ResNetRegressor

Residual Neural Network Regressor adopted from [1].

Quickstart

from sktime.regression.deep_learning.resnet import ResNetRegressor

estimator = ResNetRegressor(n_epochs=1500, callbacks=None, verbose=False, loss='mean_squared_error', metrics=None, batch_size=16, random_state=None, activation='linear', 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=[“mean_squared_error”],

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/

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.regression.deep_learning.resnet import ResNetRegressor
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test (split = "train")
>>> clf = ResNetRegressor (n_epochs = 20, batch_size = 4)
>>> clf. fit (X_train, Y_train) ResNetRegressor(
... )