Java Deep Learning Cookbook

How it works...

In step 1, ImagePreProcessingScaler normalizes the pixels in a specified range of values (0, 1) . We will use this normalizer once we create iterators for the data.

In step 2, we have added hyperparameters such as an L2 regularization coefficient, a gradient normalization strategy, a gradient update algorithm, and an activation function globally (applicable for all layers).

In step 3, ConvolutionLayer requires you to mention the kernel dimensions (11*11 for the previous code). A kernel acts as a feature detector in the context of a CNN:

• stride: Directs the space between each sample in an operation on a pixel grid.
• channels: The number of input neurons. We mention the number of color channels here (RGB: 3).
• OutGoingConnectionCount ...