keras-mdn-layer 0.3.0

An MDN Layer for Keras using TensorFlow Probability.

# Keras Mixture Density Network Layer

[](https://travis-ci.com/cpmpercussion/keras-mdn-layer)  [](https://zenodo.org/badge/latestdoi/137585470) [](https://badge.fury.io/py/keras-mdn-layer)

A mixture density network (MDN) Layer for Keras using TensorFlow’s distributions module. This makes it a bit more simple to experiment with neural networks that predict multiple real-valued variables that can take on multiple equally likely values.

This layer can help build MDN-RNNs similar to those used in [RoboJam](https://github.com/cpmpercussion/robojam), [Sketch-RNN](https://experiments.withgoogle.com/sketch-rnn-demo), [handwriting generation](https://distill.pub/2016/handwriting/), and maybe even [world models](https://worldmodels.github.io). You can do a lot of cool stuff with MDNs!

One benefit of this implementation is that you can predict any number of real-values. TensorFlow’s Mixture, Categorical, and MultivariateNormalDiag distribution functions are used to generate the loss function (the probability density function of a mixture of multivariate normal distributions with a diagonal covariance matrix). In previous work, the loss function has often been specified by hand which is fine for 1D or 2D prediction, but becomes a bit more annoying after that.

Two important functions are provided for training and prediction:

• get_mixture_loss_func(output_dim, num_mixtures): This function generates a loss function with the correct output dimensiona and number of mixtures.

• sample_from_output(params, output_dim, num_mixtures, temp=1.0): This functions samples from the mixture distribution output by the model.

## Installation

This project requires Python 3.6+. You can easily install this package from [PyPI](https://pypi.org/project/keras-mdn-layer/) via pip like so:

python3 -m pip install keras-mdn-layer

And finally, import the mdn module in Python: import mdn

Alternatively, you can clone or download this repository and then install via python setup.py install, or copy the mdn folder into your own project.

## Examples

Some examples are provided in the notebooks directory.

There’s scripts for fitting multivalued functions, a standard MDN toy problem:

<img src=”https://preview.ibb.co/mZzkpd/Keras_MDN_Demo.jpg” alt=”Keras MDN Demo” border=”0”>

There’s also a script for generating fake kanji characters:

<img src=”https://i.ibb.co/yFvtgkL/kanji-mdn-examples.png” alt=”kanji test 1” border=”0” width=”600”/>

And finally, for learning how to generate musical touch-screen performances with a temporal component:

<img src=”https://i.ibb.co/WpzSCV8/robojam-examples.png” alt=”Robojam Model Examples” border=”0”>

## How to use

The MDN layer should be the last in your network and you should use get_mixture_loss_func to generate a loss function. Here’s an example of a simple network with one Dense layer followed by the MDN.

import keras import mdn

N_HIDDEN = 15 # number of hidden units in the Dense layer N_MIXES = 10 # number of mixture components OUTPUT_DIMS = 2 # number of real-values predicted by each mixture component

model = keras.Sequential() model.add(keras.layers.Dense(N_HIDDEN, batch_input_shape=(None, 1), activation=’relu’)) model.add(mdn.MDN(OUTPUT_DIMS, N_MIXES)) model.compile(loss=mdn.get_mixture_loss_func(OUTPUT_DIMS,N_MIXES), optimizer=keras.optimizers.Adam()) model.summary()

Fit as normal:

history = model.fit(x=x_train, y=y_train)

The predictions from the network are parameters of the mixture models, so you have to apply the sample_from_output function to generate samples.

y_test = model.predict(x_test) y_samples = np.apply_along_axis(sample_from_output, 1, y_test, OUTPUT_DIMS, N_MIXES, temp=1.0)

See the notebooks directory for examples in jupyter notebooks!

### Load/Save Model

Saving models is straight forward:

model.save(‘test_save.h5’)

But loading requires cutom_objects to be filled with the MDN layer, and a loss function with the appropriate parameters:

m_2 = keras.models.load_model(‘test_save.h5’, custom_objects={‘MDN’: mdn.MDN, ‘mdn_loss_func’: mdn.get_mixture_loss_func(1, N_MIXES)})

## Acknowledgements

• Hat tip to [Omimo’s Keras MDN layer](https://github.com/omimo/Keras-MDN) for a starting point for this code.

• Super hat tip to [hardmaru’s MDN explanation, projects, and good ideas for sampling functions](http://blog.otoro.net/2015/11/24/mixture-density-networks-with-tensorflow/) etc.

• Many good ideas from [Axel Brando’s Master’s Thesis](https://github.com/axelbrando/Mixture-Density-Networks-for-distribution-and-uncertainty-estimation)

• Mixture Density Networks in Edward [tutorial](http://edwardlib.org/tutorials/mixture-density-network).

## References

1. Christopher M. Bishop. 1994. Mixture Density Networks. [Technical Report NCRG/94/004](http://publications.aston.ac.uk/373/). Neural Computing Research Group, Aston University. http://publications.aston.ac.uk/373/

2. Axel Brando. 2017. Mixture Density Networks (MDN) for distribution and uncertainty estimation. Master’s thesis. Universitat Politècnica de Catalunya.

3. 1. Graves. 2013. Generating Sequences With Recurrent Neural Networks. ArXiv e-prints (Aug. 2013). https://arxiv.org/abs/1308.0850

4. David Ha and Douglas Eck. 2017. A Neural Representation of Sketch Drawings. ArXiv e-prints (April 2017). https://arxiv.org/abs/1704.03477

5. Charles P. Martin and Jim Torresen. 2018. RoboJam: A Musical Mixture Density Network for Collaborative Touchscreen Interaction. In Evolutionary and Biologically Inspired Music, Sound, Art and Design: EvoMUSART ’18, A. Liapis et al. (Ed.). Lecture Notes in Computer Science, Vol. 10783. Springer International Publishing. DOI:[10.1007/9778-3-319-77583-8_11](http://dx.doi.org/10.1007/9778-3-319-77583-8_11)