TapNetClassifier

Time series attentional prototype network (TapNet), as described in [1].

Schnellstart

from sktime.classification.deep_learning.tapnet import TapNetClassifier

estimator = TapNetClassifier(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, rp_group=3, rp_alpha=2.0, activation='sigmoid', activation_hidden='leaky_relu', use_bias=True, use_att=True, use_lstm=True, use_cnn=True, random_state=None, padding='same', loss='binary_crossentropy', optimizer=None, metrics=None, callbacks=None, verbose=False, lstm_dropout=0.8, fc_dropout=0.0)

Parameter(25)

n_epochsint, default = 2000

number of epochs to train the model

batch_sizeint, default = 16

number of samples per update

dropoutfloat, default = 0.5

dropout rate for the convolutional layers, in the range [0, 1)

filter_sizestuple of int, default = (256, 256, 128)

number of convolutional filters in each conv block

kernel_sizetuple of int, default = (8, 5, 3)

specifying the length of the 1D convolution window

dilationint, default = 1

dilation value

layerstuple of int, default = (500, 300)

sizes of dense layers

use_rpbool, default = True

whether to use random projections

rp_groupint, default = 3

number of random permutation groups g for random dimension permutation

rp_alphafloat, default = 2.0

scale factor alpha used to compute the RDP group size: ` rp_dim = floor(n_dims * rp_alpha / rp_group) `

activationstr or callable, default = “sigmoid”

activation function for the last output layer List of available activation functions: https://keras.io/api/layers/activations/

activation_hiddenstr or callable, default = “leaky_relu”

activation function for the hidden layers List of available activation functions: https://keras.io/api/layers/activations/

use_biasbool, default = True

whether to use bias in the output dense layer

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

random_stateint or None, default = None

seed for random

paddingstr, default = ‘same’

type of padding for convolution layers

lossstr, default = “binary_crossentropy”

loss function for the classifier

optimizerstr or None, default = “Adam(lr=0.01)”

gradient updating function for the classifier

metricslist of str or None, default = None

list of metrics to be evaluated by the model during training and testing

callbackslist of keras.callbacks.Callback, optional (default=None)

List of Keras callbacks to apply during model training.

verbosebool, default = False

whether to output extra information

lstm_dropoutfloat, default = 0.8

dropout rate for the LSTM layer, in the range [0, 1)

fc_dropoutfloat, default = 0.0

dropout rate before the output layer, in the range (0, 1]

Beispiele

>>> from sktime.classification.deep_learning.tapnet import TapNetClassifier
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test (split = "train")
>>> X_test, y_test = load_unit_test (split = "test")
>>> tapnet = TapNetClassifier (n_epochs = 20, batch_size = 4)
>>> tapnet. fit (X_train, y_train) TapNetClassifier(
... )

Referenzen