Image Classification Using TensorFlow GPU
2021 International Conference on ICT for Smart Society (ICISS) | 978-1-6654-1697-9/21/$31.00 ©2021 IEEE | DOI: 10.1109/ICISS53185.2021.9532500
Derrick Yeboah
School of Computer and Information
Engineering,
Zhejiang Gongshang University,
Hangzhou, China.
derrick.yeb@gmail.com
Mahamat Saleh Adoum Sanoussi
School of Information Engineering
Huzhou University
Huzhou, China
Https://Orcid.Org/0000-0002-31812138
Abstract— There are several image classification and a
complicated methods that are been overlooked with many articles.
This article reviews the latest practices, issues, and options for
billing classification. Emphasis is placed on synthesizing
important advanced category strategies and targeting strategies
that can be used to improve ranking accuracy. Billing sorting is a
classic problem in image processing, computer vision, and
machine learning. In this article, we study deep learning-based
image classification using the TensorFlow GPU. Because the
datasets were bridges; CIFAR-10 and MNIST FASHION for the
classification module. The results show the efficiency and
accuracy of deep learning-based image classification using the
TensorFlow GPU. Additionally, some critical issues are
mentioned that affect overall performance. However, simple
research is needed to identify and reduce uncertainties in the
image processing chain to improve classification accuracy.
Keywords— Tensorflow GPU, Image Classifications, CNN,
Feature Extraction
I.
INTRODUCTION
Image classifications is becoming the trend among
developers, mainly due to the growth of facts in various early
companies, such as e-commerce etc[1]. Normally, the system
itself might be set with loads or perhaps hundreds of input
information so that you can make the ‘training’ consultation
to be greater efficient and speedy. It starts by using giving
some form of ‘education’ with all of the enter statistics.
Machine learning is also the frequent structure that has been
carried out closer to the image category. However, there are
still parts that may be stepped forward inside machine
mastering. Therefore, picture classification is going to be keen
on deep getting to know gadgets. Machine Learning has its
context while it comes to Image Classification. This
technology can understand humans, items, locations, actions,
and writing in snapshots [2]. The aggregate of synthetic
intelligence software and machine imaginative and prescient
technology can acquire the superb result of phototype. The
fundamental project of picture type is to make certain all of
the snapshots are categorized in keeping with its unique
sectors or corporations.[3]. The various applications
consisting of vehicle navigation. The primary motivation
behind the work: 1. Is to contemplate or see how pictures or
images are been arranged dependent on deep learning and
Tensorflow or applying the idea of a deep studying set of
regulations in the image category. 2. Picture Classification
using computer vision and human vision with the goal that the
pictures can be perceived both by machine and human. 3.
Image classification in respect to Deep Neural Network, in
light of TensorFlow. 4. Precision of image classification of
every level of each picture.
George K. Agordzo
School of Computer and Information
Engineering,
Zhejiang Gongshang University,
Hangzhou, China
Https://Orcid.Org/0000-0002-68183718
II.
RELATED WORKS
A. Concept of Deep Learning
In fact, the technology was introduced into machine learning
in 1986 to introduce the company through Rina Dechter and
in 2000 to colleagues connected to Igor Aizenberg and
Booleovichon's synthetic neural network portals. This
approach did not become commonplace until 2012. All
technology enthusiasts should be aware of the development of
in-depth learning. Geoffrey Hinton has been promoting
machine learning for artificial intelligence since the early
1980s. But the 1980s were not so simple. These and other
factors have led to technological leaps, such as vision, speech
recognition, and more.
B. Drawbacks 0f Deep and Machine Learning
Deep neural networks are not easy to train due to the socalled gradient disappearance problem, which can reduce the
number of layers in the neural network. When adding levels,
the gradient loss problem can lead to too long a workout with
good precision because the improvement between training
cycles is minimal. The problem does not apply to all
multilayer neural networks, but to those that use a gradientbased learning method. This means that there are several ways
to solve this problem, either by choosing the correct boot
function or by training a system with a heavy GPU.
While engineers and professionals around the world agree
that we may have entered the golden age of artificial
intelligence, we have implemented a gadget to find out the
commands you want to overcome many more obstacles than
you think. Since commissioning, the usage must be
implemented in the application. Because of the use of learning
systems, it can be difficult to admit that this is not a first-class
way to solve problems [4]. The dominance of machine
intelligence over the last ten years has changed the world as
we know it. Today, the noise of improving knowledge and
intelligence is widespread. This is probably justifiably great
because of the ability of this discipline. The number of
computerized intelligence agencies has increased enormously
in recent years and, as the report indeed shows, the number of
agencies identified by computerized intelligence services
increased by 100 in 2015. As of December 2018,% of
companies were processing at least one artificial intelligence,
and the Deloitte report focuses on the basic pace of cloud
computing and enterprise-based computing to improve
intelligence development at 87 and 83 percent revenue,
respectively. These numbers are impressive if you are
planning a short-term career change. Artificial intelligence
seems to be a very correct assessment. It's awesome and I'm a
fan of the huge system and artificial intelligence. However,
there are situations where the use of a knowledge-gaining
device is really useless, which is not the case in some cases at
the moment, and there are some cases, even if you may have
trouble applying it [5]. We are now talking about language:
insufficient training data, unrepresentative training data, data
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on incorrect, irrelevant functions, over-equipment of training
data, too good, upside down.
