TensorFlow Simplified Interface
github.com
TensorFlow Simplified Interface
1–10 of 15 posts
Re: TensorFlow Simplified Interface
#2OMG, it really does look easy to use, great work!
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/...
Re: TensorFlow Simplified Interface
#3I 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.
Re: TensorFlow Simplified Interface
#4OMG, it really does look easy to use, great work! 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/...
I was quite surprise to see that the numpy version is only about a hundred lines.
Re: TensorFlow Simplified Interface
#5How it can be compared to keras?
Re: TensorFlow Simplified Interface
#6How is tflearn different than skflow[1] or prettytensor[2], both these modules are developed by google's teams.
Re: TensorFlow Simplified Interface
#7How is tflearn different than skflow[1] or prettytensor[2], both these modules are developed by google's teams. [1] https://github.com/tensorflow/skflow/ [2] https://github.com/google/prettytensor
It's also reasonably similar to tf.slim, which we haven't released as a standalone supported component yet, but that's included in the release of the trainable inception model -- https://github.com/tensorflow/models/tree/master/inception/i...
I think it's great that we're seeing a lot of experimentation in how to express models for machine learning. It's very clear we haven't found the best ways yet, and seeing what people choose to try to make easy to express (and harder) is good for progress.
(I'm still grouchy about the state of modularity within machine learning models. It's not easy enough to reuse / have libraries at the model level yet.)
Re: TensorFlow Simplified Interface
#8How is tflearn different than skflow[1] or prettytensor[2], both these modules are developed by google's teams. [1] https://github.com/tensorflow/skflow/ [2] https://github.com/google/prettytensor
And keras? http://keras.io
Re: TensorFlow Simplified Interface
#9I 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!
Re: TensorFlow Simplified Interface
#10How is tflearn different than skflow[1] or prettytensor[2], both these modules are developed by google's teams. [1] https://github.com/tensorflow/skflow/ [2] https://github.com/google/prettytensor
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...