nn.many_to_many_rnn.ManyToManyRNNRegressor

nn.many_to_many_rnn.ManyToManyRNNRegressor(
    input_dim=1,
    output_dim=1,
    rnn_units=256,
    fc_units=256,
    act_fn='ReLU',
    dropout_prob=0.0,
    bidirectional=True,
    **kwargs,
)

ManyToManyRNN with the spotPython hyperparameter interface.

Accepts the hyperparameter names of the spotPython light_hyper_dict entry ManyToManyRNNRegressor (rnn_units, fc_units, act_fn, dropout_prob, bidirectional) plus the input_dim/output_dim convention used by spotoptim objectives. Training hyperparameters such as epochs, batch_size, patience, optimizer, and lr_mult are accepted via **kwargs and ignored here — they are consumed by the training objective.

Parameters

Name	Type	Description	Default
input_dim	int	Number of input features per time step. Defaults to 1.	`1`
output_dim	int	Number of outputs per time step. Defaults to 1.	`1`
rnn_units	int	Hidden size of the RNN layer. Defaults to 256.	`256`
fc_units	int	Width of the fully connected layer. Defaults to 256.	`256`
act_fn	Union[str, `nn`.`Module`]	Activation between the fully connected layer and the output head, either a name accepted by `get_activation` or a module instance. Defaults to “ReLU”.	`'ReLU'`
dropout_prob	float	Dropout probability applied to the RNN output. Defaults to 0.0.	`0.0`
bidirectional	bool	Whether the RNN is bidirectional. Defaults to True.	`True`
**kwargs		Ignored. Accepts surplus tuning hyperparameters.	`{}`

Attributes

Name	Type	Description
layers	ManyToManyRNN	The underlying recurrent network.

Examples

import torch
from spotoptim.nn.many_to_many_rnn import ManyToManyRNNRegressor

torch.manual_seed(0)
model = ManyToManyRNNRegressor(
    input_dim=1, output_dim=1, rnn_units=16, fc_units=16,
    act_fn="Tanh", dropout_prob=0.1, epochs=128, batch_size=2,
)
x = torch.zeros(3, 4, 1)
lengths = torch.tensor([4, 2, 3])
print(model(x, lengths).shape)

torch.Size([3, 4, 1])

Methods

Name	Description
forward	Compute per-step outputs for a padded batch.

forward

nn.many_to_many_rnn.ManyToManyRNNRegressor.forward(x, lengths)

Compute per-step outputs for a padded batch.

Parameters

Name	Type	Description	Default
x	`torch`.`Tensor`	Padded input batch, shape `(B, T_max, input_dim)`.	required
lengths	`torch`.`Tensor`	True sequence lengths, shape `(B,)`.	required

Returns

Name	Type	Description
	`torch`.`Tensor`	torch.Tensor: Padded outputs, shape `(B, T_max, output_dim)`.