本文整理汇总了Python中wandb.watch方法的典型用法代码示例。如果您正苦于以下问题：Python wandb.watch方法的具体用法？Python wandb.watch怎么用？Python wandb.watch使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类wandb的用法示例。
在下文中一共展示了wandb.watch方法的7个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: watch
# 需要导入模块: import wandb [as 别名]
# 或者: from wandb import watch [as 别名]
def watch(self, model: nn.Module) -> None:
raise NotImplementedError 示例2: on_stage_start
# 需要导入模块: import wandb [as 别名]
# 或者: from wandb import watch [as 别名]
def on_stage_start(self, runner: IRunner):
"""Initialize Weights & Biases."""
wandb.init(**self.logging_params, reinit=True, dir=str(runner.logdir))
wandb.watch(
models=runner.model, criterion=runner.criterion, log=self.log
) 示例3: train
# 需要导入模块: import wandb [as 别名]
# 或者: from wandb import watch [as 别名]
def train(self):
"""Train the agent."""
# logger
if self.args.log:
self.set_wandb()
# wandb.watch([self.actor, self.critic], log="parameters")
for self.i_episode in range(1, self.args.episode_num + 1):
state = self.env.reset()
done = False
score = 0
policy_loss_episode = list()
value_loss_episode = list()
self.episode_step = 0
while not done:
if self.args.render and self.i_episode >= self.args.render_after:
self.env.render()
action = self.select_action(state)
next_state, reward, done, _ = self.step(action)
self.episode_step += 1
policy_loss, value_loss = self.learner.update_model(self.transition)
policy_loss_episode.append(policy_loss)
value_loss_episode.append(value_loss)
state = next_state
score += reward
# logging
policy_loss = np.array(policy_loss_episode).mean()
value_loss = np.array(value_loss_episode).mean()
log_value = (self.i_episode, score, policy_loss, value_loss)
self.write_log(log_value)
if self.i_episode % self.args.save_period == 0:
self.learner.save_params(self.i_episode)
self.interim_test()
# termination
self.env.close()
self.learner.save_params(self.i_episode)
self.interim_test() 示例4: train
# 需要导入模块: import wandb [as 别名]
# 或者: from wandb import watch [as 别名]
def train(self):
"""Train the agent."""
# logger
if self.args.log:
self.set_wandb()
# wandb.watch([self.actor, self.critic], log="parameters")
# pre-training if needed
self.pretrain()
for self.i_episode in range(1, self.args.episode_num + 1):
state = self.env.reset()
done = False
score = 0
self.episode_step = 0
losses = list()
t_begin = time.time()
while not done:
if self.args.render and self.i_episode >= self.args.render_after:
self.env.render()
action = self.select_action(state)
next_state, reward, done, _ = self.step(action)
self.total_step += 1
self.episode_step += 1
if len(self.memory) >= self.hyper_params.batch_size:
for _ in range(self.hyper_params.multiple_update):
experience = self.memory.sample()
demos = self.demo_memory.sample()
experience, demos = (
numpy2floattensor(experience),
numpy2floattensor(demos),
)
loss = self.learner.update_model(experience, demos)
losses.append(loss) # for logging
state = next_state
score += reward
t_end = time.time()
avg_time_cost = (t_end - t_begin) / self.episode_step
# logging
if losses:
avg_loss = np.vstack(losses).mean(axis=0)
log_value = (self.i_episode, avg_loss, score, avg_time_cost)
self.write_log(log_value)
losses.clear()
if self.i_episode % self.args.save_period == 0:
self.learner.save_params(self.i_episode)
self.interim_test()
# termination
self.env.close()
self.learner.save_params(self.i_episode)
self.interim_test() 示例5: train
# 需要导入模块: import wandb [as 别名]
# 或者: from wandb import watch [as 别名]
def train(self):
"""Train the agent."""
