igual aessa questão Eu estava executando um algoritmo de aprendizado por reforço assíncrono e preciso executar a previsão do modelo em vários threads para obter dados de treinamento mais rapidamente. Meu código é baseado emDDPG-keras no GitHub, cuja rede neural foi construída sobre o Keras & Tensorflow. Partes do meu código são mostradas abaixo:

Criação de thread assíncrona e junção:

for roundNo in xrange(self.param['max_round']):
    AgentPool = [AgentThread(self.getEnv(), self.actor, self.critic, eps, self.param['n_step'], self.param['gamma'])]
    for agent in AgentPool:
        agent.start()
    for agent in AgentPool:
        agent.join()

Código do Encadeamento do Agente

"""Agent Thread for collecting data"""
def __init__(self, env_, actor_, critic_, eps_, n_step_, gamma_):
    super(AgentThread, self).__init__()
    self.env = env_         # type: Environment
    self.actor = actor_     # type: ActorNetwork
    # TODO: use Q(s,a)
    self.critic = critic_   # type: CriticNetwork
    self.eps = eps_         # type: float
    self.n_step = n_step_   # type: int
    self.gamma = gamma_
    self.data = {}

def run(self):
    """run behavior policy self.actor to collect experience data in self.data"""
    state = self.env.get_state()
    action = self.actor.model.predict(state[np.newaxis, :])[0]
    action = np.maximum(np.random.normal(action, self.eps, action.shape), np.ones_like(action) * 1e-3)

Ao executar esses códigos, encontrei uma exceção do Tensorflow:

Using TensorFlow backend.
create_actor_network
Exception in thread Thread-1:
Traceback (most recent call last):
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/threading.py", line 801, in __bootstrap_inner
    self.run()
  File "/Users/niyan/code/routerRL/A3C.py", line 26, in run
    action = self.actor.model.predict(state[np.newaxis, :])[0]
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/keras/engine/training.py", line 1269, in predict
    self._make_predict_function()
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/keras/engine/training.py", line 798, in _make_predict_function
    **kwargs)
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py", line 1961, in function
    return Function(inputs, outputs, updates=updates)
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py", line 1919, in __init__
    with tf.control_dependencies(self.outputs):
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3583, in control_dependencies
    return get_default_graph().control_dependencies(control_inputs)
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 3314, in control_dependencies
    c = self.as_graph_element(c)
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2405, in as_graph_element
    return self._as_graph_element_locked(obj, allow_tensor, allow_operation)
  File "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2484, in _as_graph_element_locked
    raise ValueError("Tensor %s is not an element of this graph." % obj)
ValueError: Tensor Tensor("concat:0", shape=(?, 4), dtype=float32) is not an element of this graph.

Então, como posso usar um modelo Keras treinado (usando o Tensorflow como back-end) para prever simultaneamente em vários threads?

Atualização em 2 de abril: tentei lidar com o modelo por causa do peso, mas não funcionou:

for roundNo in xrange(self.param['max_round']):
    for agent in self.AgentPool:
        agent.syncModel(self.getEnv(), self.actor, self.critic, eps)
        agent.start()
    for agent in self.AgentPool:
        agent.join()

def syncModel(self, env_, actor_, critic_, eps_):
    """synchronize A-C models before collecting data"""
    # TODO copy env, actor, critic
    self.env = env_     # shallow copy
    self.actor.model.set_weights(actor_.model.get_weights())        # deep copy, by weights
    self.critic.model.set_weights(critic_.model.get_weights())      # deep copy, by weights
    self.eps = eps_     # shallow copy
    self.data = {}

EDIT: veja issojaara / blog-AI no Github, parece

model._make_predict_function()  # have to initialize before threading

trabalho.

O autor explicou um pouco sobreesse problema. Para uma discussão mais aprofundada, consulteesse problema no Keras