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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

Description:

Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore TensorFlow debugging techniques for both TF 2 and TF 1 in this 38-minute video presentation by Software Engineer Shanqing Cai. Learn about printing Eager Tensor values, accessing graph-internal tensor values, finding device placement in pure eager execution and tf.function, visualizing function graphs, dumping Grappler outputs, step debugging, and debugging Keras models with TensorBoard callback. Gain insights into advanced debugging methods such as using tf.print() on composite tensors, fetching tensors from while loops, and accessing tf.keras Layer Activations. Discover how to optimize your TensorFlow debugging workflow and improve your machine learning development process.

Inside TensorFlow - TF Debugging

TensorFlow

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#Computer Science #Machine Learning #TensorFlow #Tensorboard