https://github.com/cpmpercussion/keras-mdn-layer
An MDN Layer for Keras using TensorFlow's distributions module
https://github.com/cpmpercussion/keras-mdn-layer
keras mdn mdn-rnn mixture-density-network neural-network tensorflow
Last synced: 3 months ago
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An MDN Layer for Keras using TensorFlow's distributions module
- Host: GitHub
- URL: https://github.com/cpmpercussion/keras-mdn-layer
- Owner: cpmpercussion
- License: mit
- Created: 2018-06-16T14:13:36.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2024-05-07T06:05:19.000Z (about 1 year ago)
- Last Synced: 2024-05-08T05:46:33.153Z (about 1 year ago)
- Topics: keras, mdn, mdn-rnn, mixture-density-network, neural-network, tensorflow
- Language: Jupyter Notebook
- Size: 3.68 MB
- Stars: 161
- Watchers: 7
- Forks: 42
- Open Issues: 16
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
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README
# Keras Mixture Density Network Layer
[](https://coveralls.io/github/cpmpercussion/keras-mdn-layer?branch=master)
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[](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+, TensorFlow and TensorFlow Probability. 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 module in Python: `import keras_mdn_layer as 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.
## Build
This project builds using `poetry`. To build a wheel use `poetry build`.
## Examples
Some examples are provided in the notebooks directory.
To run these using `poetry`, run `poetry install` and then open jupyter `poetry run jupyter lab`.
There's scripts for fitting multivalued functions, a standard MDN toy problem:
There's also a script for generating fake kanji characters:
And finally, for learning how to generate musical touch-screen performances with a temporal component:
## 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.
from tensorflow import keras
import keras_mdn_layer as 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. A. 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)