| dc.description.abstract | Artificial neural networks can be known as a
computer system modeled on the human brain and
neural system. In data classification, neural network
provides fast and efficient results. Neural Network models
are trained by using sets of labeled data. Neural networks
have the ability to work with data, based on the training.
There are thousands of interconnected nodes that belong
to interconnected hidden layers inside the neural network.
Activation function that have included in the neural
network provides the output based on given an input or
set of inputs. This research work focused on the
comparison of the effects of using several activation
functions on multiple hidden layers for classification using
MNIST (Mixed National Institute of Standards and
Technology) data set. Data classification was made using
TensorFlow library. Tensorflow library with the help of
keras used to build the neural network model. The
experiment results of Rectified Linear Unit (ReLu), Leaky
ReLU, Hyperbolic Tangent (tanH), Exponential Linear Unit
(eLu), sigmoid, softplus, softmax and softsign activation
functions. Data have been collected for the experiment in
two different methodologies. There is a hidden layer with
one activation function and multiple hidden layers with
multiple activation function. The result of the study shows
that the higher accurate rate than 88% for training and
testing when it uses multiple hidden layers with multiple
activation functions. | |
