import os import time import sys import numpy as np import time import tensorflow as tf import tensorflow.contrib.layers as layers import multi_gpu import time # os.environ["CUDA_VISIBLE_DEVICES"] = "1" os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' def residual_block(input, is_training): 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_heads(input, is_training): 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, 362, activation_fn=tf.identity, weights_regularizer=layers.l2_regularizer(1e-4)) return h def value_heads(input, is_training): 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 Network(object): def __init__(self): self.x = tf.placeholder(tf.float32, shape=[None, 19, 19, 17]) 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, 362]) self.build_network() def build_network(self): 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(19): h = residual_block(h, self.is_training) self.v = value_heads(h, self.is_training) self.p = policy_heads(h, self.is_training) # loss = tf.reduce_mean(tf.square(z-v)) - tf.multiply(pi, tf.log(tf.clip_by_value(tf.nn.softmax(p), 1e-8, tf.reduce_max(tf.nn.softmax(p))))) 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 # train_op = tf.train.MomentumOptimizer(1e-4, momentum=0.9, use_nesterov=True).minimize(total_loss) self.update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(self.update_ops): self.train_op = tf.train.RMSPropOptimizer(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) def train(self): data_path = "/home/tongzheng/data/" data_name = os.listdir("/home/tongzheng/data/") epochs = 100 batch_size = 128 result_path = "./checkpoints/" with multi_gpu.create_session() as sess: sess.run(tf.global_variables_initializer()) ckpt_file = tf.train.latest_checkpoint(result_path) if ckpt_file is not None: print('Restoring model from {}...'.format(ckpt_file)) self.saver.restore(sess, ckpt_file) for epoch in range(epochs): for name in data_name: data = np.load(data_path + name) boards = data["boards"] wins = data["wins"] ps = data["ps"] print (boards.shape) print (wins.shape) print (ps.shape) batch_num = boards.shape[0] // batch_size index = np.arange(boards.shape[0]) np.random.shuffle(index) value_losses = [] policy_losses = [] regs = [] time_train = -time.time() for iter in range(batch_num): lv, lp, r, value, prob, _ = sess.run( [self.value_loss, self.policy_loss, self.reg, self.v, tf.nn.softmax(p), self.train_op], feed_dict={self.x: boards[ index[iter * batch_size:(iter + 1) * batch_size]], self.z: wins[index[ iter * batch_size:(iter + 1) * batch_size]], self.pi: ps[index[ iter * batch_size:(iter + 1) * batch_size]], self.is_training: True}) value_losses.append(lv) policy_losses.append(lp) regs.append(r) if iter % 1 == 0: print( "Epoch: {}, Part {}, Iteration: {}, Time: {}, Value Loss: {}, Policy Loss: {}, Reg: {}".format( epoch, name, iter, time.time() + time_train, np.mean(np.array(value_losses)), np.mean(np.array(policy_losses)), np.mean(np.array(regs)))) time_train = -time.time() value_losses = [] policy_losses = [] regs = [] if iter % 20 == 0: save_path = "Epoch{}.Part{}.Iteration{}.ckpt".format(epoch, name, iter) self.saver.save(sess, result_path + save_path) del data, boards, wins, ps # def forward(call_number): # # checkpoint_path = "/home/yama/rl/tianshou/AlphaGo/checkpoints" # checkpoint_path = "/home/jialian/stuGo/tianshou/stuGo/checkpoints/" # board_file = np.genfromtxt("/home/jialian/stuGo/tianshou/leela-zero/src/mcts_nn_files/board_" + call_number, # dtype='str'); # human_board = np.zeros((17, 19, 19)) # # # TODO : is it ok to ignore the last channel? # for i in range(17): # human_board[i] = np.array(list(board_file[i])).reshape(19, 19) # # print("============================") # # print("human board sum : " + str(np.sum(human_board[-1]))) # # print("============================") # # print(human_board) # # print("============================") # # rint(human_board) # feed_board = human_board.transpose(1, 2, 0).reshape(1, 19, 19, 17) # # print(feed_board[:,:,:,-1]) # # print(feed_board.shape) # # # npz_board = np.load("/home/yama/rl/tianshou/AlphaGo/data/7f83928932f64a79bc1efdea268698ae.npz") # # print(npz_board["boards"].shape) # # feed_board = npz_board["boards"][10].reshape(-1, 19, 19, 17) # ##print(feed_board) # # show_board = feed_board[0].transpose(2, 0, 1) # # print("board shape : ", show_board.shape) # # print(show_board) # # itflag = False # with multi_gpu.create_session() as sess: # sess.run(tf.global_variables_initializer()) # ckpt_file = tf.train.latest_checkpoint(checkpoint_path) # if ckpt_file is not None: # # print('Restoring model from {}...'.format(ckpt_file)) # saver.restore(sess, ckpt_file) # else: # raise ValueError("No model loaded") # res = sess.run([tf.nn.softmax(p), v], feed_dict={x: feed_board, is_training: itflag}) # # res = sess.run([tf.nn.softmax(p),v], feed_dict={x:fix_board["boards"][300].reshape(-1, 19, 19, 17), is_training:False}) # # res = sess.run([tf.nn.softmax(p),v], feed_dict={x:fix_board["boards"][50].reshape(-1, 19, 19, 17), is_training:True}) # # print(np.argmax(res[0])) # np.savetxt(sys.stdout, res[0][0], fmt="%.6f", newline=" ") # np.savetxt(sys.stdout, res[1][0], fmt="%.6f", newline=" ") # pv_file = "/home/jialian/stuGotianshou/leela-zero/src/mcts_nn_files/policy_value" # np.savetxt(pv_file, np.concatenate((res[0][0], res[1][0])), fmt="%.6f", newline=" ") # # np.savetxt(pv_file, res[1][0], fmt="%.6f", newline=" ") # return res def forward(self): checkpoint_path = "/home/tongzheng/tianshou/AlphaGo/checkpoints/" sess = multi_gpu.create_session() sess.run(tf.global_variables_initializer()) ckpt_file = tf.train.latest_checkpoint(checkpoint_path) if ckpt_file is not None: print('Restoring model from {}...'.format(ckpt_file)) self.saver.restore(sess, ckpt_file) print('Successfully loaded') else: raise ValueError("No model loaded") # prior, value = sess.run([tf.nn.softmax(p), v], feed_dict={x: state, is_training: False}) # return prior, value return sess if __name__ == '__main__': state = np.random.randint(0, 1, [1, 19, 19, 17]) net = Network() sess = net.forward() start = time.time() for i in range(100): sess.run([tf.nn.softmax(net.p), net.v], feed_dict={net.x: state, net.is_training: False}) print("Step {}, Cumulative time {}".format(i, time.time() - start))