paddlets.models.classify.dl.inception_time

class InceptionTimeClassifier(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, activation: str = 'ReLU', kernel_size=40, block_out_size=128, block_depth=6, use_bottleneck=True, use_residual=True)

Bases: PaddleBaseClassifier

InceptionTime[1] is a time series Classification model introduced in 2019. InceptionTime an ensemble of deep Convolutional Neural Network (CNN) models, inspired by the Inception-v4 architecture.

[1] Hassan I.F, et al. “InceptionTime: Finding AlexNet for Time Series Classification”, https://arxiv.org/pdf/1909.04939v3.pdf

Parameters

• 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.

• activation (str) – Activation function,set to “ReLU” by defalut.

• kernel_size (int) – Kernel size for inception module, set to 40 by default.

• block_out_size (int) – Output size for inception block, set to 128 by default.

• block_depth (int) – Depth for inception block, set to 6 by default.

• use_bottleneck (bool) – If add residuals between Inception modules.

• use_residual (bool) – If use bottleneck layer or not, Set to True by default.