TensorFlow Serving is a library for serving TensorFlow models in a production setting, developed by Google.Īny Keras model can be exported with TensorFlow-serving (as long as it only has one input and one output, which is a limitation of TF-serving), whether or not it was training as part of a TensorFlow workflow. set_session ( sess )įor more information about using TensorFlow in a distributed setting, see this tutorial. target ) from keras import backend as K K. You can trivially make use of TensorFlow distributed training by registering with Keras a TF session linked to a cluster: eval ( feed_dict = ) Distributed training

This means that Keras will use the session we registered to initialize all variables that it creates internally.įrom trics import categorical_accuracy as accuracy acc_value = accuracy ( labels, preds ) with sess. We should start by creating a TensorFlow session and registering it with Keras. We will build a TensorFlow digits classifier using a stack of Keras Dense layers (fully-connected layers). Let's start with a simple example: MNIST digits classification. IV: Exporting a model with TensorFlow-serving I: Calling Keras layers on TensorFlow tensors Note that this tutorial assumes that you have configured Keras to use the TensorFlow backend (instead of Theano). Keras layers and models are fully compatible with pure-TensorFlow tensors, and as a result, Keras makes a great model definition add-on for TensorFlow, and can even be used alongside other TensorFlow libraries. If TensorFlow is your primary framework, and you are looking for a simple & high-level model definition interface to make your life easier, this tutorial is for you. A complete guide to using Keras as part of a TensorFlow workflow Please see the keras.io documentation for details. Keras has now been integrated into TensorFlow. It no longer reflects TensorFlow and Keras best practices.

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