C. Image Classification
With a history of more than 40 years, it was written on screen,
with the name of the classification and the appearance of the
scenes, a central element of machine vision. Due to the large
number of image and video databases, it is necessary to be
able to sort and retrieve images and videos successfully. It can
be used as a source of information and categorized in / out of
context. This element is based on a description of the scan
algorithm [6] in the classification algorithm. Telesounding
searches focusing on image classification have long attracted
the attention of the telesounding community, as classification
results form the basis of many environmental and socioeconomic applications. [7] [8].
D. Use of Machine Learning
One might ask “For what reason should machines need to
learn? Why not configuration machines to proceed as wanted
in any case?" There are a lot of reasons why AI is significant.
Machine learning is a buzzword within the era worldwide
right now, and for appropriate reason: It represents a prime
breakthrough in how computers can look [9].
Very essentially, a device mastering set of rules is given
an “education set” of facts, then requested to apply that
information to reply a query. For example, you could offer a
laptop a coaching set of photos, some of which say, “this is a
cat” and a number of which say, “This isn't always a cat.”
Then you may variety show the pc a chain of recent pics and
it might begin to select out which photos have been of cats.
Machine analyzing then keeps to characteristic to its
training set. Every photo that it identifies — successfully or
incorrectly — receives delivered to the coaching set, and this
system correctly receives “smarter” and higher at finishing its
task over time. It is specifically used to construct algorithms
that can receive enter information and use statistical
evaluation to predict the output. Some of the uses of machine
learning are: Virtual Personal Assistants, Image Recognition,
Predictions, Videos Surveillance, Social Media platforms,
Spam and Malware, Customer support, Search Engine,
Applications/Companies. [10]
practically discover the similarities in education statistics. It is
often assumed that the clusters found will be reasonably
informative due to their intuitive size. For example, a group of
substantially exclusive people can turn demographics into
only a group of wealthy organizations and be very important.
However, the information set does not have to be the same for
mapping this cluster, they can be used and the cluster can be
customized to map new models to one of the cluster options.
Therefore, the access to the data is such that it is possible to
work well with enough data with the job. The second
automatic application algorithms [12]are designed to improve
the structure of data fields. The structure of the structure is
stimulated by the cost capacity, generally limited by the
construction of the ideal parameters that will ensure the
structure of the information system. Unsupervised analysis
has been very successful, including backgammon applications
from the world and Persian machines that can use vehicles! It
may be a powerful approach whilst there is a clean manner to
assign values to actions. Clustering may be useful even as
there may be enough information to form clusters (despite the
fact that this seems to be tough at times) and specifically while
extra statistics about contributors of a cluster may be used to
offer similar results because of dependencies inside the
information. Classification getting to know is powerful whilst
the classifications are recognized to be accurate (as an instance,
simultaneously as managing sicknesses, it's far generally
instantly-in advance to decide the layout after the truth with
the aid of a post-mortem), or whilst the classifications are
absolutely arbitrary subjects that we would love the computer
so that you can recognize for us [13]. Classification gaining
knowledge is often critical at the identical time as the
selections made with the useful resource of the set of
guidelines is probably required as input a few other vicinities.
Otherwise, it would not be easy for whoever needs that input
to decide out what technique. Both concepts are often worthful
and which one you choose out want to rely on the conditions-what form of trouble is being solved, how an awful lot of time
is allocated to fixing it (supervised gaining knowledge of or
clustering is frequently quicker than reinforcement analyzing
techniques), and whether or not supervised studying is even
viable.
III. METHODS AND IMPLEMENTATION
The initial step is gathering the dataset. On the off chance
that an essential master is accessible, at that point could
propose which fields (properties, highlights) are the most
enlightening. [11]
The first method is to train the agent no longer through
explicit categorization, but through a reward system to
indicate success. Note that this type of training usually fits the
picture of selection problems, because the goal is not always
to provide a type, but to create alternatives that maximize
rewards. This approach is well generalized to the current
international level, where companies are likely to be rewarded
for positive action and punished for gaining more. The drone
studio has produced many hits, including the satisfaction of
backgammon packages from international champions or even
cars that can use cars! It's a really robust technique and there's
a great way to assign values to movements. In the unattended
study you are likely to bet on, no training data is selected, the
tool tries to explore without a coach. The second approach is
called clustering, in this type of learning the goal is not always
to maximize the functionality of the software program, but to
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Also, measure and determine the speed and accuracy of image
classification by using the two datasets. To evaluate the two
models, we conduct experiments on two standard datasets. As
we use an unsupervised approach for image classification, we
make use of the whole corpus of each dataset by aggregating
training and test sets. In this, the python was the programming
language used in creating the algorithm which was hosted on
Jupyter notebook and Anaconda prompt (Anaconda3). We
evaluated the images by comparing with MNIST FASHION
and CIFAR-10, algorithms in the aspects of training accuracy,
loss of accuracy and validation of training, loss of validation
in MNIST FASHION and CIFAR-10.
