Regressor
TapNetRegressor
Time series attentional prototype network (TapNet), as described in [1].
Quickstart
python
from sktime.regression.deep_learning.tapnet import TapNetRegressor
estimator = TapNetRegressor(n_epochs=2000, batch_size=16, dropout=0.5, filter_sizes=(256, 256, 128), kernel_size=(8, 5, 3), dilation=1, layers=(500, 300), use_rp=True, activation='linear', activation_hidden='leaky_relu', rp_params=(-1, 3), use_bias=True, use_att=True, use_lstm=True, use_cnn=True, random_state=None, padding='same', loss='mean_squared_error', optimizer=None, metrics=None, callbacks=None, verbose=False, lstm_dropout=0.8)Parameters(20)
- filter_sizesarray of int, default = (256, 256, 128)
- sets the kernel size argument for each convolutional block. Controls number of convolutional filters and number of neurons in attention dense layers.
- kernel_sizearray of int, default = (8, 5, 3)
- controls the size of the convolutional kernels
- layersarray of int, default = (500, 300)
- size of dense layers
- n_epochsint, default = 2000
- number of epochs to train the model
- batch_sizeint, default = 16
- number of samples per update
- callbackslist of keras.callbacks.Callback, optional (default=None)
- List of Keras callbacks to apply during model training.
- dropoutfloat, default = 0.5
- dropout rate, in the range [0, 1)
- lstm_dropoutfloat, default = 0.8
- dropout rate for the LSTM layer, in the range [0, 1)
- dilationint, default = 1
- dilation value
- activationstr, default = “linear”
activation function for the last output layer List of available activation functions: https://keras.io/api/layers/activations/
- activation_hiddenstr, default = “leaky_relu”
activation function for the hidden layers List of available activation functions: https://keras.io/api/layers/activations/
- lossstr, default = “mean_squared_error”
- loss function for the classifier
- optimizerstr or None, default = “Adam(lr=0.01)”
- gradient updating function for the classifier
- use_biasbool, default = True
- whether to use bias in the output dense layer
- use_rpbool, default = True
- whether to use random projections
- use_attbool, default = True
- whether to use self attention
- use_lstmbool, default = True
- whether to use an LSTM layer
- use_cnnbool, default = True
- whether to use a CNN layer
- verbosebool, default = False
- whether to output extra information
- random_stateint or None, default = None
- seed for random
References
- [1 ] Zhang et al. Tapnet: Multivariate time series classification with attentional prototypical network, Proceedings of the AAAI Conference on Artificial Intelligence 34(4), 6845-6852, 2020