在 TensorFlow 2.0 中修改 TensorBoard [英] Modifying TensorBoard in TensorFlow 2.0
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问题描述
I'm following Sentdex's DQN tutorial. I'm stuck trying to rewrite custom TensorBoard in TF 2.0. The point is to add **stats to a file, for example: {'reward_avg': -99.0, 'reward_min': -200, 'reward_max': 2, 'epsilon': 1}
Original code:
class ModifiedTensorBoard(TensorBoard):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.step = 1
self.writer = tf.summary.FileWriter(self.log_dir)
# Custom method for saving own metrics
# Creates writer, writes custom metrics and closes writer
def update_stats(self, **stats):
self._write_logs(stats, self.step)
我的尝试:
def update_stats(self, **stats):
for name, value in stats.items():
with self.writer.as_default():
tf.summary.scalar(name, value, self.step)
这种方式我得到:TypeError: unsupported operand type(s) for +: 'ModifiedTensorBoard' and 'list'
推荐答案
我遵循了相同的教程,这是我为使其工作所做的工作:
I followed the same tutorial, here's what I did to make it work:
这是 ModifiedTensorBoard 类:
Here's the ModifiedTensorBoard Class:
class ModifiedTensorBoard(TensorBoard):
# Overriding init to set initial step and writer (we want one log file for all .fit() calls)
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.step = 1
self.writer = tf.summary.create_file_writer(self.log_dir)
self._log_write_dir = os.path.join(self.log_dir, MODEL_NAME)
# Overriding this method to stop creating default log writer
def set_model(self, model):
pass
# Overrided, saves logs with our step number
# (otherwise every .fit() will start writing from 0th step)
def on_epoch_end(self, epoch, logs=None):
self.update_stats(**logs)
# Overrided
# We train for one batch only, no need to save anything at epoch end
def on_batch_end(self, batch, logs=None):
pass
# Overrided, so won't close writer
def on_train_end(self, _):
pass
def on_train_batch_end(self, batch, logs=None):
pass
# Custom method for saving own metrics
# Creates writer, writes custom metrics and closes writer
def update_stats(self, **stats):
self._write_logs(stats, self.step)
def _write_logs(self, logs, index):
with self.writer.as_default():
for name, value in logs.items():
tf.summary.scalar(name, value, step=index)
self.step += 1
self.writer.flush()
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