CS Masters Project. Scaling evolutionary neural networks for Go artificial intelligence.
- Once compiled, create a directory in the same location as the binary called "size<board size>set<set number>". For example, "size3set1".
- Run training with
./scalable_go_training <board_size> <set> <start_generation> <end_generation> <uniform> <scaled>. - Uniform and scaled are booleans (enter 0 or 1) that determine if the network is uniform, and whether it is scaling up from a smaller network. If scaling up, "importnetworks.txt" much be present, which should be a copy of "lastbestnetworks.txt" from previous training on one size smaller board.
- Run comparison with
./scalable_go_comparison <board_size> <set1_name> <set1_uniform> <set2_name> <set2_uniform>. Example:./scalable_go_comparison 5 size5set2 0 size5set6 1 - set1_uniform and set2_uniform are booleans (enter 0 or 1) that determine if the network is uniform.
- Run gogamenn benchmark with
./benchmark_gogamenn <board_size> <iterations>. Benchmark will return total time to complete iterations and iterations per second.
- gogame/: Library for defining Go game, board, and move generation
- neuralnet/: Neural Network Library
- gogamenn/: Library defining NeuralNet wrapper for Go and helper functions.
- gogameab/: Library defining AB Pruning algorithm.
- tests/: Units and regression tests
- benchmark_neuralnet.cpp: Basic benchmark of neural network performance.
- benchmark_gogamenn.cpp: Basic benchmark of gogamenn performance.
- benchmark_19x19ab_prune.cpp: Basic benchmark of worst case AB prune on 19x19 board with 0 ply.
- scalable_go_comparison.cpp: Compares 2 sets of training results.
- scalable_go_training.cpp: Training algorithm.
- Input Layer: subsection size ^2, so 9 for a 3x3 subsection
- HL1: Input Layer * (4/3) rounded down, so 12 for a 3x3 subsection
- HL2: HL1 * (1/4) rounded down, so 3 for a 3x3 subsection
- Output Layer: 1
- Input Layer: Outputs from all Layer 1 networks, in addition to pieces placed, friendly, and opponent prisoner counts
- HL1: Input Layer * (2/3) rounded down
- Output Layer: 1
Author: W. Duncan Fraser
License: Apache License V2