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Adaptive Functions (#272)

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* adaptive function improvement

---------

Co-authored-by: Dario Coscia <dariocoscia@Dario-Coscia.local>
This commit is contained in:
Dario Coscia
2024-04-02 10:11:10 +02:00
committed by GitHub
parent 71e6086645
commit cddb191fe4
14 changed files with 209 additions and 409 deletions
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5
pina/adaptive_functions/__init__.py
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@@ -1,5 +0,0 @@
from .adaptive_tanh import AdaptiveTanh
from .adaptive_sin import AdaptiveSin
from .adaptive_cos import AdaptiveCos
from .adaptive_linear import AdaptiveLinear
from .adaptive_square import AdaptiveSquare

56
pina/adaptive_functions/adaptive_cos.py
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@@ -1,56 +0,0 @@
import torch
from torch.nn.parameter import Parameter
class AdaptiveCos(torch.nn.Module):
"""
Implementation of soft exponential activation.
Shape:
- Input: (N, *) where * means, any number of additional
dimensions
- Output: (N, *), same shape as the input
Parameters:
- alpha - trainable parameter
References:
- See related paper:
https://arxiv.org/pdf/1602.01321.pdf
Examples:
>>> a1 = soft_exponential(256)
>>> x = torch.randn(256)
>>> x = a1(x)
"""
def __init__(self, alpha=None):
"""
Initialization.
INPUT:
- in_features: shape of the input
- aplha: trainable parameter
aplha is initialized with zero value by default
"""
super(AdaptiveCos, self).__init__()
# self.in_features = in_features
# initialize alpha
if alpha == None:
self.alpha = Parameter(
torch.tensor(1.0)
) # create a tensor out of alpha
else:
self.alpha = Parameter(
torch.tensor(alpha)
) # create a tensor out of alpha
self.alpha.requiresGrad = True # set requiresGrad to true!
self.scale = Parameter(torch.tensor(1.0))
self.scale.requiresGrad = True # set requiresGrad to true!
self.translate = Parameter(torch.tensor(0.0))
self.translate.requiresGrad = True # set requiresGrad to true!
def forward(self, x):
"""
Forward pass of the function.
Applies the function to the input elementwise.
"""
return self.scale * (torch.cos(self.alpha * x + self.translate))

53
pina/adaptive_functions/adaptive_exp.py
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@@ -1,53 +0,0 @@
import torch
from torch.nn.parameter import Parameter
class AdaptiveExp(torch.nn.Module):
"""
Implementation of soft exponential activation.
Shape:
- Input: (N, *) where * means, any number of additional
dimensions
- Output: (N, *), same shape as the input
Parameters:
- alpha - trainable parameter
References:
- See related paper:
https://arxiv.org/pdf/1602.01321.pdf
Examples:
>>> a1 = soft_exponential(256)
>>> x = torch.randn(256)
>>> x = a1(x)
"""
def __init__(self):
"""
Initialization.
INPUT:
- in_features: shape of the input
- aplha: trainable parameter
aplha is initialized with zero value by default
"""
super(AdaptiveExp, self).__init__()
self.scale = Parameter(
torch.normal(torch.tensor(1.0), torch.tensor(0.1))
) # create a tensor out of alpha
self.scale.requiresGrad = True # set requiresGrad to true!
self.alpha = Parameter(
torch.normal(torch.tensor(1.0), torch.tensor(0.1))
) # create a tensor out of alpha
self.alpha.requiresGrad = True # set requiresGrad to true!
self.translate = Parameter(
torch.normal(torch.tensor(0.0), torch.tensor(0.1))
) # create a tensor out of alpha
self.translate.requiresGrad = True # set requiresGrad to true!
def forward(self, x):
"""
Forward pass of the function.
Applies the function to the input elementwise.
"""
return self.scale * (x + self.translate)

