def main(cmd):
cfg = yaml.safe_load(cmd.cfg.read())
print(yaml.dump(cfg, default_flow_style=False))
root_dir = os.path.join(cfg['training']['path'], cfg['name'])
if not os.path.exists(root_dir):
os.makedirs(root_dir)
x = [
tf.placeholder(tf.float32, [None, 112, 8 * 8]),
tf.placeholder(tf.float32, [None, 1858]),
tf.placeholder(tf.float32, [None, 3]),
tf.placeholder(tf.float32, [None, 3]),
]
tfprocess = TFProcess(cfg)
tfprocess.init_net(x)
if os.path.exists(os.path.join(root_dir, 'checkpoint')):
cp = tf.train.latest_checkpoint(root_dir)
tfprocess.restore(cp)
START_FROM = cmd.start
update_global_step = tfprocess.global_step.assign(START_FROM)
tfprocess.session.run(update_global_step)
path = os.path.join(root_dir, cfg['name'])
save_path = tfprocess.saver.save(tfprocess.session,
path,
global_step=START_FROM)
tfprocess.session.close()
def main(cmd):
cfg = yaml.safe_load(cmd.cfg.read())
print(yaml.dump(cfg, default_flow_style=False))
root_dir = os.path.join(cfg['training']['path'], cfg['name'])
if not os.path.exists(root_dir):
os.makedirs(root_dir)
x = [
tf.placeholder(tf.float32, [None, 112, 8*8]),
tf.placeholder(tf.float32, [None, 1858]),
tf.placeholder(tf.float32, [None, 3]),
tf.placeholder(tf.float32, [None, 3]),
]
tfprocess = TFProcess(cfg)
tfprocess.init_net(x)
if os.path.exists(os.path.join(root_dir, 'checkpoint')):
cp = tf.train.latest_checkpoint(root_dir)
reader = tf.train.NewCheckpointReader(cp)
saved_shapes = reader.get_variable_to_shape_map()
new_names = sorted(
[var.name.split(':')[0] for var in tf.global_variables()
if var.name.split(':')[0] not in saved_shapes])
for saved_var_name in new_names:
print("New name {} will use default value".format(saved_var_name))
var_names = sorted(
[(var.name, var.name.split(':')[0]) for var in tf.global_variables()
if var.name.split(':')[0] in saved_shapes])
restore_vars = []
restore_names = []
for var_name, saved_var_name in var_names:
curr_var = tf.get_default_graph().get_tensor_by_name(var_name)
var_shape = curr_var.get_shape().as_list()
if var_shape == saved_shapes[saved_var_name]:
restore_vars.append(curr_var)
restore_names.append(saved_var_name)
else:
print("Dropping {} due to shape change".format(saved_var_name))
legacy_names = sorted(
[name for name in saved_shapes.keys()
if name not in restore_names])
for saved_var_name in legacy_names:
print("Dropping {} as no longer used".format(saved_var_name))
opt_saver = tf.train.Saver(restore_vars)
opt_saver.restore(tfprocess.session, cp)
else:
print("No checkpoint to upgrade!")
exit(1)
steps = tf.train.global_step(tfprocess.session, tfprocess.global_step)
path = os.path.join(root_dir, cfg['name'])
save_path = tfprocess.saver.save(tfprocess.session, path, global_step=steps)
tfprocess.session.close()
def main(cmd):
cfg = yaml.safe_load(cmd.cfg.read())
print(yaml.dump(cfg, default_flow_style=False))
root_dir = os.path.join(cfg['training']['path'], cfg['name'])
if not os.path.exists(root_dir):
os.makedirs(root_dir)
tfprocess = TFProcess(cfg)
tfprocess.init_net()
tfprocess.restore()
START_FROM = cmd.start
tfprocess.global_step.assign(START_FROM)
tfprocess.manager.save(checkpoint_number=START_FROM)
name='x')
probs = tf.placeholder(tf.float32, [None, BOARD_SIZE * BOARD_SIZE + 1])
winner = tf.placeholder(tf.float32, [None, 1])
else:
planes = tf.placeholder(tf.float32,
[None, FEATURES, BOARD_SIZE, BOARD_SIZE],
name='x')
probs = tf.placeholder(tf.float32, [None, BOARD_SIZE * BOARD_SIZE + 1])
winner = tf.placeholder(tf.float32, [None, 1])
tfprocess = TFProcess()
tfprocess.TFCOREML = True
tfprocess.DATA_FORMAT = data_format
tfprocess.BOARD_SIZE = BOARD_SIZE
tfprocess.INPUT_DIM = 2
tfprocess.FEATURES = FEATURES
tfprocess.RESIDUAL_FILTERS = filters
tfprocess.RESIDUAL_BLOCKS = blocks
if BOARD_SIZE == 9:
tfprocess.VALUE_FULLY_CONNECTED = 64
tfprocess.training = False # batch normalizationをコンバートするため
tfprocess.init_net(planes, probs, winner)
tfprocess.replace_weights(weights)
tf.train.write_graph(tf.get_default_graph(),
os.path.dirname(sys.argv[3]),
os.path.basename(sys.argv[3]),
as_text=True)
with tf.get_default_graph().as_default():
saver = tf.train.Saver()
print(saver.save(tfprocess.session, "./tmp/model.ckpt"))
print("Version", line.strip())
if line != '1\n':
raise ValueError("Unknown version {}".format(line.strip()))
else:
weights.append(list(map(float, line.split(' '))))
if e == 2:
filters = len(line.split(' '))
print("Channels", filters)
blocks = e - (4 + 14)
if blocks % 8 != 0:
raise ValueError("Inconsistent number of weights in the file")
blocks //= 8
print("Blocks", blocks)
cfg['model']['filters'] = filters
cfg['model']['residual_blocks'] = blocks
print(yaml.dump(cfg, default_flow_style=False))
x = [
tf.placeholder(tf.float32, [None, 120, 8 * 8]),
tf.placeholder(tf.float32, [None, 1924]),
tf.placeholder(tf.float32, [None, 1])
]
tfprocess = TFProcess(cfg)
tfprocess.init_net(x)
tfprocess.replace_weights(weights)
path = os.path.join(os.getcwd(), cfg['name'])
save_path = tfprocess.saver.save(tfprocess.session, path, global_step=0)
print("Writted model to {}".format(path))
