fishy.models.deep.rcnn

Residual Convolutional Neural Network (RCNN) for spectral classification.

This model uses residual blocks with 1D convolutions to capture spectral patterns while allowing for deeper architectures via skip connections.

Classes

class fishy.models.deep.rcnn.RCNN(input_dim: int, output_dim: int, hidden_dim: int = 128, num_layers: int = 6, dropout: float = 0.2, **kwargs)

Bases: Module

Improved Residual Convolutional Neural Network (RCNN) model.

__init__(input_dim: int, output_dim: int, hidden_dim: int = 128, num_layers: int = 6, dropout: float = 0.2, **kwargs) → None

Initialize internal Module state, shared by both nn.Module and ScriptModule.

forward(x: Tensor) → Tensor

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.

class fishy.models.deep.rcnn.ResidualBlock(channels, dropout=0.2)

Bases: Module

__init__(channels, dropout=0.2)

Initialize internal Module state, shared by both nn.Module and ScriptModule.

forward(x)

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.

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