What Is CNN Architecture? Layers of Convolutional Neural Network

What Is CNN Architecture: Exploring the Key Concepts and Basic Architecture

Edited By Team Careers360 | Updated on Jan 08, 2024 05:03 PM IST | #Neural Networks

CNN architecture is a class of deep neural networks that have proven to be highly effective in image-related tasks. It is inspired by the human visual system and employs a hierarchical approach to process visual data. The primary layers of the Convolutional neural network include the Convolutional layer in CNN, the Pooling layer in CNN, and the fully connected layer in CNN.

What Is CNN Architecture: Exploring the Key Concepts and Basic Architecture

In this article, we will explore the fundamentals of CNN architecture, exploring its basic structure, and take a closer look at the different layers of CNN and in the end LeNet-5 CNN architecture as an illustrative example. But before starting your preparation about these basic Convolutional Neural Network Architecture frameworks, take a look at these Neural Network Certification Courses.

This basic architecture of CNN is specifically designed to process and analyse visual data, making it particularly well-suited for tasks such as image recognition and computer vision. Architecture of CNN consists of interconnected neurons that process visual information hierarchically, starting from simple features and gradually building up to more complex ones. CNNs aim to mimic this process by automatically learning and extracting meaningful features from images.

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Characteristics of CNN Architecture

Convolutional Neural Networks (CNNs) have emerged as a groundbreaking technology in the realm of deep learning, particularly in the field of computer vision. These neural networks possess distinctive characteristics that make them exceptionally well-suited for processing and analysing visual data. At their core, CNN uses a set of sample images to understand common patterns inside the visuals. More evolved versions also support texture and feature understanding along with the ability to create new similar patterns. The following are the characteristics of Convolutional Neural Network Architecture:

Convolutional Layers

Convolutional layer in CNN are used to scan the input image with learnable filters or kernels. These filters slide over the image in small, overlapping regions and perform element-wise multiplication and summation. This process captures local patterns and features, such as edges, textures, and shapes.

Activation Functions

After convolutional operations, activation functions, such as Rectified Linear Units (ReLU), are applied element-wise to introduce non-linearity into the network. This nonlinearity allows the network to learn complex patterns and relationships in the data.

Pooling Layers

Pooling layer in CNN, often called Max Pooling or Average Pooling, reduce the spatial dimensions of the feature maps produced by the convolutional layers. Pooling helps to create translation-invariant representations, making the network more robust to variations in object position and orientation.

Fully Connected Layers

The fully connected layer in CNN are similar to traditional neural network layers and serve as the final stages of the network. These layers take the flattened feature maps from the previous layers and make predictions based on them. They are commonly used for classification or regression tasks.

Hierarchical Feature Learning

CNNs are capable of learning hierarchical representations of the input data. The early layers capture low-level features such as edges, while deeper layers combine these low-level features to detect more complex structures and patterns, ultimately making high-level predictions.

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The Basic CNN Architecture

The Basic CNN architecture is a fundamental building block in the field of deep learning and computer vision. CNNs have revolutionised image and video analysis by enabling machines to automatically learn and extract meaningful features from visual data. These neural networks are particularly designed to process grid-like data, such as images, and have played a pivotal role in various applications, including image classification, object detection, and image segmentation.

Key Components of CNN Architecture

In this section of the CNN Basic Architecture, we will explore its core components and operations, providing a foundational understanding of how these networks work and paving the way for more advanced and specialised CNN architectures. The basic architecture of CNN typically consists of several key components, each with a specific role in feature extraction and classification. Let us take a look at the different layers in CNN and their functions:

Input Layer

The input layer is where the image data is fed into the network. Each image is represented as a grid of pixel values, and this layer is responsible for passing this information to the subsequent layers. The size of this layer corresponds to the dimensions of the input image, typically in the form of a 3D tensor (width, height, and colour channels).

Convolutional Layer

Convolutional layers are the workhorses of CNNs. They apply a set of learnable filters (also known as kernels) to the input image. These filters slide over the image and perform element-wise multiplication and summation to detect local patterns and features. Convolutional layers are responsible for feature extraction and help the network learn hierarchical representations of the input data.

Activation Layer

After the convolution operation, an activation function, often ReLU (Rectified Linear Unit), is applied element-wise to introduce non-linearity into the network. This helps CNNs learn complex patterns and representations in the data.

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Pooling Layer

Pooling layer in CNN reduces the spatial dimensions of the feature maps produced by the convolutional layers. They do this by aggregating the information within a small region of the feature map, typically by taking the maximum (Max Pooling) or the average (Average Pooling). This reduces the computational load and helps in achieving translational invariance.

Fully Connected Layer

The fully connected layer in CNN takes the output from the previous layers and flatten it into a vector. These layers are similar to those in a traditional neural network and are responsible for making final predictions. They often consist of one or more dense layers.

