  Convolutional Neural Networks has been implemented in many complex machine
learning takes such as image classification, object identification, autonomous
vehicle and robotic vision tasks. However, ConvNet architecture efficiency and
accuracy depend on a large number of fac- tors. Also, the complex architecture
requires a significant amount of data to train and involves with a large number
of hyperparameters that increases the computational expenses and difficul-
ties. Hence, it is necessary to address the limitations and techniques to
overcome the barriers to ensure that the architecture performs well in complex
visual tasks. This article is intended to develop an efficient ConvNet
architecture for multi-class image categorical classification applica- tion. In
the development of the architecture, large pool of grey scale images are taken
as input information images and split into training and test datasets. The
numerously available technique is implemented to reduce the overfitting and
poor generalization of the network. The hyperpa- rameters of determined by
Bayesian Optimization with Gaussian Process prior algorithm. ReLu non-linear
activation function is implemented after the convolutional layers. Max pooling
op- eration is carried out to downsampling the data points in pooling layers.
Cross-entropy loss function is used to measure the performance of the
architecture where the softmax is used in the classification layer. Mini-batch
gradient descent with Adam optimizer algorithm is used for backpropagation.
Developed architecture is validated with confusion matrix and classification
report.
