Tensorflow: From CSV to API

Code from this tutorial about creating a REST API which uses a model trained using TensorFlow. The code goes through the entire process from the downloading of a CSV training file to hosting the model in an API.

Installation

Using Python 3 in a virtual environment, install the required packages via pip.

pip install -r requirements.txt

You might need to edit the version of TensorFlow in the requirements file. Details are found on TensorFlow's installation guide.

Running

There are four commands which include a number of arguments.

Command	Explanation
python iris-network-download.py -vv	Download example Iris data from UCI and convert the CSV into a test and train dataset with the Iris species converted to a one-hot vector representation.
python iris-network-train.py -vv	Train a Neural Network using feed forward learning and save checkpoint models to a directory.
python iris-network-test.py -vv	Test the trained Neural Network to check for accuracy.
python iris-network-predict.py --feature 5.5,4.2,1.4,0.2 -vv	Check the prediction for an Iris described by the features 5.5,4.2,1.4,0.2 which correspond to Sepal Length, Sepal Width, Petal Length and Petal Width.
gunicorn web.iris:api	Start a gunicorn webserver to host the Falcon API.

Testing

This code is not meant to be used in production and doesn't provide necessary tests to validate its functionality.

If you're writing TensorFlow tests there is a useful Python class which makes testing a graph fairly trivial.