TensorFlow on iOS demo
This is the code that accompanies my blog post Getting started with TensorFlow on iOS.
It uses TensorFlow to train a basic binary classifier on the Gender Recognition by Voice and Speech Analysis dataset.
This project includes the following:
- The dataset in the file voice.csv.
- Python scripts to train the model with TensorFlow on your Mac.
- An iOS app that uses the TensorFlow C++ API to do inference.
- An iOS app that uses Metal to do inference using the trained model.
Training the model
To train the model, do the following:
- Make sure these are installed:
- Run the split_data.py script to divide the dataset into a training set and a test set. This creates 4 new files:
- Run the train.py script. This trains the logistic classifier and saves the model to
/tmp/voiceevery 10,000 training steps. Training happens in an infinite loop and goes on forever, so press Ctrl+C when you're happy with the training set accuracy and the loss no longer becomes any lower.
- Run the test.py script to compute the accuracy on the test set. This also prints out a report with precision / recall / f1-score and a confusion matrix.
Using the model with the iOS TensorFlow app
To run the model on the iOS TensorFlow app, do the following:
- Clone TensorFlow and build the iOS library.
- Open the VoiceTensorFlow Xcode project. In Build Settings, Other Linker Flags and Header Search Paths, change the paths to your local installation of TensorFlow.
The model is already included in the app as inference.pb. If you train the model with different settings, you need to run the
optimize_for_inference tools to create a new inference.pb.
Using the model with the iOS Metal app
To run the model on the iOS Metal app, do the following:
- Run the export_weights.py script. This creates two new files that contain the model's learned parameters:
W.binfor the weights and
b.binfor the bias.
b.bininto the VoiceMetal Xcode project and build the app.
You need to run the Metal app on a device, it won't work in the simulator.