potential_based
PotentialBased(input_size, hidden_size, activation_nonlin, tau_init, tau_learnable, kappa_init, kappa_learnable, potentials_init=None, output_size=None, input_embedding=None, output_embedding=None, device='cpu')
¶
Base class for all potential-based recurrent neutral networks.
hidden_size: Number of neurons with potential per hidden layer. For all use cases conceived at this point,
we only use one recurrent layer. However, there is the possibility to extend the networks to multiple
potential-based layers.
activation_nonlin: Nonlinearity used to compute the activations from the potential levels.
tau_init: Initial value for the shared time constant of the potentials.
tau_learnable: Whether the time constant is a learnable parameter or fixed.
kappa_init: Initial value for the cubic decay, pass 0 to disable the cubic decay.
kappa_learnable: Whether the cubic decay is a learnable parameter or fixed.
potentials_init: Initial for the potentials, i.e., the network's hidden state.
output_size: Number of output dimensions. By default, the number of outputs is equal to the number of
hidden neurons.
input_embedding: Optional (custom) [Module][torch.nn.Module] to extract features from the inputs.
This module must transform the inputs such that the dimensionality matches the number of
neurons of the neural field, i.e., `hidden_size`. By default, a [linear layer][torch.nn.Linear]
without biases is used.
output_embedding: Optional (custom) [Module][torch.nn.Module] to compute the outputs from the activations.
This module must map the activations of shape (`hidden_size`,) to the outputs of shape (`output_size`,)
By default, a [linear layer][torch.nn.Linear] without biases is used.
device: Device to move this module to (after initialization).
Source code in neuralfields/potential_based.py
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device: torch.device
property
¶
Get the device this model is located on. This assumes that all parts are located on the same device.
hidden_size: int
property
¶
Get the number of neurons in the neural field layer, i.e., the ones with the in-/exhibition dynamics.
kappa: Union[torch.Tensor, nn.Parameter]
property
¶
Get the cubic decay parameter \(\kappa\).
param_values: torch.Tensor
property
writable
¶
Get the module's parameters as a 1-dimensional array. The values are copied, thus modifying the return value does not propagate back to the module parameters.
stimuli_external: torch.Tensor
property
¶
Get the neurons' external stimuli, resulting from the current inputs. This property is useful for recording during a simulation / rollout.
stimuli_internal: torch.Tensor
property
¶
Get the neurons' internal stimuli, resulting from the previous activations of the neurons. This property is useful for recording during a simulation / rollout.
tau: Union[torch.Tensor, nn.Parameter]
property
¶
Get the timescale parameter, called \(\tau\) in the original paper [Amari_77].
transform_to_img_space: Callable[[torch.Tensor], torch.Tensor] = torch.exp
class-attribute
instance-attribute
¶
Function to map parameters to the image space of the original problem.
transform_to_opt_space: Callable[[torch.Tensor], torch.Tensor] = torch.log
class-attribute
instance-attribute
¶
Function to map parameters to the optimization space.
forward(inputs, hidden=None)
¶
Compute the external and internal stimuli, advance the potential dynamics for one time step, and return the model's output for several time steps in a row.
This method essentially calls forward_one_step several times in a row.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
inputs | Tensor | Inputs of shape | required |
hidden | Optional[Tensor] | Initial values of the hidden states, i.e., the potentials. By default, the network initialized the hidden state to be all zeros. However, via this argument one can set a specific initial value for the potentials. Depending on the shape of | None |
Returns:
Type | Description |
---|---|
Tensor | The outputs, i.e., the (linearly combined) activations, and all intermediate potential values, both of |
Tensor | shape |
Source code in neuralfields/potential_based.py
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forward_one_step(inputs, hidden=None)
abstractmethod
¶
Compute the external and internal stimuli, advance the potential dynamics for one time step, and return the model's output.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
inputs | Tensor | Inputs of the current time step, of shape | required |
hidden | Optional[Tensor] | Hidden state which are for the model in this package the potentials, of shape | None |
Returns:
Type | Description |
---|---|
Tensor | The outputs, i.e., the (linearly combined) activations, and the most recent potential values, both of shape |
Tensor |
|
Source code in neuralfields/potential_based.py
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init_hidden(batch_size=None, potentials_init=None)
¶
Provide initial values for the hidden parameters. This usually is a zero tensor.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
batch_size | Optional[int] | Number of batches, i.e., states to track in parallel. | None |
potentials_init | Optional[Tensor] | Initial values for the potentials to override the networks default values with. | None |
Returns:
Type | Description |
---|---|
Union[Tensor, Parameter] | Tensor of shape |
Source code in neuralfields/potential_based.py
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potentials_dot(potentials, stimuli)
abstractmethod
¶
Compute the derivative of the neurons' potentials w.r.t. time.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
potentials | Tensor | Potential values at the current point in time, of shape | required |
stimuli | Tensor | Sum of external and internal stimuli at the current point in time, of shape | required |
Returns:
Type | Description |
---|---|
Tensor | Time derivative of the potentials $ rac{dp}{dt}$, of shape |
Source code in neuralfields/potential_based.py
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