class_docstr: Base class for MLP classification and regression.
  
  Warning: This class should not be used directly.
  Use derived classes instead.
  
  .. versionadded:: 0.18
code: def __init__(
      self,
      hidden_layer_sizes,
      activation,
      solver,
      alpha,
      batch_size,
      learning_rate,
      learning_rate_init,
      power_t,
      max_iter,
      loss,
      shuffle,
      random_state,
      tol,
      verbose,
      warm_start,
      momentum,
      nesterovs_momentum,
      early_stopping,
      validation_fraction,
      beta_1,
      beta_2,
      epsilon,
  ):
      self.activation = activation
      self.solver = solver
      self.alpha = alpha
      self.batch_size = batch_size
      self.learning_rate = learning_rate
      self.learning_rate_init = learning_rate_init
      self.power_t = power_t
      self.max_iter = max_iter
      self.loss = loss
      self.hidden_layer_sizes = hidden_layer_sizes
      self.shuffle = shuffle
      self.random_state = random_state
      self.tol = tol
      self.verbose = verbose
      self.warm_start = warm_start
      self.momentum = momentum
      self.nesterovs_momentum = nesterovs_momentum
      self.early_stopping = early_stopping
      self.validation_fraction = validation_fraction
      self.beta_1 = beta_1
      self.beta_2 = beta_2
      self.epsilon = epsilon
  
imports: import numpy
  from abc import ABCMeta, abstractmethod
  from scipy.optimize import fmin_l_bfgs_b
  import warnings
  from ..base import BaseEstimator, ClassifierMixin, RegressorMixin
  from ._base import ACTIVATIONS, DERIVATIVES, LOSS_FUNCTIONS
  from ._stochastic_optimizers import SGDOptimizer, AdamOptimizer
  from ..model_selection import train_test_split
  from ..externals import six
  from ..preprocessing import LabelBinarizer
  from ..utils import gen_batches, check_random_state
  from ..utils import shuffle
  from ..utils import check_array, check_X_y, column_or_1d
  from ..exceptions import ConvergenceWarning
  from ..utils.extmath import safe_sparse_dot
  from ..utils.validation import check_is_fitted
  from ..utils.multiclass import _check_partial_fit_first_call, unique_labels
  from ..utils.multiclass import type_of_target
label_desc: Use image node_id 1 to create a new BaseMultilayerPerceptron object from inherited base classes:  with example: obj = BaseMultilayerPerceptron(hidden_layer_sizes, activation, solver, alpha, batch_size, learning_rate, learning_rate_init, power_t, max_iter, loss, shuffle, random_state, tol, verbose, warm_start, momentum, nesterovs_momentum, early_stopping, validation_fraction, beta_1, beta_2, epsilon)