working code #71
Description
Can you please publish code that is work ?
I have this error:
('Failed to import pydot. You must pip install pydot and install graphviz (https://graphviz.gitlab.io/download/), ', 'for pydotprint to work.')
ITERATION NUMBER 1
BEST PLAYER VERSION 0
SELF PLAYING 30 EPISODES...
1
UnimplementedError Traceback (most recent call last)
in
63 ######## SELF PLAY ########
64 print('SELF PLAYING ' + str(config.EPISODES) + ' EPISODES...')
---> 65 _, memory, _, _ = playMatches(best_player, best_player, config.EPISODES, lg.logger_main, turns_until_tau0 = config.TURNS_UNTIL_TAU0, memory = memory)
66 print('\n')
67
~\Desktop\finantial risk\DeepReinforcementLearning-master\funcs.py in playMatches(player1, player2, EPISODES, logger, turns_until_tau0, memory, goes_first)
84 #### Run the MCTS algo and return an action
85 if turn < turns_until_tau0:
---> 86 action, pi, MCTS_value, NN_value = players[state.playerTurn]['agent'].act(state, 1)
87 else:
88 action, pi, MCTS_value, NN_value = players[state.playerTurn]['agent'].act(state, 0)
~\Desktop\finantial risk\DeepReinforcementLearning-master\agent.py in act(self, state, tau)
84 lg.logger_mcts.info('****** SIMULATION %d ', sim + 1)
85 lg.logger_mcts.info('*********************')
---> 86 self.simulate()
87
88 #### get action values
~\Desktop\finantial risk\DeepReinforcementLearning-master\agent.py in simulate(self)
66
67 ##### EVALUATE THE LEAF NODE
---> 68 value, breadcrumbs = self.evaluateLeaf(leaf, value, done, breadcrumbs)
69
70 ##### BACKFILL THE VALUE THROUGH THE TREE
~\Desktop\finantial risk\DeepReinforcementLearning-master\agent.py in evaluateLeaf(self, leaf, value, done, breadcrumbs)
134 if done == 0:
135
--> 136 value, probs, allowedActions = self.get_preds(leaf.state)
137 lg.logger_mcts.info('PREDICTED VALUE FOR %d: %f', leaf.state.playerTurn, value)
138
~\Desktop\finantial risk\DeepReinforcementLearning-master\agent.py in get_preds(self, state)
108 inputToModel = np.array([self.model.convertToModelInput(state)])
109
--> 110 preds = self.model.predict(inputToModel)
111 value_array = preds[0]
112 logits_array = preds[1]
~\Desktop\finantial risk\DeepReinforcementLearning-master\model.py in predict(self, x)
28
29 def predict(self, x):
---> 30 return self.model.predict(x)
31
32 def fit(self, states, targets, epochs, verbose, validation_split, batch_size):
~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in predict(self, x, batch_size, verbose, steps, callbacks, max_queue_size, workers, use_multiprocessing)
1627 for step in data_handler.steps():
1628 callbacks.on_predict_batch_begin(step)
-> 1629 tmp_batch_outputs = self.predict_function(iterator)
1630 if data_handler.should_sync:
1631 context.async_wait()
~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in call(self, *args, **kwds)
826 tracing_count = self.experimental_get_tracing_count()
827 with trace.Trace(self._name) as tm:
--> 828 result = self._call(*args, **kwds)
829 compiler = "xla" if self._experimental_compile else "nonXla"
830 new_tracing_count = self.experimental_get_tracing_count()
~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds)
892 *args, **kwds)
893 # If we did not create any variables the trace we have is good enough.
--> 894 return self._concrete_stateful_fn._call_flat(
895 filtered_flat_args, self._concrete_stateful_fn.captured_inputs) # pylint: disable=protected-access
896
~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1916 and executing_eagerly):
1917 # No tape is watching; skip to running the function.
-> 1918 return self._build_call_outputs(self._inference_function.call(
1919 ctx, args, cancellation_manager=cancellation_manager))
1920 forward_backward = self._select_forward_and_backward_functions(
~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in call(self, ctx, args, cancellation_manager)
553 with _InterpolateFunctionError(self):
554 if cancellation_manager is None:
--> 555 outputs = execute.execute(
556 str(self.signature.name),
557 num_outputs=self._num_outputs,
~\anaconda3\lib\site-packages\tensorflow\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
57 try:
58 ctx.ensure_initialized()
---> 59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
60 inputs, attrs, num_outputs)
61 except core._NotOkStatusException as e:
UnimplementedError: The Conv2D op currently only supports the NHWC tensor format on the CPU. The op was given the format: NCHW
[[node model_5/conv2d_65/Conv2D (defined at C:\Users\Admin\Desktop\finantial risk\DeepReinforcementLearning-master\model.py:30) ]] [Op:__inference_predict_function_6283]
Function call stack:
predict_function