TFLearn is a library that wraps a lot of new TensorFlow APIs with the nice and familiar scikit-learn API.
TensorFlow is all about a building and executing graphs. This is a very powerful concept, but it is also cumbersome to start with.
Looking under the hood of TF.Learn, we just used three parts:
- layers: A set of advanced TensorFlow functions that allow us to easily build complex graphs, from fully connected layers, convolution, and batch norm to losses and optimization.
- graph_actions: A set of tools to perform training, evaluating, and running inference on TensorFlow graphs.
- Estimator: This packages everything into a class that follows scikit-learn interface and provides a way to easily build and train custom TensorFlow models.