Convolution Neural Networks for Binary Classification
K. N. H. Srinivas, Kavitha Rani Balmuri, M. Varaprasad Rao, G. Madhukar, Bendi Venkata Ramana · Journal of Computational and Theoretical Nanoscience · 2019
Convolutional neural networks (CNNs) are similar to “ordinary” neural networks in the sense that they are made up of hidden layers consisting of neurons with “learnable” parameters. These neurons receive inputs, perform a dot product, and then follows it with a non-linearity. The whole network expresses the mapping between raw image pixels and their class scores. Conventionally, the Softmax function is the classifier used at the last layer of this network. However, there have been studies conducted to challenge this norm. Empirical data has shown that the CNN model was able to achieve a test accuracy of ≈99.04% using the MNIST dataset. MNIST dataset consists of 60,000 training images and 10,000 testing images. This experiment was inspired by following the experiment on MNIST dataset. The dataset we used in this experiment is collection of images consisting of cats and dogs. These images are gathered from different sources over internet. This dataset consists of 10,000 images of each class i.e., Cats and Dogs. The overall accuracy of training and validation set is 96.85%. The said results may be improved if data pre-processing techniques were employed on the datasets, and if the base CNN model was alternatively more sophisticated than the one used in this study.