LSTMFCNRegressor

class LSTMFCNRegressor(n_epochs=2000, batch_size=128, dropout=0.8, kernel_sizes=(8, 5, 3), filter_sizes=(128, 256, 128), lstm_size=8, attention=False, callbacks=None, random_state=None, verbose=0, activation='linear', activation_hidden='relu')[source]

Implementation of LSTMFCNRegressor from Karim et al (2019) [1].

Parameters:

n_epochsint, default=2000: the number of epochs to train the model
batch_sizeint, default=128: the number of samples per gradient update.
dropoutfloat, default=0.8: controls dropout rate of LSTM layer
kernel_sizeslist of ints, default=[8, 5, 3]: specifying the length of the 1D convolution windows
filter_sizesint, list of ints, default=[128, 256, 128]: size of filter for each conv layer
lstm_sizeint, default=8: output dimension for LSTM layer
attentionboolean, default=False: If True, uses custom attention LSTM layer
callbackskeras callbacks, default=ReduceLRonPlateau: Keras callbacks to use such as learning rate reduction or saving best model based on validation error
random_stateint or None, default=None: Seed for random, integer.
verbose‘auto’, 0, 1, or 2. Verbosity mode.: 0 = silent, 1 = progress bar, 2 = one line per epoch. ‘auto’ defaults to 1 for most cases, but 2 when used with ParameterServerStrategy. Note that the progress bar is not particularly useful when logged to a file, so verbose=2 is recommended when not running interactively (eg, in a production environment).
activationstring or a tf callable, default=”linear”: Activation function used in the output layer. List of available activation functions: https://keras.io/api/layers/activations/
activation_hiddenstring or a tf callable, default=”relu”: Activation function used in the hidden layers. List of available activation functions: https://keras.io/api/layers/activations/

Attributes:

is_fitted: Whether fit has been called.

References

[1]

Karim et al. Multivariate LSTM-FCNs for Time Series Classification, 2019

https://arxiv.org/pdf/1801.04503.pdf

Examples

>>> from sktime.datasets import load_unit_test
>>> from sktime.regression.deep_learning.lstmfcn import LSTMFCNRegressor
>>> 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")
>>> regressor = LSTMFCNRegressor()
>>> regressor.fit(X_train, y_train)
LSTMFCNRegressor(...)
>>> y_pred = regressor.predict(X_test)

Methods

`build_model`(input_shape, **kwargs)	Construct a compiled, un-trained, keras model that is ready for training.
`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(X, y)	Fit time series regressor to training data.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_custom_objects`()	Return the custom objects needed for loading the model.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`predict`(X)	Predicts labels for sequences in X.
`reset`()	Reset the object to a clean post-init state.
`save`([path])	Save serialized self to bytes-like object or to (.zip) file.
`score`(X, y[, multioutput])	Scores predicted labels against ground truth labels on X.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.