SimpleRNNRegressor

class SimpleRNNRegressor(n_epochs=100, batch_size=1, units=6, callbacks=None, add_default_callback=True, random_state=0, verbose=False, loss='mean_squared_error', metrics=None, activation='linear', activation_hidden='tanh', use_bias=True, optimizer=None, dropout=0.0)[source]

Simple recurrent neural network.

Parameters:

n_epochsint, default = 100: the number of epochs to train the model
batch_sizeint, default = 1: the number of samples per gradient update.
unitsint, default = 6: number of units in the network
callbackslist of tf.keras.callbacks.Callback objects, default = None
add_default_callbackbool, default = True: whether to add default callback
random_stateint or None, default=0: Seed for random number generation.
verboseboolean, default = False: whether to output extra information
lossstring, default=”mean_squared_error”: fit parameter for the keras model
metricslist of strings, default=[“accuracy”]: metrics to use in fitting the neural network
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=”tanh”: 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 = RMSprop(lr=0.001): specify the optimizer and the learning rate to be used.
dropoutfloat, default = 0.0: The dropout rate for the RNN layer.

Attributes:

is_fitted: Whether fit has been called.

References

..[1] benchmark forecaster in M4 forecasting competition: https://github.com/Mcompetitions/M4-methods

Examples

>>> from sktime.regression.deep_learning.rnn import SimpleRNNRegressor
>>> from sktime.datasets import load_unit_test
>>> X_train, Y_train = load_unit_test(split="train")
>>> clf = SimpleRNNRegressor(n_epochs=20, batch_size=4)
>>> clf.fit(X_train, Y_train)
SimpleRNNRegressor(...)

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.