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GiovanniCanaliGiovanniCanali
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implement interface + base class structure
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‎pina/_src/adaptive_function/adaptive_celu.py‎

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"""Module for the Adaptive CELU activation function."""
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import torch
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from pina._src.adaptive_function.base_adaptive_function import (
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BaseAdaptiveFunction,
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)
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class AdaptiveCELU(BaseAdaptiveFunction):
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r"""
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Adaptive, trainable variant of the :class:`~torch.nn.CELU` activation.
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This module extends the standard CELU by introducing learnable scaling
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and shifting parameters applied to both the input and the output.
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Given the function :math:`\text{CELU}:\mathbb{R}^n\rightarrow\mathbb{R}^n`,
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the corresponding adaptive activation
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:math:`\text{CELU}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n` is
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defined as:
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.. math::
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\text{CELU}_{\text{adaptive}}({x})=\alpha\,\text{CELU}(\beta{x}+\gamma),
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where :math:`\alpha`, :math:`\beta`, and :math:`\gamma` are trainable
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parameters controlling output scaling, input scaling, and input shifting,
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respectively.
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The CELU function is defined elementwise as:
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.. math::
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\text{CELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x) - 1))
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.. seealso::
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**Original reference**: Godfrey, L. B., Gashler, M. S. (2015).
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*A continuum among logarithmic, linear, and exponential functions,
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and its potential to improve generalization in neural networks.*
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7th international joint conference on knowledge discovery, knowledge
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engineering and knowledge management (IC3K), Vol. 1.
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DOI: `arXiv preprint arXiv:1602.01321.
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<https://arxiv.org/abs/1602.01321>`_.
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**Original reference**: Jagtap, A. D., Karniadakis, G. E. (2020).
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*Adaptive activation functions accelerate convergence in deep and
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physics-informed neural networks*.
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Journal of Computational Physics, 404.
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DOI: `JCP 10.1016 <https://doi.org/10.1016/j.jcp.2019.109136>`_.
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"""
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def __init__(self, alpha=None, beta=None, gamma=None, fixed=None):
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"""
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Initialization of the :class:`AdaptiveCELU` class.
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:param alpha: The output scaling parameter of the adaptive function.
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If ``None``, it is initialized to ``1``. Default is ``None``.
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:type alpha: int | float | complex
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:param beta: The input scaling parameter of the adaptive function.
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If ``None``, it is initialized to ``1``. Default is ``None``.
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:type beta: int | float | complex
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:param gamma: The input shifting parameter of the adaptive function.
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If ``None``, it is initialized to ``0``. Default is ``None``.
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:type gamma: int | float | complex
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:param fixed: The names of parameters to keep fixed during training.
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These parameters will not be optimized and will have
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``requires_grad=False``. Available options are ``"alpha"``,
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``"beta"``, and ``"gamma"``. If ``None``, all parameters are
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trainable. Default is ``None``.
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:type fixed: str | list[str]
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:raises ValueError: If alpha, when provided, is not a number.
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:raises ValueError: If beta, when provided, is not a number.
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:raises ValueError: If gamma, when provided, is not a number.
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:raises ValueError: If fixed, when provided, is neither a string nor a
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list of strings.
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:raises ValueError: If fixed contains invalid parameter names.
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"""
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super().__init__(alpha, beta, gamma, fixed)
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self.func = torch.nn.CELU()

