CNN Architecture
Dr.A.Sumithra Gavaskar
Associate Professor at Sns College of Technology , Research Co-ordinator of Dept of CSE
Introduction
A convolutional neural network (CNN), is a network architecture for deep learning which learns directly from data. CNNs are particularly useful for finding patterns in images to recognize objects. They can also be quite effective for classifying non-image data such as audio, time series, and signal data.
Stride
Stride is a parameter of the neural network’s filter that modifies the amount of movement over the image or video. we had stride 1 so it will take one by one. If we give stride 2 then it will take value by skipping the next 2 pixels.
Padding
Padding is a term relevant to convolutional neural networks as it refers to the number of pixels added to an image when it is being processed by the kernel of a CNN. For example, if the padding in a CNN is set to zero, then every pixel value that is added will be of value zero. When we use the filter or Kernel to scan the image, the size of the image will go smaller. We have to avoid that because we wanna preserve the original size of the image to extract some low-level features. Therefore, we will add some extra pixels outside the image.
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Layers used to build CNN
Convolutional neural networks are distinguished from other neural networks by their superior performance with image, speech, or audio signal inputs. They have three main types of layers, which are:
Convolutional layer
This layer is the first layer that is used to extract the various features from the input images. In this layer, We use a filter or Kernel method to extract features from the input image.
Pooling layer
The primary aim of this layer is to decrease the size of the convolved feature map to reduce computational costs. This is performed by decreasing the connections between layers and independently operating on each feature map. Depending upon the method used, there are several types of Pooling operations. We have Max pooling and average pooling.