MACNNClassifier

class MACNNClassifier(n_epochs=1500, batch_size=4, padding='same', pool_size=3, strides=2, repeats=2, filter_sizes=(64, 128, 256), kernel_size=(3, 6, 12), reduction=16, loss='categorical_crossentropy', activation='sigmoid', activation_hidden='relu', use_bias=True, metrics=None, optimizer=None, callbacks=None, random_state=0, verbose=False)[source]

Multi-Scale Attention Convolutional Neural Classifier, as described in [1].

Parameters:

n_epochsint, optional (default=1500): The number of epochs to train the model.
batch_sizeint, optional (default=4): The number of sample per gradient update.
paddingstr, optional (default=”same”): The type of padding to be provided in MACNN Blocks. Accepts all the string values that keras.layers supports. Note: For Conv1D layers within MACNN Blocks, padding is always set to “same” to ensure consistent output lengths for multi-scale convolutions. This parameter only affects the pooling layers between MACNN Blocks.
pool_sizeint, optional (default=3): A single value representing pooling windows which are applied between two MACNN Blocks.
stridesint, optional (default=2): A single value representing strides to be taken during the pooling operation.
repeatsint, optional (default=2): The number of MACNN Blocks to be stacked.
filter_sizestuple, optional (default=(64, 128, 256)): The input size of Conv1D layers within each MACNN Block.
kernel_sizetuple, optional (default=(3, 6, 12)): The output size of Conv1D layers within each MACNN Block.
reductionint, optional (default = 16): The factor by which the first dense layer of a MACNN Block will be divided by.
lossstr, optional (default=”categorical_crossentropy”): The name of the loss function to be used during training, should be supported by keras.
activationstr, optional (default=”sigmoid”): The activation function to apply at the output. 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_biasbool, optional (default=True): Whether bias should be included in the output layer.
metricsNone or string, optional (default=None): The string which will be used during model compilation. If left as None, then “accuracy” is passed to model.compile().
optimizer: None or keras.optimizers.Optimizer instance, optional (default=None): The optimizer that is used for model compiltation. If left as None, then keras.optimizers.Adam(learning_rate=0.0001) is used.
callbacksNone or list of keras.callbacks.Callback, optional (default=None): The callback(s) to use during training.
random_stateint, optional (default=0): The seed to any random action.
verbosebool, optional (default=False): Verbosity during model training, making it True will print model summary, training information etc.

Attributes:

is_fitted: Whether fit has been called.

References

[1]

Wei Chen et. al, Multi-scale Attention Convolutional

Neural Network for time series classification, Neural Networks, Volume 136, 2021, Pages 126-140, ISSN 0893-6080, https://doi.org/10.1016/j.neunet.2021.01.001.

Examples

>>> from sktime.classification.deep_learning.macnn import MACNNClassifier
>>> 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")
>>> macnn = MACNNClassifier(n_epochs=3)
>>> macnn.fit(X_train, y_train)
MACNNClassifier(...)

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.