This project attempts evolving transfer functions for neural networks using symbolic regression. Project is implemented using DeepLearning4Java.

The installation procedure can be found here

• Defines common structures and procedures for the evolution process.

• Evolutionary search algorithms and structures.

• Methods for search and analysis of the initial architecture for a given dataset.

• Custom data structures and pipelines.
• Includes Parser for DPAv2/4 CSV files.

• Demos on the usage of DL4J and TF frameworks.

• Various common utilities.

• Wrapper for the neural network, used to hide unnecessary features
• Softly prevents unwanted model modifications (for comparable results).

• Defines the common procedures and structures used for model training.
• Ensures comparability of experiment results.

• Defines immutable parameters used for model training.
• Ensures comparability of experiment results.

• Defines model performance on the test set.

• Workhorse for procedures related to storing and loading experiments.

• Defines the storage paths of an experiment.

• Used to run custom experiments using the DL4J model and data API

• Demonstrates the usage of the custom data pipeline.

• Demonstrates learning a neural network using DeepLearning4Java on MNIST and custom datasets.

• Demonstrates learning a neural network using Tensorflow Java Ops API.

To load data construct a pipeline.

• Parser - reads dataset description markers and passes actual data through
• Cacher - loads the whole stream into an array (for faster inference) and additionally applying some data modifiers
• Batcher - constructs batches from input stream
• DatumF - wrapper class used to construct and hold a Tensor pair (doesn't matter if batch or not)
• IModifier - interface for constructing modifiers used to modify the data (normalization, randomization before batching, etc.)
  • Randomizer - randomizes the order of instances in dataset

Method get() fetches the next data, method reset() generally resets internals to start over the inference procedure.