Tensorflow 2.0 implementation of Sinusodial Representation networks (SIREN) from the paper Implicit Neural Representations with Periodic Activation Functions.

• Pip install

$ pip install --upgrade tf_siren

• Pip install (test support)

$ pip install --upgrade tf_siren[tests]

Copy the tf_siren folder to your local directory and import either SinusodialRepresentationDense or SIRENModel.

from tf_siren import SinusodialRepresentationDense
from tf_siren import SIRENModel

# You can use SinusodialRepresentationDense exactly like you ordinarily use Dense layers.
ip = tf.keras.layers.Input(shape=[2])
x = SinusodialRepresentationDense(32,
                                  activation='sine', # default activation function
                                  w0=1.0)(ip)        # w0 represents sin(w0 * x) where x is the input.
                                  
model = tf.keras.Model(inputs=ip, outputs=x)

# Or directly use the model class to build a multi layer SIREN
model = SIRENModel(units=256, final_units=3, final_activation='sigmoid',
                   num_layers=5, w0=1.0, w0_initial=30.0)

A partial implementation of the image inpainting task is available as the train_inpainting_siren.py and eval_inpainting_siren.py scripts inside the scripts directory.

Weight files are made available in the repository under the Release tab of the project. Extract the weights and place the checkpoints folder at the scripts directory

These weights generates the following output after 5000 epochs of training with batch size 8192 while using only 10% of the available pixels in the image during training phase.

If we train for using only 20% of the available pixels in the image during training phase -

If we train for using only 20% of the available pixels in the image during training phase -

@misc{sitzmann2020implicit,
    title={Implicit Neural Representations with Periodic Activation Functions},
    author={Vincent Sitzmann and Julien N. P. Martel and Alexander W. Bergman and David B. Lindell and Gordon Wetzstein},
    year={2020},
    eprint={2006.09661},
    archivePrefix={arXiv},
    primaryClass={cs.CV}
}

• Tensorflow 2.0+
• Matplotlib to visualize eval result