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Documentation for v0.1 version (#199)

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* Adding Equations, solving typos
* improve _code.rst
* the team rst and restuctore index.rst
* fixing errors

---------

Co-authored-by: Dario Coscia <dariocoscia@dhcp-015.eduroam.sissa.it>
This commit is contained in:
Dario Coscia
2023-11-08 14:39:00 +01:00
committed by Nicola Demo
parent 3f9305d475
commit 8b7b61b3bd
144 changed files with 2741 additions and 1766 deletions
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1
pina/adaptive_functions/__init__.py
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@@ -1,4 +1,3 @@
from .adaptive_tanh import AdaptiveTanh
from .adaptive_sin import AdaptiveSin
from .adaptive_cos import AdaptiveCos

16
pina/adaptive_functions/adaptive_cos.py
View File

@@ -1,6 +1,7 @@
import torch
from torch.nn.parameter import Parameter
class AdaptiveCos(torch.nn.Module):
'''
Implementation of soft exponential activation.
@@ -18,7 +19,8 @@ class AdaptiveCos(torch.nn.Module):
>>> x = torch.randn(256)
>>> x = a1(x)
'''
def __init__(self, alpha = None):
def __init__(self, alpha=None):
'''
Initialization.
INPUT:
@@ -31,16 +33,18 @@ class AdaptiveCos(torch.nn.Module):
# initialize alpha
if alpha == None:
self.alpha = Parameter(torch.tensor(1.0)) # create a tensor out of alpha
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.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.scale.requiresGrad = True # set requiresGrad to true!
self.translate = Parameter(torch.tensor(0.0))
self.translate.requiresGrad = True # set requiresGrad to true!
self.translate.requiresGrad = True # set requiresGrad to true!
def forward(self, x):
'''

20
pina/adaptive_functions/adaptive_exp.py
View File

@@ -1,6 +1,7 @@
import torch
from torch.nn.parameter import Parameter
class AdaptiveExp(torch.nn.Module):
'''
Implementation of soft exponential activation.
@@ -18,6 +19,7 @@ class AdaptiveExp(torch.nn.Module):
>>> x = torch.randn(256)
>>> x = a1(x)
'''
def __init__(self):
'''
Initialization.
@@ -28,14 +30,20 @@ class AdaptiveExp(torch.nn.Module):
'''
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.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.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!
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):
'''

6
pina/adaptive_functions/adaptive_linear.py
View File

@@ -2,6 +2,7 @@
import torch
from torch.nn.parameter import Parameter
class AdaptiveLinear(torch.nn.Module):
'''
Implementation of soft exponential activation.
@@ -19,6 +20,7 @@ class AdaptiveLinear(torch.nn.Module):
>>> x = torch.randn(256)
>>> x = a1(x)
'''
def __init__(self):
'''
Initialization.
@@ -30,10 +32,10 @@ class AdaptiveLinear(torch.nn.Module):
super(AdaptiveLinear, self).__init__()
self.scale = Parameter(torch.tensor(1.0))
self.scale.requiresGrad = True # set requiresGrad to true!
self.scale.requiresGrad = True # set requiresGrad to true!
self.translate = Parameter(torch.tensor(0.0))
self.translate.requiresGrad = True # set requiresGrad to true!
self.translate.requiresGrad = True # set requiresGrad to true!
def forward(self, x):
'''

8
pina/adaptive_functions/adaptive_relu.py
View File

@@ -1,6 +1,7 @@
import torch
from torch.nn.parameter import Parameter
class AdaptiveReLU(torch.nn.Module, Parameter):
'''
Implementation of soft exponential activation.
@@ -18,6 +19,7 @@ class AdaptiveReLU(torch.nn.Module, Parameter):
>>> x = torch.randn(256)
>>> x = a1(x)
'''
def __init__(self):
'''
Initialization.
@@ -29,10 +31,10 @@ class AdaptiveReLU(torch.nn.Module, Parameter):
super(AdaptiveReLU, self).__init__()
self.scale = Parameter(torch.rand(1))
self.scale.requiresGrad = True # set requiresGrad to true!
self.scale.requiresGrad = True # set requiresGrad to true!
self.translate = Parameter(torch.rand(1))
self.translate.requiresGrad = True # set requiresGrad to true!
self.translate.requiresGrad = True # set requiresGrad to true!
def forward(self, x):
'''
@@ -40,4 +42,4 @@ class AdaptiveReLU(torch.nn.Module, Parameter):
Applies the function to the input elementwise.
'''
#x += self.translate
return torch.relu(x+self.translate)*self.scale
return torch.relu(x + self.translate) * self.scale