46
pina/adaptive_functions/adaptive_linear.py
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@@ -1,46 +0,0 @@
""" Implementation of adaptive linear layer. """
import torch
from torch.nn.parameter import Parameter
class AdaptiveLinear(torch.nn.Module):
"""
Implementation of soft exponential activation.
Shape:
- Input: (N, *) where * means, any number of additional
dimensions
- Output: (N, *), same shape as the input
Parameters:
- alpha - trainable parameter
References:
- See related paper:
https://arxiv.org/pdf/1602.01321.pdf
Examples:
>>> a1 = soft_exponential(256)
>>> x = torch.randn(256)
>>> x = a1(x)
"""
def __init__(self):
"""
Initialization.
INPUT:
- in_features: shape of the input
- aplha: trainable parameter
aplha is initialized with zero value by default
"""
super(AdaptiveLinear, self).__init__()
self.scale = Parameter(torch.tensor(1.0))
self.scale.requiresGrad = True # set requiresGrad to true!
self.translate = Parameter(torch.tensor(0.0))
self.translate.requiresGrad = True # set requiresGrad to true!
def forward(self, x):
"""
Forward pass of the function.
Applies the function to the input elementwise.
"""
return self.scale * (x + self.translate)

45
pina/adaptive_functions/adaptive_relu.py
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@@ -1,45 +0,0 @@
import torch
from torch.nn.parameter import Parameter
class AdaptiveReLU(torch.nn.Module, Parameter):
"""
Implementation of soft exponential activation.
Shape:
- Input: (N, *) where * means, any number of additional
dimensions
- Output: (N, *), same shape as the input
Parameters:
- alpha - trainable parameter
References:
- See related paper:
https://arxiv.org/pdf/1602.01321.pdf
Examples:
>>> a1 = soft_exponential(256)
>>> x = torch.randn(256)
>>> x = a1(x)
"""
def __init__(self):
"""
Initialization.
INPUT:
- in_features: shape of the input
- aplha: trainable parameter
aplha is initialized with zero value by default
"""
super(AdaptiveReLU, self).__init__()
self.scale = Parameter(torch.rand(1))
self.scale.requiresGrad = True # set requiresGrad to true!
self.translate = Parameter(torch.rand(1))
self.translate.requiresGrad = True # set requiresGrad to true!
def forward(self, x):
"""
Forward pass of the function.
Applies the function to the input elementwise.
"""
# x += self.translate
return torch.relu(x + self.translate) * self.scale

54
pina/adaptive_functions/adaptive_sin.py
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@@ -1,54 +0,0 @@
import torch
from torch.nn.parameter import Parameter
class AdaptiveSin(torch.nn.Module):
"""
Implementation of soft exponential activation.
Shape:
- Input: (N, *) where * means, any number of additional
dimensions
- Output: (N, *), same shape as the input
Parameters:
- alpha - trainable parameter
References:
- See related paper:
https://arxiv.org/pdf/1602.01321.pdf
Examples:
>>> a1 = soft_exponential(256)
>>> x = torch.randn(256)
>>> x = a1(x)
"""
def __init__(self, alpha=None):
"""
Initialization.
INPUT:
- in_features: shape of the input
- aplha: trainable parameter
aplha is initialized with zero value by default
"""
super(AdaptiveSin, self).__init__()
# initialize alpha
self.alpha = Parameter(
torch.normal(torch.tensor(1.0), torch.tensor(0.1))
) # create a tensor out of alpha
self.alpha.requiresGrad = True # set requiresGrad to true!
self.scale = Parameter(
torch.normal(torch.tensor(1.0), torch.tensor(0.1))
)
self.scale.requiresGrad = True # set requiresGrad to true!
self.translate = Parameter(
torch.normal(torch.tensor(0.0), torch.tensor(0.1))
)
self.translate.requiresGrad = True # set requiresGrad to true!
def forward(self, x):
"""
Forward pass of the function.
Applies the function to the input elementwise.
"""
return self.scale * (torch.sin(self.alpha * x + self.translate))

44
pina/adaptive_functions/adaptive_softplus.py
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@@ -1,44 +0,0 @@
import torch
from torch.nn.parameter import Parameter
class AdaptiveSoftplus(torch.nn.Module):
"""
Implementation of soft exponential activation.
Shape:
- Input: (N, *) where * means, any number of additional
dimensions
- Output: (N, *), same shape as the input
Parameters:
- alpha - trainable parameter
References:
- See related paper:
https://arxiv.org/pdf/1602.01321.pdf
Examples:
>>> a1 = soft_exponential(256)
>>> x = torch.randn(256)
>>> x = a1(x)
"""
def __init__(self):
"""
Initialization.
INPUT:
- in_features: shape of the input
- aplha: trainable parameter
aplha is initialized with zero value by default
"""
super().__init__()
self.soft = torch.nn.Softplus()
self.scale = Parameter(torch.rand(1))
self.scale.requiresGrad = True # set requiresGrad to true!
def forward(self, x):
"""
Forward pass of the function.
Applies the function to the input elementwise.
"""
# x += self.translate
return self.soft(x) * self.scale

