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.

• 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

Check out an example in the getting started notebook

Get the code:

git clone https://github.com/scientific-computing/FKB

Dependencies:

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

• 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

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>

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')

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/

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')