I agree that sklearn-compatibility is the strong point of Skflow. If you are familiar with Keras, note that you can do the same with any Keras model, via the sklearn wrapper: https://github.com/fchollet/keras/blob/master/keras/wrappers...
> I don't think the SKFlow author(s) are at Google. Prettytensor's are, but it isn't Google supported.
I believe they are. Also I do believe that PrettyTensor is an internal Google project.
I didn't know about the Keras/SKLearn wrapper - nice.
(Keras is a beautiful piece of work, so thanks for all your work on that BTW)
I think it's a maintained by google, this is one of the main contributors of skflow https://github.com/ilblackdragon.
Also the repo is under tensorflow organisation.
I haven't used tflearn or prettytensor, but I have used skflow (and a bit of raw TensorFlow).
SKFlow is nice if you are already using scikit learn because you can drop it straight into your sklean Pipelines[1]. This is great in terms of making it usable alongside other systems.
For example, I currently have a project using an ensemble of regression methods (2 different RandomForest regressors, and 3 XGB methods, then multiple different seeds for each method). SKFlow lets me drop in a TensorFlow regressor as well.
(In actual fact I can't get TF to perform as well as a RF on my featureset, and XGB outperforms it by far. This is using a relatively simple NN though).
[1] http://scikit-learn.org/stable/modules/generated/sklearn.pip...