Convolutional Neural Network (CNN) in a brief
What is Convolutional Neural Network (CNN) ?
- A neural network in which at least one layer is a convolutional layer.
- Depending on features, we categorize the images (classify) using CNN.
- Yann Lecun is considered the grandfather of Convolutional neural networks.
What is a Convolutional Layer ?
These are the layers of convolutional neural network where filters are applied to the original image.
Steps involved in constructing a Convolutional Neural Network:
- Convolution Operation.
- Stride.
- ReLU Layer.
- Pooling.
- Flattening.
- Full Connection.
1. Convolution Operation :
- In this process, we reduce the size of the image by passing the input image through a Feature detector/Filter/Kernel so as to convert it into a Feature Map/ Convolved feature/ Activation Map
- It helps remove the unnecessary details from the image.
- We can create many feature maps (detects certain features from the image) to obtain our first convolution layer.
- Involves element-wise multiplication of convolutional filter with the slice of an input matrix and finally the summation of all values in the resulting matrix.
1.1. Stride:
The number of pixels by which we are moving the filter over the input matrix is called a stride.
1.2. ReLU Activation Function :
- ReLU is the most commonly used activation function in the world.
- When applying convolution, there is a risk we might create something linear and there we need to break linearity.
- Rectified Linear unit can be described by the function f(x) = max(x, 0).
- We are applying the rectifier to increase the non-linearity in our image/CNN. Rectifier keeps only non-negative values of an image.
2. Pooling :
- It helps to reduce the spatial size of the convolved feature which in-turn helps to to decrease the computational power required to process the data.
- Here we are able to preserve the dominant features, thus helping in the process of effectively training the model.
- Converts the Feature Map into a Pooled Feature Map.
Pooling is divided into 2 types: 1. Max Pooling - Returns the max value from the portion of the image covered by the kernel. 2. Average Pooling - Returns the average of all values from the portion of the image covered by the kernel.
3. Flattening :
Involves converting a Pooled feature Map into one-dimensional Column vector.
4. Full Connection :
- The flattened output is fed to a feed-forward neural network with backpropagation applied to every iteration.
- Over a series of epochs, the model is able to identify dominating features and low-level features in images and classify them using the Softmax Classification technique (It brings the output values between 0 and 1).
