Keras Layers API (Part 2)
Learn about the pooling, normalization, and dropout layers of a neural network, and use Keras to implement these layers.
We'll cover the following
Pooling layers
A pooling layer reduces network parameters and saves subsequent computations. It replaces the input data with a summary statistic of the nearby points. It’s a type of nonlinear downsampling. Usually, we use:
Max pooling: This selects the maximum value within a rectangular neighborhood.
Average pooling: This computes the average output.
The following code implements both pooling types in a 2x2 neighborhood with a stride (slide) of two units, though we can use other size localities and strides in pooling layers.
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