This course teaches learners how to debug TensorFlow for TF 2 and TF 1. The learning outcomes include understanding debugging concepts in machine learning, printing Eager Tensor values, accessing and fetching graph-internal tensors programmatically, finding device placement, plotting function graphs, and debugging Keras models. The course utilizes methods such as tf.print(), programmatically accessing tensors, step debugging, and using TensorBoard callback. The intended audience for this course is individuals interested in TensorFlow debugging techniques.
Overview
Syllabus
Intro
Scope of this talk: "debugging" is an overloaded term in ML
Printing Eager Tensor values
Printing the value of graph-internal tensors
Homework: tf.print() on composite tensors
Programmatically access graph-internal tensor values
Programmatically fetching graph-internal tensors: While loop?
Finding device placement: Pure eager execution
Finding out device placement: tf.function
Getting and plotting the graph of a function: Colab (google3 only)
Dumping Grappler outputs: The graph that actually (almost) gets executed at runtime (bazel builds)
t.print: may change runtime graph optimization
t.config.experimental_run_functions_eagerly
Step debugging: Using tf.config.experimental_run_functions_eagerly
Step debugging: What happens inside a non-eagerly-executing function?
tf.config.experimental_run_functions eagerly does not work on tf.data.Dataset.mapo
Getting Access to tf.keras Layer Activations
Debugging Keras Models with TensorBoard callback
Parting notes
Taught by
TensorFlow
