class_docstr: Multi-layer Perceptron classifier.
  
  This model optimizes the log-loss function using LBFGS or stochastic
  gradient descent.
  
  .. versionadded:: 0.18
  
  Parameters
  ----------
  hidden_layer_sizes : tuple, length = n_layers - 2, default (100,)
      The ith element represents the number of neurons in the ith
      hidden layer.
  
  activation : {'identity', 'logistic', 'tanh', 'relu'}, default 'relu'
      Activation function for the hidden layer.
  
      - 'identity', no-op activation, useful to implement linear bottleneck,
        returns f(x) = x
  
      - 'logistic', the logistic sigmoid function,
        returns f(x) = 1 / (1 + exp(-x)).
  
      - 'tanh', the hyperbolic tan function,
        returns f(x) = tanh(x).
  
      - 'relu', the rectified linear unit function,
        returns f(x) = max(0, x)
  
  solver : {'lbfgs', 'sgd', 'adam'}, default 'adam'
      The solver for weight optimization.
  
      - 'lbfgs' is an optimizer in the family of quasi-Newton methods.
  
      - 'sgd' refers to stochastic gradient descent.
  
      - 'adam' refers to a stochastic gradient-based optimizer proposed
        by Kingma, Diederik, and Jimmy Ba
  
      Note: The default solver 'adam' works pretty well on relatively
      large datasets (with thousands of training samples or more) in terms of
      both training time and validation score.
      For small datasets, however, 'lbfgs' can converge faster and perform
      better.
  
  alpha : float, optional, default 0.0001
      L2 penalty (regularization term) parameter.
  
  batch_size : int, optional, default 'auto'
      Size of minibatches for stochastic optimizers.
      If the solver is 'lbfgs', the classifier will not use minibatch.
      When set to "auto", `batch_size=min(200, n_samples)`
  
  learning_rate : {'constant', 'invscaling', 'adaptive'}, default 'constant'
      Learning rate schedule for weight updates.
  
      - 'constant' is a constant learning rate given by
        'learning_rate_init'.
  
      - 'invscaling' gradually decreases the learning rate ``learning_rate_``
        at each time step 't' using an inverse scaling exponent of 'power_t'.
        effective_learning_rate = learning_rate_init / pow(t, power_t)
  
      - 'adaptive' keeps the learning rate constant to
        'learning_rate_init' as long as training loss keeps decreasing.
        Each time two consecutive epochs fail to decrease training loss by at
        least tol, or fail to increase validation score by at least tol if
        'early_stopping' is on, the current learning rate is divided by 5.
  
      Only used when ``solver='sgd'``.
  
  max_iter : int, optional, default 200
      Maximum number of iterations. The solver iterates until convergence
      (determined by 'tol') or this number of iterations.
  
  random_state : int or RandomState, optional, default None
      State or seed for random number generator.
  
  shuffle : bool, optional, default True
      Whether to shuffle samples in each iteration. Only used when
      solver='sgd' or 'adam'.
  
  tol : float, optional, default 1e-4
      Tolerance for the optimization. When the loss or score is not improving
      by at least tol for two consecutive iterations, unless `learning_rate`
      is set to 'adaptive', convergence is considered to be reached and
      training stops.
  
  learning_rate_init : double, optional, default 0.001
      The initial learning rate used. It controls the step-size
      in updating the weights. Only used when solver='sgd' or 'adam'.
  
  power_t : double, optional, default 0.5
      The exponent for inverse scaling learning rate.
      It is used in updating effective learning rate when the learning_rate
      is set to 'invscaling'. Only used when solver='sgd'.
  
  verbose : bool, optional, default False
      Whether to print progress messages to stdout.
  
  warm_start : bool, optional, default False
      When set to True, reuse the solution of the previous
      call to fit as initialization, otherwise, just erase the
      previous solution.
  
