TFLearn

4.0
(5)

TFlearn is a modular and transparent deep learning library built on top of Tensorflow that provide a higher-level API to TensorFlow in order to facilitate and speed-up experimentations, while remaining fully transparent and compatible with it.

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TFLearn review by Chathuri J.
Chathuri J.
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"An easy-to-use and efficient API for building deep NNs very fast"

What do you like best?

We can use Tensorflow to build up neural networks easily. yet, TFlearn has made this task even easier with its built-in functions and this leaves me to do less amount of coding. While Tensorflow needs around 12 lines of coding to build a fully connected neural networks, TFLearn builds the same neural network with only five lines od coding. Further, TFLearn provides very useful and descriptive visualization on the built deep NN. It supports not only deep NNs but also other NN architectures such as CNN, LSTM etc. as well.

What do you dislike?

One of the drawbacks of TFLearn is, it is possible to have issues in executing your algorithms after updating the API due to depreciation of certain functions. Yet, this also not be the case sometime. Yet, it is better if the developers of TFLearn can take care of this issue as well.

Recommendations to others considering the product

TFLearn is a very useful tool to have in your Machine Learning toolkit if you are dealing with neural networks more often. The TFlearn tutorial is also available which will give a thorough guide in starting to use the API. TFlearn has the ability to build up different types of deep learning models in a very short time with less effort. Therefore, TFLearn can be highly recommended to any ML practitioner.

What business problems are you solving with the product? What benefits have you realized?

Currently I am working on a project which is involved with machine vision. There, I had to implement a Convolution Neural network to extract some information from image data. In completing this task, TFLearn was a very useful tool as it reduces the whole coding amount and its tutorials were also very helpful to me.

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TFLearn review by Aruna J.
Aruna J.
Validated Reviewer
Verified Current User
Review Source

"rapid prototyping tool for deep learning models"

What do you like best?

The best thing related to TFLearn is it have inbuilt functions for all the machine learning functions and equations in a single line of code most of the time. Therefore I feel like it is the best rapid prototyping tool that can be used to develop fast deep learning models. The next best thing that I love is TFlearn has not of tutorials and backup support. The matrix operations are handled by the Tensorflow developed by Google. The TFLearn runs on top of Tensorflow. The next best thing is that it supports normal CPU operation and also the GPU operation. It runs very fast on CUDA cored GPU. Easy to test models on different devices.

What do you dislike?

It is a bigger library. Updates are done to the library very frequently. Once I had an issue with the version of the library. After installing the previous version of the TFLearn the problem was solved. Other than that problem no other problems were occurred as for my experience.

Recommendations to others considering the product

TFLearn is the best prototyping tool for featuring more high-level function for Tensorflow. Most of the new Machine learning and deep learning products are developed on top of Tensorflow library. Therefore if you do experiments with deep learning and ai models TFLearns speed up them. Just you can implement models with less number of code lines.

What business problems are you solving with the product? What benefits have you realized?

Developing deep learning models, rapid prototyping deep learning models, Testing different activation functions with the output behaviour.

What Artificial Neural Network solution do you use?

Thanks for letting us know!
TFLearn review by Ahmad A.
Ahmad A.
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Verified Current User
Review Source

"If you are not expert in machine learning and want to start somewhere, TFLearn is the best"

What do you like best?

It is very easy to learn. It is higher-level API to TensorFlow and makes building up a machine learning model much faster and easier with less complications.

What do you dislike?

The explanations on its website about different parts and models and libraries is way far from being comprehensive, In most of the cases, a feature is just explained with few lines.

Recommendations to others considering the product

Do not just use the library as a black box, but also try to read and understand the modules of the library,

it is easy to understand and will help you gain confidence in changing something in the functions of the library, in case needed

What business problems are you solving with the product? What benefits have you realized?

As a researcher and engineer, it gave me the ability to easily integrate my medium knowledge of machine learning into the real life application that I needed.

TFLearn review by G2 Crowd User
G2 Crowd User
Validated Reviewer
Review Source

"Good but not that great"

What do you like best?

A good higher level abstraction for using deep learning models out of the box. Saves the headache of having to create a manual configuration from scratch in tensorflow (other than if you want to use the Estimator API, which isn't that configurable). If I need to test a new architecture for my business-case, this can easily spin up one for me, the default configurations are quite usable.

What do you dislike?

Quite a few things, firstly why hasn't this merged with Tensorflow yet? Why is there no one addressing Github issues promptly? This is one of the few opensource libraries with over 500 open issues most of which seem to be legit on opening manually, and no one but the developer can address it as its not a usage doubt, but a bug.

Recommendations to others considering the product

If you want easy and speedily available test networks, go for it but in the long run more stable networks on the original tensorflow will be better.

What business problems are you solving with the product? What benefits have you realized?

Trying out deep learning models wasn't easier ever. TFLearn can give a workable solution very easily and its graph visualization helps in explaining the technical process to non-technical beneficiaries very conveniently. I was very much used to sklearn's API and now seeing something similar for tensorflow is like a win-win situation where I get the ease of a consistent API and power of the new Tensorflow library.

TFLearn review by G2 Crowd User in Higher Education
G2 Crowd User in Higher Education
Validated Reviewer
Verified Current User
Review Source

"good framework to start with"

What do you like best?

simple abstraction on tensorflow, much easier to use than tf

What do you dislike?

not as powerful than tensorflow in terms of functionality

What business problems are you solving with the product? What benefits have you realized?

machine learning

Kate from G2 Crowd

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