Classifier

ResNetClassifier

Residual Neural Network as described in [1].

Adapted from the implementation from source code https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/resnet.py

Quickstart

python

from sktime.classification.deep_learning.resnet import ResNetClassifier

estimator = ResNetClassifier(n_epochs=1500, callbacks=None, verbose=False, loss='categorical_crossentropy', metrics=None, batch_size=16, random_state=None, activation='sigmoid', activation_hidden='relu', use_bias=True, optimizer=None)

Parameters(12)

n_epochsint, default = 1500: the number of epochs to train the model
batch_sizeint, default = 16: the number of samples per gradient update.
callbackslist of keras.callbacks.Callback, optional (default=None): List of Keras callbacks to apply during model training.
random_stateint or None, default=None: Seed for random number generation.
verboseboolean, default = False: whether to output extra information
lossstring, default=”mean_squared_error”: fit parameter for the keras model
optimizerkeras.optimizer, default=keras.optimizers.Adam(),
metricslist of strings, default=[“accuracy”],
activationstring or a tf callable, default=”sigmoid”: 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/
use_biasboolean, default = True: whether the layer uses a bias vector.
optimizerkeras.optimizers object, default = Adam(lr=0.01): specify the optimizer and the learning rate to be used.

Examples

>>> from sktime.classification.deep_learning.resnet import ResNetClassifier
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test (split = "train")
>>> clf = ResNetClassifier (n_epochs = 20)
>>> clf. fit (X_train, y_train) ResNetClassifier(
... )