Exercise

# Keras pooling layers

Keras implements a pooling operation as a layer that can be added to CNNs between other layers. In this exercise, you will construct a convolutional neural network similar to the one you have constructed before:

**Convolution => Convolution => Flatten => Dense**

However, you will also add a pooling layer. The architecture will add a single max-pooling layer between the convolutional layer and the dense layer with a pooling of 2x2:

**Convolution => Max pooling => Convolution => Flatten => Dense**

A Sequential `model`

along with `Dense`

, `Conv2D`

, `Flatten`

, and `MaxPool2D`

objects are available in your workspace.

Instructions

**100 XP**

- Add an input convolutional layer (15 units, kernel size of 2,
`relu`

activation). - Add a maximum pooling operation (pooling over windows of size 2x2).
- Add another convolution layer (5 units, kernel size of 2,
`relu`

activation). - Flatten the output of the second convolution and add a
`Dense`

layer for output (3 categories,`softmax`

activation).