Always private
DuckDuckGo never tracks your searches.
Learn More
You can hide this reminder in Search Settings
All regions
Argentina
Australia
Austria
Belgium (fr)
Belgium (nl)
Brazil
Bulgaria
Canada (en)
Canada (fr)
Catalonia
Chile
China
Colombia
Croatia
Czech Republic
Denmark
Estonia
Finland
France
Germany
Greece
Hong Kong
Hungary
Iceland
India (en)
Indonesia (en)
Ireland
Israel (en)
Italy
Japan
Korea
Latvia
Lithuania
Malaysia (en)
Mexico
Netherlands
New Zealand
Norway
Pakistan (en)
Peru
Philippines (en)
Poland
Portugal
Romania
Russia
Saudi Arabia
Singapore
Slovakia
Slovenia
South Africa
Spain (ca)
Spain (es)
Sweden
Switzerland (de)
Switzerland (fr)
Taiwan
Thailand (en)
Turkey
Ukraine
United Kingdom
US (English)
US (Spanish)
Vietnam (en)
Safe search: moderate
Strict
Moderate
Off
Any time
Any time
Past day
Past week
Past month
Past year
  1. Eager evaluation

    In computer programming, eager evaluation, also known as strict evaluation or greedy evaluation, is the evaluation strategy used by most traditional programming languages. In eager evaluation, an expression is evaluated as soon as it is bound to a variable. An opposite alternative to eager evaluation is lazy evaluation, where expressions are evaluated only when a dependent expression is evaluated. Wikipedia

    Was this helpful?
  2. stackoverflow.com

    Eager execution evaluates immediately. In context of TensorFlow, it does not create a graph. tf.constant creates a constant, with a certain value, right then and there. If compared to the Rube Goldberg machine, you could imagine building the first part of the machine, drop the marble in, then stop it when the marble exits, before building the ...
  3. Was this helpful?
  4. towardsdatascience.com

    · Eager execution runs by default on CPU, to use GPU include below code: with tf.device('/gpu:0') · Eager execution doesn't create Tensor Graph, to build graph just remove the tf.enable_eager_execution(). · Eager execution is good for R&D but for production you should use graph execution.
  5. research.google

    The same code that executes operations when eager execution is enabled will construct a graph describing the computation when it is not. To convert your models to graphs, simply run the same code in a new Python session where eager execution hasn't been enabled, as seen, for example, in the MNIST example. The value of model variables can be ...
  6. blog.tensorflow.org

    August 10, 2018 — By Xuechen Li, Software Engineering Intern OverviewEager execution simplifies the model building experience in TensorFlow, whereas graph execution can provide optimizations that make models run faster with better memory efficiency. This blog post showcases how to write TensorFlow code so that models built using eager execution with the tf.keras API can be converted to ...
  7. docs.w3cub.com

    Eager execution is a flexible machine learning platform for research and experimentation, providing: An intuitive interface—Structure your code naturally and use Python data structures. Quickly iterate on small models and small data. Easier debugging—Call ops directly to inspect running models and test changes. Use standard Python debugging ...
  8. tensorflow.org

    Aug 15, 2024In TensorFlow 2, eager execution is turned on by default. The user interface is intuitive and flexible (running one-off operations is much easier and faster), but this can come at the expense of performance and deployability. You can use tf.function to make graphs out of your programs. It is a transformation tool that creates Python-independent ...
  9. tensorflow.org

    Mar 23, 2024Use eager execution to run your code step-by-step to inspect shapes, data types and values. Certain APIs, like tf.function, tf.keras, etc. are designed to use Graph execution, for performance and portability. When debugging, use tf.config.run_functions_eagerly(True) to use eager execution inside this code.
  10. en.wikipedia.org

    Eager execution is a form of speculative execution where both sides of the conditional branch are executed; however, the results are committed only if the predicate is true. With unlimited resources, eager execution (also known as oracle execution) would in theory provide the same performance as perfect branch prediction.
  11. tensorflow.org

    Aug 15, 2024While eager execution has several unique advantages, graph execution enables portability outside Python and tends to offer better performance. Graph execution means that tensor computations are executed as a TensorFlow graph, sometimes referred to as a tf.Graph or simply a "graph."
  12. analyticsindiamag.com

    When Eager Execution is disabled, the tensor manipulation is limited. For example, tensor operations will yield a tensor object and not the result of the operations. Refer to the code example below. Model Output Before Training. Even without training, call the model and inspect the output in eager execution. A TensorFlow model needs to be ...

    Can’t find what you’re looking for?

    Help us improve DuckDuckGo searches with your feedback

  1. Lazy execution delays evaluation as much as possible. In context of TensorFlow, it will create a plan of execution (a graph) before it does anything, then when everything is ready, it will feed the input into the graph and calculate everything to return the output. tf.constant creates an execution node in the graph that will receive a constant value when the execution starts. You can compare lazy evaluation to a Rube Goldberg machine: you build the whole thing, then you drop a marble into it and watch the magic unfold.

    Eager execution evaluates immediately. In context of TensorFlow, it does not create a graph. tf.constant creates a constant, with a certain value, right then and there. If compared to the Rube Goldberg machine, you could imagine building the first part of the machine, drop the marble in, then stop it when the marble exits, before building the next part of the machine, and reintroducing the marble with the same position and velocity. :P

    TensorFlow is much more efficient in the lazy evaluation mode, but the eager mode is easier to develop and debug on.

    --Amadan

    Was this helpful?
Custom date rangeX