I am writing a custom attention mechanism in TensorFlow and keep getting "Incompatible shapes" errors that are hard to trace. Even with eager execution enabled, I find it difficult to inspect the intermediate tensors during the forward pass. Is there a better way to use tf.print or the Python debugger without slowing down the training loop too much for my daily work?
3 answers
Debugging custom logic in TensorFlow can be tricky because of the way the graph is compiled. If you are using eager execution, you can actually use standard Python breakpoints with pdb, but this only works if you aren't inside a @tf.function decorated block. When you move to a graph context, tf.print is your best friend. It allows you to log the values and shapes of tensors directly from the GPU execution. A pro tip is to use tf.debugging.assert_shapes at the start of your custom layer. This will throw an immediate, clear error if the input doesn't match your expectations, which is much better than a vague C++ error later on.
Do you find that using TensorFlow's TensorBoard debugger plugin provides enough visual detail for your specific attention weights?
Try wrapping your code in a simple try-except block and use tf.config.run_functions_eagerly(True) specifically for the debugging session.
Great advice, Laura! Temporarily forcing eager mode is a classic TensorFlow move that makes the variable inspection much more "Pythonic" and less frustrating.
I’ve tried the TensorBoard debugger, Jason, but it feels a bit laggy for real-time inspection. I usually end up just logging the "mean" and "std" of my weights as scalars to see if they are exploding. For this TensorFlow project, I really need to see the actual distribution of the attention scores to make sure the softmax isn't saturating. I think I’ll try the shape assertion tip Ashley mentioned; it sounds like it would catch the 4D vs 3D tensor mix-ups that keep crashing my training script in the third epoch.