LSTMFCNRegressor

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

Schnellstart

from sktime.regression.deep_learning.lstmfcn import LSTMFCNRegressor

estimator = 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')

Parameter(12)

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 or tuple of int, default=(8, 5, 3)

Length of the 1D convolution windows for each convolutional layer. The number of convolutional layers is len(kernel_sizes). Must have the same length as filter_sizes. Defaults match Karim et al. (2019): three layers with kernels 8, 5, 3.

filter_sizeslist or tuple of int, default=(128, 256, 128)

Number of filters for each convolutional layer. The number of convolutional layers is len(filter_sizes). Must have the same length as kernel_sizes. Defaults match Karim et al. (2019): three layers with 128, 256, 128 filters.

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

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).

random_stateint or None, default=None

Seed for random, integer.

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/

Beispiele

>>> 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)

Referenzen