CNNClassifier

CNNClassifier

class CNNClassifier(n_epochs=2000, batch_size=16, kernel_size=7, avg_pool_size=3, n_conv_layers=2, callbacks=None, verbose=False, loss='categorical_crossentropy', metrics=None, random_state=None, activation='softmax', activation_hidden='changing_from_sigmoid_to_relu_in_0.42.0', use_bias=True, optimizer=None, filter_sizes=None, padding='auto')[source]

Time 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=”categorical_crossentropy”

fit parameter for the keras model

metricslist of strings, default=[“accuracy”]

random_stateint or None, default=None

Seed for random number generation.

activationstring or tf callable, default=”softmax”

Activation function used in the output layer. List of available activation functions: https://keras.io/api/layers/activations/

activation_hiddenstring or tf callable, default=”sigmoid”

Activation function used in the hidden layers. List of available activation functions: https://keras.io/api/layers/activations/ Default value of activation_hidden will change to “relu” in version ‘0.42.0’.

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.

References

[1]

Zhao et al., Convolutional neural networks for time series classification,

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

Examples

>>> from sktime.classification.deep_learning.cnn import CNNClassifier
>>> 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")
>>> cnn = CNNClassifier(n_epochs=20, batch_size=4)
>>> cnn.fit(X_train, y_train)
CNNClassifier(...)

Methods

build_model(input_shape, n_classes, **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 classifier to training data.
fit_predict(X, y[, cv, change_state])	Fit and predict labels for sequences in X.
fit_predict_proba(X, y[, cv, change_state])	Fit and predict labels probabilities for sequences in X.
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_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.
predict_proba(X)	Predicts labels probabilities for sequences in X.
reset()	Reset the object to a clean post-init state.
save([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
score(X, y)	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.
summary()	Summary function to return the losses/metrics for model fit.