InceptionNetwork#

class InceptionNetwork(nb_filters=32, nb_conv_per_layer=3, kernel_size=40, use_max_pooling=True, max_pool_size=3, strides=1, dilation_rate=1, padding='same', activation='relu', use_bias=False, use_residual=True, use_bottleneck=True, bottleneck_size=32, depth=6, use_custom_filters=True, random_state=0)[source]#

InceptionTime Network.

depthint, default = 6,
the number of inception modules used

nb_filtersint or list of int32, default = 32,
the number of filters used in one inception module, if not a list, the same number of filters is used in all inception modules

nb_conv_per_layerint or list of int, default = 3,
the number of convolution layers in each inception module, if not a list, the same number of convolution layers is used in all inception modules

kernel_sizeint or list of int, default = 40,
the head kernel size used for each inception module, if not a list, the same is used in all inception modules

use_max_poolingbool or list of bool, default = True,
conditioning whether or not to use max pooling layer in inception modules,if not a list, the same is used in all inception modules

max_pool_sizeint or list of int, default = 3,
the size of the max pooling layer, if not a list, the same is used in all inception modules

stridesint or list of int, default = 1,
the strides of kernels in convolution layers for each inception module, if not a list, the same is used in all inception modules

dilation_rateint or list of int, default = 1,
the dilation rate of convolutions in each inception module, if not a list, the same is used in all inception modules

paddingstr or list of str, default = ‘same’,
the type of padding used for convoltuon for each inception module, if not a list, the same is used in all inception modules

activationstr or list of str, default = ‘relu’,
the activation function used in each inception module, if not a list, the same is used in all inception modules

use_biasbool or list of bool, default = False,
conditioning whether or not convolutions should use bias values in each inception module, if not a list, the same is used in all inception modules

use_residualbool, default = True,
condition whether or not to use residual connections all over Inception

use_bottleneckbool, default = True,
condition whether or not to use bottlenecks all over Inception

bottleneck_sizeint, default = 32,
the bottleneck size in case use_bottleneck = True

use_custom_filtersbool, default = True,
condition on whether or not to use custom filters in the first inception module.

random_state : int, default = 0,

Adapted from the implementation from Fawaz et. al

https://github.com/hfawaz/InceptionTime/blob/master/classifiers/ inception.py

and

https://github.com/MSD-IRIMAS/CF-4-TSC/blob/main/classifiers/H_Inception.py for the custom filters

Network originally defined in:

@article{IsmailFawaz2019inceptionTime, Title = { InceptionTime: Finding AlexNet for Time Series Classification}, Author

= {Ismail Fawaz, Hassan and Lucas, Benjamin and Forestier, Germain and Pelletier, Charlotte and Schmidt, Daniel F. and Weber, Jonathan and Webb, Geoffrey I. and Idoumghar, Lhassane and Muller, Pierre-Alain and Petitjean, François}, journal = { ArXiv}, Year = {2019} }

Custom filters defined in:

@inproceedings{ismail-fawaz2022hccf, author = {Ismail-Fawaz, Ali and Devanne, Maxime and Weber, Jonathan and Forestier, Germain}, title = {Deep Learning For Time Series Classification Using New Hand-Crafted Convolution Filters}, booktitle = {2022 IEEE International Conference on Big Data (IEEE BigData 2022)}, city = {Osaka}, country = {Japan}, pages = {972-981}, url = {doi.org/10.1109/BigData55660.2022.10020496}, year = {2022}, organization = {IEEE} }

Methods

`build_network`(input_shape, **kwargs)	Construct a network and return its input and output layers.
`clone`()	Obtain a clone of the object with same hyper-parameters.
`clone_tags`(estimator[, tag_names])	Clone/mirror tags from another estimator as dynamic override.
`create_test_instance`([parameter_set])	Construct Estimator instance if possible.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`get_class_tag`(tag_name[, tag_value_default])	Get tag value from estimator class (only class tags).
`get_class_tags`()	Get class tags from estimator class and all its parent classes.
`get_param_defaults`()	Get parameter defaults for the object.
`get_param_names`()	Get parameter names for the object.
`get_params`([deep])	Get parameters for this estimator.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from estimator class.
`get_tags`()	Get tags from estimator class.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`hybrid_layer`(input_tensor, input_channels[, ...])	Construct the hybrid layer to compute features of cutom filters.
`is_composite`()	Check if the object is composite.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`reset`()	Reset the object to a clean post-init state.
`save`([path])	Save serialized self to bytes-like object or to (.zip) file.
`set_params`(**params)	Set the parameters of this object.
`set_tags`(**tag_dict)	Set dynamic tags to given values.

hybrid_layer(input_tensor, input_channels, kernel_sizes=None)[source]#

Construct the hybrid layer to compute features of cutom filters.

Parameters:

input_tensortensorflow tensor, usually the input layer of the model.
input_channelsint, the number of input channels in case of multivariate.
kernel_sizeslist of int, default = [2,4,8,16,32,64],
the size of the hand-crafted filters.

