Simplified AI 1.1: Understand concepts behind CNN Architecture
Read on to learn more about how many layers a CNN uses, and what their purpose is.
We have already discussed applications and introduction to CNNs now let’s understand CNN’s architecture. AI and deep learning advances have enabled rapid development in the fields of computer vision and image analysis. The emergence and progress of the Convolutional Neural Networks (CNNs) makes all of this possible.
A CNN has two major sections of it:
- A convolution tool that splits the different characteristics of the image for analysis
- A completely connected layer, which uses the convolution layer output to predict the image 's best definition.
CNN architecture is inspired by the visual cortex 's organisation and functionality, and designed to mimic neuronal connectivity patterns within the human brain.
Within a CNN the neurons are divided into a three-dimensional structure, with each group of neurons analyzing a specific region or image function. In other words, each group of neurons is specialized in one part of the picture being described. CNNs use the layer predictions to produce a final output that presents a vector of probability scores to represent the likelihood that a particular characteristic belongs to a certain class.
- Convolutional layer- creates a feature map to predict the class probabilities for each feature by applying a filter that scans the entire image at a time of a few pixels.
- Pooling layer (down-sampling)- scales down the amount of information produced by the convolution layer for each function and preserves the most important information (usually repeats many times the cycle of the Convolutional and pooling layers).
- Fully connected input layer - the outputs generated by previous layers are "flattened" to turn them into a single vector that can be used as an input for the next layer.
- Fully connected layer - applies weights generated by the feature analysis to predict a precise label over the input.
- Fully connected output layer - generates the final probabilities for the image class to be calculated.
Readers Note:
I hope I have been able to give you an intuition into convolutional neural networks through this article. I wasn’t going into CNN 's complicated maths and coding. If you like to learn the same thing-stay tuned, there’s a lot more lined up for you. Let me know your findings, and use the Comments section for it.
Sources
- https://missinglink.ai/guides/convolutional-neural-networks/convolutional-neural-network-architecture-forging-pathways-future/
- https://medium.com/@RaghavPrabhu/understanding-of-convolutional-neural-network-cnn-deep-learning-99760835f148
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