TensorBoard: Difference between revisions
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==Custom Usage== | ==Custom Usage== | ||
If you're using a custom training loop (i.e. gradient tape), then you'll need to set everything up manually. | If you're using a custom training loop (i.e. gradient tape), then you'll need to set everything up manually. | ||
First create a <code>SummaryWriter<code> | |||
<syntaxhighlight lang="python"> | |||
train_log_dir = os.path.join(args.checkpoint_dir, "logs", "train") | |||
train_summary_writer = tf.summary.create_file_writer(train_log_dir) | |||
</syntaxhighlight> | |||
===Scalars=== | ===Scalars=== | ||
Add scalars using <code>tf.summary.scalar</code>: | |||
<syntaxhighlight lang="python"> | |||
with train_summary_writer.as_default(): | |||
tf.summary.scalar("training_loss", m_loss.numpy(), step=int(ckpt.step)) | |||
</syntaxhighlight> | |||
==Resources== | ==Resources== | ||
* [https://www.tensorflow.org/tensorboard/get_started Getting started with TensorBoard] | * [https://www.tensorflow.org/tensorboard/get_started Getting started with TensorBoard] | ||
Revision as of 13:14, 18 June 2020
Custom Usage
If you're using a custom training loop (i.e. gradient tape), then you'll need to set everything up manually.
First create a SummaryWriter
train_log_dir = os.path.join(args.checkpoint_dir, "logs", "train")
train_summary_writer = tf.summary.create_file_writer(train_log_dir)
Scalars
Add scalars using tf.summary.scalar:
with train_summary_writer.as_default():
tf.summary.scalar("training_loss", m_loss.numpy(), step=int(ckpt.step))
Resources