IRJET- Machine Learning based Object Identification System using Python

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 03 | Mar 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 1814
MACHINE LEARNING BASED OBJECT IDENTIFICATION SYSTEM
USING PYTHON
K. Rajendra Prasad1, P. Chandana Sravani3, P.S.N. Mounika3, N. Navya4, M. Shyamala5
1,2,3,4,5Department of Electronics and Communication Engineering, Vignan’s Institute of Engineering for Women,
Visakhapatnam-46, India
-----------------------------------------------------------------------***------------------------------------------------------------------------
Abstract- Nowadays, large amount of data is available
everywhere. Therefore, it is very important to analyse this
data in order to extract some useful information and to
develop an algorithm based on its analysis. This can be
achieved through Machine Learning (ML). ML is a subset of
Artificial Intelligence (AI) that provides systems the ability
to automatically learn and improve from experience without
being explicitly programmed [9]. As the Human visual
system is fast and accurate and can perform complex tasks
like identifying multiple objects and detect obstacles with
little conscious thought. Our idea is to implement an Object
Identification system by using classification type algorithms
of ML i.e. Convolution Neural Networks (CNN) in Python. In
this project we use different predefined training and test
data sets which are used to predict various objects. The main
motive of this idea is to identify and obtain the required data
of an object by taking the image as input. This is a
fundamental approach for many prediction type
applications like Self-Driving system.
Key Words: CNN, Machine Learning, Object
Identification System, Python, Training Set, Test Set.
1. INTRODUCTION
Machine learning has been gaining momentum over last
decades: self-driving cars, efficient web search, speech and
image recognition. The successful results gradually
propagate into our daily lives. ML is a class of AI methods,
which allows the computer to operate in a self-learning
mode, without being explicitly programmed.It has wide
range of applications in various fields such as Bio-
informatics, Intrusion detection, Information retrieval,
Game playing, Marketing, Malware detection and Image
deconvolution. It is a very interesting and complex topic,
which could drive the future of technology [9].
Neural network is a machine learning algorithm, which is
built on the principle of the organization and functioning of
biological neural networks. It consists of individual units
called Neuron. Neurons are located in a series of groups-
layers. Neurons in each layer are connected to neurons of
the next layer. Data comes from the input layer to the
output layer along these compounds. Each individual node
performs a simple mathematical calculation. Then it
transmits its data to all the nodes it is connected to.
Fig-1: Neural Network
The last wave of neural networks came in connection with
the increase in computing power and the accumulation of
experience. That brought Deep learning, where
technological structures of neural networks have become
more complex and able to solve a wide range of tasks that
could not be effectively solved before [3].
Deep learning is a type of machine learning that requires
computer systems to iteratively perform calculations to
determine patterns by itself [8]. It aims at learning feature
hierarchies with features from higher levels of the
hierarchy formed by the composition of lower level
features. Automatically learning features at multiple levels
of abstraction allow a system to learn complex functions
mapping the input to the output directly from data,
without depending completely on human-crafted features
[1].

One of the major problem in image classification using
deep learning is low performance caused due to over
fitting. Over fitting happens when the model fits too well to
the training set. It then becomes difficult for the model to
generalize the new examples that were not in the training
set. In order to avoid this problem and to improve
performance, large dataset is needed which can be
provided by using CNN [2] [6].
Convolutional neural network (CNN) is the frequently used
deep learning method for image classification [6]. CNN
learns directly from the input image that eliminates
manual feature extraction [4].
In this paper, a deep learning convolutional neural
network based on keras and tensorflow is developed using
python for image classification. Here, we use collection of
different images i.e., dataset which contains two types of
animals, namely cat and dog which are used to train the
system.
Fig-2: Sample Dataset
In this paper, different classifiers such as softmax, sigmoid
in combination with an activation function Relu of CNN are
compared.
Tensorflow is an open-source software library for dataflow
programming across a range of tasks. It is used for image
classification in deep learning and also for machine
learning applications such as neural networks.
Keras is an open source neural network library written in
python. It is designed to enable fast experimentation with
deep neural networks.
2. BASIC THEORY
2.1. Convolution Neural Network
In CNN, the neuron in a layer is only connected to a small
region of the layer before it, instead of all the neurons in a
fully connected manner, so CNN handle fewer amounts of
weights and also less number of neurons. In machine
learning, convolution neural networks are complex feed
forward neural networks. CNNs are used for image
classification and recognition because of its high accuracy
[4].
Fig-3: Convolutional Neural Network
2.2. Classifier Functions:
Classifiers are used when we would want our neural
networks to work on complicated tasks like language
translations and image classifications.
2.2.1. Sigmoid or Logistic Classifier:
The Sigmoid Function curve looks like S-shape.
Fig-4: Sigmoid Function
The main reason why we use sigmoid function is because it
exists between (0 to 1).Therefore, it is especially used for
models where we have to predict the probability as an

