TFLearn is a library that wraps a lot of new APIs by TensorFlow with the nice and familiar scikit-learn API.

TensorFlow is all about building and executing a graph. This is a very powerful concept, but it is also cumbersome to start with.

Looking under the hood of TFLearn, we used just three parts:

• Layers: This is a set of advanced TensorFlow functions that allows you to easily build complex graphs, from fully connected layers, convolution, and batch norm, to losses and optimization.
• Graph actions: This is a set of tools to perform training and evaluating, and run inference on TensorFlow graphs.
• Estimator: This packages everything in a class that follows the scikit-learn interface, and provides a way to easily build and train custom TensorFlow models. Subclasses of Estimator, such as linear classifier, linear regressor, DNN classifier, and so on ,  are pre-packaged models similar to scikit-learn logistic regression that can be used in one line.