Recurrent quantile regression neural network module
Source:R/EQRN_seq_network_structures.R
QRNN_RNN_net.RdA recurrent neural network as a torch::nn_module,
designed for quantile regression.
Usage
QRNN_RNN_net(
type = c("lstm", "gru"),
nb_input_features,
hidden_size,
num_layers = 1,
dropout = 0
)Arguments
- type
the type of recurrent architecture, can be one of
"lstm"(default) or"gru",- nb_input_features
the input size (i.e. the number of features),
the dimension of the hidden latent state variables in the recurrent network,
- num_layers
the number of recurrent layers,
- dropout
probability parameter for dropout before each hidden layer for regularization during training.
Details
The constructor allows specifying:
- type
the type of recurrent architecture, can be one of
"lstm"(default) or"gru",- nb_input_features
the input size (i.e. the number of features),
- hidden_size
the dimension of the hidden latent state variables in the recurrent network,
- num_layers
the number of recurrent layers,
- dropout
probability parameter for dropout before each hidden layer for regularization during training.