This is my implementation of a Convolutional Neural Network(CNN), a Machine Learning Model which is used for image classification. The final classification accuracy was 94.71%, compared to a Multilayer Perceptron which only reached a maximum classification accuracy of 80%.
Convolutional Neural Networks(CNN) form the foundation for Modern Computer Vision(CV). What sets CNNS apart from traditional Multilayer Perceptrons is their usage of Convolutional Filters. In inference, these filters contain different feature map matrices which we use to scan over the image: interpreting our image as a matrix of pixels, we take the element-wise product of our feature map with a region of our image, and store the product in a product matrix-each number in this matrix represents a different element-wise product. We then feed these product matrices element-wise into an Activation Function, and they are now ready for the next layer! We do this for all possible regions of our image and all of our filters, with each distinct filter having its corresponding product matrix.
See the below gif for a visualization of the scanning/multiplication process, where the feature map is in dark blue, the image is light blue, and the product matrix is white:
Once we have repeated this an arbitrary number of times, we reshape it into a single vector and feed it into a Multilayer Perceptron, calculate the loss function, and backpropagate it.
Final Accuracy: 94.71%
1st layer: 6 5x5 Filters, ReLU Activation Function
2nd layer: 12 6x5x5 Filters, ReLU Activation Function
3rd layer: Fully Connected Layer, output of length of 136, ReLU Activation
4th layer: Fully Connected Layer, output of length 10 for classification, Cross-Entropy Loss.