zoo

LSTMRegressorInitialized(n_features=10, loss_fn='binary_cross_entropy_with_logits', optimizer_fn='sgd', lr=0.001, output_is_logit=True, is_feature_incremental=False, device='cpu', seed=42, **kwargs)

Bases: RollingRegressorInitialized

LinearRegressionInitialized(n_features=10, loss_fn='binary_cross_entropy_with_logits', optimizer_fn='sgd', lr=0.001, output_is_logit=True, is_feature_incremental=False, device='cpu', seed=42, **kwargs)

Bases: RegressorInitialized

Linear Regression model for regression.

PARAMETER	DESCRIPTION
loss_fn	Loss function to be used for training the wrapped model. TYPE: str or Callable DEFAULT: 'binary_cross_entropy_with_logits'
optimizer_fn	Optimizer to be used for training the wrapped model. TYPE: str or Callable DEFAULT: 'sgd'
lr	Learning rate of the optimizer. TYPE: float DEFAULT: 0.001
output_is_logit	Whether the module produces logits as output. If true, either softmax or sigmoid is applied to the outputs when predicting. TYPE: bool DEFAULT: True
is_class_incremental	Whether the classifier should adapt to the appearance of previously unobserved classes by adding an unit to the output layer of the network. TYPE: bool
is_feature_incremental	Whether the model should adapt to the appearance of previously features by adding units to the input layer of the network. TYPE: bool DEFAULT: False
device	Device to run the wrapped model on. Can be "cpu" or "cuda". TYPE: str DEFAULT: 'cpu'
seed	Random seed to be used for training the wrapped model. TYPE: int DEFAULT: 42
**kwargs	Parameters to be passed to the build_fn function aside from n_features. DEFAULT: {}

MultiLayerPerceptronInitialized(n_features=10, n_width=5, n_layers=5, loss_fn='binary_cross_entropy_with_logits', optimizer_fn='sgd', lr=0.001, output_is_logit=True, is_feature_incremental=False, device='cpu', seed=42, **kwargs)

Bases: RegressorInitialized

Linear Regression model for regression.

PARAMETER	DESCRIPTION
loss_fn	Loss function to be used for training the wrapped model. TYPE: str or Callable DEFAULT: 'binary_cross_entropy_with_logits'
optimizer_fn	Optimizer to be used for training the wrapped model. TYPE: str or Callable DEFAULT: 'sgd'
lr	Learning rate of the optimizer. TYPE: float DEFAULT: 0.001
output_is_logit	Whether the module produces logits as output. If true, either softmax or sigmoid is applied to the outputs when predicting. TYPE: bool DEFAULT: True
is_class_incremental	Whether the classifier should adapt to the appearance of previously unobserved classes by adding an unit to the output layer of the network. TYPE: bool
is_feature_incremental	Whether the model should adapt to the appearance of previously features by adding units to the input layer of the network. TYPE: bool DEFAULT: False
device	Device to run the wrapped model on. Can be "cpu" or "cuda". TYPE: str DEFAULT: 'cpu'
seed	Random seed to be used for training the wrapped model. TYPE: int DEFAULT: 42
**kwargs	Parameters to be passed to the build_fn function aside from n_features. DEFAULT: {}