GRU¶

class pytorch_forecasting.models.nn.rnn.GRU(*args, **kwargs)[source]¶

Bases: pytorch_forecasting.models.nn.rnn.RNN, torch.nn.modules.rnn.GRU

GRU that can handle zero-length sequences

Methods

`handle_no_encoding`(hidden_state, ...)	Mask the hidden_state where there is no encoding.
`init_hidden_state`(x)	Initialise a hidden_state.
`repeat_interleave`(hidden_state, n_samples)	Duplicate the hidden_state n_samples times.

handle_no_encoding(hidden_state: Union[Tuple[torch.Tensor, torch.Tensor], torch.Tensor], no_encoding: torch.BoolTensor, initial_hidden_state: Union[Tuple[torch.Tensor, torch.Tensor], torch.Tensor]) → Union[Tuple[torch.Tensor, torch.Tensor], torch.Tensor][source]¶

Mask the hidden_state where there is no encoding.

Parameters

hidden_state (HiddenState) – hidden state where some entries need replacement
no_encoding (torch.BoolTensor) – positions that need replacement
initial_hidden_state (HiddenState) – hidden state to use for replacement

Returns

hidden state with propagated initial hidden state where appropriate

Return type

HiddenState

init_hidden_state(x: torch.Tensor) → Union[Tuple[torch.Tensor, torch.Tensor], torch.Tensor][source]¶

Initialise a hidden_state.

Parameters: x (torch.Tensor) – network input
Returns: default (zero-like) hidden state
Return type: HiddenState

repeat_interleave(hidden_state: Union[Tuple[torch.Tensor, torch.Tensor], torch.Tensor], n_samples: int) → Union[Tuple[torch.Tensor, torch.Tensor], torch.Tensor][source]¶

Duplicate the hidden_state n_samples times.

Parameters

hidden_state (HiddenState) – hidden state to repeat
n_samples (int) – number of repetitions

Returns

repeated hidden state

Return type

HiddenState

MultiEmbedding

LSTM