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Understanding LeNet-5 CNN Architecture

One of the most iconic and pioneering CNN architectures in the field of deep learning is LeNet CNN architecture. Developed by Yann LeCun and his team, LeNet-5 represents a pivotal milestone in the evolution of Convolutional Neural Networks. This innovative CNN layer architecture was initially designed for handwritten digit recognition, but its principles have had a lasting impact on the development of modern CNNs.

In this introductory exploration, we will explore the details of the LeNet CNN architecture, examining its key components and highlighting the significant role it played in the popularisation and advancement of CNN neural network architecture.

LeNet-5 consists of seven layers, including two convolutional layer in CNN and three fully connected layer in CNN. This section breaks down the different CNN layers explained in LeNet CNN architecture and key components of LetNet-5 CNN Architecture:

Key Components of LeNet-5 CNN Architecture

The LeNet-5 Convolutional Neural Network architecture, developed by Yann LeCun and his team in the late 1990s, is a pioneering model that laid the foundation for modern CNNs. LeNet-5 was primarily designed for handwritten digit recognition, but its key components have since become fundamental in the development of more advanced convolutional neural networks. Let us explore the essential components of the LeNet-5 layer in CNN:

Input Layer

LeNet-5 takes as input grayscale images with a fixed size of 32x32 pixels. This size was standard for digit recognition tasks, and it can be adjusted for other applications.

Convolutional Layers

LeNet-5 features two convolutional layers. The first convolutional layer employs 6 learnable filters of size 5x5x1, where 5x5 is the filter size, and 1 represents the single input channel for grayscale images.The second convolutional layer uses 16 filters of size 5x5x6, building on the features extracted by the first layer. Convolution is followed by a ReLU activation function, introducing non-linearity to the network.

Pooling Layers

After each convolutional layer, LeNet-5 includes a Max Pooling layer. The first Max Pooling layer has a 2x2 window, while the second has a 2x2 window with overlapping regions.

Fully Connected Layers

LeNet-5 has three fully connected layers, often referred to as traditional neural network layers. The first fully connected layer consists of 120 neurons, which enables the network to learn complex representations. The second fully connected layer contains 84 neurons, further capturing high-level features. The final fully connected layer consists of 10 neurons, corresponding to the 10 possible digits (0-9). It employs the softmax activation function for classification.

Output Layer

The output layer of LeNet-5 is responsible for making predictions. In the case of digit recognition, it determines which digit the input image represents based on the network's learned features.

Convolutional and Fully Connected Layer Activation

LeNet-5 uses the Rectified Linear Unit (ReLU) activation function after both convolutional and fully connected layers. ReLU introduces non-linearity, helping the network learn complex patterns in the data.

These key components of LeNet-5 collectively contribute to its ability to recognise handwritten digits with impressive accuracy. The two convolutional layers capture essential features, while the pooling layers reduce spatial dimensions and enhance translation-invariance.

The fully connected layers process these features for final classification, and the use of ReLU activation fosters effective feature learning. LeNet-5's architectural principles have not only shaped the development of subsequent CNNs but also continue to serve as a reference point for understanding the fundamentals of convolutional neural network design.

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Conclusion

Convolutional Neural Networks are a cornerstone in the field of deep learning, particularly for image-related tasks. The basic CNN architecture, as well as the LeNet-5 example, showcase the key elements of these networks, including different CNN architectures, CNN layer architecture, activation functions, pooling, and fully connected layer in CNN. Understanding these fundamental concepts is crucial for anyone looking to work with CNNs and harness their capabilities in various applications, from image recognition to object detection.

As technology continues to advance, CNN architecture will undoubtedly evolve, but the principles outlined in this article will remain central to their design and functionality. This CNN basic architecture will help students learn and start their careers as machine learning engineers.

Frequently Asked Questions (FAQs)

1. What is a CNN, and how does it differ from traditional neural networks?

It is a deep learning architecture designed for processing visual data. It differs from traditional neural networks by using convolutional layers, which are specifically tailored for handling grid-like data like images.

2. Can you explain the key components of a CNN architecture?

The key components of a CNN include convolutional layers for feature extraction, activation functions (often ReLU) for introducing non-linearity, pooling layers for spatial dimension reduction, fully connected layers for making predictions, and the use of hierarchical feature learning.

3. What is the purpose of a pooling layer in a CNN?

A pooling layer reduces the spatial dimensions of feature maps produced by convolutional layers. It helps create translational invariance, making the network robust to variations in object position and orientation.

4. What is the purpose of fully connected layers in a CNN?

Fully connected layers take the flattened feature maps from previous layers and make final predictions. They are typically used for classification or regression tasks.

5. What are the types of CNN architectures?

Some common types include LeNet, AlexNet, VGGNet, GoogLeNet, ResNet, and MobileNet, each with varying depths and structures designed for different tasks and levels of computational efficiency.