Deep HyperNEAT: Extending HyperNEAT to Evolve the Architecture and Depth of Deep Networks

NOTE: This implementation is under development. Updates will be pushed over time, bringing in new functionality, tests, and various other elements. The purpose of this repo is to allow others to have a codebase to understand, use, or improve upon DeepHyperNEAT.

Using DeepHyperNEAT

To run DHN in its current form, you need to create a task file. For reference, see xor_study.py.

This task file must contain:

Necessary imports:

 from genome import Genome # Genome class
 from population import Population # Population class
 from phenomes import FeedForwardCPPN # CPPN class
 from decode import decode # Decoder for CPPN -> Substrate
 from visualize import draw_net # optional, for visualizing networks

Substrate parameters

Input dimensions
Output dimensions
Sheet dimensions (optional)

 sub_in_dims = [1,2] # Is of type list
 sub_sh_dims = [1,3] # Is of type list
 sub_o_dims = 1 # Is of type integer

Evolutionary parameters

Population size
Population elitism
Max number of generations

 pop_key = 0 # Key for population
 pop_size = 150
 pop_elitism = 2 # Number of members of pop to keep each generation

The task (defined as a function in python)

Task parameters:
- Task inputs
- Expected outputs (optional)

 def task(genomes):
 	task_inputs = [1,2,3]
 	expected_outputs = [2,4,6]
 	for key, genome in genomes:
 		cppn = CPPN.create(genome) # Create cppn from genome
 		substrate = decode(cppn,sub_in_dims,sub_o_dims,sub_sh_dims) # Decode cppn into substrate
 		error = 0.0 # Initialize error for current genome
 		for inputs, expected in zip(xor_inputs, expected_outputs):
 			inputs = inputs + (1.0,) # Append inputs with bias value
 			actual_output = substrate.activate(inputs)[0] # Query substrate
 			error += error_func(actual_output,expected) # Evaluate error
 		genome.fitness = 1.0 - error # Assign fitness

A call to DHN to attempt to solve the task

 pop = Population(pop_key, pop_size, pop_elitism)
 solution = pop.run(task,num_generations) # Returns the solution to the task

Primary Modules

These modules are associated with the primary function of the DeepHyperNEAT (DHN) algorihtm.

genome.py

Contains all functionality of the genome, a Compositional Pattern Producing Network (CPPN) and its mutation operators.

phenomes.py

Contains multiple representations for feed-forward and recurrent neural networks for the CPPN and the Substrate.

population.py

Contains all functionality and information of the populations used in DHN.

activations.py

A library of activation functions that can be used for the CPPN and Substrate.

reproduction.py

Contains all functionality needed for the reproductive behavior in DHN.

species.py

Contains all functionality needed for speciation in DHN.

stagnation.py

Contains all functionality needed for stagnation schemes used in speciation.

decode.py

Contains all functionality needed to decode a given CPPN into a Substrate.

Secondary Modules

These modules are intended for secondary functionality such as reporting evolutionary statistics, visualizing the CPPN and Substrate, and various utility functions used throughout the primary modules.

reporters.py

Contains various functions for reporting evolutionary statistics during and after an evolutionary run.

visualize.py

Contains functions for visualizing a CPPN or Substrate.

util.py

Contains common functions and iterators used throughout DHN.

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