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ValueError: Could not find trained model in model_dir: ./output/sa_nn1.
Hi, thanks for your amazing work. When I tried to run the main.py, it occured this error. How can I resolve it please?Below are the traceback:
Traceback (most recent call last): File "main_with_actor_in_it.py", line 162, in <module> main() File "main_with_actor_in_it.py", line 97, in main actions = env.act(state) File "/home/my/.conda/envs/tensorflow1.8/lib/python3.6/site-packages/pommerman/envs/v0.py", line 137, in act return self.model.act(agents, obs, self.action_space) File "/home/my/.conda/envs/tensorflow1.8/lib/python3.6/site-packages/pommerman/forward_model.py", line 122, in act ret.append(act_ex_communication(agent)) File "/home/my/.conda/envs/tensorflow1.8/lib/python3.6/site-packages/pommerman/forward_model.py", line 101, in act_ex_communication return agent.act(obs[agent.agent_id], action_space=action_space) File "/home/my/playground/magnet/models/ddpg_agent.py", line 98, in act input_to_ddpg = self.__input_to_ddpg__(prev_state, curr_state) File "/home/my/playground/magnet/models/ddpg_agent.py", line 83, in __input_to_ddpg__ graph_predictions = np.asmatrix(list(itertools.islice(y_generator, prev_state.shape[0]))[0]['graph']) File "/home/my/.conda/envs/tensorflow1.8/lib/python3.6/site-packages/tensorflow/python/estimator/estimator.py", line 488, in predict self._model_dir)) ValueError: Could not find trained model in model_dir: ./output/sa_nn1.
Thank you for having interest in this repo. The code does not have the referred error messages but the deprecated messages have shown. Please check the updated code and try to run via python main.py.
Thank you for having interest in this repo. The code does not have the referred error messages but the deprecated messages have shown. Please check the updated code and try to run via
python main.py.
Thank you. It runs well until it restore parameters from ./output/sa_nn1/model.ckpt-3:
It went well when restoring parameters from ./output/sa_nn1/model.ckpt-2 and model.ckpt-1:
Why this happened?
@mamengyiyi I also encountered this issue and what I do is to modify the code of ddpg_agent.py like below, def act(self, obs, action_space): action = action_space.sample()
self.prev_state = self.curr_state
if self.pr_action is not None:
self.curr_state = state_to_matrix_with_action(obs, action=self.pr_action)
if self.prev_state is not None:
curr_state = self.curr_state
prev_state = self.prev_state
input_to_ddpg = self.__input_to_ddpg__(prev_state, curr_state)
curr_state_matrix = self.curr_state
prev_state_matrix = self.prev_state
#modify start pred_input_NN1 = tf.estimator.inputs.numpy_input_fn( x={"state1": prev_state_matrix.astype("float32"), "state2": curr_state_matrix.astype("float32"), "y": np.asmatrix(self.graph.flatten())}, y=np.asmatrix(self.graph.flatten()), batch_size=1, num_epochs=None, shuffle=False) #modify end # Predict the estimator y_generator = self.estimator_nn1.predict(input_fn=pred_input_NN1)
graph_predictions = np.asmatrix(list(itertools.islice(y_generator, prev_state_matrix.shape[0]))[0])
input_to_ddpg = np.concatenate([self.curr_state, graph_predictions], axis=1)
print(input_to_ddpg.shape)
# action = self.actor.predict(np.expand_dims(input_to_ddpg, 0))[0, 0]
self.pr_action = action
return action
@mamengyiyi I also encountered this issue and what I do is to modify the code of ddpg_agent.py like below, def act(self, obs, action_space): action = action_space.sample()
self.prev_state = self.curr_state if self.pr_action is not None: self.curr_state = state_to_matrix_with_action(obs, action=self.pr_action) if self.prev_state is not None: curr_state = self.curr_state prev_state = self.prev_state input_to_ddpg = self.__input_to_ddpg__(prev_state, curr_state) curr_state_matrix = self.curr_state prev_state_matrix = self.prev_state#modify start pred_input_NN1 = tf.estimator.inputs.numpy_input_fn( x={"state1": prev_state_matrix.astype("float32"), "state2": curr_state_matrix.astype("float32"), "y": np.asmatrix(self.graph.flatten())}, y=np.asmatrix(self.graph.flatten()), batch_size=1, num_epochs=None, shuffle=False) #modify end
Predict the estimator
y_generator = self.estimator_nn1.predict(input_fn=pred_input_NN1)
graph_predictions = np.asmatrix(list(itertools.islice(y_generator, prev_state_matrix.shape[0]))[0]) input_to_ddpg = np.concatenate([self.curr_state, graph_predictions], axis=1) print(input_to_ddpg.shape) # action = self.actor.predict(np.expand_dims(input_to_ddpg, 0))[0, 0] self.pr_action = action return action
Thank you, it work!
