Average pooling

Average Pooling is a pooling operation that calculates the average value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer.

1. What is max-pooling and average pooling?
2. Why use average pooling?
3. Why average pooling is used in CNN?
4. What does average pooling 2d do?

What is max-pooling and average pooling?

At max pooling, each filter is taken the maximum value, then arranged into a new output with a size of 2x2 pixels. While the average pooling value taken is the average value of the filter size. Classification layer is a layer consisting of flattening, hidden layer and activation functions.

Why use average pooling?

Average pooling method smooths out the image and hence the sharp features may not be identified when this pooling method is used. Max pooling selects the brighter pixels from the image. It is useful when the background of the image is dark and we are interested in only the lighter pixels of the image.

Why average pooling is used in CNN?

Why to use Pooling Layers? Pooling layers are used to reduce the dimensions of the feature maps. Thus, it reduces the number of parameters to learn and the amount of computation performed in the network. The pooling layer summarises the features present in a region of the feature map generated by a convolution layer.

What does average pooling 2d do?

AveragePooling2D class

Downsamples the input along its spatial dimensions (height and width) by taking the average value over an input window (of size defined by pool_size ) for each channel of the input. The window is shifted by strides along each dimension.