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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

by mmq 11y ago

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

dgacmu 11y ago
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/inception/slim 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.)
radarsat1 11y ago
And keras? http://keras.io http://keras.io
nl 11y ago
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.pipeline.Pipeline.html http://scikit-learn.org/stable/modules/generated/sklearn.pip...
- fchollet 11y ago
  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/scikit_learn.py#L229 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.
  - nl 11y ago
    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)
- mmq 11y ago
  I think it's a maintained by google, this is one of the main contributors of skflow https://github.com/ilblackdragon https://github.com/ilblackdragon. Also the repo is under tensorflow organisation.

by mmq 11y ago

dgacmu 11y ago

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/inception/slim 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.)

radarsat1 11y ago

And keras? http://keras.io http://keras.io

nl 11y ago

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.pipeline.Pipeline.html http://scikit-learn.org/stable/modules/generated/sklearn.pip...

fchollet 11y ago
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/scikit_learn.py#L229 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.
- nl 11y ago
  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)
mmq 11y ago
I think it's a maintained by google, this is one of the main contributors of skflow https://github.com/ilblackdragon https://github.com/ilblackdragon. Also the repo is under tensorflow organisation.