def parse_sgf_to_examples(sgf_path):
"""Return supervised examples from positions
NOTE: last move is not played because no p.next_move after.
"""
return zip(*[(p.position, p.next_move, p.result)
for p in sgf_wrapper.replay_sgf_file(sgf_path)])
def make_dataset_from_sgf(board_size, sgf_filename, tf_record):
pwcs = sgf_wrapper.replay_sgf_file(board_size, sgf_filename)
def make_tf_example_from_pwc(pwcs):
return _make_tf_example_from_pwc(board_size, pwcs)
tf_examples = map(make_tf_example_from_pwc, pwcs)
write_tf_examples(tf_record, tf_examples)
def main(argv):
network = dual_net.DualNetwork('minigo-models/models/000737-fury') # add path to model
board = np.zeros([N, N], dtype=np.int8)
pos_w_con = list(replay_sgf_file('go_puzzles/10458/10494.sgf')) # Loading a puzzle from go_puzzles folder
board += pos_w_con[0].position.board # Setting up the board
# Let's add new pieces from another puzzle
pos_w_con = list(replay_sgf_file('go_puzzles/14511/14515.sgf'))
board += pos_w_con[0].position.board
# Load the board position
pos = Position(board = board)
print(pos)
# Generate saliency maps, see results folder
play_network(network, board)
def main(argv):
features, labels = dual_net.get_inference_input()
tf_tensors = dual_net.model_inference_fn(features, False)
if len(tf_tensors) != 4:
print("oneoffs/embeddings.py requires you modify")
print("dual_net.model_inference_fn and add a fourth param")
sys.exit(1)
p_out, v_out, logits, shared = tf_tensors
predictions = {'shared': shared}
sess = tf.Session()
tf.train.Saver().restore(sess, FLAGS.model)
try:
progress = tqdm(get_files())
embeddings = []
metadata = []
for i, f in enumerate(progress):
short_f = os.path.basename(f)
short_f = short_f.replace('minigo-cc-evaluator', '')
short_f = short_f.replace('-000', '-')
progress.set_description('Processing %s' % short_f)
processed = []
for idx, p in enumerate(sgf_wrapper.replay_sgf_file(f)):
if idx < FLAGS.first: continue
if idx > FLAGS.last: break
if idx % FLAGS.every != 0: continue
processed.append(features_lib.extract_features(p.position))
metadata.append((f, idx))
if len(processed) > 0:
# If len(processed) gets too large may have to chunk.
res = sess.run(predictions, feed_dict={features: processed})
for r in res['shared']:
embeddings.append(r.flatten())
except:
# Raise shows us the error but only after the finally block executes.
raise
finally:
with open(FLAGS.embedding_file, 'wb') as pickle_file:
pickle.dump([metadata, np.array(embeddings)], pickle_file)
def make_dataset_from_sgf(sgf_filename, tf_record):
pwcs = sgf_wrapper.replay_sgf_file(sgf_filename)
tf_examples = map(_make_tf_example_from_pwc, pwcs)
write_tf_examples(tf_record, tf_examples)
def final_position_sgf(sgf_path):
for pwc in sgf_wrapper.replay_sgf_file(sgf_path):
pass
return pwc.position.play_move(pwc.next_move)
def main(argv):
network = dual_net.DualNetwork(
'minigo-models/models/000737-fury') # add path to model
board = np.zeros([N, N], dtype=np.int8)
# pos_w_con = list(replay_sgf_file('go_puzzles/14511/14511.sgf'))
# pos_w_con = list(replay_sgf_file('go_puzzles/10/10.sgf'))
# board += pos_w_con[0].position.board
# pos_w_con = list(replay_sgf_file('go_puzzles/9225/9225.sgf'))
# board += pos_w_con[0].position.board
# pos_w_con = list(replay_sgf_file('go_puzzles/14571/14587.sgf'))
# board += pos_w_con[0].position.board
# pos_w_con = list(replay_sgf_file('go_puzzles/14054/14064.sgf'))
# board += pos_w_con[0].position.board
# pos_w_con = list(replay_sgf_file('go_puzzles/10458/7592.sgf'))
# board += pos_w_con[0].position.board
# pos_w_con = list(replay_sgf_file('go_puzzles/10458/10458.sgf'))
# board += pos_w_con[0].position.board
# pos_w_con = list(replay_sgf_file('go_puzzles/10458/10495.sgf'))
# board += pos_w_con[0].position.board
pos_w_con = list(replay_sgf_file('go_puzzles/10458/10494.sgf'))
board += pos_w_con[0].position.board
# pos_w_con = list(replay_sgf_file('go_puzzles/10458/7593.sgf'))
# board += pos_w_con[0].position.board
pos_w_con = list(replay_sgf_file('go_puzzles/14511/14515.sgf'))
board += pos_w_con[0].position.board
# pos_w_con = list(replay_sgf_file('go_puzzles/10458/7589.sgf'))
# board += pos_w_con[0].position.board
# for i in pos_w_con:
# print(i.position)
# board[5, 7] = -1
# board[6][7] = -1
# board[8][4:6] = -1
# board[3][8] = -1
# board[5][3] = -1
# board[[11,12,13],:] = 0
pos = Position(board=board)
# board = board + pos_w_con[0].position.board
# print(pos)
# board[0][3] = -1
# board[0][4] = 1
# board[1][1] = -1
# board[1][3] = -1
# board[1][4] = 1
# board[2][0] = -1
# board[2, 2] = -1
# board[2,3:5] = 1
# board[3, 0:2] = -1
# board[3, [2, 4]] = 1
# board[4, 0] = -1
# board[4, [1, 3]] = 1
# board[5, :3] = 1
# snap back
# board = np.zeros([19, 19], dtype=np.int8)
# board[0, 2] = 1
# board[0, [5,6]] = -1
# board[1][[1,5]] = 1
# board[1][[2,3,4,6]] = -1
# board[2][[0, 2,3,4,5]] = 1
# board[[2,3], 6] = -1
# Noise
# board[2,-2] = 1
# # board[4, 11] = -1
# board[5, 15] = 1
# board[8, 15] = -1
# board[10, -1] = 1
# # board[12, 10] = -1
# # board[12, 13] = 1
# board[17, 16] = -1
# board[abs(board)==1] *= -1 # to invert the board colors
pos = Position(board=board)
print(pos)
# simulate(network, board, steps=10)
play_network(network, board)
def make_dataset_from_sgf(sgf_filename, tf_record):
pwcs = sgf_wrapper.replay_sgf_file(sgf_filename)
tf_examples = map(_make_tf_example_from_pwc, pwcs)
write_tf_examples(tf_record, tf_examples)
