kerex 1.0.10

Keras Extensions for Keras3

Keras Extensions

This package offers a wide range of extensions and tools for the Keras3 framework. Please note that this package is still in development. While all methods have a comprehensive docstring, there is no API documentation yet.

The implementation of all layers, blocks and models aligns with official Keras3 layer implementations, and subclass either keras.layers.Layer or keras.models.Model. All layers are fully serializable and can be used in model.save().

Installation

The package is hosted on pypi.org and can be installed using pip

$ pip install kerex

which automatically installs keras>3.0.0. Additionally, you can use the installer with options

$ pip install kerex[jax]

which installs jax and jaxlib packages, or

$ pip install kerex[tensorflow]

which installs the tensorflow backend.

Contents

Layers

The module kerex.layers implements a wide variety of additional layers for Keras3.

Fourier Neural Operator

The first class of layers are Fourier Neural Operators (FNO), cf. Zongyi et al. (2021). The FNO layer has two parallel paths. The first path applies a spectral convolution, where the input is transformed to Fourier space, truncated to the lowest $m$ modes, multiplied by the weights, padded and then transformed back to space (or time) domain. The second path applies a $1\times1$ convolution to learn higher order dynamics.

>>> from keras import ops
>>> from kerex.layers import FNO1D
>>> fno_layer = FNO1D(filters=8, modes=8)
>>> fno_layer.build(input_shape=(None, 32, 3))
>>> x = ops.ones((1, 32, 3))
>>> y = fno_layer(x)
>>> y.shape
(1, 32, 8)

Note

The Keras3 framework does not have a complex data type. Therefore, the implementation of the spectral convolution treats the real- and imaginary parts of the weights and the inputs as two separate tensors. The multiplication of the complex weights with the complex inputs

$$wx = (w_{\text{real}} + i w_{\text{imag}})(x_{\text{real}} + i x_{\text{imag}})$$

is therefore explicitly performed as

$$y_{\text{real}} = w_{\text{real}}x_{\text{real}} - w_{\text{imag}}x_{\text{imag}}$$

and

$$y_{\text{imag}} = w_{\text{real}}x_{\text{imag}} + w_{\text{imag}}x_{\text{real}}$$

Moreover, the Tensorflow implementation requires a custom_gradient function as the automatic differentiation has trouble propagating throught the layer.

Merge layer

This module implements additional merge layer, that are not implemented in Keras3 by default.

Wrapper

This module implements wrapper, which can be used to augment any Keras3 layer with additional functionality.

Blocks

This module implements larger blocks from layers. All blocks subclass keras.layers.Layer and are serializable using model.save().

Attention

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Autoencoder

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TSMixer

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Models

Autoencoder

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Neural Operator

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TSMixer

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