Keras Deep Learning Cookbook

Introduction – shared input layer

In this section, we show how multiple convolutional layers with differently sized kernels interpret an image input. The model takes colored CIFAR images with a size of 32 x 32 x 3 pixels. There are two CNN feature extraction submodels that share this input; the first has a kernel size of 4, the second a kernel size of 8. The outputs from these feature extraction sub-models are flattened into vectors and concatenated into one long vector, and this is passed on to a fully connected layer for interpretation before a final output layer makes a binary classification.

This is the model topology:

• One input layer
• Two feature extraction layers
• One interpretation layer
• One dense output layer