The Tool Desk
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What TensorBoard shows
TensorFlow describes TensorBoard as “A suite of visualization tools to understand, debug, and optimize TensorFlow programs for ML experimentation.” Its dashboards offer complementary views: time-series plots show how metrics change, graph views show model structure, and histograms reveal how tensor values evolve. Other tools can display images or embeddings and help diagnose execution bottlenecks. No single view substitutes for the others.
Log a Keras training run
Create a distinct directory for each run so its event files can be inspected separately. This small example uses a timestamp to avoid reusing a previous run’s directory:
from datetime import datetime
import tensorflow as tf
logdir = "logs/fit/" + datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=logdir)
model = tf.keras.Sequential([
tf.keras.layers.Input(shape=(784,)),
tf.keras.layers.Dense(128, activation="relu"),
tf.keras.layers.Dense(10, activation="softmax"),
])
model.compile(
optimizer="adam",
loss="sparse_categorical_crossentropy",
metrics=["accuracy"],
)
# x_train and y_train should contain data shaped for this model.
model.fit(
x_train,
y_train,
epochs=5,
validation_data=(x_val, y_val),
callbacks=[tensorboard_callback],
)The model and dataset are illustrative; substitute your own inputs and labels. The important connection is the callback passed to model.fit(), which writes summaries for the run to logdir. Keep this directory dedicated to TensorBoard output rather than sharing it with another callback. Callback options can vary by TensorFlow version: for example, the TensorFlow v2.16.1 API reference marks write_graph as “Not supported at this time.” Check the API for your installed version before relying on an option: TensorBoard callback API.
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Open the run in TensorBoard
From a shell
Run this from the environment where TensorFlow wrote the files:
tensorboard --logdir=logs/fitOpen the local address printed by TensorBoard in a browser. Point --logdir at the parent directory when it contains multiple run-specific subdirectories; TensorBoard can then list those runs for comparison.
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From a notebook
In a supported notebook, use the TensorBoard magic with the same log directory:
%load_ext tensorboard
%tensorboard --logdir logs/fitThe magic is the notebook counterpart to starting TensorBoard from a shell. Hosted notebook environments do not necessarily expose every dashboard, so availability depends on the environment as well as the installed TensorBoard and plugin versions. See the TensorBoard notebook guide.
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Choose a dashboard for the question
| Dashboard or view | What it helps answer | What to look for |
|---|---|---|
| Scalars | How did loss, accuracy, or another logged metric change across steps or epochs? | Compare training and validation curves for changes over time. |
| Graphs | What structure did TensorFlow or Keras construct? | Inspect the operation-level execution graph or, when available, the conceptual Keras graph. |
| Histograms and distributions | How did tensor values change during training? | Track the shape and spread of logged values over time. |
| Images | What do logged inputs, weights, or generated tensors look like? | Review image summaries at the steps where they were recorded. |
| Embedding Projector | Which high-dimensional points appear near one another? | Explore a lower-dimensional view of embeddings and inspect neighboring points or terms. |
| Profiler | Where might execution time or resource use be a bottleneck? | Inspect recorded profiling traces; support and setup depend on versions and plugins. |
Start with Scalars
Use scalar plots to follow metrics such as loss and accuracy through training. A curve that changes unexpectedly can indicate when to investigate, but the plot alone does not explain the cause. Compare runs or training and validation metrics only when the corresponding data was logged.
Inspect Graphs
Use the Graphs dashboard to understand how operations are connected and to inspect the model structure captured in the run. Depending on the model and logging path, TensorBoard may expose both an op-level execution graph and a more conceptual Keras graph. A graph helps answer what was constructed; it does not report whether the model is learning well.
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Track Histograms and distributions
These views show how tensor values vary across training, rather than reducing a run to one scalar per step. They can help you notice changes in weights or activations that are not visible in a loss curve. The useful tensors depend on what your code logs.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Optional: inspect images and embeddings
Log image summaries
Image summaries can show tensors or other image data, including inputs, weights, generated tensors, and diagnostic examples. Use them when seeing the actual content is more informative than a scalar or distribution. The TensorFlow guide demonstrates image summary workflows: Image summaries.
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Use the Embedding Projector
The Projector can display high-dimensional embeddings in a lower-dimensional view so you can explore neighborhood structure. It is not enough to open the dashboard: the workflow needs checkpoint data and metadata for the layer you want to examine. Follow the Embedding Projector guide for the required files and setup.
Optional: profile execution
TensorBoard’s profiler views are for investigating runtime behavior and locating possible bottlenecks, not for judging model quality from training metrics. Profiler support and plugin setup can differ between TensorFlow and TensorBoard versions. Consult the current TensorFlow Profiler guide for the workflow and requirements that apply to your environment.
Quick Recap
When a dashboard or option is missing
- No run appears: confirm that the path supplied to
--logdiror%tensorboardcontains the event files written during training, and that you are viewing the same environment that created them. - A dashboard is unavailable in a hosted notebook: the notebook environment may not expose that dashboard. Check its TensorBoard integration and the relevant plugin requirements rather than assuming every local dashboard is available.
- An older code example uses an unsupported callback option: check the TensorBoard callback API for your installed TensorFlow version. In the v2.16.1 reference,
write_graphis marked unsupported. - The Projector has no embedding to display: verify that the layer’s checkpoint data and metadata are available as described in the Projector guide.
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