Sign in Subscribe

CNN (Convolutional neural network)

Last updated on 

Introduction:

Convolutional Neural Network (CNN) is a type of deep learning neural network that is commonly used in computer vision tasks such as image classification, object detection, and image segmentation. CNNs have revolutionized the field of computer vision by enabling the development of highly accurate algorithms for image analysis tasks that were previously considered challenging. In this article, we will explain CNN architecture and how it works.

CNN Architecture:

The architecture of a CNN consists of several layers, each of which performs a different type of computation. The most common layers used in a CNN are:

  1. Convolutional Layer: This layer performs the convolution operation, which is the fundamental operation of a CNN. It takes the input image and applies a set of filters to it. Each filter convolves over the input image and produces an activation map. The filters used in this layer learn to recognize specific features in the input image, such as edges, corners, and other visual patterns.
  2. Pooling Layer: This layer reduces the dimensionality of the output of the convolutional layer. The most commonly used pooling operation is max-pooling, which selects the maximum value from a rectangular region of the activation map. Max-pooling reduces the size of the feature map, making it computationally less expensive to process.
  3. Activation Layer: This layer applies an activation function to the output of the previous layer. The activation function is typically a non-linear function that introduces non-linearity into the network, enabling it to model complex non-linear relationships between the input and output.
  4. Fully Connected Layer: This layer takes the output of the previous layer and performs a matrix multiplication with a set of weights. The weights are learned during the training process, and they determine the final output of the network.

Working of CNN:

The working of a CNN can be broken down into the following steps:

  1. Input Image: The first step in the CNN is to input an image into the network. The image is typically represented as a 3D array, where the dimensions represent the height, width, and color channels of the image.
  2. Convolutional Layer: The next step is to apply the convolutional layer to the input image. This layer convolves the image with a set of filters to produce a set of activation maps. Each filter is designed to recognize a specific feature in the input image.
  3. Activation Layer: The activation layer applies a non-linear activation function to the output of the convolutional layer. The most commonly used activation function is the Rectified Linear Unit (ReLU) function, which sets all negative values in the activation map to zero.
  4. Pooling Layer: The pooling layer reduces the dimensionality of the output of the activation layer by selecting the maximum value from a rectangular region of the activation map. This reduces the size of the feature map, making it computationally less expensive to process.
  5. Fully Connected Layer: The fully connected layer takes the output of the previous layer and performs a matrix multiplication with a set of weights. The weights are learned during the training process, and they determine the final output of the network.
  6. Output Layer: The output layer produces the final output of the network. In image classification tasks, the output layer typically consists of a set of neurons, each of which represents a class. The neuron with the highest activation value is selected as the predicted class of the input image.

Training of CNN:

The training of a CNN involves two stages: forward propagation and backpropagation.

  1. Forward Propagation: In the forward propagation stage, an input image is passed through the network, and the output is calculated. The output is then compared to the actual output to calculate the loss function. The loss function measures the difference between the predicted output and the actual output.
  2. Backpropagation: In the backpropagation stage, the error is propagated backward through the network to update the weights. The goal of backpropagation is to minimize the loss function by adjusting the weights of the network.

The backpropagation algorithm involves calculating the gradient of the loss function with respect to each weight in the network. The gradient indicates the direction in which the weight should be adjusted to reduce the loss function. The weights are updated using an optimization algorithm such as stochastic gradient descent (SGD) or Adam.

During the training process, the weights of the network are adjusted iteratively until the loss function is minimized. Once the training process is complete, the network can be used to make predictions on new, unseen data.

Applications of CNN:

CNNs have a wide range of applications in computer vision, including:

  1. Image Classification: CNNs are commonly used for image classification tasks, such as identifying whether an image contains a cat or a dog.
  2. Object Detection: CNNs can be used for object detection tasks, such as identifying the location of objects within an image.
  3. Image Segmentation: CNNs can be used for image segmentation tasks, such as identifying the boundaries of different objects within an image.
  4. Facial Recognition: CNNs can be used for facial recognition tasks, such as identifying individuals from a database of faces.
  5. Autonomous Driving: CNNs are used in autonomous driving systems to identify and track objects in the environment, such as pedestrians and other vehicles.

Conclusion:

Convolutional Neural Networks (CNNs) are a powerful tool for analyzing images and have become an essential component in many computer vision applications. They work by applying a set of filters to an input image to learn and recognize specific features. The filters are learned during the training process, and the weights are updated iteratively to minimize the loss function. CNNs have a wide range of applications, including image classification, object detection, and facial recognition. As technology continues to advance, CNNs will continue to play an increasingly important role in computer vision and machine learning.