Modules

flood_forecast.da_rnn.modules.init_hidden(x, hidden_size: int)

Train the initial value of the hidden state: https://r2rt.com/non-zero-initial-states-for-recurrent-neural-networks.html

class flood_forecast.da_rnn.modules.Encoder(input_size: int, hidden_size: int, T: int)

__init__(input_size: int, hidden_size: int, T: int)

input size: number of underlying factors (81) T: number of time steps (10) hidden_size: dimension of the hidden state

forward(input_data: Tensor)

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.

class flood_forecast.da_rnn.modules.Decoder(encoder_hidden_size: int, decoder_hidden_size: int, T: int, out_feats=1)

__init__(encoder_hidden_size: int, decoder_hidden_size: int, T: int, out_feats=1)

Initialize internal Module state, shared by both nn.Module and ScriptModule.

forward(input_encoded, y_history)

Define the computation performed at every call.

Should be overridden by all subclasses.

Note

Although the recipe for forward pass needs to be defined within this function, one should call the Module instance afterwards instead of this since the former takes care of running the registered hooks while the latter silently ignores them.