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fix tensorboard path so that users can track all historical models

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This commit is contained in:
robcaulk 2022-08-31 16:50:39 +02:00
parent 2493e0c8a5
commit 240b529533
3 changed files with 6 additions and 6 deletions
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4
config_examples/config_freqai-rl.example.json
View File

@ -72,8 +72,8 @@
"5m", "5m",
"30m" "30m"
], ],
"indicator_max_period_candles": 10, "indicator_max_period_candles": 20,
"indicator_periods_candles": [5] "indicator_periods_candles": [14]
}, },
"data_split_parameters": { "data_split_parameters": {
"test_size": 0.5, "test_size": 0.5,

4
freqtrade/freqai/prediction_models/ReinforcementLearner.py
View File

@ -28,14 +28,14 @@ class ReinforcementLearner(BaseReinforcementLearningModel):
if dk.pair not in self.dd.model_dictionary or not self.continual_learning: if dk.pair not in self.dd.model_dictionary or not self.continual_learning:
model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs, model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
tensorboard_log=Path(dk.data_path / "tensorboard"), tensorboard_log=Path(
dk.full_path / "tensorboard" / dk.pair.split('/')[0]),
**self.freqai_info['model_training_parameters'] **self.freqai_info['model_training_parameters']
) )
else: else:
logger.info('Continual training activated - starting training from previously ' logger.info('Continual training activated - starting training from previously '
'trained agent.') 'trained agent.')
model = self.dd.model_dictionary[dk.pair] model = self.dd.model_dictionary[dk.pair]
model.tensorboard_log = Path(dk.data_path / "tensorboard")
model.set_env(self.train_env) model.set_env(self.train_env)
model.learn( model.learn(

4
freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py
View File

@ -31,14 +31,14 @@ class ReinforcementLearner_multiproc(BaseReinforcementLearningModel):
if dk.pair not in self.dd.model_dictionary or not self.continual_learning: if dk.pair not in self.dd.model_dictionary or not self.continual_learning:
model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs, model = self.MODELCLASS(self.policy_type, self.train_env, policy_kwargs=policy_kwargs,
tensorboard_log=Path(dk.full_path / "tensorboard"), tensorboard_log=Path(
dk.full_path / "tensorboard" / dk.pair.split('/')[0]),
**self.freqai_info['model_training_parameters'] **self.freqai_info['model_training_parameters']
) )
else: else:
logger.info('Continual learning activated - starting training from previously ' logger.info('Continual learning activated - starting training from previously '
'trained agent.') 'trained agent.')
model = self.dd.model_dictionary[dk.pair] model = self.dd.model_dictionary[dk.pair]
model.tensorboard_log = Path(dk.data_path / "tensorboard")
model.set_env(self.train_env) model.set_env(self.train_env)
model.learn( model.learn(