paddlets.models.forecasting.dl.tcn
- class TCNRegressor(in_chunk_len: int, out_chunk_len: int, skip_chunk_len: int = 0, sampling_stride: int = 1, loss_fn: ~typing.Callable[[...], ~paddle.Tensor] = <function mse_loss>, optimizer_fn: ~typing.Callable[[...], ~paddle.optimizer.optimizer.Optimizer] = <class 'paddle.optimizer.adam.Adam'>, optimizer_params: ~typing.Dict[str, ~typing.Any] = {'learning_rate': 0.001}, eval_metrics: ~typing.List[str] = [], callbacks: ~typing.List[~paddlets.models.common.callbacks.callbacks.Callback] = [], batch_size: int = 32, max_epochs: int = 100, verbose: int = 1, patience: int = 10, seed: ~typing.Union[None, int] = None, hidden_config: ~typing.Optional[~typing.List[int]] = None, kernel_size: int = 3, dropout_rate: float = 0.2)[source]
Bases:
PaddleBaseModelImplTemporal Convolution Net[1].
[1] Bai S, et al. “An empirical evaluation of generic convolutional and recurrent networks for sequence modeling”, https://arxiv.org/pdf/1803.01271
- Parameters
in_chunk_len (int) – The size of the loopback window, i.e. the number of time steps feed to the model.
out_chunk_len (int) – The size of the forecasting horizon, i.e. the number of time steps output by the model.
skip_chunk_len (int) – Optional, the number of time steps between in_chunk and out_chunk for a single sample. The skip chunk is neither used as a feature (i.e. X) nor a label (i.e. Y) for a single sample. By default it will NOT skip any time steps.
sampling_stride (int) – Sampling intervals between two adjacent samples.
loss_fn (Callable[..., paddle.Tensor]) – Loss function.
optimizer_fn (Callable[..., Optimizer]) – Optimizer algorithm.
optimizer_params (Dict[str, Any]) – Optimizer parameters.
eval_metrics (List[str]) – Evaluation metrics of model.
callbacks (List[Callback]) – Customized callback functions.
batch_size (int) – Number of samples per batch.
max_epochs (int) – Max epochs during training.
verbose (int) – Verbosity mode.
patience (int) – Number of epochs to wait for improvement before terminating.
seed (int|None) – Global random seed.
hidden_config (List[int]|None) – Hidden layer configuration.
kernel_size (int) – The filter size.
dropout_rate (float) – Probability of setting units to zero.
- _in_chunk_len
The size of the loopback window, i.e. the number of time steps feed to the model.
- Type
int
- _out_chunk_len
The size of the forecasting horizon, i.e. the number of time steps output by the model.
- Type
int
- _skip_chunk_len
Optional, the number of time steps between in_chunk and out_chunk for a single sample. The skip chunk is neither used as a feature (i.e. X) nor a label (i.e. Y) for a single sample. By default it will NOT skip any time steps.
- Type
int
- _sampling_stride
Sampling intervals between two adjacent samples.
- Type
int
- _loss_fn
Loss function.
- Type
Callable[…, paddle.Tensor]
- _optimizer_fn
Optimizer algorithm.
- Type
Callable[…, Optimizer]
- _optimizer_params
Optimizer parameters.
- Type
Dict[str, Any]
- _eval_metrics
Evaluation metrics of model.
- Type
List[str]
- _batch_size
Number of samples per batch.
- Type
int
- _max_epochs
Max epochs during training.
- Type
int
- _verbose
Verbosity mode.
- Type
int
- _patience
Number of epochs to wait for improvement before terminating.
- Type
int
- _seed
Global random seed.
- Type
int|None
- _stop_training
- Type
bool
Hidden layer configuration.
- Type
List[int]|None
- _kernel_size
The filter size.
- Type
int
- _dropout_rate
Probability of setting units to zero.
- Type
float