# logger
if self.args.log:
self.set_wandb()
# wandb.watch([self.actor, self.critic1, self.critic2], log="parameters")
for self.i_episode in range(1, self.args.episode_num + 1):
state = self.env.reset()
done = False
score = 0
loss_episode = list()
self.episode_step = 0
t_begin = time.time()
while not done:
if self.args.render and self.i_episode >= self.args.render_after:
self.env.render()
action = self.select_action(state)
next_state, reward, done, _ = self.step(action)
self.total_step += 1
self.episode_step += 1
state = next_state
score += reward
if len(self.memory) >= self.hyper_params.batch_size:
experience = self.memory.sample()
experience = numpy2floattensor(experience)
loss = self.learner.update_model(experience)
loss_episode.append(loss) # for logging
t_end = time.time()
avg_time_cost = (t_end - t_begin) / self.episode_step
# logging
if loss_episode:
avg_loss = np.vstack(loss_episode).mean(axis=0)
log_value = (
self.i_episode,
avg_loss,
score,
self.hyper_params.policy_update_freq,
avg_time_cost,
)
self.write_log(log_value)
if self.i_episode % self.args.save_period == 0:
self.learner.save_params(self.i_episode)
self.interim_test()
# termination
self.env.close()
self.learner.save_params(self.i_episode)
self.interim_test() 示例6: train
# 需要导入模块: import wandb [as 别名]
# 或者: from wandb import watch [as 别名]
def train(self):
"""Train the agent."""
# logger
if self.args.log:
self.set_wandb()
# wandb.watch([self.actor, self.critic], log="parameters")
# pre-training if needed
self.pretrain()
for self.i_episode in range(1, self.args.episode_num + 1):
state = self.env.reset()
done = False
score = 0
self.episode_step = 0
losses = list()
t_begin = time.time()
while not done:
if self.args.render and self.i_episode >= self.args.render_after:
self.env.render()
action = self.select_action(state)
next_state, reward, done, _ = self.step(action)
self.total_step += 1
self.episode_step += 1
if len(self.memory) >= self.hyper_params.batch_size:
for _ in range(self.hyper_params.multiple_update):
experience = self.memory.sample()
experience = numpy2floattensor(experience)
loss = self.learner.update_model(experience)
losses.append(loss) # for logging
state = next_state
score += reward
t_end = time.time()
avg_time_cost = (t_end - t_begin) / self.episode_step
# logging
if losses:
avg_loss = np.vstack(losses).mean(axis=0)
log_value = (self.i_episode, avg_loss, score, avg_time_cost)
self.write_log(log_value)
losses.clear()
if self.i_episode % self.args.save_period == 0:
self.learner.save_params(self.i_episode)
self.interim_test()
# termination
self.env.close()
self.learner.save_params(self.i_episode)
self.interim_test() 示例7: __init__
# 需要导入模块: import wandb [as 别名]
# 或者: from wandb import watch [as 别名]
def __init__(
self,
metric_names: List[str] = None,
log_on_batch_end: bool = False,
log_on_epoch_end: bool = True,
log: str = None,
**logging_params,
):
"""
Args:
metric_names (List[str]): list of metric names to log,
if None - logs everything
log_on_batch_end (bool): logs per-batch metrics if set True
log_on_epoch_end (bool): logs per-epoch metrics if set True
log (str): wandb.watch parameter. Can be "all", "gradients"
or "parameters"
**logging_params: any parameters of function `wandb.init`
except `reinit` which is automatically set to `True`
and `dir` which is set to `<logdir>`
"""
super().__init__(
order=CallbackOrder.logging,
node=CallbackNode.master,
scope=CallbackScope.experiment,
)
self.metrics_to_log = metric_names
self.log_on_batch_end = log_on_batch_end
self.log_on_epoch_end = log_on_epoch_end
self.log = log
if not (self.log_on_batch_end or self.log_on_epoch_end):
raise ValueError("You have to log something!")
if (self.log_on_batch_end and not self.log_on_epoch_end) or (
not self.log_on_batch_end and self.log_on_epoch_end
):
self.batch_log_suffix = ""
self.epoch_log_suffix = ""
else:
self.batch_log_suffix = "_batch"
self.epoch_log_suffix = "_epoch"
self.logging_params = logging_params