Create setting for RNN model with python
setRNNTorch( hidden_size = c(50, 100), epochs = c(20, 50), seed = 0, class_weight = 0, type = "RNN" )
hidden_size | The hidden size |
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epochs | The number of epochs |
seed | A seed for the model |
class_weight | The class weight used for imbalanced data: 0: Inverse ratio between positives and negatives -1: Focal loss |
type | It can be normal 'RNN', 'BiRNN' (bidirectional RNN) and 'GRU' |
if (FALSE) { model.rnnTorch <- setRNNTorch() }