Returns:

hybrid_layertensorflow tensor containing the concatenation
of the output features extracted form hand-crafted convolution filters.

build_network(input_shape, **kwargs)[source]#

Construct a network and return its input and output layers.

input_shapetuple: The shape of the data fed into the input layer

Returns:

input_layera keras layer
output_layera keras layer

clone()[source]#

Obtain a clone of the object with same hyper-parameters.

A clone is a different object without shared references, in post-init state. This function is equivalent to returning sklearn.clone of self. Equal in value to type(self)(**self.get_params(deep=False)).

Returns:

instance of type(self), clone of self (see above)

clone_tags(estimator, tag_names=None)[source]#

Clone/mirror tags from another estimator as dynamic override.

Parameters:

estimatorobject: Estimator inheriting from :class:BaseEstimator.
tag_namesstr or list of str, default = None: Names of tags to clone. If None then all tags in estimator are used as tag_names.

Returns:

Self: Reference to self.

Notes

Changes object state by setting tag values in tag_set from estimator as dynamic tags in self.

classmethod create_test_instance(parameter_set='default')[source]#

Construct Estimator instance if possible.

Parameters:

parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

instanceinstance of the class with default parameters.

Notes

get_test_params can return dict or list of dict. This function takes first or single dict that get_test_params returns, and constructs the object with that.

classmethod create_test_instances_and_names(parameter_set='default')[source]#

Create list of all test instances and a list of names for them.

Parameters:

parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

objslist of instances of cls: i-th instance is cls(**cls.get_test_params()[i]).
nameslist of str, same length as objs: i-th element is name of i-th instance of obj in tests convention is {cls.__name__}-{i} if more than one instance otherwise {cls.__name__}.
parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

classmethod get_class_tag(tag_name, tag_value_default=None)[source]#

Get tag value from estimator class (only class tags).

Parameters:

tag_namestr: Name of tag value.
tag_value_defaultany type: Default/fallback value if tag is not found.

Returns:

tag_value: Value of the tag_name tag in self. If not found, returns tag_value_default.

See also

get_tag: Get a single tag from an object.
get_clas_tags: Get all tags from a class.
get_class_tag: Get a single tag from a class.

Examples

>>> from aeon.classification import DummyClassifier
>>> d = DummyClassifier()
>>> tags = d.get_tags()

classmethod get_test_params(parameter_set='default')[source]#

Return testing parameter settings for the estimator.

Parameters:

parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

paramsdict or list of dict, default = {}: Parameters to create testing instances of the class. Each dict are parameters to construct an “interesting” test instance, i.e., MyClass(**params) or MyClass(**params[i]) creates a valid test instance. create_test_instance uses the first (or only) dictionary in params.

is_composite()[source]#

Check if the object is composite.

A composite object is an object which contains objects, as parameters. Called on an instance, since this may differ by instance.

Returns:

composite: bool: Whether self contains a parameter which is BaseObject.

classmethod load_from_path(serial)[source]#

Load object from file location.

Parameters:

serialobject: Result of ZipFile(path).open(“object).

Returns:

deserialized self resulting in output at path, of cls.save(path)

classmethod load_from_serial(serial)[source]#

Load object from serialized memory container.

Parameters:

serialobject: First element of output of cls.save(None).

Returns:

deserialized self resulting in output serial, of cls.save(None).

reset()[source]#

Reset the object to a clean post-init state.

Equivalent to sklearn.clone but overwrites self. After self.reset() call, self is equal in value to type(self)(**self.get_params(deep=False))

Detail behaviour: removes any object attributes, except:

hyper-parameters = arguments of __init__ object attributes containing double-underscores, i.e., the string “__”

runs __init__ with current values of hyper-parameters (result of get_params)

Not affected by the reset are: object attributes containing double-underscores class and object methods, class attributes

save(path=None)[source]#

Save serialized self to bytes-like object or to (.zip) file.

Behaviour: if path is None, returns an in-memory serialized self if path is a file location, stores self at that location as a zip file

saved files are zip files with following contents: _metadata - contains class of self, i.e., type(self) _obj - serialized self. This class uses the default serialization (pickle).

Parameters:

pathNone or file location (str or Path).: if None, self is saved to an in-memory object if file location, self is saved to that file location. If:

path=”estimator” then a zip file estimator.zip will be made at cwd. path=”/home/stored/estimator” then a zip file estimator.zip will be stored in /home/stored/.

Returns:

if path is None - in-memory serialized self
if path is file location - ZipFile with reference to the file.

set_params(**params)[source]#

Set the parameters of this object.

The method works on simple estimators as well as on nested objects. The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: BaseObject parameters

Returns:

selfreference to self (after parameters have been set)

set_tags(**tag_dict)[source]#

Set dynamic tags to given values.

Parameters:

**tag_dictdict: Dictionary of tag name : tag value pairs.

Returns:

Self: Reference to self.

Notes

Changes object state by settting tag values in tag_dict as dynamic tags in self.