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A two-way deep learning bridge between Keras and Fortran

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The Fortran-Keras Bridge (FKB)

This two-way bridge connects environments where deep learning resources are plentiful, with those where they are scarce. You can find the paper here.

@article{ott2020fortran,
  title={A Fortran-Keras Deep Learning Bridge for Scientific Computing},
  author={Ott, Jordan and Pritchard, Mike and Best, Natalie and Linstead, Erik and Curcic, Milan and Baldi, Pierre},
  journal={arXiv preprint arXiv:2004.10652},
  year={2020}
}

This library allows users to convert models built and trained in Keras to ones usable in Fortran. In order to make this possible FKB implements a neural network library in Fortran. The foundations of which are derived from Milan Curcic's original work.

Additions

  • An extendable layer type
    • The original library was only capable of a dense layer
      • Forward and backward operations occurred outside the layer (in the network module)
    • Ability to implement arbitrary layers
      • Simply extend the layer_type and specify these functions:
        • forward
        • backward
  • Training
    • Backprop takes place inside the extended layer_type
    • Ability to training arbitrary cost functions
  • Implemented layers
    • Dense
    • Dropout
    • Batch Normalization
  • Ensembles
    • Read in a directory of network configs
    • Create a network for each config
    • Run in parallel using $OMP PARALLEL directives
    • Average results of all predictions in ensemble
  • A two-way bridge between Keras and Fortran
    • Convert model trained in Keras (h5 file) to Fortran
      • Any of the above layers are allowed
      • Sequential or Functional API
    • Convert Fortran models back to Keras
    • Check out this for supported model types

Getting started

Check out an example in the getting started notebook

Get the code:

git clone https://github.com/sungdukyu/FKB64

Dependencies:

  • Fortran 2018-compatible compiler
  • OpenCoarrays (optional, for parallel execution, gfortran only)
  • BLAS, MKL (optional)

Build

  • Tests and examples will be built in the bin/ directory
  • To use a different compiler modify FC=mpif90 cmake .. -DSERIAL=1
sh build_steps.sh

Examples

Loading a model trained in Keras

python convert_weights.py --weights_file path/to/keras_model.h5 --output_file path/to/model_config.txt

This would create the model_config.txt file with the following:

9                         --> How many total layers (includes input and activations)
input	5                 --> 5 inputs
dense	3                 --> Hidden layer 1 has 3 nodes
leakyrelu	0.3       --> Hidden layer 1 activation LeakyReLU with alpha = 0.3
dense	4                 --> Hidden layer 2 has 4 nodes
leakyrelu	0.3       --> Hidden layer 2 activation LeakyReLU with alpha = 0.3
dense	3                 --> Hidden layer 3 has 3 nodes
leakyrelu	0.3       --> Hidden layer 3 activation LeakyReLU with alpha = 0.3
dense	2                 --> 2 outputs in the last layer
linear	0                 --> Linear activation with no alpha
0.5                       --> Learning rate
<BIASES>
.
.
.
<DENSE LAYER WEIGHTS>
.
.
.
<BATCH NORMALIZATION PARAMETERS>

Creating a network

Architecture descriptions are specified in a config text file:

9                         --> How many total layers (includes input and activations)
input	5                 --> 5 inputs
dense	3                 --> Hidden layer 1 has 3 nodes
leakyrelu	0.3       --> Hidden layer 1 activation LeakyReLU with alpha = 0.3
dense	4                 --> Hidden layer 2 has 4 nodes
leakyrelu	0.3       --> Hidden layer 2 activation LeakyReLU with alpha = 0.3
dense	3                 --> Hidden layer 3 has 3 nodes
leakyrelu	0.3       --> Hidden layer 3 activation LeakyReLU with alpha = 0.3
dense	2                 --> 2 outputs in the last layer
linear	0                 --> Linear activation with no alpha
0.5                       --> Learning rate

Then the network configuration can be loaded into FORTRAN:

use mod_network, only: network_type
type(network_type) :: net

call net % load('model_config.txt')

Ensembles

mod_ensemble allows ensembles of neural networks to be run in parallel. The ensemble_type will read all networks provided in the user specified directory. Calling average passes the input through all networks in the ensemble and averages their output. noise_perturbation is used to perturb the input to each model with Gaussian noise.

Put the names of the model files in ensemble_members.txt:

simple_model.txt
simple_model_with_weights.txt

Then to run an ensemble:

ensemble = ensemble_type('$HOME/Desktop/neural-fortran/ExampleModels/', noise_perturbation)

result1 = ensemble % average(input)

You can run the test_ensembles.F90 file:

./test_ensembles $HOME/Desktop/neural-fortran/ExampleModels/

Saving and loading from file

To save a network to a file, do:

call net % save('model_config.txt')

Loading from file works the same way:

call net % load('model_config.txt')

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  • Fortran 43.0%
  • Jupyter Notebook 29.3%
  • Python 23.5%
  • CMake 3.3%
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