@mamengyiyi I also encountered this issue and what I do is to modify the code of ddpg_agent.py like below, def act(self, obs, action_space): action = action_space.sample()
self.prev_state = self.curr_state if self.pr_action is not None: self.curr_state = state_to_matrix_with_action(obs, action=self.pr_action) if self.prev_state is not None: curr_state = self.curr_state prev_state = self.prev_state input_to_ddpg = self.__input_to_ddpg__(prev_state, curr_state) curr_state_matrix = self.curr_state prev_state_matrix = self.prev_state#modify start pred_input_NN1 = tf.estimator.inputs.numpy_input_fn( x={"state1": prev_state_matrix.astype("float32"), "state2": curr_state_matrix.astype("float32"), "y": np.asmatrix(self.graph.flatten())}, y=np.asmatrix(self.graph.flatten()), batch_size=1, num_epochs=None, shuffle=False) #modify end
Predict the estimator
y_generator = self.estimator_nn1.predict(input_fn=pred_input_NN1)
graph_predictions = np.asmatrix(list(itertools.islice(y_generator, prev_state_matrix.shape[0]))[0]) input_to_ddpg = np.concatenate([self.curr_state, graph_predictions], axis=1) print(input_to_ddpg.shape) # action = self.actor.predict(np.expand_dims(input_to_ddpg, 0))[0, 0] self.pr_action = action return actionThank you, it work!
Excuse me , can you run the project successfully? I still have problem as above..I' ve sent a e-mail to you. Would you like to communicate with me,plz
@mamengyiyi I also encountered this issue and what I do is to modify the code of ddpg_agent.py like below, def act(self, obs, action_space): action = action_space.sample()
self.prev_state = self.curr_state if self.pr_action is not None: self.curr_state = state_to_matrix_with_action(obs, action=self.pr_action) if self.prev_state is not None: curr_state = self.curr_state prev_state = self.prev_state input_to_ddpg = self.__input_to_ddpg__(prev_state, curr_state) curr_state_matrix = self.curr_state prev_state_matrix = self.prev_state#modify start pred_input_NN1 = tf.estimator.inputs.numpy_input_fn( x={"state1": prev_state_matrix.astype("float32"), "state2": curr_state_matrix.astype("float32"), "y": np.asmatrix(self.graph.flatten())}, y=np.asmatrix(self.graph.flatten()), batch_size=1, num_epochs=None, shuffle=False) #modify end
Predict the estimator
y_generator = self.estimator_nn1.predict(input_fn=pred_input_NN1)
graph_predictions = np.asmatrix(list(itertools.islice(y_generator, prev_state_matrix.shape[0]))[0]) input_to_ddpg = np.concatenate([self.curr_state, graph_predictions], axis=1) print(input_to_ddpg.shape) # action = self.actor.predict(np.expand_dims(input_to_ddpg, 0))[0, 0] self.pr_action = action return actionThank you, it work!
Excuse me , can you run the project successfully? I still have problem as above..I' ve sent a e-mail to you. Would you like to communicate with me,plz
hi, can you solve it? I have the same question
