Hands-On Transfer Learning with Python

Visualizing feature maps

Visualizing a CNN model involves looking at the intermediate layer feature maps that are output by various convolution and pooling layers in a network, given a certain input. This gives a view into how an input is processed by the network, and how various image features are hierarchically extracted. All feature maps have three dimensions: width, height, and depth (channels). We will try to visualize them for the InceptionV3 model.

Let's take the following input photo of a Labrador dog, and try to visualize various feature maps. As the InceptionV3 model has huge depth, we will visualize just a few of the layers: