Convolutional Neural Networks (CNN)

Convolutional Neural Network(CNN) is a subclass of an artificial neural network(ANN) which is mostly used for image-related applications. The input for a CNN is an image, and there are different operations performed on that image to extract its important features of it and then decide the weights to give the correct output. These features are learned using filters. Filters help to detect certain image properties such as horizontal lines, vertical lines, edges, corners, etc. As we go deep into the network, the network learns to defect complex features such as objects, face, background, foreground, etc.

CNNs have three main types of layers:

1. Convolutional Layer: This layer is the main layer of CNN. When an image is fed into the convolution layer, a filter or a kernel of varying size but generally of size 3×3 is used to detect the features. The dot product is carried out with the image, and the kernel is the output is stored in a cell of a matrix which is called a feature map or an activation map. Once the operation is done, the filter moves by a distance and then repeats the process. This distance is called a stride. After each convolution operation, a ReLu transformation is applied to the feature map to introduce non-linearity into the model.
2. Pooling Layer: This layer is responsible for reducing the number of parameters in the next layer. It is also known as downsampling or dimensionality reduction. 
3. Fully Connected Layer: Neurons in this layer have full connectivity to all the neurons in the preceding layer and the succeeding layer. FC layer helps to map the input with the output.

Deep learning is a subfield of machine learning related to artificial neural networks. The word deep means bigger neural networks with a lot of hidden units. Deep learning’s CNN’s have proved to be the state-of-the-art technique for image recognition tasks. Keras is a deep learning library in Python which provides an interface for creating an artificial neural network. It is an open-sourced program. It is built on top of Tensorflow.

The prime objective of this article is to implement a CNN to perform image classification on the famous fashion MNIST dataset. In this, we will be implementing our own CNN architecture. The process will be divided into three steps: data analysis, model training, and prediction.

First, let’s include all the required libraries