Classifier
GRUClassifier
Schnellstart
python
from sktime.classification.deep_learning.gru import GRUClassifier
estimator = GRUClassifier(hidden_dim: int=256, n_layers: int=4, batch_first: bool=False, bias: bool=True, init_weights: bool=True, dropout: float=0.0, fc_dropout: float=0.0, bidirectional: bool=False, num_epochs: int=10, batch_size: int=8, optimizer: str=None, criterion: str=None, criterion_kwargs: dict=None, optimizer_kwargs: dict=None, lr: float=0.001, verbose: bool=False, random_state: int=None)Parameter(18)
- hidden_dimint
- Number of features in the hidden state.
- n_layersint
- Number of recurrent layers.
- batch_firstbool
- If True, then the input and output tensors are provided as (batch, seq, feature), default is False.
- biasbool
- If False, then the layer does not use bias weights, default is True.
- init_weightsbool
- If True, then the weights are initialized, default is True.
- dropoutfloat
- Dropout rate to apply. default is 0.0
- fc_dropoutfloat
- Dropout rate to apply to the fully connected layer. default is 0.0
- bidirectionalbool
- If True, then the GRU is bidirectional, default is False.
- num_epochsint, optional (default=10)
- The number of epochs to train the model.
- optimizerstr, optional (default=None)
- The optimizer to use. If None, Adam will be used.
- activationstr, optional (default=”relu”)
- The activation function to use. Options: [“relu”, “softmax”].
- batch_sizeint, optional (default=8)
- The size of each mini-batch during training.
- criterioncallable, optional (default=None)
- The loss function to use. If None, CrossEntropyLoss will be used.
- criterion_kwargsdict, optional (default=None)
- Additional keyword arguments to pass to the loss function.
- optimizer_kwargsdict, optional (default=None)
- Additional keyword arguments to pass to the optimizer.
- lrfloat, optional (default=0.001)
- The learning rate to use for the optimizer.
- verbosebool, optional (default=False)
- Whether to print progress information during training.
- random_stateint, optional (default=None)
- Seed to ensure reproducibility.
Referenzen
[1]
Cho, Kyunghyun, et al. “Learning phrase representations using RNN encoder-decoder for statistical machine translation.” arXiv preprint arXiv:1406.1078 (2014).
[2]
Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, Yoshua Bengio. Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling. arXiv preprint arXiv:1412.3555 (2014).