Module PINNacle.src.model.fnn
Classes
FNN(layer_sizes, activation, kernel_initializer)
: Fully-connected neural network.
Initializes internal Module state, shared by both nn.Module and ScriptModule.
### Ancestors (in MRO)
* deepxde.nn.pytorch.nn.NN
* torch.nn.modules.module.Module
### Class variables
`call_super_init: bool`
:
`dump_patches: bool`
:
`training: bool`
:
### Methods
`forward(self, inputs) ‑> Callable[..., Any]`
: Defines the computation performed at every call.
Should be overridden by all subclasses.
.. note::
Although the recipe for forward pass needs to be defined within
this function, one should call the :class:`Module` instance afterwards
instead of this since the former takes care of running the
registered hooks while the latter silently ignores them.PFNN(layer_sizes, activation, kernel_initializer, split_mask=None)
: Parallel fully-connected network that uses independent sub-networks for each
network output.
Args:
layer_sizes: A nested list that defines the architecture of the neural network
(how the layers are connected). If `layer_sizes[i]` is an int, it represents
one layer shared by all the outputs; if `layer_sizes[i]` is a list, it
represents `len(layer_sizes[i])` sub-layers, each of which is exclusively
used by one output. Note that `len(layer_sizes[i])` should equal the number
of outputs. Every number specifies the number of neurons in that layer.
Initializes internal Module state, shared by both nn.Module and ScriptModule.
### Ancestors (in MRO)
* deepxde.nn.pytorch.nn.NN
* torch.nn.modules.module.Module
### Class variables
`call_super_init: bool`
:
`dump_patches: bool`
:
`training: bool`
:
### Methods
`forward(self, inputs) ‑> Callable[..., Any]`
: Defines the computation performed at every call.
Should be overridden by all subclasses.
.. note::
Although the recipe for forward pass needs to be defined within
this function, one should call the :class:`Module` instance afterwards
instead of this since the former takes care of running the
registered hooks while the latter silently ignores them.