Package: keras-mdn-layer
# Keras Mixture Density Network Layer[![Build Status](https://travis-ci.com/cpmpercussion/keras-mdn-layer.svg?branch=master)](https://travis-ci.com/cpmpercussion/keras-mdn-layer)
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[![PyPI version](https://badge.fury.io/py/keras-mdn-layer.svg)](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’sMixture,Categorical, andMultivariateNormalDiagdistribution 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.## InstallationThis project requires Python 3.6+. You can easily install this package from [PyPI](https://pypi.org/project/keras-mdn-layer/) viapiplike so:python3 -m pip install keras-mdn-layerAnd finally, import themdnmodule in Python:import mdnAlternatively, you can clone or download this repository and then install viapython setup.py install, or copy themdnfolder into your own project.## ExamplesSome 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 useThe MDN layer should be the last in your network and you should useget_mixture_loss_functo generate a loss function. Here’s an example of a simple network with one Dense layer followed by the MDN.import keras
import mdnN_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 componentmodel = 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 thesample_from_outputfunction 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 ModelSaving models is straight forward:model.save(‘test_save.h5’)But loading requirescutom_objectsto 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)})## AcknowledgementsHat 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).## ReferencesChristopher 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/Axel Brando. 2017. Mixture Density Networks (MDN) for distribution and uncertainty estimation. Master’s thesis. Universitat Politècnica de Catalunya.Graves. 2013. Generating Sequences With Recurrent Neural Networks. ArXiv e-prints (Aug. 2013).https://arxiv.org/abs/1308.0850David Ha and Douglas Eck. 2017. A Neural Representation of Sketch Drawings. ArXiv e-prints (April 2017).https://arxiv.org/abs/1704.03477Charles 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)