Overview
TensorBoard is a visualization tool included with TensorFlow that
enables you to visualize your TensorFlow graph, plot quantitative
metrics about the execution of your graph, and show additional data like
images that pass through it.
Models built using tfestimators automatically contain most of the
necessary information to visualize the TensorFlow graph, variables,
etc., for you so you can easily launch the TensorBoard without
additional manual specifications.
Examples
To start the TensorBoard, you trained a model in a similar fashion as
follows:
estimator(
model_fn = model_fn,
model_dir = "/tmp/test"
) %>% train(input_fn = input, steps = 100L)
and then you can call
tensorboard(log_dir = "/tmp/test", launch_browser = TRUE)
with log_dir being the directory you used to save the
model checkpoints, to launch the TensorBoard in browser.
For example, here’s a TensorBoard display for a model’s training
loss:
Here’s another TensorBoard display for this model’s TensorFlow graph
that demonstrates the architecture of the model:
You can also click to see each subgraph’s details:
