CNNRegressor

class CNNRegressor(n_epochs=2000, batch_size=16, kernel_size=7, avg_pool_size=3, n_conv_layers=2, callbacks=None, verbose=False, loss='mean_squared_error', metrics=None, random_state=0, activation='linear', activation_hidden='relu', use_bias=True, optimizer=None, filter_sizes=None, padding='auto')[source]

Time Series Convolutional Neural Network (CNN), as described in [1].

Zhao et al. 2017 uses sigmoid activation in the hidden layers. To obtain same behaviour as Zhao et al. 2017, set activation_hidden to “sigmoid”.

Adapted from the implementation from Fawaz et. al https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/cnn.py

Parameters:

n_epochsint, default = 2000

the number of epochs to train the model

batch_sizeint, default = 16

the number of samples per gradient update.

kernel_sizeint, default = 7

the length of the 1D convolution window

avg_pool_sizeint, default = 3

size of the average pooling windows

n_conv_layersint, default = 2

the number of convolutional plus average pooling layers

callbackslist of keras.callbacks, default = None

verboseboolean, default = False

whether to output extra information

lossstring, default=”mean_squared_error”

fit parameter for the keras model

metricslist of strings, default=[“accuracy”],

random_stateint or None, default=None

Seed for random number generation.

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.

filter_sizesarray of shape (n_conv_layers) default = [6, 12]

paddingstring, default = “auto”

Controls padding logic for the convolutional layers, i.e. whether 'valid' and 'same' are passed to the Conv1D layer. - “auto”: as per original implementation, "same" is passed if

input_shape[0] < 60 in the input layer, and "valid" otherwise.

“valid”, “same”, and other values are passed directly to Conv1D

Attributes:

is_fitted: Whether fit has been called.

Notes

Adapted from the implementation from Fawaz et. al https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/cnn.py

References

[1]

Zhao et. al, Convolutional neural networks for

time series classification, Journal of Systems Engineering and Electronics, 28(1):2017.

Examples

>>> from sktime.datasets import load_unit_test
>>> from sktime.regression.deep_learning.cnn import CNNRegressor
>>> 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 = CNNRegressor()
>>> regressor.fit(X_train, y_train)
CNNRegressor(...)
>>> y_pred = regressor.predict(X_test)

Methods

`build_model`(input_shape, **kwargs)	Construct a compiled, un-trained, keras model that is ready for training.
`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(X, y)	Fit time series regressor to training data.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_custom_objects`()	Return the custom objects needed for loading the model.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`predict`(X)	Predicts labels for sequences in X.
`reset`()	Reset the object to a clean post-init state.
`save`([path])	Save serialized self to bytes-like object or to (.zip) file.
`score`(X, y[, multioutput])	Scores predicted labels against ground truth labels on X.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.