How does one debug NaN values in TensorFlow?

There are a couple of reasons WHY you can get a NaN-result, often it is because of too high a learning rate but plenty other reasons are possible like for example corrupt data in your input-queue or a log of 0 calculation.

Anyhow, debugging with a print as you describe cannot be done by a simple print (as this would result only in the printing of the tensor-information inside the graph and not print any actual values).

However, if you use tf.print as an op in bulding the graph (tf.print) then when the graph gets executed you will get the actual values printed (and it IS a good exercise to watch these values to debug and understand the behavior of your net).

However, you are using the print-statement not entirely in the correct manner. This is an op, so you need to pass it a tensor and request a result-tensor that you need to work with later on in the executing graph. Otherwise the op is not going to be executed and no printing occurs. Try this:

Z = tf.sqrt(Delta_tilde)
Z = tf.Print(Z,[Z], message="my Z-values:") # <-------- TF PRINT STATMENT
Z = Transform(Z) # potentially some transform, currently I have it to return Z for debugging (the identity)
Z = tf.pow(Z, 2.0)

I used to find it's much tougher to pinpoint where the nans and infs may occur than to fix the bug. As a complementary to @scai's answer, I'd like to add some points here:

The debug module, you can imported by:

from tensorflow.python import debug as tf_debug

is much better than any print or assert.

You can just add the debug function by changing your wrapper you session by:

sess = tf_debug.LocalCLIDebugWrapperSession(sess)
sess.add_tensor_filter("has_inf_or_nan", tf_debug.has_inf_or_nan)

And you'll prompt an command line interface, then you enter: run -f has_inf_or_nan and lt -f has_inf_or_nan to find where the nans or infs are. The first one is the first place where the catastrophe occurs. By the variable name you can trace the origin in your code.

Reference: https://developers.googleblog.com/2017/02/debug-tensorflow-models-with-tfdbg.html

It look like you can call it after you complete making the graph.

check = tf.add_check_numerics_ops()

I think this will add the check for all floating point operations. Then in the sessions run function you can add the check operation.

sess.run([check, ...])