class_docstr: Simple recurrent neural network.
  
  Parameters
  ----------
  n_epochs : int, default = 100
      the number of epochs to train the model
  batch_size : int, default = 1
      the number of samples per gradient update.
  units : int, default = 6
      number of units in the network
  callbacks : list of tf.keras.callbacks.Callback objects, default = None
  add_default_callback : bool, default = True
      whether to add default callback
  random_state : int or None, default=0
      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
  metrics : list of strings, default=["accuracy"]
      metrics to use in fitting the neural network
  activation : string or a tf callable, default="sigmoid"
      Activation function used in the output 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 = RMSprop(lr=0.001)
      specify the optimizer and the learning rate to be used.
  
  References
  ----------
  ..[1] benchmark forecaster in M4 forecasting competition:
  https://github.com/Mcompetitions/M4-methods
code: def __init__(
      self,
      num_epochs=None,
      batch_size=1,
      units=6,
      callbacks=None,
      add_default_callback=True,
      random_state=0,
      verbose=False,
      loss="mean_squared_error",
      metrics=None,
      activation="sigmoid",
      use_bias=True,
      optimizer=None,
      n_epochs=100,
  ):
      _check_dl_dependencies(severity="error")
      super().__init__()
      # todo: 0.26.0 - remove this, replace by
      # self._n_epochs = n_epochs
      # Deprecated Parameter Handling
      if num_epochs is not None:
          warn(
              "In SimpleRNNClassifier, the parameter 'num_epochs' is deprecated "
              "and will be removed in v0.26.0. It will be renamed to n_epochs. "
              "To avoid this warning, update your code to use 'n_epochs'. ",
              DeprecationWarning,
              stacklevel=2,
          )
          self._n_epochs = num_epochs
      else:
          self._n_epochs = n_epochs
      self.num_epochs = num_epochs
      # end remove
      self.batch_size = batch_size
      self.verbose = verbose
      self.units = units
      self.callbacks = callbacks
      self.add_default_callback = add_default_callback
      self.random_state = random_state
      self.loss = loss
      self.metrics = metrics
      self.activation = activation
      self.use_bias = use_bias
      self.optimizer = optimizer
      self.history = None
      self._network = RNNNetwork(random_state=random_state, units=units)
      self.n_epochs = n_epochs
  
file_path: sktime/sktime/classification/deep_learning/rnn.py
imports: from copy import deepcopy
  from sklearn.utils import check_random_state
  from sktime.classification.deep_learning.base import BaseDeepClassifier
  from sktime.networks.rnn import RNNNetwork
  from sktime.utils.validation._dependencies import _check_dl_dependencies
  from sktime.utils.warnings import warn
label_desc: Use image node_id 1 to create a new SimpleRNNClassifier object from inherited base classes: BaseDeepClassifier with example: obj = SimpleRNNClassifier(num_epochs, batch_size, units, callbacks, add_default_callback, random_state, verbose, loss, metrics, activation, use_bias, optimizer, n_epochs)