def load_model(self):
self.data_node = tf.placeholder(tf.float32, shape=(1, INPUT_WIDTH, INPUT_WIDTH, INPUT_DEPTH))
self.sess=tf.Session()
if self.is_value_net :
from valuenet import ValueNet
self.vnet=ValueNet()
self.keep_prob_node=tf.placeholder(tf.float32)
self.value=self.vnet.model(self.data_node, keep_prob_node=self.keep_prob_node)
self.position_values=np.ndarray(dtype=np.float32, shape=(BOARD_SIZE**2,))
else:
self.net = SLNetwork()
self.net.declare_layers(num_hidden_layer=5)
self.logit=self.net.model(self.data_node)
saver = tf.train.Saver()
saver.restore(self.sess, self.model_path)
class NNAgent(object):
def __init__(self, model_location, name, is_value_net=False):
self.model_path=model_location
self.agent_name=name
self.is_value_net=is_value_net
self.initialize_game()
def initialize_game(self):
self.game_state=[]
self.boardtensor=np.zeros(dtype=np.float32, shape=(1, INPUT_WIDTH, INPUT_WIDTH, INPUT_DEPTH))
make_empty_board_tensor(self.boardtensor)
self.load_model()
def load_model(self):
self.data_node = tf.placeholder(tf.float32, shape=(1, INPUT_WIDTH, INPUT_WIDTH, INPUT_DEPTH))
self.sess=tf.Session()
if self.is_value_net :
from valuenet import ValueNet
self.vnet=ValueNet()
self.keep_prob_node=tf.placeholder(tf.float32)
self.value=self.vnet.model(self.data_node, keep_prob_node=self.keep_prob_node)
self.position_values=np.ndarray(dtype=np.float32, shape=(BOARD_SIZE**2,))
else:
self.net = SLNetwork()
self.net.declare_layers(num_hidden_layer=5)
self.logit=self.net.model(self.data_node)
saver = tf.train.Saver()
saver.restore(self.sess, self.model_path)
def reinitialize(self):
self.game_state = []
make_empty_board_tensor(self.boardtensor)
#0-black player, 1-white player
def play_move(self, intplayer, intmove):
update_tensor(self.boardtensor, intplayer, intmove)
self.game_state.append(intmove)
def generate_move(self, intplayer=None):
if self.is_value_net :
s=list(self.game_state)
empty_positions=[i for i in range(BOARD_SIZE**2) if i not in s]
self.position_values.fill(0.0)
for intmove in empty_positions:
update_tensor(self.boardtensor, intplayer, intmove)
v=self.sess.run(self.value, feed_dict={self.data_node:self.boardtensor})
undo_update_tensor(self.boardtensor,intplayer, intmove)
self.position_values[intmove]=v
im=softmax_selection(self.position_values, self.game_state, temperature=0.1)
#im=max_selection(self.position_values, self.game_state)
return im
else:
logits=self.sess.run(self.logit, feed_dict={self.data_node:self.boardtensor})
intmove=softmax_selection(logits, self.game_state)
#intmove=max_selection(logits, self.game_state)
raw_move=intmove_to_raw(intmove)
assert(ord('a') <= ord(raw_move[0]) <= ord('z') and 0<= int(raw_move[1:]) <BOARD_SIZE**2)
return raw_move
def close_all(self):
self.sess.close()
