smiling.rr

The Model Architecture
The model we used is built with Keras using Convolutional Neural Networks (CNN). A convolutional neural network is a special type of deep neural network which performs extremely well for image classification purposes. A CNN basically consists of an input layer, an output layer and a hidden layer which can have multiple layers. A convolution operation is performed on these layers using a filter that performs 2D matrix multiplication on the layer and filter.

The CNN model architecture consists of the following layers:

Convolutional layer; 32 nodes, kernel size 3
Convolutional layer; 32 nodes, kernel size 3
Convolutional layer; 64 nodes, kernel size 3
Fully connected layer; 128 nodes
The final layer is also a fully connected layer with 2 nodes. A Relu activation function is used in all the layers except the output layer in which we used Softmax.