Regressor

MCDCNNRegressor

Multi Channel Deep Convolutional Neural Regressor, adopted from [1].

Quickstart

python

from sktime.regression.deep_learning.mcdcnn import MCDCNNRegressor

estimator = MCDCNNRegressor(n_epochs=120, batch_size=16, kernel_size=5, pool_size=2, filter_sizes=(8, 8), dense_units=732, conv_padding='same', pool_padding='same', loss='mean_squared_error', activation='linear', activation_hidden='relu', use_bias=True, callbacks=None, metrics=None, optimizer=None, verbose=False, random_state=0)

Parameters(16)

n_epochsint, optional (default=120): The number of epochs to train the model.
batch_sizeint, optional (default=16): The number of samples per gradient update.
kernel_sizeint, optional (default=5): The size of kernel in Conv1D layer.
pool_sizeint, optional (default=2): The size of kernel in (Max) Pool layer.
filter_sizestuple, optional (default=(8, 8)): The sizes of filter for Conv1D layer corresponding to each Conv1D in the block.
dense_unitsint, optional (default=732): The number of output units of the final Dense layer of this Network. This is NOT the final layer but the penultimate layer.
conv_paddingstr or None, optional (default=”same”): The type of padding to be applied to convolutional layers.
pool_paddingstr or None, optional (default=”same”): The type of padding to be applied to pooling layers.
lossstr, optional (default=”mean_squared_error”): The name of the loss function to be used during training, should be supported by keras.
activationstr, optional (default=”linear”): 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 “mean_squared_error” 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.SGD is used with the following parameters - learning_rate=0.01, momentum=0.9, weight_decay=0.0005.
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.

Examples

>>> from sktime.regression.deep_learning.mcdcnn import MCDCNNRegressor
>>> from sktime.datasets import load_unit_test
>>> X_train, y_train = load_unit_test (split = "train")
>>> mcdcnn = MCDCNNRegressor (n_epochs = 1, kernel_size = 4)
>>> mcdcnn. fit (X_train, y_train) MCDCNRegressor(
... )

References

[1 ] Zheng et. al, Time series classification using multi-channels deep convolutional neural networks, International Conference on Web-Age Information Management, Pages 298-310, year 2014, organization: Springer.