ftrl | A Java version of ftrl algorithm | Learning library

by happynoom Java Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(4)Vulnerabilities Install Support

kandi X-RAY | ftrl Summary

ftrl is a Java library typically used in Tutorial, Learning, Example Codes applications. ftrl has no bugs, it has no vulnerabilities, it has build file available and it has low support. You can download it from GitHub.

ftrl is a Java version of Follow-the-Regularized-Leader algorithm published in paper "Follow-the-Regularized-Leader and Mirror Descent: Equivalence Theorems and L1 Regularization". It can be used for classification problems with online convex optimization.

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ftrl has a low active ecosystem.

It has 23 star(s) with 9 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 1407 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of ftrl is current.

Quality

ftrl has 0 bugs and 0 code smells.

Security

ftrl has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

ftrl code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

ftrl does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

ftrl releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

ftrl saves you 384 person hours of effort in developing the same functionality from scratch.

It has 915 lines of code, 90 functions and 12 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed ftrl and discovered the below as its top functions. This is intended to give you an instant insight into ftrl implemented functionality, and help decide if they suit your requirements.

Main entry point
Trains the given problem
Cross validation
Groups the classes in the given problem
Main program
Runs the test case
Get or return the value of the data
Read a problem from a file
Log a formatted message at the DEBUG level
Log an info message
Calculate a hash code
Returns the filename of the class
Returns a string representation of the file
Compares two features

Get all kandi verified functions for this library.

ftrl Key Features

No Key Features are available at this moment for ftrl.

ftrl Examples and Code Snippets

No Code Snippets are available at this moment for ftrl.

Community Discussions

Trending Discussions on ftrl

ai-platform: No eval folder or export folder in outputs when running TensorFlow 2.1 training job using Estimators

TensorFlow 2.0: Cant run minimal TF Tutorial: TypeError: Can not convert a int64 into a Tensor or Operation

"ValueError: Unknown optimizer: momentum" correct name for Momentum Optimizer?

Why TensorFlow in Go didn't find the optimizer as python?

QUESTION

ai-platform: No eval folder or export folder in outputs when running TensorFlow 2.1 training job using Estimators

Asked 2020-Jun-12 at 23:16

The Problem

My code works locally, but I am not able to get any evaluation data or exports from my TensorFlow estimator when submitting online training jobs after having upgraded to TensorFlow 2.1. Here's the bulk of my code:

...

ANSWER

Answered 2020-Jun-12 at 23:16

Found the answer...

Based on documentation about the TF_CONFIG environment variable...

master is a deprecated task type in TensorFlow. master represented a task that performed a similar role as chief but also acted as an evaluator in some configurations. TensorFlow 2 does not support TF_CONFIG environment variables that contain a master task.

So previously we were using TF 1.X, which used a master worker. But, master has been deprecated when training TF 2.X jobs. Now the default is chief, but chief by default does not act as an evaluator. In order to get evaluation data, we needed to update our config yaml to explicitly allocate an evaluator.

https://cloud.google.com/ai-platform/training/docs/distributed-training-details#tf-config-format

We updated our config.yaml with evaluatorType and evaluatorCount

Source https://stackoverflow.com/questions/62337037

QUESTION

TensorFlow 2.0: Cant run minimal TF Tutorial: TypeError: Can not convert a int64 into a Tensor or Operation

Asked 2020-Jan-04 at 04:57

im new to machine learning and i am trying to follow this tutorial to get a grasp :

https://www.tensorflow.org/tutorials/estimator/boosted_trees

...

ANSWER

Answered 2020-Jan-04 at 04:57

I was able to reproduce the correct results from TF's Boosted_Trees Estimator example as follows (without any modifications to their code):

I guess you might be getting error mainly because of an incorrect version of TensorFlow (or other dependencies) installed.

Check the version of tensorflow you're using by running tf.__version__ in the terminal after importing it.

Hope this helps!

Source https://stackoverflow.com/questions/59586949

QUESTION

"ValueError: Unknown optimizer: momentum" correct name for Momentum Optimizer?

Asked 2019-Nov-24 at 23:00

I am trying to train my program using the Momentum optimizer but when I input "momentum" as the optimizer, it gives me this error:

...

ANSWER

Answered 2019-Nov-24 at 23:00

Tensorflow has no plain "momentum" optimizer: tensorflow.org/api_docs/python/tf/optimizers in TensorFlow. Though Tutorialpoints references to it.

Nevertheless it has MomentumOptimizer() class.

So you should first define a MomentumOptimizer() class instance, then you can pass through as parameter to the compile() method.

Note: lr(learning rate) and m(momentum) parameters need to be defined by you.

Source https://stackoverflow.com/questions/59011145

QUESTION

Why TensorFlow in Go didn't find the optimizer as python?

Asked 2017-Sep-16 at 20:06

I am a newbie of TensorFlow in Go.

There are some doubts during my first traing demo. I just find one optimizer in Go's wrappers.go.

But i learn the demos of python,they has serveral optimizers. Like

...

ANSWER

Answered 2017-Sep-16 at 08:33

You can't train a Tensorflow model using Go.

The only thing you can do is load a pre-trained model and use it for the inference.

You can't because the Go implementation lacks the Variable support, therefore it's impossible to train anything at the moment.

Source https://stackoverflow.com/questions/46249621

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install ftrl

You can download it from GitHub.
You can use ftrl like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the ftrl component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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