import os import time import sys import numpy as np import tensorflow as tf import tensorflow.contrib.layers as layers import multi_gpu os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' def residual_block(input, is_training): """ one residual block :param input: a tensor, input of the residual block :param is_training: a placeholder, indicate whether the model is training or not :return: a tensor, output of the residual block """ normalizer_params = {'is_training': is_training, 'updates_collections': tf.GraphKeys.UPDATE_OPS} h = layers.conv2d(input, 256, kernel_size=3, stride=1, activation_fn=tf.nn.relu, normalizer_fn=layers.batch_norm, normalizer_params=normalizer_params, weights_regularizer=layers.l2_regularizer(1e-4)) h = layers.conv2d(h, 256, kernel_size=3, stride=1, activation_fn=tf.identity, normalizer_fn=layers.batch_norm, normalizer_params=normalizer_params, weights_regularizer=layers.l2_regularizer(1e-4)) h = h + input return tf.nn.relu(h) def policy_head(input, is_training, action_num): """ the head of policy branch :param input: a tensor, input of the policy head :param is_training: a placeholder, indicate whether the model is training or not :param action_num: action_num: an integer, number of unique actions at any state :return: a tensor: output of the policy head, shape [batch_size, action_num] """ normalizer_params = {'is_training': is_training, 'updates_collections': tf.GraphKeys.UPDATE_OPS} h = layers.conv2d(input, 2, kernel_size=1, stride=1, activation_fn=tf.nn.relu, normalizer_fn=layers.batch_norm, normalizer_params=normalizer_params, weights_regularizer=layers.l2_regularizer(1e-4)) h = layers.flatten(h) h = layers.fully_connected(h, action_num, activation_fn=tf.identity, weights_regularizer=layers.l2_regularizer(1e-4)) return h def value_head(input, is_training): """ the head of value branch :param input: a tensor, input of the value head :param is_training: a placeholder, indicate whether the model is training or not :return: a tensor, output of the value head, shape [batch_size, 1] """ normalizer_params = {'is_training': is_training, 'updates_collections': tf.GraphKeys.UPDATE_OPS} h = layers.conv2d(input, 2, kernel_size=1, stride=1, activation_fn=tf.nn.relu, normalizer_fn=layers.batch_norm, normalizer_params=normalizer_params, weights_regularizer=layers.l2_regularizer(1e-4)) h = layers.flatten(h) h = layers.fully_connected(h, 256, activation_fn=tf.nn.relu, weights_regularizer=layers.l2_regularizer(1e-4)) h = layers.fully_connected(h, 1, activation_fn=tf.nn.tanh, weights_regularizer=layers.l2_regularizer(1e-4)) return h class ResNet(object): def __init__(self, board_size, action_num, history_length=1, residual_block_num=20, checkpoint_path=None): """ the resnet model :param board_size: an integer, the board size :param action_num: an integer, number of unique actions at any state :param history_length: an integer, the history length to use, default is 1 :param residual_block_num: an integer, the number of residual block, default is 20, at least 1 :param checkpoint_path: a string, the path to the checkpoint, default is None, """ self.board_size = board_size self.action_num = action_num self.history_length = history_length self.x = tf.placeholder(tf.float32, shape=[None, self.board_size, self.board_size, 2 * self.history_length + 1]) self.is_training = tf.placeholder(tf.bool, shape=[]) self.z = tf.placeholder(tf.float32, shape=[None, 1]) self.pi = tf.placeholder(tf.float32, shape=[None, self.action_num]) self._build_network(residual_block_num, checkpoint_path) def _build_network(self, residual_block_num, checkpoint_path): """ build the network :param residual_block_num: an integer, the number of residual block :param checkpoint_path: a string, the path to the checkpoint, if None, use random initialization parameter :return: None """ h = layers.conv2d(self.x, 256, kernel_size=3, stride=1, activation_fn=tf.nn.relu, normalizer_fn=layers.batch_norm, normalizer_params={'is_training': self.is_training, 'updates_collections': tf.GraphKeys.UPDATE_OPS}, weights_regularizer=layers.l2_regularizer(1e-4)) for i in range(residual_block_num - 1): h = residual_block(h, self.is_training) self.v = value_head(h, self.is_training) self.p = policy_head(h, self.is_training, self.action_num) self.value_loss = tf.reduce_mean(tf.square(self.z - self.v)) self.policy_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=self.pi, logits=self.p)) self.reg = tf.add_n(tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)) self.total_loss = self.value_loss + self.policy_loss + self.reg self.update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(self.update_ops): self.train_op = tf.train.AdamOptimizer(1e-4).minimize(self.total_loss) self.var_list = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES) self.saver = tf.train.Saver(max_to_keep=10, var_list=self.var_list) self.sess = multi_gpu.create_session() self.sess.run(tf.global_variables_initializer()) if checkpoint_path is not None: ckpt_file = tf.train.latest_checkpoint(checkpoint_path) if ckpt_file is not None: print('Restoring model from {}...'.format(ckpt_file)) self.saver.restore(self.sess, ckpt_file) print('Successfully loaded') else: raise ValueError("No model in path {}".format(checkpoint_path)) def __call__(self, state): """ :param history: a list, the history :param color: a string, indicate which one to play :return: a list of tensor, the predicted value and policy given the history and color """ history, color = state if len(history) != self.history_length: raise ValueError( 'The length of history cannot meet the need of the model, given {}, need {}'.format(len(history), self.history_length)) state = self._history2state(history, color) return self.sess.run([self.p, self.v], feed_dict={self.x: state, self.is_training: False}) def _history2state(self, history, color): """ convert the history to the state we need :param history: a list, the history :param color: a string, indicate which one to play :return: a ndarray, the state """ state = np.zeros([1, self.board_size, self.board_size, 2 * self.history_length + 1]) for i in range(self.history_length): state[0, :, :, i] = np.array(np.array(history[i]) == np.ones(self.board_size ** 2)).reshape(self.board_size, self.board_size) state[0, :, :, i + self.history_length] = np.array( np.array(history[i]) == -np.ones(self.board_size ** 2)).reshape(self.board_size, self.board_size) # TODO: need a config to specify the BLACK and WHITE if color == +1: state[0, :, :, 2 * self.history_length] = np.ones([self.board_size, self.board_size]) if color == -1: state[0, :, :, 2 * self.history_length] = np.zeros([self.board_size, self.board_size]) return state #TODO: design the interface between the environment and training def train(self, mode='memory', *args, **kwargs): pass