import mcts, uuid, os
import json
__all__ = ['store', 'load']
SIZE = mcts.board_size()
KOMI = 7.5
# Format for games:
# games = [game]
# game = (moves, <score>)
# moves = ['B'|'W', <move>, search_count]
# search_count = [int]
def store(filename, games):
tmp_fname = '{}.{}'.format(filename, str(uuid.uuid4()))
f = open(tmp_fname, 'w')
for moves, score in games:
f.write('Game: {} {} {} {}\n'.format(SIZE, KOMI, len(moves), score))
for m in moves:
f.write('{} {} |'.format(m[0], m[1]))
assert len(m[2]) == SIZE * SIZE + 1, '{} != {}'.format(
m[2], SIZE * SIZE + 1)
for count in m[2]:
f.write(' {}'.format(count))
f.write('\n')
f.close()
os.rename(tmp_fname, filename)
#!/usr/bin/env python3
import random, sys, glob
import numpy as np
import mcts
import tensorflow as tf
__all__ = ['Network']
size = mcts.board_size()
def keras_residual_block(input_layer):
v = tf.keras.layers.ZeroPadding2D(
padding=1, data_format='channels_first')(input_layer)
v = tf.keras.layers.Conv2D(
128,
3,
data_format='channels_first',
kernel_regularizer=tf.keras.regularizers.l2(1.e-4))(v)
v = tf.keras.layers.LeakyReLU(0.01)(v)
v = tf.keras.layers.ZeroPadding2D(padding=1,
data_format='channels_first')(v)
v = tf.keras.layers.Conv2D(
128,
3,
data_format='channels_first',
kernel_regularizer=tf.keras.regularizers.l2(1.e-4))(v)
merged = tf.keras.layers.Add()([input_layer, v])
return tf.keras.layers.LeakyReLU(0.01)(merged)