LSTMFCNClassifier

LSTMFCNClassifier

class LSTMFCNClassifier(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='softmax', activation_hidden='relu')[source]

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

Combines an LSTM arm with a CNN arm. Optionally uses an attention mechanism in the LSTM which the author indicates provides improved performance.

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=”softmax”

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

>>> import sktime.classification.deep_learning as dl_clf
>>> from dl_clf.lstmfcn import LSTMFCNClassifier
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test(split="train", return_X_y=True)
>>> X_test, y_test = load_unit_test(split="test", return_X_y=True)
>>> lstmfcn = FCNClassifier(n_epochs=20,batch_size=4)
>>> lstmfcn.fit(X_train, y_train)
FCNClassifier(...)

Methods

build_model(input_shape, n_classes, **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 classifier to training data.
fit_predict(X, y[, cv, change_state])	Fit and predict labels for sequences in X.
fit_predict_proba(X, y[, cv, change_state])	Fit and predict labels probabilities for sequences in X.
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_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.
predict_proba(X)	Predicts labels probabilities for sequences in X.
reset()	Reset the object to a clean post-init state.
save([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
score(X, y)	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.
summary()	Summary function to return the losses/metrics for model fit.