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June 30, 2017 15:04
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keras-rl's TrainIntervalLogger but using tqdm for jupyter notebook
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from rl.callbacks import TrainIntervalLogger | |
from tqdm import tqdm_notebook | |
import timeit | |
class TrainIntervalLoggerTQDMNotebook(TrainIntervalLogger): | |
"""TrainIntervalLogger using tqdm_notebook for jupyter-notebook.""" | |
def reset(self): | |
self.interval_start = timeit.default_timer() | |
self.metrics = [] | |
self.infos = [] | |
self.info_names = None | |
self.episode_rewards = [] | |
def on_train_begin(self, logs): | |
self.progbar = tqdm_notebook(desc='', total=self.params['nb_steps'], leave=True) | |
self.train_start = timeit.default_timer() | |
self.metrics_names = self.model.metrics_names | |
print('Training for {} steps ...'.format(self.params['nb_steps'])) | |
def on_step_end(self, step, logs): | |
if self.info_names is None: | |
self.info_names = logs['info'].keys() | |
values = [('reward', logs['reward'])] | |
self.progbar.desc = 'reward={reward: 2.6f}'.format( | |
reward=logs['reward']) | |
self.progbar.update(1) # update | |
self.step += 1 | |
self.metrics.append(logs['metrics']) | |
if len(self.info_names) > 0: | |
self.infos.append([logs['info'][k] for k in self.info_names]) |
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