immuneML.ml_methods.pytorch_implementations package¶
Submodules¶
immuneML.ml_methods.pytorch_implementations.PyTorchLogisticRegression module¶
immuneML.ml_methods.pytorch_implementations.PyTorchReceptorCNN module¶
- class immuneML.ml_methods.pytorch_implementations.PyTorchReceptorCNN.PyTorchReceptorCNN(*args: Any, **kwargs: Any)[source]¶
Bases:
Module
This class implements the ReceptorCNN using PyTorch. This is one specific implementation of the architecture proposed in
ReceptorCNN
.- forward(x)[source]¶
Implements the forward pass through the network by applying kernels to the one-hot encoded receptors, followed by ReLU activation and max pooling. The obtained output is then concatenated to get the receptor representation. A fully-connected layer is then applied to the representation to predict the class assignment.
- Parameters:
x – input data consisting of one-hot encoded immune receptors with optional positional information
- Returns:
predictions of class assignment
immuneML.ml_methods.pytorch_implementations.SimpleLSTMGenerator module¶
immuneML.ml_methods.pytorch_implementations.SimpleVAEGenerator module¶
- class immuneML.ml_methods.pytorch_implementations.SimpleVAEGenerator.Decoder(*args: Any, **kwargs: Any)[source]¶
Bases:
Module
- class immuneML.ml_methods.pytorch_implementations.SimpleVAEGenerator.Encoder(*args: Any, **kwargs: Any)[source]¶
Bases:
Module