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Debugging and Monitoring TensorFlow Programs

Debugging and Monitoring TensorFlow Programs

1425/month
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Self paced
English
Course by
PluralsightCourses from Pluralsight
Certificate awarded
Intermediate
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Checklist

Certification

You will get a certificate on completing this course.

University

This course is not affiliated with any university.

Price

This course costs very less.

Edvicer's Rewards

You can get a cashback of ₹ 100 on buying this course.

Why should you choose this course?

Description

This course goes deep into two specific tools in the TensorFlow toolkit - tfdbg and TensorBoard. These tools can be used to examine the internal state of TensorFlow programs and to visualize execution metrics and state.

Syllabus

Course Overview
Module Overview
Prerequisites and Course Overview
A Brief Overview of Computation Graphs
Debugging TensorFlow Programs
Fetching Tensors: Computing Intermediate Values
Fetching Tensors: A Neural Network Example
Fetching Tensors: Side Effects
Partial Runs
Introducing tf.Print()
tf.Print(): A Neural Network Example
Introducing tf.Assert()
Traditional Python Debuggers
Interposing Python Code in Computation Graphs
Introducing tfdbg and TensorBoard
Module Overview
The Curses Library for tfdbg
Introducing tfdbg Commands
Debugging Shortcuts and Multiple Session Runs
Using has_inf_or_nan and Custom Filters
Using Filters with Neural Networks
Debugging Estimators and Experiments
Debugging Keras Models
Module Overview
Introducing TensorBoard
Naming Tensors and Nodes
Using Named Scopes
Scalar Summaries
Histograms
Moving Mean Normal Distribution
More Histograms
Runtime Statistics
Working with Images

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