44
pina/adaptive_functions/adaptive_square.py
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@@ -1,44 +0,0 @@
import torch
from torch.nn.parameter import Parameter
class AdaptiveSquare(torch.nn.Module):
"""
Implementation of soft exponential activation.
Shape:
- Input: (N, *) where * means, any number of additional
dimensions
- Output: (N, *), same shape as the input
Parameters:
- alpha - trainable parameter
References:
- See related paper:
https://arxiv.org/pdf/1602.01321.pdf
Examples:
>>> a1 = soft_exponential(256)
>>> x = torch.randn(256)
>>> x = a1(x)
"""
def __init__(self, alpha=None):
"""
Initialization.
INPUT:
- in_features: shape of the input
- aplha: trainable parameter
aplha is initialized with zero value by default
"""
super(AdaptiveSquare, self).__init__()
self.scale = Parameter(torch.tensor(1.0))
self.scale.requiresGrad = True # set requiresGrad to true!
self.translate = Parameter(torch.tensor(0.0))
self.translate.requiresGrad = True # set requiresGrad to true!
def forward(self, x):
"""
Forward pass of the function.
Applies the function to the input elementwise.
"""
return self.scale * (x + self.translate) ** 2

62
pina/adaptive_functions/adaptive_tanh.py
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@@ -1,62 +0,0 @@
import torch
from torch.nn.parameter import Parameter
class AdaptiveTanh(torch.nn.Module):
"""
Implementation of soft exponential activation.
Shape:
- Input: (N, *) where * means, any number of additional
dimensions
- Output: (N, *), same shape as the input
Parameters:
- alpha - trainable parameter
References:
- See related paper:
https://arxiv.org/pdf/1602.01321.pdf
Examples:
>>> a1 = soft_exponential(256)
>>> x = torch.randn(256)
>>> x = a1(x)
"""
def __init__(self, alpha=None):
"""
Initialization.
INPUT:
- in_features: shape of the input
- aplha: trainable parameter
aplha is initialized with zero value by default
"""
super(AdaptiveTanh, self).__init__()
# self.in_features = in_features
# initialize alpha
if alpha == None:
self.alpha = Parameter(
torch.tensor(1.0)
) # create a tensor out of alpha
else:
self.alpha = Parameter(
torch.tensor(alpha)
) # create a tensor out of alpha
self.alpha.requiresGrad = True # set requiresGrad to true!
self.scale = Parameter(torch.tensor(1.0))
self.scale.requiresGrad = True # set requiresGrad to true!
self.translate = Parameter(torch.tensor(0.0))
self.translate.requiresGrad = True # set requiresGrad to true!
def forward(self, x):
"""
Forward pass of the function.
Applies the function to the input elementwise.
"""
x += self.translate
return (
self.scale
* (torch.exp(self.alpha * x) - torch.exp(-self.alpha * x))
/ (torch.exp(self.alpha * x) + torch.exp(-self.alpha * x))
)

2
pina/model/layers/__init__.py
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@@ -11,6 +11,7 @@ __all__ = [
"PODBlock",
"PeriodicBoundaryEmbedding",
"AVNOBlock",
"AdaptiveActivationFunction"
]
from .convolution_2d import ContinuousConvBlock
@@ -24,3 +25,4 @@ from .fourier import FourierBlock1D, FourierBlock2D, FourierBlock3D
from .pod import PODBlock
from .embedding import PeriodicBoundaryEmbedding
from .avno_layer import AVNOBlock
from .adaptive_func import AdaptiveActivationFunction

