Train da

flood_forecast.da_rnn.train_da.da_rnn(train_data: flood_forecast.da_rnn.custom_types.TrainData, n_targs: int, encoder_hidden_size=64, decoder_hidden_size=64, T=10, learning_rate=0.01, batch_size=128, param_output_path='', save_path: str = None) → Tuple[dict, flood_forecast.da_rnn.custom_types.DaRnnNet]

n_targs: The number of target columns (not steps) T: The number timesteps in the window

flood_forecast.da_rnn.train_da.train(net: flood_forecast.da_rnn.custom_types.DaRnnNet, train_data: flood_forecast.da_rnn.custom_types.TrainData, t_cfg: flood_forecast.da_rnn.custom_types.TrainConfig, train_config='', n_epochs=10, save_plots=True, wandb=False, tensorboard=False)

flood_forecast.da_rnn.train_da.prep_train_data(batch_idx: numpy.ndarray, t_cfg: flood_forecast.da_rnn.custom_types.TrainConfig, train_data: flood_forecast.da_rnn.custom_types.TrainData) → Tuple

flood_forecast.da_rnn.train_da.adjust_learning_rate(net: flood_forecast.da_rnn.custom_types.DaRnnNet, n_iter: int) → None

flood_forecast.da_rnn.train_da.train_iteration(t_net: flood_forecast.da_rnn.custom_types.DaRnnNet, loss_func: Callable, X, y_history, y_target)

flood_forecast.da_rnn.train_da.predict(t_net: flood_forecast.da_rnn.custom_types.DaRnnNet, t_dat: flood_forecast.da_rnn.custom_types.TrainData, train_size: int, batch_size: int, T: int, on_train=False)