In this chapter, we looked at some TensorFlow-based libraries for DL research and development. We introduced tf.estimator, which is a simplified interface for DL/ML, and is now part of TensorFlow and a high-level ML API that makes it easy to train, configure, and evaluate a variety of ML models. We used the estimator feature to implement a classifier for the Iris dataset.
We also had a look at the TFLearn library, which wraps a lot of TensorFlow APIs. In the example, we used TFLearn to estimate the chance of survival of passengers on the Titanic. To tackle this task, we built a DNN classifier.
Then, we introduced PrettyTensor, which allows TensorFlow operations to be wrapped to chain any number of layers. We implemented a convolutional model in the style of LeNet to quickly resolve the handwritten classification model.
Then we had a quick look at Keras, which is designed for minimalism and modularity, allowing the user to quickly define DL models. Using Keras, we have learned how to...