Fig. 1. Data Flow/Processing Diagram
Fig. 3. Images from FASHION MNIST
Setting up the Environment (TensorFlow GPU)
TensorFlow is an open source learning platform with open
source code. It has a large and flexible ecosystem of
community-based tools, libraries, and resources to increase
the need to develop well-developed machines and
technologies to support and improve machines. All models
implemented in neural networks use TensorFlow and CUDA
to reduce the use of GPU recognition and expert optimization.
A document analysis tool must also be installed and
configured to use Python Widget Manager packages on a
Jupiter with laptop.
• Create a Python "virtual environment" for TensorFlow
using conda
• Install TensorFlow-GPU from Anaconda Repositories
• Simple check TensorFlow is working with your GPU
• Create Jupyter Notebook Kernel for TensorFlow
• Testing the environment
IV.
RESULTS AND DISCUSSIONS
A. CIFAR-10
In this chapter, a standardized CIFAR-10 dataset and MNIST
Fashion dataset will be employed which contains 60,000
images. The two datasets are going to be compared and
determine which one is the best to classify the images.
Fig. 4. Image of the dataset (CIFAR-10)
B. Training
During the training, we trained CNN Fashion MNIST and
CIFAR-10 models in fashion stores and each of us 20 times.
Of the 30,000 images in the MNIST database, we used 30,000
images to model the model and 5,000 images to test the
model. I also used CIFAR-10 data. CIFAR-10 is a subset of
80 million small actions and contains 60,000 32 * 32 color
photographs of one of ten object categories to test a model
with 6,000 images per image. Category.
Fig. 2. FASHION MNIST
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Fig. 5. Loss and accuracy evolution image
Fig. 9. Test Image, their Predicted and True Labels
C. Qualitative analysis and discussion
Fig. 6. Dataset image (MNIST FASHION)
This section talks about the above results which describes the
qualitative and quantitative analysis of all the results of the
both models. The MNIST FASHION dataset, this dataset
consumes the least time in the training process, when
classifying the images, it takes less time to process but the
accuracy is not good and the losses are also less. The test
accuracy is 87%. The second dataset is the CIFAR-10 dataset,
the dataset consumes more time in the training process when
classifying the images. The accuracy is good but the losses
are more. The test accuracy is 89.59%. By comparing the two
dataset, I will choose the CIFAR-10 because it has better test
accuracy.
D. Uses of CNN
Fig. 7. Trained image
The LeNet5 model you proposed is the oldest Convolutional
model in the neural network. According [14]. The rise of this
network architecture has made progress in image processing.
The traditional neural network convolution model consists of
two parts: function extraction and data classification. After
extracting the image features that meet the algorithm
requirements through a specific network structure design, the
classification module classifies the target image based on the
extracted features. These convolution layers can extract
recognition functions into an image using convolution
markers of different sizes. When multiple convolution layers
extract advanced image features, the classification layer can
use these features that give the image category. Convoluting
neural networks essentially reduce the dimensionality of the
image by increasing the depth of the hidden layer, allowing
the model to extract rare image functions in low-dimensional
space [15][16]The Deep Learning Algorithm selects
convoluted neural networks (CNN), which work well within
computer vision.
V. CONCLUSIONS
Fig. 8. Dataset image (MNIST FASHION)
These experimental data show that the neural network
standard is fantastic and is the perfect method for image
classification. The two networks have in common the fact that
they all evaluate the identification of the orientation, size and
other characteristics of their network structure and, therefore,
the establishment of internal connections. Among them is
CNN, a convolutional neural network with the characteristics
of various parameters, rapid formation, high scores, easy
migration, a great improvement over the conventional neural
network method. This convolutional layer is the key to
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achieving this growth, which can be described as the
lifeblood of the entire convolutional neural network. As
previously mentioned, CNN has lost a lot of knowledge about
the team. Therefore, it is not possible to recognize the
orientation of the objects to improve the extraction accuracy
of the image function. Most of these images were essentially
the same, although CNN was unable to identify them, which
is often the dominant output factor. Thanks to a full merge
network without a full link layer (FC), CNN can handle a
variety of input formats. The unwind layer allows you to scale
your data, contributing to more accurate results. The structure
of the jumps in the results of different deep layers is
combined to ensure the durability and precision of the results.
Due to a combination of the two factors mentioned above,
CNN's membership level. Meanwhile, when we selected
images from the test set, and thus the training images were
roughly the same, even higher recognition accuracy was
achieved for CNN in CIFAR-10. The database we use was
carefully designed to form or not perform a recognition task
purely, suggesting that an object is often recognized from
different perspectives. With such an attitude, CIFAR-10
defeated the revolutionary MNIST in a database. In additions,
it is often recommended to carry out various comparative
studies of classifications. Strategies or procedures related to
the implementation and capability of key classification
algorithms need to be developed for future research. Given
the future work in the field, it needs to be researched,
evaluated and further development: data collection and
preparation, integration with other applications.
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
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