EmbCudaLSTM

class neuralhydrology.modelzoo.embcudalstm.EmbCudaLSTM(cfg: Config)

Bases: BaseModel

EmbCudaLSTM model class, which adds embedding networks for static inputs to the standard LSTM.

Deprecated since version 0.9.11-beta: Use neuralhydrology.modelzoo.cudalstm.CudaLSTM with statics_embedding.

This class extends the standard CudaLSTM class to preprocess the static inputs by an embedding network, prior to concatenating those values to the dynamic (time series) inputs. Use the config argument statics_embedding to specify the architecture of the fully-connected embedding network. No activation function is applied to the outputs of the embedding network. To control the initial forget gate bias, use the config argument initial_forget_bias. Often it is useful to set this value to a positive value at the start of the model training, to keep the forget gate closed and to facilitate the gradient flow. The EmbCudaLSTM class only supports single timescale predictions. Use MTSLSTM to train a model and get predictions on multiple temporal resolutions at the same time.

Parameters:

cfg (Config) – The run configuration.

forward(data: Dict[str, Tensor]) → Dict[str, Tensor]

Perform a forward pass on the EmbCudaLSTM model.

Parameters:

data (Dict[str, torch.Tensor]) – Dictionary, containing input features as key-value pairs.

Returns:

Model outputs and intermediate states as a dictionary.

  • y_hat: model predictions of shape [batch size, sequence length, number of target variables].

  • h_n: hidden state at the last time step of the sequence of shape [1, batch size, hidden size].

  • c_n: cell state at the last time step of the sequence of shape [1, batch size, hidden size].

Return type:

Dict[str, torch.Tensor]

module_parts = ['embedding_net', 'lstm', 'head']