Pooling
Now that you have found the important features of the image, still, the amount of input is very large and our machine could not be able to handle this amount of inputs. So here is where pooling comes.
Pooling is just reducing the size of the image without losing the features that we found with convolution. For example, a MaxPooling method will take in a shape of a matrix and return the larger value in that range. By doing this we can compress the image without losing the important features of this image.
Working of Convolutional Neural Network (CNN) in Tensorflow
In this article, we are going to see the working of convolution neural networks with TensorFlow a powerful machine learning library to create neural networks.
Now to know, how a convolution neural network lets break it into parts. the 3 most important parts of this convolution neural networks are,
- Convolution
- Pooling
- Flattening
These 3 actions are the very special things that make convolution neural networks perform way better compared with other artificial neural networks. Now, let’s discuss them in detail,