class_docstr: Base class for Neural Network algorithms.
  
  Parameters
  ----------
  step : float
      Learning rate, defaults to ``0.1``.
  
  show_epoch : int
      This property controls how often the network will display
      information about training. It has to be defined as positive
      integer. For instance, number ``100`` mean that network shows
      summary at 1st, 100th, 200th, 300th ... and last epochs.
  
      Defaults to ``1``.
  
  shuffle_data : bool
      If it's ``True`` than training data will be shuffled before
      the training. Defaults to ``True``.
  
  signals : dict, list or function
      Function that will be triggered after certain events during
      the training.
  
  {Verbose.Parameters}
  
  Methods
  -------
  {BaseSkeleton.fit}
  
  predict(X)
      Propagates input ``X`` through the network and
      returns produced output.
  
  plot_errors(logx=False, show=True, **figkwargs)
      Using errors collected during the training this method
      generates plot that can give additional insight into the
      performance reached during the training.
  
  Attributes
  ----------
  errors : list
      Information about errors. It has two main attributes, namely
      ``train`` and ``valid``. These attributes provide access to
      the training and validation errors respectively.
  
  last_epoch : int
      Value equals to the last trained epoch. After initialization
      it is equal to ``0``.
  
  n_updates_made : int
      Number of training updates applied to the network.
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code: def __init__(self, *args, **options):
      super(BaseNetwork, self).__init__(*args, **options)
  
      self.last_epoch = 0
      self.n_updates_made = 0
      self.errors = base_signals.ErrorCollector()
  
      signals = list(
          as_tuple(
              base_signals.ProgressbarSignal(),
              base_signals.PrintLastErrorSignal(),
              self.errors,
              self.signals,
          )
      )
  
      for i, signal in enumerate(signals):
          if inspect.isfunction(signal):
              signals[i] = base_signals.EpochEndSignal(signal)
  
          elif inspect.isclass(signal):
              signals[i] = signal()
  
      self.events = Events(network=self, signals=signals)
  
imports: from __future__ import division, absolute_import, unicode_literals
  import time
  import inspect
  from abc import abstractmethod
  import numpy
  from neupy.exceptions import StopTraining
  from neupy.core.logs import Verbose
  from neupy.core.config import ConfigurableABC
  from neupy.core.properties import Property, NumberProperty, IntProperty
  from neupy.algorithms import signals
  from neupy.algorithms.plots import plot_optimizer_errors
  from neupy.utils import iters, as_tuple
label_desc: Use image node_id 1 to create a new BaseNetwork object from inherited base classes: BaseSkeleton with example: obj = BaseNetwork()