MLPRegressor

class MLPRegressor(n_epochs=2000, batch_size=16, callbacks=None, verbose=False, loss='mean_squared_error', metrics=None, random_state=None, activation='linear', activation_hidden='relu', use_bias=True, optimizer=None, dropout=(0.1, 0.2, 0.2, 0.3), n_layers=3, hidden_dim=500)[source]

Multi Layer Perceptron Network (MLP), as described in [1].

Adapted from the implementation by hfawaz in https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/mlp.py

Parameters:

should inherited fields be listed here?
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.
random_stateint or None, default=None: 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”],
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=”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.
dropoutfloat or tuple, default=(0.1, 0.2, 0.2, 0.3): The dropout rate for the hidden layers. If float, the same rate is used for all layers. If tuple, length must equal n_layers + 1, where the first n_layers elements correspond to dropout applied before each hidden Dense layer, and the last element corresponds to the dropout applied after the final hidden layer (before the output layer).
n_layersint, default=3: Number of hidden Dense layers in the MLP.
hidden_dimint or tuple, default=500: Number of units in each hidden Dense layer. If int, the same number of units is used for all hidden layers. If list or tuple, length must equal n_layers, with each element specifying the number of units for the corresponding hidden layer.

Attributes:

is_fitted: Whether fit has been called.

References

[1]

Wang et al, Time series classification from

scratch with deep neural networks: A strong baseline, International joint conference on neural networks (IJCNN), 2017.

Examples

>>> from sktime.datasets import load_unit_test
>>> from sktime.regression.deep_learning.mlp import MLPRegressor
>>> X_train, y_train = load_unit_test(return_X_y=True, split="train")
>>> X_test, y_test = load_unit_test(return_X_y=True, split="test")
>>> regressor = MLPRegressor()
>>> regressor.fit(X_train, y_train)
MLPRegressor(...)
>>> y_pred = regressor.predict(X_test)

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.