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Create setting for neural network model with python
setMLPTorch( size = c(500, 1000), w_decay = c(5e-04, 0.005), epochs = c(20, 50), seed = 0, class_weight = 0, mlp_type = "MLP", autoencoder = FALSE, vae = FALSE )
The number of hidden nodes
The l2 regularisation
The number of epochs
A seed for the model
The class weight used for imbalanced data: 0: Inverse ratio between positives and negatives -1: Focal loss
The type of multiple layer network, inlcuding MLP and SNN (self-normalizing neural network)
First learn stakced autoencoder for input features, then train MLP on the encoded features.
First learn stakced varational autoencoder for input features, then train MLP on the encoded features.
# NOT RUN { model.mlpTorch <- setMLPTorch() # }
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