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Showing posts with the label pooling layer

Importance of pooling layer in CNN

I recently came across a bunch of question regarding pooling in CNN. Will removing the max pooling layer from the CNN architecture effect the accuracy? Is pooling necessary for convolutional neural network? What will happen if we remove pooling layers from the General CNN architecture? Does removing pooling layers from CNN will improve results?   There are many other similar questions like these and this article will answers all those questions. Photo by Thomas Tucker on Unsplash So, firstly if you don't know what pooling is then you can go through this article , it will help you in understanding pooling deeply. And if you just want to know the answers to the above questions then continue with this article. So, we know that pooling helps in reducing the dimension. But why we want to reduce the dimension? The answer is to reduce the computational power required to train the model. If we don't reduce the dimension then our model will take very long or most probably our machine w

How Pooling layer helps in reducing dimension in convolutional neural networks?

One of the most important layer in a CNN architecture is Pooling layer. In this article we will understand what is pooling layer? what does pooling layer do? and how pooling layer works? we will also look at different types of pooling such as max pooling, average pooling and global average pooling. Photo by Andras Kerekes What is Pooling layer and what does pooling layer do?  In simple words Pooling is used for dimensionality reduction in CNN. Why dimensionality reduction? For decreasing the computational power required to process the data. But pooling is not just for reducing the dimension only, it also helps in extracting the dominant features like edges in the image. How pooling layer works? So, now we know that pooling is used for dimensionality reduction but how pooling reduces dimension? Pooling works similar to filters. Consider the below image where we are using a filter of size 2 X 2. In case of filters, we used to multiply filter values to the input element wise and c