output. Since probability of anything exists only between
the range of 0 and 1, sigmoid is the right choice.
2.2.2. Softmax Classifier:
The softmax function is also a type of sigmoid function but
is handy when we are trying to handle classification
problems. The sigmoid function as we saw earlier was able
to handle just two classes. What shall we do when we are
trying to handle multiple classes? Just classifying yes or no
for a single class would not help then. The softmax
function would squeeze the outputs for each class between
0 and 1 and would also divide by the sum of the outputs.
This essentially gives the probability of the input being in a
particular class. It can be defined as
∑
for j = 1, 2, 3 ……….., K. - eq(1)
Fig-5: Softmax Function
Let’s say for example we have the outputs as-
[1.2, 0.9, 0.75], When we apply the softmax function we
would get [0.42, 0.31, 0.27]. So now we can use these as
probabilities for the value to be in each class.
The softmax function is ideally used in the output layer of
the classifier where we are actually trying to attain the
probabilities to define the class of each input.
2.3. Activation Function
They basically decide whether a neuron should be
activated or not. Whether the information that the neuron
is receiving is relevant for the given information or should
it be ignored.
- eq(2)
The activation function is the non-linear transformation
that we do over the input signal. This transformed output
is then sent to the next layer of neurons as input.
When we do not have the activation function the weights
and bias would simply do a linear transformation. A linear
equation is simple to solve but is limited in its capacity to
solve complex problems. A neural network without an
activation function is essentially just a linear regression
model. The activation function does the non-linear
transformation to the input making it capable to learn and
perform more complex tasks. We would want our neural
networks to work on complicated tasks like language
translations and image classifications. Linear
transformations would never be able to perform such
tasks.
2.3.1. ReLU (Rectified Linear Unit) Activation Function
The ReLU is the most used activation function in the world
right now. Since, it is used in almost all the convolutional
neural networks or deep learning.
Fig-6: ReLU Function
As you can see, the ReLU is half rectified (from bottom). f(z)
is zero when z is less than zero and f(z) is equal to z when z
is above or equal to zero.
Range: [0 to infinity)
3. EXPERIMENTAL SETUP
In this paper, we perform experiments on windows 10 in
python 3.7 on CPU system and create the CNN model based
on keras and tensorflow libraries. The CNN model used for
experiments is shown in fig 7. This model mainly consists
of four layers including, convolutional, pooling, flattening
and fully connected layers.

Fig-7: Structure of CNN
The convolution function is taking 4 arguments, the first is
the number of filters i.e., 32, the second is the shape of
each filter of size 3x3, the third is the shape of type of
image (RGB or black and white) of each image i.e., the
input to CNN is 64x64 resolution and 3 stands for RGB, the
fourth argument is the activation function.
We need to perform pooling operation on the resultant
feature maps in order to compress the size of image using
a window of size 2x2 pixels.
For performance measurement we use activation function
namely, ReLU (Rectified linear unit), and two classifiers
namely Softmax, Sigmoid. In experiment, we use
combination of activation function with different
classifiers in building the identification system, and
analyze that which combination gives better accuracy for
image identification.
After implementing all above parameters in python, we
train and test CNN model using training and test datasets,
and then obtain accuracy for different CNN structures.
After then we compare the obtained accuracies and finds
which CNN structure results in higher accuracy.
4. RESULTS
The obtained accuracies of different CNN structures are
listed in the below Table-1 [5].
Table-1: Showing the Results
Number of
convolutional
layers
Activation
Function
Classifier Classification
Accuracy
2 ReLU Sigmoid 90.56
2 ReLU Softmax 50.96
Fig-8: Graphical Representation of accuracy vs number of
epochs for different Classifier functions.
5. CONCLUSIONS
In this paper, a deep learning convolutional neural
network based on keras and tensorflow is developed using
python 3.7 for image classification. Here, we compared two
different structures of CNN, with different combinations of
classifier and activation function.
From experiments, we obtained results for each
combination and observed that ReLU activation function
and sigmoid classifier combination gives better
classification accuracy (nearly 90.5% [5]) than any other
combination of activation function and classifier.
ACKNOWLEDGEMENTS
The Authors express the deepest gratitude to
Dr.J.Sudhakar, Head of the Department, Department of
Electronics and Communication Engineering, for his
cooperation and encouragement during the present work.
We express our sincere thanks to AUTHOURITIES of
Vignan’s Institute of Engineering for Women, for their
cooperation in extending the necessary facilities to carry
out our research work.
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