Articles about tensorflow

Articles about Google's deep learning framework

Cyclical learning rates with Tensorflow Implementation

The learning rate is considered as the most important hyperparameter in a neural network (Bengio 2012). Finding the right one is thus quite crucial. Even better is to find a good learning rate scheduling: modifying the learning rate during the training so that the model has a bigger chance to reach a better optimum. The goal of this article is to describe a learning rate scheduling that seems to work well, along its Tensorflow implementation and an example with a simple CNN on the MNIST dataset.

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Transforming Keras Model into Tensorflow Estimator

A Tensorflow Estimator is a convenient object to manage models, especially for production. And Keras is a convenient library to build models. Thus combining both is a powerful way to leverage their strenghts. Especially since Keras will be the standard for building models in Tensorflow 2.0 Let see how it works:

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