Learnaholic Learnaholic - 1 month ago 36
Python Question

Show training and validation accuracy in TensorFlow using same graph

I have a TensorFlow model, and one part of this model evaluates the accuracy. The

accuracy
is just another node in the tensorflow graph, that takes in
logits
and
labels
.

When I want to plot the training accuracy, this is simple: I have something like:

tf.scalar_summary("Training Accuracy", accuracy)
tf.scalar_summary("SomethingElse", foo)
summary_op = tf.merge_all_summaries()
writer = tf.train.SummaryWriter('/me/mydir/', graph=sess.graph)


Then, during my training loop, I have something like:

for n in xrange(1000):
...
summary, ..., ... = sess.run([summary_op, ..., ...], feed_dict)
writer.add_summary(summary, n)
...


Also inside that for loop, every say, 100 iterations, I want to evaluate the validation accuracy. I have a separate feed_dict for this, and I am able to evaluate the validation accuracy very nicely in python.

However, here is my problem: I want to make another summary for the validation accuracy, by using the
accuracy
node. I am not clear on how to do this though. Since I have the
accuracy
node it makes sense that I should be able to re-use it, but I am unsure how to do this exactly, such that I can also get the validation accuracy written out as a separate scalar_summary...

How might this be possible?

wih wih
Answer

You can reuse the the accuracy node but you need to use two different SummaryWriters, one for the training runs and one for the test data. Also you have to assign the scalar summary for accuracy to a variable.

accuracy_summary = tf.scalar_summary("Training Accuracy", accuracy)
tf.scalar_summary("SomethingElse", foo)
summary_op = tf.merge_all_summaries()
summaries_dir = '/me/mydir/'
train_writer = tf.train.SummaryWriter(summaries_dir + '/train', sess.graph)
test_writer = tf.train.SummaryWriter(summaries_dir + '/test')

Then in your training loop you have the normal training and record your summaries with the train_writer. In addition you run the graph on the test set each 100th iteration and record only the accuracy summary with the test_writer.

# Record train set summaries, and train
summary, _ = sess.run([summary_op, train_step], feed_dict=...)
train_writer.add_summary(summary, n)
if n % 100 == 0:  # Record summaries and test-set accuracy
  summary, acc = sess.run([accuracy_summary, accuracy], feed_dict=...)
  test_writer.add_summary(summary, n)
  print('Accuracy at step %s: %s' % (n, acc))

You can then point TensorBoard to the parent directory (summaries_dir) and it will load both data sets.

This can be also found in the TensorFlow HowTo's https://www.tensorflow.org/versions/r0.11/how_tos/summaries_and_tensorboard/index.html

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