FCNRegressor

FCNRegressor

class FCNRegressor(n_epochs=2000, batch_size=16, callbacks=None, verbose=False, loss='mean_squared_error', metrics=None, random_state=None, activation='sigmoid', activation_hidden='relu', use_bias=True, optimizer=None, filter_sizes=(128, 256, 128), kernel_sizes=(8, 5, 3))[source]

Fully Connected Neural Network (FCN), as described in [1].

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

callbackslist of keras.callbacks.Callback, optional (default=None)

List of Keras callbacks to apply during model training.

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=”sigmoid”

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_sizeslist or tuple of int , default = (128,256,128)

number of filters for each convolutional layer. must have length equal to kernel_sizes.

kernel_sizeslist or tuple of int , default = (8,5,3)

kernel size for each convolutional layer. must have length equal to filter_sizes.

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.

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.