Personally, I find Keras better than tflearn. But you need to know the fundamentals on TF and NNs (RNNs,LSTMs, etc...). Keras makes it easier to build on those

Personally, I find Keras better than tflearn. But you need to know the fundamentals on TF and NNs (RNNs,LSTMs, etc...). Keras makes it easier to build on those concepts with less programming. I've found TFLearn to be slightly complicated. Both Keras and tflearn make it simpler to deal with TF. Creating a good train-test dataset is general ML problem. Keras doesn't solve that and isn't meant to do that. However, Keras (and tflearn too) makes it easy to throw a statistically bad dataset to an NN, add multiple layers and then let TF take over and derive a inefficient model in a few hours. The amazing part is that the inefficient NN (driven by TF) might still return a slightly acceptable accuracy. This is awesome because you may be an amateur and yet have some okay results to start with. Later you can improvise the dataset to improve the accuracy. In general, throwing NNs at everything isn't good. They result in hard-to-decompile blackbox models. If NNs give you good classification, you could also try the same with other classifiers. You could also start looking into scikit-learn algos and see if those could be used in your case.