The computations you'll use TensorBoard for - like training a massive deep neural network - can be complex and confusing. To make it easier to understand, debug, and optimize TensorFlow programs, we've included a suite of visualization tools called TensorBoard. You can use TensorBoard to visualize your TensorFlow graph, plot quantitative metrics about the execution of your graph, and show additional data like images that pass through it. When TensorBoard is fully configured, it looks like this:
TensorBoard operates by reading TensorFlow events files, which contain summary data that you can generate when running TensorFlow. Here's the general lifecycle for summary data within TensorBoard.
First, create the TensorFlow graph that you'd like to collect summary data from, and decide which nodes you would like to annotate with [summary operations] (../../api_docs/python/train.md#summary-operations).
For example, suppose you are training a convolutional neural network for
recognizing MNIST digits. You'd like to record how the learning rate
varies over time, and how the objective function is changing. Collect these by
to the nodes that output the learning rate and loss respectively. Then, give
scalar_summary a meaningful
'learning rate' or
Perhaps you'd also like to visualize the distributions of activations coming
off a particular layer, or the distribution of gradients or weights. Collect
this data by attaching
histogram_summary ops to
the gradient outputs and to the variable that holds your weights, respectively.
For details on all of the summary operations avaiable, check out the docs on [summary operations] (../../api_docs/python/train.md#summary-operations).
Operations in TensorFlow don't do anything until you run them, or an op that
depends on their output. And the summary nodes that we've just created are
peripheral to your graph: none of the ops you are currently running depend on
them. So, to generate summaries, we need to run all of these summary nodes.
Managing them by hand would be tedious, so use
to combine them into a single op that generates all the summary data.
Then, you can just run the merged summary op, which will generate a serialized
Summary protobuf object with all of your summary data at a given step.
Finally, to write this summary data to disk, pass the summary protobuf to a
SummaryWriter takes a logdir in its constructor - this logdir is quite
important, it's the directory where all of the events will be written out.
SummaryWriter can optionally take a
GraphDef in its constructor.
If it receives one, then TensorBoard will visualize your graph as well.
Now that you've modified your graph and have a
SummaryWriter, you're ready to
start runing your network! If you want, you could run the merged summary op
every single step, and record a ton of training data. That's likely to be more
data than you need, though. Instead, consider running the merged summary op
every hundred steps or so, as in the following code example.
merged_summary_op = tf.merge_all_summaries() summary_writer = tf.train.SummaryWriter('/tmp/mnist_logs', sess.graph) total_step = 0 while training: total_step += 1 session.run(training_op) if total_step % 100 == 0: summary_str = session.run(merged_summary_op) summary_writer.add_summary(summary_str, total_step)
You're now all set to visualize this data using TensorBoard.
To run TensorBoard, use the command
python tensorflow/tensorboard/tensorboard.py --logdir=path/to/log-directory
logdir points to the directory where the
SummaryWriter serialized its
data. If this
logdir directory contains subdirectories which contain
serialized data from separate runs, then TensorBoard will visualize the data
from all of those runs. Once TensorBoard is running, navigate your web browser
localhost:6006 to view the TensorBoard.
If you have pip installed TensorBoard, you can use the simpler command
When looking at TensorBoard, you will see the navigation tabs in the top right corner. Each tab represents a set of serialized data that can be visualized. For any tab you are looking at, if the logs being looked at by TensorBoard do not contain any data relevant to that tab, a message will be displayed indicating how to serialize data that is applicable to that tab.
For in depth information on how to use the graph tab to visualize your graph, see TensorBoard: Visualizing your graph.