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.
Using Python 3 in a virtual environment, install the required packages via pip.
pip install -r requirements.txtYou might need to edit the version of TensorFlow in the requirements file. Details are found on TensorFlow's installation guide.
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. |
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.