20
pina/adaptive_functions/adaptive_sin.py
View File

@@ -1,6 +1,7 @@
import torch
from torch.nn.parameter import Parameter
class AdaptiveSin(torch.nn.Module):
'''
Implementation of soft exponential activation.
@@ -18,7 +19,8 @@ class AdaptiveSin(torch.nn.Module):
>>> x = torch.randn(256)
>>> x = a1(x)
'''
def __init__(self, alpha = None):
def __init__(self, alpha=None):
'''
Initialization.
INPUT:
@@ -29,14 +31,18 @@ class AdaptiveSin(torch.nn.Module):
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.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.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!
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):
'''

6
pina/adaptive_functions/adaptive_softplus.py
View File

@@ -1,6 +1,7 @@
import torch
from torch.nn.parameter import Parameter
class AdaptiveSoftplus(torch.nn.Module):
'''
Implementation of soft exponential activation.
@@ -18,6 +19,7 @@ class AdaptiveSoftplus(torch.nn.Module):
>>> x = torch.randn(256)
>>> x = a1(x)
'''
def __init__(self):
'''
Initialization.
@@ -31,7 +33,7 @@ class AdaptiveSoftplus(torch.nn.Module):
self.soft = torch.nn.Softplus()
self.scale = Parameter(torch.rand(1))
self.scale.requiresGrad = True # set requiresGrad to true!
self.scale.requiresGrad = True # set requiresGrad to true!
def forward(self, x):
'''
@@ -39,4 +41,4 @@ class AdaptiveSoftplus(torch.nn.Module):
Applies the function to the input elementwise.
'''
#x += self.translate
return self.soft(x)*self.scale
return self.soft(x) * self.scale

8
pina/adaptive_functions/adaptive_square.py
View File

@@ -1,6 +1,7 @@
import torch
from torch.nn.parameter import Parameter
class AdaptiveSquare(torch.nn.Module):
'''
Implementation of soft exponential activation.
@@ -18,7 +19,8 @@ class AdaptiveSquare(torch.nn.Module):
>>> x = torch.randn(256)
>>> x = a1(x)
'''
def __init__(self, alpha = None):
def __init__(self, alpha=None):
'''
Initialization.
INPUT:
@@ -29,10 +31,10 @@ class AdaptiveSquare(torch.nn.Module):
super(AdaptiveSquare, self).__init__()
self.scale = Parameter(torch.tensor(1.0))
self.scale.requiresGrad = True # set requiresGrad to true!
self.scale.requiresGrad = True # set requiresGrad to true!
self.translate = Parameter(torch.tensor(0.0))
self.translate.requiresGrad = True # set requiresGrad to true!
self.translate.requiresGrad = True # set requiresGrad to true!
def forward(self, x):
'''

22
pina/adaptive_functions/adaptive_tanh.py
View File

@@ -1,6 +1,7 @@
import torch
from torch.nn.parameter import Parameter
class AdaptiveTanh(torch.nn.Module):
'''
Implementation of soft exponential activation.
@@ -18,7 +19,8 @@ class AdaptiveTanh(torch.nn.Module):
>>> x = torch.randn(256)
>>> x = a1(x)
'''
def __init__(self, alpha = None):
def __init__(self, alpha=None):
'''
Initialization.
INPUT:
@@ -31,17 +33,19 @@ class AdaptiveTanh(torch.nn.Module):
# initialize alpha
if alpha == None:
self.alpha = Parameter(torch.tensor(1.0)) # create a tensor out of alpha
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.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.scale.requiresGrad = True # set requiresGrad to true!
self.translate = Parameter(torch.tensor(0.0))
self.translate.requiresGrad = True # set requiresGrad to true!
self.translate.requiresGrad = True # set requiresGrad to true!
def forward(self, x):
'''
@@ -49,4 +53,6 @@ class AdaptiveTanh(torch.nn.Module):
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))
return self.scale * (torch.exp(self.alpha * x) - torch.exp(
-self.alpha * x)) / (torch.exp(self.alpha * x) +
torch.exp(-self.alpha * x))