openFrameworks addon for Running trained Keras Deep Learning models in C++.
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Failed to load latest commit information.
example-ios-imageregression
example-mnist_mlp
images
libs
src
.gitignore
.gitmodules
LICENSE
README.md
addon_config.mk
convert_keras_model.py

README.md

ofxKerasify

openFrameworks addon for Running trained Keras Deep Learning models in C++.

based on moof2k's wonderful kerasify project. https://github.com/moof2k/kerasify

Disclaimer

Example 1: MNIST MLP (example-mnist_mlp)

  1. Train MLP model for MNIST using Keras example https://github.com/fchollet/keras/blob/master/examples/mnist_mlp.py

  2. Save trained model model.save('mnist_mlp.h5')

  3. "Kerasify" the saved model using python script

$ python convert_keras_model.py mnist_mlp.h5 mnist_mlp.model
  1. Copy the converted model into "data" folder of ofApp

  2. Load model

kerasify.loadModel("mnist_mlp.model");
  1. Inference
// Image -> vector
ofPixels pix = img.getPixels();
std::vector<float> input;
for (int i = 0; i < pix.size(); i++){
    input.push_back(pix.getData()[i]/255.0);
}

// INFERENCE
output.resize(10); // output vector should be [10]. because we have 10 digits! ;-)
kerasify.predict(input, output);
  1. Voila!

screenshot

Example 2: Image Regression for iOS (example-ios-imageregression)

This sample project is for oF iOS

"Image Regression" - "treats the pixels of an image as a learning problem: it takes the (x,y) position on a grid and learns to predict the color at that point using regression to (r,g,b). It's a bit like compression, since the image information is encoded in the weights of the network, but almost certainly not of practical kind :)"" by @karpathy.

Here is his great online demo.

I trained a simple fully-connected 8-layer network for this image of dog. https://github.com/naotokui/ChainerPainter/blob/master/ChainerPainter.ipynb
screenshot

You can run the trained model on iOS now!
(Don't forget to use convert_keras_model.py)

screenshot

Let's see what happens when you randomly disable one of trained layers.

screenshot