CNNRegressorTorch

Time Convolutional Neural Network (CNN) in PyTorch, as described in [1].

Quickstart

from sktime.regression.deep_learning.cnn import CNNRegressorTorch

estimator = CNNRegressorTorch(num_epochs: int=2000, batch_size: int=16, kernel_sizes: tuple [int, ... ]=(7, 7), avg_pool_size: int=3, filter_sizes: tuple [int, ... ]=(6, 12), use_bias: bool=True, padding: str='auto', activation: str | Callable | None=None, activation_hidden: str | Callable='Sigmoid', optimizer: str | None | Callable='Adam', optimizer_kwargs: dict | None=None, criterion: str | None | Callable='MSELoss', criterion_kwargs: dict | None=None, callbacks: None | str | tuple [str, ... ]='ReduceLROnPlateau', callback_kwargs: dict | None=None, lr: float=0.01, verbose: bool=False, init_weights: str | None=None, random_state: int | None=None)

Parameters(19)

num_epochsint, default = 2000

Number of epochs to train the model.

batch_sizeint, default = 16

Size of each mini-batch.

kernel_sizestuple of int, default = (7, 7)

A tuple of length equal to the number of conv layers with each entry in the tuple specifies the kernel size for the corresponding convolutional layer. The length of kernel_sizes must be equal to the length of filter_sizes.

avg_pool_sizeint, default = 3

Size of the average pooling window.

filter_sizestuple of int, default = (6, 12)

A tuple of length equal to the number of conv layers with each entry in the tuple specifies the filter size for the corresponding convolutional layer. The length of filter_sizes must be equal to the length of kernel_sizes.

use_biasbool, default = True

Whether to use bias in output layer.

paddingstring, default = “auto”

Controls padding logic for the convolutional layers, i.e. whether 'valid' and 'same' are passed to the Conv1D layer. - “auto”: as per original implementation, "same" is passed if

input_shape[0] < 60 in the input layer, and "valid" otherwise.

• “valid”, “same”, and other values are passed directly to Conv1D

activationstr or Callable or None, default = None

Activation for the output layer.

activation_hiddenstr or Callable, default = “Sigmoid”

Activation for hidden conv layers: “Sigmoid”, “ReLU” or “Tanh”.

optimizerstr or callable, default = “Adam”

Optimizer to use. Same as TF default (Adam).

optimizer_kwargsdict or None, default = None

Additional keyword arguments for the optimizer.

criterionstr or callable, default = “MSELoss”

Loss function (TF uses mean_squared_error).

criterion_kwargsdict or None, default = None

Additional keyword arguments for the criterion.

callbacksNone or str or tuple of str, default = “ReduceLROnPlateau”

Learning rate schedulers as callbacks.

callback_kwargsdict or None, default = None

Keyword arguments for callbacks.

lrfloat, default = 0.01

Learning rate (TF CNN uses Adam(lr=0.01)).

verbosebool, default = False

Whether to print progress during training.

init_weights: str or None, default = None

The method to initialize the weights of the conv layers. Supported values are ‘kaiming_uniform’, ‘kaiming_normal’, ‘xavier_uniform’, ‘xavier_normal’, or None for default PyTorch initialization.

random_stateint or None, default = None

Seed for reproducibility.

Examples

>>> from sktime.regression.deep_learning.cnn import CNNRegressorTorch
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test (return_X_y = True, split = "train")
>>> X_test, y_test = load_unit_test (return_X_y = True, split = "test")
>>> reg = CNNRegressorTorch (num_epochs = 20, batch_size = 4)
>>> reg. fit (X_train, y_train) CNNRegressorTorch(
... )

References

1. [1 ] Zhao et al. Convolutional neural networks for time series classification, Journal of Systems Engineering and Electronics, 28(1):2017.