Training and saving a TensorFlow model
To deploy a deep learning model in production, you first need a model to begin with. For that we've prepared a
Python script called
train.py for you that does the following steps:
- It loads the MNIST dataset containing handwritten digits and their labels.
- It defines a small Multilayer Perceptron (MLP) model in Tensorflow.
- It then trains this model on MNIST data and stores the resulting model to the file
If you're interested in the details, click on
train.py in the editor on the right. If you're
ready to go, you can simply run this script by pasting this snippet to the interactive shell
in the lower right window:
As the script runs through, you'll see that
model.pb gets created in the file tree of
your editor window.
That completes the first step. We can now move on to deploying your TensorFlow model with the Skymind Intelligence Layer (SKIL).