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.