  momentum : float, default 0.9
      Momentum for gradient descent update. Should be between 0 and 1. Only
      used when solver='sgd'.
  
  nesterovs_momentum : boolean, default True
      Whether to use Nesterov's momentum. Only used when solver='sgd' and
      momentum > 0.
  
  early_stopping : bool, default False
      Whether to use early stopping to terminate training when validation
      score is not improving. If set to true, it will automatically set
      aside 10% of training data as validation and terminate training when
      validation score is not improving by at least tol for two consecutive
      epochs.
      Only effective when solver='sgd' or 'adam'
  
  validation_fraction : float, optional, default 0.1
      The proportion of training data to set aside as validation set for
      early stopping. Must be between 0 and 1.
      Only used if early_stopping is True
  
  beta_1 : float, optional, default 0.9
      Exponential decay rate for estimates of first moment vector in adam,
      should be in [0, 1). Only used when solver='adam'
  
  beta_2 : float, optional, default 0.999
      Exponential decay rate for estimates of second moment vector in adam,
      should be in [0, 1). Only used when solver='adam'
  
  epsilon : float, optional, default 1e-8
      Value for numerical stability in adam. Only used when solver='adam'
  
  Attributes
  ----------
  `classes_` : array or list of array of shape (n_classes,)
      Class labels for each output.
  
  `loss_` : float
      The current loss computed with the loss function.
  
  `coefs_` : list, length n_layers - 1
      The ith element in the list represents the weight matrix corresponding
      to layer i.
  
  `intercepts_` : list, length n_layers - 1
      The ith element in the list represents the bias vector corresponding to
      layer i + 1.
  
  n_iter_ : int,
      The number of iterations the solver has ran.
  
  n_layers_ : int
      Number of layers.
  
  `n_outputs_` : int
      Number of outputs.
  
  `out_activation_` : string
      Name of the output activation function.
  
  Notes
  -----
  MLPClassifier trains iteratively since at each time step
  the partial derivatives of the loss function with respect to the model
  parameters are computed to update the parameters.
  
  It can also have a regularization term added to the loss function
  that shrinks model parameters to prevent overfitting.
  
  This implementation works with data represented as dense numpy arrays or
  sparse scipy arrays of floating point values.
  
  References
  ----------
  Hinton, Geoffrey E.
      "Connectionist learning procedures." Artificial intelligence 40.1
      (1989): 185-234.
  
  Glorot, Xavier, and Yoshua Bengio. "Understanding the difficulty of
      training deep feedforward neural networks." International Conference
      on Artificial Intelligence and Statistics. 2010.
  
  He, Kaiming, et al. "Delving deep into rectifiers: Surpassing human-level
      performance on imagenet classification." arXiv preprint
      arXiv:1502.01852 (2015).
  
  Kingma, Diederik, and Jimmy Ba. "Adam: A method for stochastic
      optimization." arXiv preprint arXiv:1412.6980 (2014).
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docstr: Return the log of probability estimates.
  
  Parameters
  ----------
  X : array-like, shape (n_samples, n_features)
      The input data.
  
  Returns
  -------
  log_y_prob : array-like, shape (n_samples, n_classes)
      The predicted log-probability of the sample for each class
      in the model, where classes are ordered as they are in
      `self.classes_`. Equivalent to log(predict_proba(X))
code: def predict_log_proba(self, X):
      """Return the log of probability estimates.
  
      Parameters
      ----------
      X : array-like, shape (n_samples, n_features)
          The input data.
  
      Returns
      -------
      log_y_prob : array-like, shape (n_samples, n_classes)
          The predicted log-probability of the sample for each class
          in the model, where classes are ordered as they are in
          `self.classes_`. Equivalent to log(predict_proba(X))
      """
      y_prob = self.predict_proba(X)
      return np.log(y_prob, out=y_prob)
  
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 6 for calling the MLPClassifier obj's underlying member method code with example usage: obj.predict_log_proba(X) and returns: np