Model Components¶
Activation Functions¶
- class doc_octopy.models.components.activation_functions.PSerf(gamma=1.0, sigma=1.25, stabilisation_term=1e-12)[source]¶
PSerf activation function from Biswas et al.
- Parameters:
References
Biswas, K., Kumar, S., Banerjee, S., Pandey, A.K., 2021. ErfAct and PSerf: Non-monotonic smooth trainable Activation Functions. arXiv:2109.04386 [cs].
Initialize internal Module state, shared by both nn.Module and ScriptModule.
- forward(x)[source]¶
Define 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
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.- Return type:
- class doc_octopy.models.components.activation_functions.SAU(alpha=0.15, n=20000)[source]¶
SAU activation function from Biswas et al.
- alphafloat, optional
The alpha parameter, by default 0.15.
- nint, optional
The n parameter, by default 20000.
Biswas, K., Kumar, S., Banerjee, S., Pandey, A.K., 2021. SAU: Smooth activation function using convolution with approximate identities. arXiv:2109.13210 [cs].
Initialize internal Module state, shared by both nn.Module and ScriptModule.
- forward(x)[source]¶
Define 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
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.- Return type:
- class doc_octopy.models.components.activation_functions.SMU(alpha=0.01, mu=2.5)[source]¶
SMU activation function from Biswas et al.
- Parameters:
References
Biswas, K., Kumar, S., Banerjee, S., Pandey, A.K., 2022. SMU: smooth activation function for deep networks using smoothing maximum technique. arXiv:2111.04682 [cs].
Notes
This version also make alpha trainable.
Initialize internal Module state, shared by both nn.Module and ScriptModule.
- forward(x)[source]¶
Define 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
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.- Return type:
- class doc_octopy.models.components.activation_functions.SMU_old(alpha=0.01, mu=2.5)[source]¶
SMU activation function from Biswas et al. This is an older version of the SMU activation function and should not be used.
Warning
This is an older version of the SMU activation function and should not be used.
- Parameters:
References
Biswas, K., Kumar, S., Banerjee, S., Pandey, A.K., 2022. SMU: smooth activation function for deep networks using smoothing maximum technique. arXiv:2111.04682 [cs].
Initialize internal Module state, shared by both nn.Module and ScriptModule.
- forward(x)[source]¶
Define 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
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.- Return type:
Utilities¶
- class doc_octopy.models.components.utils.CircularPad[source]¶
Circular padding layer used in the paper [1].
- Parameters:
x (torch.Tensor) – Input tensor.
- Returns:
Padded tensor.
- Return type:
References
[1] Sîmpetru, R.C., Osswald, M., Braun, D.I., Oliveira, D.S., Cakici, A.L., Del Vecchio, A., 2022. Accurate Continuous Prediction of 14 Degrees of Freedom of the Hand from Myoelectrical Signals through Convolutive Deep Learning, in: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 702–706. https://doi.org/10.1109/EMBC48229.2022.9870937
Initialize internal Module state, shared by both nn.Module and ScriptModule.
- forward(x)[source]¶
Define 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
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.- Return type:
- class doc_octopy.models.components.utils.WeightedSum(alpha=0.5)[source]¶
Initialize internal Module state, shared by both nn.Module and ScriptModule.
- forward(x, y)[source]¶
Define 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
Module
instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.- Return type: