clinicadl.networks.nn.SEResNet101¶

class clinicadl.networks.nn.SEResNet101(num_outputs: int | None, output_act: ActFunction | tuple[ActFunction, dict[str, Any]] | None = None) → None[source]¶

SEResNet-101, from Squeeze-and-Excitation Networks.

Only the last fully connected layer will be changed to match num_outputs.

Warning

Only works with 2D images with 3 channels.

Parameters:

num_outputs (Optional[int]) – Number of output variables after the last linear layer. If None, the feature map before the last fully connected layer will be returned.
output_act (Optional[ActivationParameters], default=None) –
A potential activation layer applied to the output of the network, and optionally its arguments. Must be passed as activation_name or (activation_name, arguments), where arguments is a dictionary. If None, no activation will be used.

activation_name can be any value in {"celu", "elu", "gelu", "leakyrelu", "logsoftmax", "mish", "prelu", "relu", "relu6", "selu", "sigmoid", "softmax", "tanh"}. Please refer to PyTorch activation functions to know the arguments for each of them.