class_docstr: Time Series Convolutional Neural Network (CNN), as described in [1].
  
  Parameters
  ----------
  should inherited fields be listed here?
  n_epochs       : int, default = 2000
      the number of epochs to train the model
  batch_size      : int, default = 16
      the number of samples per gradient update.
  kernel_size     : int, default = 7
      the length of the 1D convolution window
  avg_pool_size   : int, default = 3
      size of the average pooling windows
  n_conv_layers   : int, default = 2
      the number of convolutional plus average pooling layers
  filter_sizes    : array of shape (n_conv_layers) default = [6, 12]
  random_state    : int or None, default=None
      Seed for random number generation.
  verbose         : boolean, default = False
      whether to output extra information
  loss            : string, default="mean_squared_error"
      fit parameter for the keras model
  activation      : keras.activations or string, default `linear`
      function to use in the output layer.
  optimizer       : keras.optimizers or string, default `None`.
      when `None`, internally uses `keras.optimizers.Adam(0.01)`
  use_bias        : bool, default=True
      whether to use bias in the output layer.
  metrics         : list of strings, default=["accuracy"],
  
  References
  ----------
  .. [1] Zhao et. al, Convolutional neural networks for
  time series classification, Journal of
  Systems Engineering and Electronics, 28(1):2017.
  
  Notes
  -----
  Adapted from the implementation from Fawaz et. al
  https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/cnn.py
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code: def __init__(
      self,
      n_epochs=2000,
      batch_size=16,
      kernel_size=7,
      avg_pool_size=3,
      n_conv_layers=2,
      callbacks=None,
      verbose=False,
      loss="mean_squared_error",
      metrics=None,
      random_state=0,
      activation="linear",
      use_bias=True,
      optimizer=None,
  ):
      _check_dl_dependencies(severity="error")
      super().__init__(
          batch_size=batch_size,
      )
      self.n_conv_layers = n_conv_layers
      self.avg_pool_size = avg_pool_size
      self.kernel_size = kernel_size
      self.callbacks = callbacks
      self.n_epochs = n_epochs
      self.batch_size = batch_size
      self.verbose = verbose
      self.loss = loss
      self.metrics = metrics
      self.random_state = random_state
      self.activation = activation
      self.use_bias = use_bias
      self.optimizer = optimizer
      self.history = None
      self._network = CNNNetwork(
          kernel_size=self.kernel_size,
          avg_pool_size=self.avg_pool_size,
          n_conv_layers=self.n_conv_layers,
          activation=self.activation,
          random_state=self.random_state,
      )
  
file_path: sktime/sktime/regression/deep_learning/cnn.py
imports: from copy import deepcopy
  from sklearn.utils import check_random_state
  from sktime.networks.cnn import CNNNetwork
  from sktime.regression.deep_learning.base import BaseDeepRegressor
  from sktime.utils.validation._dependencies import _check_dl_dependencies
label_desc: Use image node_id 1 to create a new CNNRegressor object from inherited base classes: BaseDeepRegressor with example: obj = CNNRegressor(n_epochs, batch_size, kernel_size, avg_pool_size, n_conv_layers, callbacks, verbose, loss, metrics, random_state, activation, use_bias, optimizer)