151
pina/model/layers/adaptive_func.py Normal file
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@@ -0,0 +1,151 @@
""" Module for adaptive functions. """
import torch
from pina.utils import check_consistency
class AdaptiveActivationFunction(torch.nn.Module):
r"""
The :class:`~pina.model.layers.adaptive_func.AdaptiveActivationFunction`
class makes a :class:`torch.nn.Module` activation function into an adaptive
trainable activation function.
Given a function :math:`f:\mathbb{R}^n\rightarrow\mathbb{R}^m`, the adaptive
function :math:`f_{\text{adaptive}}:\mathbb{R}^n\rightarrow\mathbb{R}^m`
is defined as:
.. math::
f_{\text{adaptive}}(\mathbf{x}) = \alpha\,f(\beta\mathbf{x}+\gamma),
where :math:`\alpha,\,\beta,\,\gamma` are trainable parameters.
:Example:
>>> import torch
>>> from pina.model.layers import AdaptiveActivationFunction
>>>
>>> # simple adaptive function with all trainable parameters
>>> AdaptiveTanh = AdaptiveActivationFunction(torch.nn.Tanh())
>>> AdaptiveTanh(torch.rand(3))
tensor([0.1084, 0.3931, 0.7294], grad_fn=<MulBackward0>)
>>> AdaptiveTanh.alpha
Parameter containing:
tensor(1., requires_grad=True)
>>>
>>> # simple adaptive function with trainable parameters fixed alpha
>>> AdaptiveTanh = AdaptiveActivationFunction(torch.nn.Tanh(),
... fixed=['alpha'])
>>> AdaptiveTanh.alpha
tensor(1.)
>>> AdaptiveTanh.beta
Parameter containing:
tensor(1., requires_grad=True)
>>>
.. seealso::
**Original reference**: Godfrey, Luke B., and Michael S. Gashler.
*A continuum among logarithmic, linear, and exponential functions,
and its potential to improve generalization in neural networks.*
2015 7th international joint conference on knowledge discovery,
knowledge engineering and knowledge management (IC3K).
Vol. 1. IEEE, 2015. DOI: `arXiv preprint arXiv:1602.01321.
<https://arxiv.org/abs/1602.01321>`_.
"""
def __init__(self, func, alpha=None, beta=None, gamma=None, fixed=None):
"""
Initializes the AdaptiveActivationFunction module.
:param callable func: The original collable function. It could be an
initialized :meth:`torch.nn.Module`, or a python callable function.
:param float | complex alpha: Scaling parameter alpha.
Defaults to ``None``. When ``None`` is passed,
the variable is initialized to 1.
:param float | complex beta: Scaling parameter beta.
Defaults to ``None``. When ``None`` is passed,
the variable is initialized to 1.
:param float | complex gamma: Shifting parameter gamma.
Defaults to ``None``. When ``None`` is passed,
the variable is initialized to 1.
:param list fixed: List of parameters to fix during training,
i.e. not optimized (``requires_grad`` set to ``False``).
Options are ['alpha', 'beta', 'gamma']. Defaults to None.
"""
super().__init__()
# see if there are fixed variables
if fixed is not None:
check_consistency(fixed, str)
if not all(key in ['alpha', 'beta', 'gamma'] for key in fixed):
raise TypeError("Fixed keys must be in "
"['alpha', 'beta', 'gamma'].")
# initialize alpha, beta, gamma if they are None
if alpha is None:
alpha = 1.
if beta is None:
beta = 1.
if gamma is None:
gamma = 0.
# checking consistency
check_consistency(alpha, (float, complex))
check_consistency(beta, (float, complex))
check_consistency(gamma, (float, complex))
if not callable(func):
raise ValueError("Function must be a callable function.")
# registering as tensors
alpha = torch.tensor(alpha, requires_grad=False)
beta = torch.tensor(beta, requires_grad=False)
gamma = torch.tensor(gamma, requires_grad=False)
# setting not fixed variables as torch.nn.Parameter with gradient
# registering the buffer for the one which are fixed, buffers by
# default are saved alongside trainable parameters
if 'alpha' not in (fixed or []):
self._alpha = torch.nn.Parameter(alpha, requires_grad=True)
else:
self.register_buffer('alpha', alpha)
if 'beta' not in (fixed or []):
self._beta = torch.nn.Parameter(beta, requires_grad=True)
else:
self.register_buffer('beta', beta)
if 'gamma' not in (fixed or []):
self._gamma = torch.nn.Parameter(gamma, requires_grad=True)
else:
self.register_buffer('gamma', gamma)
# registering function
self._func = func
def forward(self, x):
"""
Forward pass of the function.
Applies the function to the input elementwise.
"""
return self.alpha * (self._func(self.beta * x + self.gamma))
@property
def alpha(self):
"""
The alpha variable
"""
return self._alpha
@property
def beta(self):
"""
The alpha variable
"""
return self._beta
@property
def gamma(self):
"""
The alpha variable
"""
return self._gamma