
TensorFlow Simplified Interface - aymericdamien
https://github.com/tflearn/tflearn
======
feylikurds
OMG, it really does look easy to use, great work!

[http://tflearn.org/getting_started/#high-level-api-
usage](http://tflearn.org/getting_started/#high-level-api-usage)

Incredible, the Shakespearean text generator is only 42 lines long!

[https://github.com/tflearn/tflearn/blob/master/examples/nlp/...](https://github.com/tflearn/tflearn/blob/master/examples/nlp/lstm_generator_shakespeare.py)

~~~
wodenokoto
I was quite surprise to see that the numpy version is only about a hundred
lines.

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mmq
How is tflearn different than skflow[1] or prettytensor[2], both these modules
are developed by google's teams.

[1]
[https://github.com/tensorflow/skflow/](https://github.com/tensorflow/skflow/)

[2]
[https://github.com/google/prettytensor](https://github.com/google/prettytensor)

~~~
nl
I don't think the SKFlow author(s) are at Google. Prettytensor's are, but it
isn't Google supported.

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...](http://scikit-
learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html)

~~~
fchollet
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...](https://github.com/fchollet/keras/blob/master/keras/wrappers/scikit_learn.py#L229)

> 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.

~~~
nl
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)

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mrdrozdov
I had doubts reading on Yann Lecun's fb feed that machine learning could be as
big as the web, but all these jQuery-like libraries for ml have me second
guessing.

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kwrobel
How it can be compared to keras?

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dvcrn
I think the progress in neural networks is amazing. Every week the usage of
tensorflow and friends seem to become a good chunk easier to use. Even people
like me with close to no mathematical background can now spin up tensorflow
and run a neural network. I'm loving it!

~~~
danieldk
I would encourage you to try to use Tensorflow directly. Once you understand
placeholders, variables, etc., it is not that much harder than most higher
level wrappers.

It requires a bit more thinking ahead-of-time thinking, but also gives far
more flexibility.

That said, I have found Keras to be excellent for quick experiments. Even
more, because it supports both Tensorflow and Theano as backends.

------
estefan
What's the best way to learn about these algorithms? Do a course on deep
learning & neural nets, or is there some other less time-intensive way?

