TensorBoard: Difference between revisions
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TensorBoard is a way to visualize your model and various statistics during or after training. | TensorBoard is a way to visualize your model and various statistics during or after training. | ||
== | ==CLI Usage== | ||
===CLI=== | |||
<pre> | |||
tensorboard --logdir [logs] | |||
</pre> | |||
;Flags | |||
*<code>--samples_per_plugin</code> indices the number of samples to show for each tab. Non-scalar objects are sampled using reservoir sampling. | |||
** <code>--samples_per_plugin images=10000</code> samples approximately 10000 images. | |||
==Training 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. | ||
| Line 15: | Line 25: | ||
with train_summary_writer.as_default(): | with train_summary_writer.as_default(): | ||
tf.summary.scalar("training_loss", m_loss.numpy(), step=int(ckpt.step)) | tf.summary.scalar("training_loss", m_loss.numpy(), step=int(ckpt.step)) | ||
</syntaxhighlight> | |||
==PyTorch== | |||
PyTorch also supports output tensorboard logs. | |||
See [https://pytorch.org/docs/stable/tensorboard.html https://pytorch.org/docs/stable/tensorboard.html]. | |||
There is also [https://github.com/lanpa/tensorboardX lanpa/tensorboardX] but I haven't tried it. | |||
<syntaxhighlight lang="python"> | |||
from torch.utils.tensorboard import SummaryWriter | |||
writer = SummaryWriter(log_dir="./runs") | |||
writer.add_scalar("train_loss", loss_np, step) | |||
# Optionally flush e.g. at checkpoints | |||
writer.flush() | |||
# Close the writer (will flush) | |||
writer.close() | |||
</syntaxhighlight> | </syntaxhighlight> | ||
Latest revision as of 14:22, 11 November 2020
TensorBoard is a way to visualize your model and various statistics during or after training.
CLI Usage
CLI
tensorboard --logdir [logs]
- Flags
--samples_per_pluginindices the number of samples to show for each tab. Non-scalar objects are sampled using reservoir sampling.--samples_per_plugin images=10000samples approximately 10000 images.
Training 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))
PyTorch
PyTorch also supports output tensorboard logs.
See https://pytorch.org/docs/stable/tensorboard.html.
There is also lanpa/tensorboardX but I haven't tried it.
from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter(log_dir="./runs")
writer.add_scalar("train_loss", loss_np, step)
# Optionally flush e.g. at checkpoints
writer.flush()
# Close the writer (will flush)
writer.close()