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Visualizing your Deep learning using TensorBoard (TensorFlow) Sung Kim <[email protected]>

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Visualizing your Deep learning using TensorBoard (TensorFlow)

Sung Kim <[email protected]>

TensorBoard: TF logging/debugging tool

• Visualize your TF graph

• Plot quantitative metrics

• Show additional data

https://www.tensorflow.org/versions/r0.7/how_tos/summaries_and_tensorboard/index.html

Old fashion: print, print, print

New way!

5 steps of using tensorboard

• From TF graph, decide which node you want to annotate- with tf.name_scope("test") as scope:

- tf.histogram_summary("weights", W), tf.scalar_summary(“accuracy", accuracy)

• Merge all summaries- merged = tf.merge_all_summaries()

• Create writer- writer = tf.train.SummaryWriter("/tmp/mnist_logs", sess.graph_def)

• Run summary merge and add_summary- summary = sess.run(merged, …); writer.add_summary(summary);

• Launch Tensorboard- tensorboard --logdir=/tmp/mnist_logs

Name variables

Add scope for better graph hierarch

Add histogram

Add scalar variables

Merge summaries and create writer after creating session

Run merged summary and write (add summary)

Launch tensorboard

• tensorboard —logdir=/tmp/mnist_logs

• (You can navigate to http://0.0.0.0:6006)

Add scope for better graph hierarch

Add scope for better graph hierarch

Add histogram

Add scalar variables

5 steps of using tensorboard

• From TF graph, decide which node you want to annotate- with tf.name_scope("test") as scope:

- tf.histogram_summary("weights", W), tf.scalar_summary(“accuracy", accuracy)

• Merge all summaries- merged = tf.merge_all_summaries()

• Create writer- writer = tf.train.SummaryWriter("/tmp/mnist_logs", sess.graph_def)

• Run summary merge and add_summary- summary = sess.run(merged, …); writer.add_summary(summary);

• Launch Tensorboard- tensorboard --logdir=/tmp/mnist_logs

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