def main_train(trainer):
from tartist.plugins.trainer_enhancer import summary
summary.enable_summary_history(trainer,
extra_summary_types={
'async/train/score': 'async_scalar',
'async/inference/score': 'async_scalar'
})
summary.enable_echo_summary_scalar(trainer,
summary_spec={
'async/train/score': ['avg', 'max'],
'async/inference/score':
['avg', 'max']
})
from tartist.plugins.trainer_enhancer import progress
progress.enable_epoch_progress(trainer)
from tartist.plugins.trainer_enhancer import snapshot
snapshot.enable_snapshot_saver(trainer)
from tartist.core import register_event
from common_hp_a3c import main_inference_play_multithread
def on_epoch_after(trainer):
if trainer.epoch > 0 and trainer.epoch % 2 == 0:
main_inference_play_multithread(trainer, make_player=make_player)
register_event(trainer, 'epoch:after', on_epoch_after, priority=5)
trainer.train()
def main_train(trainer):
from tartist.plugins.trainer_enhancer import summary
summary.enable_summary_history(trainer)
summary.enable_echo_summary_scalar(trainer)
summary.set_error_summary_key(trainer, 'error')
from tartist.plugins.trainer_enhancer import progress
progress.enable_epoch_progress(trainer)
from tartist.plugins.trainer_enhancer import snapshot
snapshot.enable_snapshot_saver(trainer)
from tartist.plugins.trainer_enhancer import inference
inference.enable_inference_runner(trainer, make_dataflow_inference)
from tartist.core import register_event
def on_epoch_after(trainer):
if trainer.epoch == 5:
trainer.optimizer.set_learning_rate(
trainer.optimizer.learning_rate * 0.1)
register_event(trainer, 'epoch:after', on_epoch_after)
trainer.train()
def enable_param_clippping(trainer):
# apply clip on params of discriminator
limit = get_env('trainer.clip_limit', 0.01)
ops = []
with trainer.env.as_default():
sess = trainer.env.session
var_list = tf.trainable_variables()
for v in var_list:
if v.name.startswith(GANGraphKeys.DISCRIMINATOR_VARIABLES + '/'):
ops.append(v.assign(tf.clip_by_value(v, -limit, limit)))
op = tf.group(*ops)
def do_clip_params(trainer, inp, out):
trainer.env.session.run(op)
from tartist.core import register_event
register_event(trainer, 'iter:after', do_clip_params)