Keras VS TFLearn

Compare Keras vs TFLearn and see what are their differences.

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Keras TFLearn
32 1
53,748 9,576
0.9% 0.1%
9.8 0.0
3 days ago 12 months ago
Python Python
GNU General Public License v3.0 or later MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

Keras

Posts with mentions or reviews of Keras. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-01-11.

TFLearn

Posts with mentions or reviews of TFLearn. We have used some of these posts to build our list of alternatives and similar projects.
  • Base ball
    1 project | dev.to | 20 Mar 2021
    Both the teams in a game are given their individual ID values and are made into vectors. Relevant data like the home and away team, home runs, RBI’s, and walk’s are all taken into account and passed through layers. There’s no need to reinvent the wheel here, there's a multitude of libraries that enable a coder to implement machine learning theories efficiently. In this case we will be using a library called TFlearn, documentation available from http://tflearn.org. The program will output the home and away teams as well as their respective score predictions.

What are some alternatives?

When comparing Keras and TFLearn you can also consider the following projects:

scikit-learn - scikit-learn: machine learning in Python

MLP Classifier - A handwritten multilayer perceptron classifer using numpy.

tensorflow - An Open Source Machine Learning Framework for Everyone

xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

skflow - Simplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning

PyBrain

Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

gensim - Topic Modelling for Humans

H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.