ForeTiS.model._model_classes
Module Contents
Classes
Base class for all neural network modules. |
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Base class for all neural network modules. |
|
Base class for all neural network modules. |
|
Base class for all neural network modules. |
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The Matern 5/2 kernel. Functions drawn from a GP with this kernel are twice |
- class ForeTiS.model._model_classes.PrintLayer
Bases:
torch.nn.Module
Base class for all neural network modules.
Your models should also subclass this class.
Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:
import torch.nn as nn import torch.nn.functional as F class Model(nn.Module): def __init__(self): super().__init__() self.conv1 = nn.Conv2d(1, 20, 5) self.conv2 = nn.Conv2d(20, 20, 5) def forward(self, x): x = F.relu(self.conv1(x)) return F.relu(self.conv2(x))
Submodules assigned in this way will be registered, and will have their parameters converted too when you call
to()
, etc.Note
As per the example above, an
__init__()
call to the parent class must be made before assignment on the child.- Variables:
training (bool) – Boolean represents whether this module is in training or evaluation mode.
- forward(x)
- class ForeTiS.model._model_classes.GetOutputZero
Bases:
torch.nn.Module
Base class for all neural network modules.
Your models should also subclass this class.
Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:
import torch.nn as nn import torch.nn.functional as F class Model(nn.Module): def __init__(self): super().__init__() self.conv1 = nn.Conv2d(1, 20, 5) self.conv2 = nn.Conv2d(20, 20, 5) def forward(self, x): x = F.relu(self.conv1(x)) return F.relu(self.conv2(x))
Submodules assigned in this way will be registered, and will have their parameters converted too when you call
to()
, etc.Note
As per the example above, an
__init__()
call to the parent class must be made before assignment on the child.- Variables:
training (bool) – Boolean represents whether this module is in training or evaluation mode.
- forward(x)
- class ForeTiS.model._model_classes.PrepareForlstm
Bases:
torch.nn.Module
Base class for all neural network modules.
Your models should also subclass this class.
Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:
import torch.nn as nn import torch.nn.functional as F class Model(nn.Module): def __init__(self): super().__init__() self.conv1 = nn.Conv2d(1, 20, 5) self.conv2 = nn.Conv2d(20, 20, 5) def forward(self, x): x = F.relu(self.conv1(x)) return F.relu(self.conv2(x))
Submodules assigned in this way will be registered, and will have their parameters converted too when you call
to()
, etc.Note
As per the example above, an
__init__()
call to the parent class must be made before assignment on the child.- Variables:
training (bool) – Boolean represents whether this module is in training or evaluation mode.
- forward(x)
- class ForeTiS.model._model_classes.PrepareForDropout
Bases:
torch.nn.Module
Base class for all neural network modules.
Your models should also subclass this class.
Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:
import torch.nn as nn import torch.nn.functional as F class Model(nn.Module): def __init__(self): super().__init__() self.conv1 = nn.Conv2d(1, 20, 5) self.conv2 = nn.Conv2d(20, 20, 5) def forward(self, x): x = F.relu(self.conv1(x)) return F.relu(self.conv2(x))
Submodules assigned in this way will be registered, and will have their parameters converted too when you call
to()
, etc.Note
As per the example above, an
__init__()
call to the parent class must be made before assignment on the child.- Variables:
training (bool) – Boolean represents whether this module is in training or evaluation mode.
- forward(lstm_out)
- class ForeTiS.model._model_classes.SafeMatern52(variance=1.0, lengthscales=1.0, **kwargs)
Bases:
gpflow.kernels.Matern52
The Matern 5/2 kernel. Functions drawn from a GP with this kernel are twice differentiable. The kernel equation is
k(r) = σ² (1 + √5r + 5/3r²) exp{-√5 r}
where: r is the Euclidean distance between the input points, scaled by the lengthscales parameter ℓ, σ² is the variance parameter.
- Parameters:
variance (gpflow.base.TensorType) –
lengthscales (gpflow.base.TensorType) –
kwargs (Any) –
- euclid_dist(X, X2)