4. Check that your prompt changed to:

(SwiftNLC) $

5. Launch Jupyter Notebook:

jupyter notebook

6. Open your browser to:

http://localhost:

To create a basic model with Keras/TensorFlow and export it with

CoreMLTools, open createModelWithNSLinguisticTaggerEmbedding in

your Jupyter browsing session and execute any cells in order to create,

save, and export the Keras Model using Core ML exporting tools.

The basic Core ML model will be saved in the current folder.

Keras / TensorFlow model consideration

As discussed above, the Swift Embedder project is generally responsible

for massaging the dataset, and responsible in particular for creating the

one-hot word encoding for stem words from sample utterances and for

creating a corpus of stemmed, encoded documents to be used for

training the model.

For this reason, the Python code to create the model is simplied with

very little pre-processing, and it quickly starts to create the TensorFlow

model used for learning.

This model is a simple, fully connected network that receives as input

an array of embedded 0's and 1's for each sample utterance. It uses

‘relu’ as an activator for the rst and an internal hidden layer, and then

at the end, as this is a multi-class classier, it uses ‘softmax’ to

emphasize the winner prediction.