class_docstr: Multi Layer Perceptron Network (MLP), 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.
  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
  optimizer       : keras.optimizer, default=keras.optimizers.Adam(),
  metrics         : list of strings, default=["accuracy"],
  activation      : string or a tf callable, default="sigmoid"
      Activation function used in the output linear layer.
      List of available activation functions:
      https://keras.io/api/layers/activations/
  use_bias        : boolean, default = True
      whether the layer uses a bias vector.
  optimizer       : keras.optimizers object, default = Adam(lr=0.01)
      specify the optimizer and the learning rate to be used.
  
  Notes
  -----
  Adapted from the implementation from source code
  https://github.com/hfawaz/dl-4-tsc/blob/master/classifiers/mlp.py
  
  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.classification.deep_learning.mlp import MLPClassifier
  >>> from sktime.datasets import load_unit_test
  >>> X_train, y_train = load_unit_test(split="train")
  >>> mlp = MLPClassifier(n_epochs=20,batch_size=4)  # doctest: +SKIP
  >>> mlp.fit(X_train, y_train)  # doctest: +SKIP
  MLPClassifier(...)
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code: def __init__(
      self,
      n_epochs=2000,
      batch_size=16,
      callbacks=None,
      verbose=False,
      loss="categorical_crossentropy",
      metrics=None,
      random_state=None,
      activation="sigmoid",
      use_bias=True,
      optimizer=None,
  ):
      _check_dl_dependencies(severity="error")
      super().__init__()
      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 = MLPNetwork(
          random_state=self.random_state,
      )
  
file_path: sktime/sktime/classification/deep_learning/mlp.py
imports: from copy import deepcopy
  from sklearn.utils import check_random_state
  from sktime.classification.deep_learning.base import BaseDeepClassifier
  from sktime.networks.mlp import MLPNetwork
  from sktime.utils.validation._dependencies import _check_dl_dependencies
label_desc: Use image node_id 1 to create a new MLPClassifier object from inherited base classes: BaseDeepClassifier with example: obj = MLPClassifier(n_epochs, batch_size, callbacks, verbose, loss, metrics, random_state, activation, use_bias, optimizer)