我花了数周的时间试图编写可直接与TPU通信的Python级Tensorflow代码。在没有Estimator API的情况下，如何实现可以在TPU上运行的系统？
我尝试过的资源：
我尝试的方法：
初始化TPUClusterResolver并将其作为tf.Session（）的参数传递，它只是挂起而不执行session.run（）
还尝试了sess.run（tpu.initialize_system（）），并且卡住了
试图在那里查看TPUEstimator API
def train_model(self, env, episodes=100,
load_model = False, # load model from checkpoint if available:?
model_dir = '/tmp/pgmodel/', log_freq=10 ) :
# initialize variables and load model
init_op = tf.global_variables_initializer()
self._sess.run(init_op)
if load_model:
ckpt = tf.train.get_checkpoint_state(model_dir)
print tf.train.latest_checkpoint(model_dir)
if ckpt and ckpt.model_checkpoint_path:
savr = tf.train.import_meta_graph(ckpt.model_checkpoint_path+'.meta')
out = savr.restore(self._sess, ckpt.model_checkpoint_path)
print("Model restored from ",ckpt.model_checkpoint_path)
else:
print('No checkpoint found at: ',model_dir)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
episode = 0
observation = env.reset()
xs,rs,ys = [],[],[] # environment info
running_reward = 0
reward_sum = 0
# training loop
day = 0
simrors = np.zeros(episodes)
mktrors = np.zeros(episodes)
alldf = None
victory = False
while episode < episodes and not victory:
# stochastically sample a policy from the network
x = observation
feed = {self._tf_x: np.reshape(x, (1,-1))}
aprob = self._sess.run(self._tf_aprob,feed)
aprob = aprob[0,:] # we live in a batched world :/
action = np.random.choice(self._num_actions, p=aprob)
label = np.zeros_like(aprob) ; label[action] = 1 # make a training 'label'
# step the environment and get new measurements
observation, reward, done, info = env.step(action)
#print observation, reward, done, info
reward_sum += reward
# record game history
xs.append(x)
ys.append(label)
rs.append(reward)
day += 1
if done:
running_reward = running_reward * 0.99 + reward_sum * 0.01
epx = np.vstack(xs)
epr = np.vstack(rs)
epy = np.vstack(ys)
xs,rs,ys = [],[],[] # reset game history
df = env.env.sim.to_df()
#pdb.set_trace()
simrors[episode]=df.bod_nav.values[-1]-1 # compound returns
mktrors[episode]=df.mkt_nav.values[-1]-1
alldf = df if alldf is None else pd.concat([alldf,df], axis=0)
feed = {self._tf_x: epx, self._tf_epr: epr, self._tf_y: epy}
_ = self._sess.run(self._train_op,feed) # parameter update
if episode % log_freq == 0:
log.info('year #%6d, mean reward: %8.4f, sim ret: %8.4f, mkt ret: %8.4f, net: %8.4f', episode,
running_reward, simrors[episode],mktrors[episode], simrors[episode]-mktrors[episode])
save_path = self._saver.save(self._sess, model_dir+'model.ckpt',
global_step=episode+1)
if episode > 100:
vict = pd.DataFrame( { 'sim': simrors[episode-100:episode],
'mkt': mktrors[episode-100:episode] } )
vict['net'] = vict.sim - vict.mkt
if vict.net.mean() > 0.0:
victory = True
log.info('Congratulations, Warren Buffet! You won the trading game.')
#print("Model saved in file: {}".format(save_path))
episode += 1
observation = env.reset()
reward_sum = 0
day = 0
return alldf, pd.DataFrame({'simror':simrors,'mktror':mktrors})
Estimator API实现存在的问题：