225 lines
9.0 KiB
Python
225 lines
9.0 KiB
Python
import argparse
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import sys
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import os
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import time
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# Parse command line arguments
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parser = argparse.ArgumentParser(description="Backgammon games")
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parser.add_argument('--episodes', action='store', dest='episode_count',
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type=int, default=1000,
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help='number of episodes to train')
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parser.add_argument('--model', action='store', dest='model',
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default='default',
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help='name of Tensorflow model to use')
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parser.add_argument('--eval-methods', action='store',
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default=['random'], nargs='*',
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help='specifies evaluation methods')
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parser.add_argument('--eval', action='store_true',
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help='evaluate the neural network with a random choice bot')
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parser.add_argument('--bench-eval-scores', action='store_true',
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help='benchmark scores of evaluation measures. episode counts and model specified as options are ignored.')
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parser.add_argument('--train', action='store_true',
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help='train the neural network')
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parser.add_argument('--eval-after-train', action='store_true', dest='eval_after_train',
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help='evaluate after each training session')
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parser.add_argument('--play', action='store_true',
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help='play with the neural network')
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parser.add_argument('--start-episode', action='store', dest='start_episode',
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type=int, default=0,
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help='episode count to start at; purely for display purposes')
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parser.add_argument('--train-perpetually', action='store_true',
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help='start new training session as soon as the previous is finished')
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parser.add_argument('--list-models', action='store_true',
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help='list all known models')
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parser.add_argument('--force-creation', action='store_true',
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help='force model creation if model does not exist')
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parser.add_argument('--board-rep', action='store', dest='board_rep',
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default='tesauro',
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help='name of board representation to use as input to neural network')
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parser.add_argument('--use-baseline', action='store_true',
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help='use the baseline model, note, has size 28')
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parser.add_argument('--verbose', action='store_true',
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help='If set, a lot of stuff will be printed')
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parser.add_argument('--ply', action='store', dest='ply',
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help='defines the amount of ply used when deciding what move to make')
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args = parser.parse_args()
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if args.model == "baseline_model":
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print("Model name 'baseline_model' not allowed")
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exit()
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config = {
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'model': args.model,
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'episode_count': args.episode_count,
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'eval_methods': args.eval_methods,
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'train': args.train,
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'play': args.play,
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'eval': args.eval,
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'bench_eval_scores': args.bench_eval_scores,
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'eval_after_train': args.eval_after_train,
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'start_episode': args.start_episode,
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'train_perpetually': args.train_perpetually,
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'model_storage_path': 'models',
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'bench_storage_path': 'bench',
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'board_representation': args.board_rep,
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'force_creation': args.force_creation,
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'use_baseline': args.use_baseline,
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'global_step': 0,
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'verbose': args.verbose,
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'ply': args.ply
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}
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# Create models folder
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if not os.path.exists(config['model_storage_path']):
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os.makedirs(config['model_storage_path'])
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model_path = lambda: os.path.join(config['model_storage_path'], config['model'])
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# Make sure directories exist
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log_path = os.path.join(model_path(), 'logs')
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if not os.path.isdir(model_path()):
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os.mkdir(model_path())
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if not os.path.isdir(log_path):
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os.mkdir(log_path)
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# Define helper functions
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def log_train_outcome(outcome, diff_in_values, trained_eps = 0, log_path = os.path.join(model_path(), 'logs', "train.log")):
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format_vars = { 'trained_eps': trained_eps,
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'count': len(outcome),
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'sum': sum(outcome),
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'mean': sum(outcome) / len(outcome),
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'time': int(time.time()),
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'average_diff_in_vals': diff_in_values/len(outcome)
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}
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with open(log_path, 'a+') as f:
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f.write("{time};{trained_eps};{count};{sum};{mean};{average_diff_in_vals}".format(**format_vars) + "\n")
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def log_eval_outcomes(outcomes, trained_eps = 0, log_path = os.path.join(model_path(), 'logs', "eval.log")):
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"""
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:param outcomes:
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:param average_diff_in_value:
