gluonts.mx.block.enc2dec module#
- class gluonts.mx.block.enc2dec.FutureFeatIntegratorEnc2Dec(**kwargs)[source]#
Bases:
gluonts.mx.block.enc2dec.Seq2SeqEnc2Dec
Integrates the encoder_output_dynamic and future_features_dynamic into one and passes them through as the dynamic input to the decoder.
- hybrid_forward(F, encoder_output_static: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], encoder_output_dynamic: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], future_features_dynamic: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) Tuple[Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]] [source]#
- Parameters
encoder_output_static – shape (batch_size, channels_seq[-1] + 1) or (N, C)
encoder_output_dynamic – shape (batch_size, sequence_length, channels_seq[-1] + 1) or (N, T, C)
future_features_dynamic – shape (batch_size, sequence_length, prediction_length=decoder_length, num_feat_dynamic) or (N, T, P, C`)
- Returns
Tensor – shape (batch_size, channels_seq[-1] + 1) or (N, C)
Tensor – shape (batch_size, prediction_length=decoder_length, channels_seq[-1] + 1 + decoder_length * num_feat_dynamic) or (N, T, C)
- class gluonts.mx.block.enc2dec.PassThroughEnc2Dec(**kwargs)[source]#
Bases:
gluonts.mx.block.enc2dec.Seq2SeqEnc2Dec
Simplest class for passing encoder tensors do decoder.
Passes through tensors, except that future_features_dynamic is dropped.
- hybrid_forward(F, encoder_output_static: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], encoder_output_dynamic: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], future_features_dynamic: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) Tuple[Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]] [source]#
- Parameters
encoder_output_static – shape (batch_size, channels_seq[-1] + 1) or (N, C)
encoder_output_dynamic – shape (batch_size, sequence_length, channels_seq[-1] + 1) or (N, T, C)
future_features_dynamic – shape (batch_size, sequence_length, prediction_length=decoder_length, num_feat_dynamic) or (N, T, P, C`)
- Returns
Tensor – shape (batch_size, channels_seq[-1] + 1) or (N, C)
Tensor – shape (batch_size, sequence_length, channels_seq[-1] + 1) or (N, T, C)
- class gluonts.mx.block.enc2dec.Seq2SeqEnc2Dec(**kwargs)[source]#
Bases:
mxnet.gluon.block.HybridBlock
Abstract class for any module that pass encoder to decoder, such as attention network.
- hybrid_forward(F, encoder_output_static: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], encoder_output_dynamic: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], future_features_dynamic: Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]) Tuple[Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol], Union[mxnet.ndarray.ndarray.NDArray, mxnet.symbol.symbol.Symbol]] [source]#
- Parameters
encoder_output_static – shape (batch_size, channels_seq[-1] + 1) or (N, C)
encoder_output_dynamic – shape (batch_size, sequence_length, channels_seq[-1] + 1) or (N, T, C)
future_features_dynamic – shape (batch_size, sequence_length, prediction_length=decoder_length, num_feat_dynamic) or (N, T, P, C`)