‎pina/_src/adaptive_function/adaptive_elu.py‎

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"""Module for the Adaptive ELU activation function."""
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import torch
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from pina._src.adaptive_function.base_adaptive_function import (
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BaseAdaptiveFunction,
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)
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class AdaptiveELU(BaseAdaptiveFunction):
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r"""
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Adaptive, trainable variant of the :class:`~torch.nn.ELU` activation.
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This module extends the standard ELU by introducing learnable scaling
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and shifting parameters applied to both the input and the output.
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Given the function :math:`\text{ELU}:\mathbb{R}^n\rightarrow\mathbb{R}^n`,
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the corresponding adaptive activation
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:math:`\text{ELU}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n` is
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defined as:
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.. math::
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\text{ELU}_{\text{adaptive}}({x}) = \alpha\,\text{ELU}(\beta{x}+\gamma),
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where :math:`\alpha`, :math:`\beta`, and :math:`\gamma` are trainable
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parameters controlling output scaling, input scaling, and input shifting,
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respectively.
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The ELU function is defined elementwise as:
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.. math::
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\text{ELU}(x) = \begin{cases}
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x, & \text{ if }x > 0\\
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\exp(x) - 1, & \text{ if }x \leq 0
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\end{cases}
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.. seealso::
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**Original reference**: Godfrey, L. B., Gashler, M. S. (2015).
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*A continuum among logarithmic, linear, and exponential functions,
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and its potential to improve generalization in neural networks.*
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7th international joint conference on knowledge discovery, knowledge
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engineering and knowledge management (IC3K), Vol. 1.
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DOI: `arXiv preprint arXiv:1602.01321.
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<https://arxiv.org/abs/1602.01321>`_.
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**Original reference**: Jagtap, A. D., Karniadakis, G. E. (2020).
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*Adaptive activation functions accelerate convergence in deep and
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physics-informed neural networks*.
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Journal of Computational Physics, 404.
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DOI: `JCP 10.1016 <https://doi.org/10.1016/j.jcp.2019.109136>`_.
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"""
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def __init__(self, alpha=None, beta=None, gamma=None, fixed=None):
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"""
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Initialization of the :class:`AdaptiveELU` class.
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:param alpha: The output scaling parameter of the adaptive function.
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If ``None``, it is initialized to ``1``. Default is ``None``.
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:type alpha: int | float | complex
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:param beta: The input scaling parameter of the adaptive function.
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If ``None``, it is initialized to ``1``. Default is ``None``.
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:type beta: int | float | complex
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:param gamma: The input shifting parameter of the adaptive function.
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If ``None``, it is initialized to ``0``. Default is ``None``.
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:type gamma: int | float | complex
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:param fixed: The names of parameters to keep fixed during training.
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These parameters will not be optimized and will have
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``requires_grad=False``. Available options are ``"alpha"``,
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``"beta"``, and ``"gamma"``. If ``None``, all parameters are
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trainable. Default is ``None``.
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:type fixed: str | list[str]
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:raises ValueError: If alpha, when provided, is not a number.
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:raises ValueError: If beta, when provided, is not a number.
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:raises ValueError: If gamma, when provided, is not a number.
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:raises ValueError: If fixed, when provided, is neither a string nor a
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list of strings.
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:raises ValueError: If fixed contains invalid parameter names.
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"""
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super().__init__(alpha, beta, gamma, fixed)
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self.func = torch.nn.ELU()

‎pina/_src/adaptive_function/adaptive_exp.py‎

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"""Module for the Adaptive Exp activation function."""
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import torch
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from pina._src.adaptive_function.base_adaptive_function import (
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BaseAdaptiveFunction,
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)
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class AdaptiveExp(BaseAdaptiveFunction):
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r"""
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Adaptive, trainable variant of the :obj:`~torch.exp` activation.
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This module extends the standard exponential function by introducing
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learnable scaling and shifting parameters applied to both the input and the
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output.
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Given the function :math:`\text{exp}:\mathbb{R}^n\rightarrow\mathbb{R}^n`,
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the corresponding adaptive activation
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:math:`\text{exp}_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^n`
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is defined as:
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.. math::
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\text{exp}_{\text{adaptive}}({x}) = \alpha\,\text{exp}(\beta{x}+\gamma),
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where :math:`\alpha`, :math:`\beta`, and :math:`\gamma` are trainable
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parameters controlling output scaling, input scaling, and input shifting,
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respectively.
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.. seealso::
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**Original reference**: Godfrey, L. B., Gashler, M. S. (2015).
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*A continuum among logarithmic, linear, and exponential functions,
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and its potential to improve generalization in neural networks.*
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7th international joint conference on knowledge discovery, knowledge
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engineering and knowledge management (IC3K), Vol. 1.
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DOI: `arXiv preprint arXiv:1602.01321.
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<https://arxiv.org/abs/1602.01321>`_.
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**Original reference**: Jagtap, A. D., Karniadakis, G. E. (2020).
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*Adaptive activation functions accelerate convergence in deep and
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physics-informed neural networks*.
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Journal of Computational Physics, 404.
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DOI: `JCP 10.1016 <https://doi.org/10.1016/j.jcp.2019.109136>`_.
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"""
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def __init__(self, alpha=None, beta=None, gamma=None, fixed=None):
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"""
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Initialization of the :class:`AdaptiveExp` class.
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:param alpha: The output scaling parameter of the adaptive function.
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If ``None``, it is initialized to ``1``. Default is ``None``.
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:type alpha: int | float | complex
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:param beta: The input scaling parameter of the adaptive function.
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If ``None``, it is initialized to ``1``. Default is ``None``.
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:type beta: int | float | complex
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:param gamma: The input shifting parameter of the adaptive function.
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If ``None``, it is initialized to ``0``. Default is ``None``.
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:type gamma: int | float | complex
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:param fixed: The names of parameters to keep fixed during training.
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These parameters will not be optimized and will have
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``requires_grad=False``. Available options are ``"alpha"``,
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``"beta"``, and ``"gamma"``. If ``None``, all parameters are
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trainable. Default is ``None``.
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:type fixed: str | list[str]
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:raises ValueError: If alpha, when provided, is not a number.
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:raises ValueError: If beta, when provided, is not a number.
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:raises ValueError: If gamma, when provided, is not a number.
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:raises ValueError: If fixed, when provided, is neither a string nor a
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list of strings.
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:raises ValueError: If fixed contains invalid parameter names.
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"""
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super().__init__(alpha, beta, gamma, fixed)
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self.func = torch.exp

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