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:param trained_eps:
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:param log_path:
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:return:
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"""
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for outcome in outcomes:
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scores = outcome[1]
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format_vars = { 'trained_eps': trained_eps,
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'method': outcome[0],
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'count': len(scores),
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'sum': sum(scores),
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'mean': sum(scores) / len(scores),
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'time': int(time.time())
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}
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with open(log_path, 'a+') as f:
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f.write("{time};{method};{trained_eps};{count};{sum};{mean}".format(**format_vars) + "\n")
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def log_bench_eval_outcomes(outcomes, log_path, index, time, trained_eps = 0):
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for outcome in outcomes:
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scores = outcome[1]
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format_vars = { 'trained_eps': trained_eps,
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'method': outcome[0],
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'count': len(scores),
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'sum': sum(scores),
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'mean': sum(scores) / len(scores),
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'time': time,
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'index': index,
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}
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with open(log_path, 'a+') as f:
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f.write("{method};{count};{index};{time};{sum};{mean}".format(**format_vars) + "\n")
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# Do actions specified by command-line
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if args.list_models:
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def get_eps_trained(folder):
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with open(os.path.join(folder, 'episodes_trained'), 'r') as f:
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return int(f.read())
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model_folders = [ f.path
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for f
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in os.scandir(config['model_storage_path'])
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if f.is_dir() ]
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models = [ (folder, get_eps_trained(folder)) for folder in model_folders ]
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sys.stderr.write("Found {} model(s)\n".format(len(models)))
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for model in models:
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sys.stderr.write(" {name}: {eps_trained}\n".format(name = model[0], eps_trained = model[1]))
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exit()
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if __name__ == "__main__":
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# Set up network
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from network import Network
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# Set up variables
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episode_count = config['episode_count']
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if args.train:
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network = Network(config, config['model'])
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start_episode = network.episodes_trained
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while True:
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train_outcome, diff_in_values = network.train_model(episodes = episode_count, trained_eps = start_episode)
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start_episode += episode_count
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log_train_outcome(train_outcome, diff_in_values, trained_eps = start_episode)
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if config['eval_after_train']:
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eval_outcomes = network.eval(trained_eps = start_episode)
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log_eval_outcomes(eval_outcomes, trained_eps = start_episode)
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if not config['train_perpetually']:
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break
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elif args.eval:
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network = Network(config, config['model'])
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start_episode = network.episodes_trained
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# Evaluation measures are described in `config`
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outcomes = network.eval(config['episode_count'])
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log_eval_outcomes(outcomes, trained_eps = start_episode)
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# elif args.play:
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# g.play(episodes = episode_count)
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elif args.bench_eval_scores:
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# Make sure benchmark directory exists
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if not os.path.isdir(config['bench_storage_path']):
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os.mkdir(config['bench_storage_path'])
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config = config.copy()
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config['model'] = 'bench'
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network = Network(config, config['model'])
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start_episode = network.episodes_trained
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if start_episode == 0:
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print("Model not trained! Beware of using non-existing models!")
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exit()
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sample_count = 20
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episode_counts = [25, 50, 100, 250, 500, 1000, 2500, 5000,
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10000, 20000]
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def do_eval():
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for eval_method in config['eval_methods']:
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result_path = os.path.join(config['bench_storage_path'],
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eval_method) + "-{}.log".format(int(time.time()))
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for n in episode_counts:
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for i in range(sample_count):
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start_time = time.time()
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# Evaluation measure to be benchmarked are described in `config`
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outcomes = network.eval(episode_count = n)
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time_diff = time.time() - start_time
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log_bench_eval_outcomes(outcomes,
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time = time_diff,
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index = i,
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trained_eps = start_episode,
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log_path = result_path)
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# CMM: oh no
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import tensorflow as tf
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network.restore_model()
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do_eval()
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