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1.
Visualizing your Deep
learning using TensorBoard (TensorFlow) Sung Kim <hunkim+ml@gmail.com>
2.
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
3.
Old fashion: print,
print, print
4.
New way!
5.
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
6.
Name variables
7.
Add scope for
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8.
Add histogram
9.
Add scalar variables
10.
Merge summaries and
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11.
Run merged summary
and write (add summary)
12.
Launch tensorboard • tensorboard
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13.
Add scope for
better graph hierarch
14.
Add scope for
better graph hierarch
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Add histogram
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Add scalar variables
17.
5 steps of
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