$ cd installSwapfile
$ ./installSwapfile.sh
$ cd ..

You’re now ready to install librealsense.

$ git clone https://github.com/jetsonhacksnano/installLibrealsense
$ cd installLibrealsense
$ ./installLibrealsense.sh

While the installLibrealsense.sh script has the option to compile the librealsense with CUDA support, we do not select that option. If you are using the T265 alone, there is no advantage in using CUDA, as the librealsense CUDA routines only convert images from the RealSense Depth cameras (D415, D435 and so on).

The location of librealsense SDK products:

  • The library is installed in /usr/local/lib
  • The header files are in /usr/local/include
  • The demos and tools are located in /usr/local/bin

Go to the demos and tools directory, and checkout the realsense-viewer application and all of the different demonstrations!


The Intel RealSense T265 is a powerful tool for use in robotics and augmented/virtual reality. Well worth checking out!


  • Tested on Jetson Nano L4T 32.1.0
  • If you have a mobile robot, you can send wheel odometry to the RealSense T265 through the librealsense SDK for better accuracy. The details are still being worked out.

Thanks, Cheers.

Jetson Nano – RealSense Tracking Camera