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 : array-like of shape(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, default=0.0001
      Strength of the L2 regularization term. The L2 regularization term
      is divided by the sample size when added to the loss.
  
  batch_size : int, 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 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'``.
  
  learning_rate_init : float, 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 : float, 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'.
  
  max_iter : int, default=200
      Maximum number of iterations. The solver iterates until convergence
      (determined by 'tol') or this number of iterations. For stochastic
      solvers ('sgd', 'adam'), note that this determines the number of epochs
      (how many times each data point will be used), not the number of
      gradient steps.
  
  shuffle : bool, default=True
      Whether to shuffle samples in each iteration. Only used when
      solver='sgd' or 'adam'.
  
  random_state : int, RandomState instance, default=None
      Determines random number generation for weights and bias
      initialization, train-test split if early stopping is used, and batch
      sampling when solver='sgd' or 'adam'.
      Pass an int for reproducible results across multiple function calls.
      See :term:`Glossary <random_state>`.
  
  tol : float, default=1e-4
      Tolerance for the optimization. When the loss or score is not improving
      by at least ``tol`` for ``n_iter_no_change`` consecutive iterations,
      unless ``learning_rate`` is set to 'adaptive', convergence is
      considered to be reached and training stops.
  
  verbose : bool, default=False
      Whether to print progress messages to stdout.
  
  warm_start : bool, default=False
      When set to True, reuse the solution of the previous
      call to fit as initialization, otherwise, just erase the
      previous solution. See :term:`the Glossary <warm_start>`.
  
  momentum : float, default=0.9
      Momentum for gradient descent update. Should be between 0 and 1. Only
      used when solver='sgd'.
  
  nesterovs_momentum : bool, 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
      ``n_iter_no_change`` consecutive epochs. The split is stratified,
      except in a multilabel setting.
      If early stopping is False, then the training stops when the training
      loss does not improve by more than tol for n_iter_no_change consecutive
      passes over the training set.
      Only effective when solver='sgd' or 'adam'.
  
  validation_fraction : float, 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, 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, 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, default=1e-8
      Value for numerical stability in adam. Only used when solver='adam'.
  
  n_iter_no_change : int, default=10
      Maximum number of epochs to not meet ``tol`` improvement.
      Only effective when solver='sgd' or 'adam'.
  
      .. versionadded:: 0.20
  
  max_fun : int, default=15000
      Only used when solver='lbfgs'. Maximum number of loss function calls.
      The solver iterates until convergence (determined by 'tol'), number
      of iterations reaches max_iter, or this number of loss function calls.
      Note that number of loss function calls will be greater than or equal
      to the number of iterations for the `MLPClassifier`.
  
      .. versionadded:: 0.22
  
  Attributes
  ----------
  classes_ : ndarray or list of ndarray of shape (n_classes,)
      Class labels for each output.
  
  loss_ : float
      The current loss computed with the loss function.
  
  best_loss_ : float or None
      The minimum loss reached by the solver throughout fitting.
      If `early_stopping=True`, this attribute is set to `None`. Refer to
      the `best_validation_score_` fitted attribute instead.
  
  loss_curve_ : list of shape (`n_iter_`,)
      The ith element in the list represents the loss at the ith iteration.
  
  validation_scores_ : list of shape (`n_iter_`,) or None
      The score at each iteration on a held-out validation set. The score
      reported is the accuracy score. Only available if `early_stopping=True`,
      otherwise the attribute is set to `None`.
  
  best_validation_score_ : float or None
      The best validation score (i.e. accuracy score) that triggered the
      early stopping. Only available if `early_stopping=True`, otherwise the
      attribute is set to `None`.
  
  t_ : int
      The number of training samples seen by the solver during fitting.
  
  coefs_ : list of shape (n_layers - 1,)
      The ith element in the list represents the weight matrix corresponding
      to layer i.
  
  intercepts_ : list of shape (n_layers - 1,)
      The ith element in the list represents the bias vector corresponding to
      layer i + 1.
  
  n_features_in_ : int
      Number of features seen during :term:`fit`.
  
      .. versionadded:: 0.24
  
  feature_names_in_ : ndarray of shape (`n_features_in_`,)
      Names of features seen during :term:`fit`. Defined only when `X`
      has feature names that are all strings.
  
      .. versionadded:: 1.0
  
  n_iter_ : int
      The number of iterations the solver has run.
  
  n_layers_ : int
      Number of layers.
  
  n_outputs_ : int
      Number of outputs.
  
  out_activation_ : str
      Name of the output activation function.
  
  See Also
  --------
  MLPRegressor : Multi-layer Perceptron regressor.
  BernoulliRBM : Bernoulli Restricted Boltzmann Machine (RBM).
  
  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.
  
  :arxiv:`He, Kaiming, et al (2015). "Delving deep into rectifiers:
  Surpassing human-level performance on imagenet classification." <1502.01852>`
  
  :arxiv:`Kingma, Diederik, and Jimmy Ba (2014)
  "Adam: A method for stochastic optimization." <1412.6980>`
  
  Examples
  --------
  >>> from sklearn.neural_network import MLPClassifier
  >>> from sklearn.datasets import make_classification
  >>> from sklearn.model_selection import train_test_split
  >>> X, y = make_classification(n_samples=100, random_state=1)
  >>> X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y,
  ...                                                     random_state=1)
  >>> clf = MLPClassifier(random_state=1, max_iter=300).fit(X_train, y_train)
  >>> clf.predict_proba(X_test[:1])
  array([[0.038..., 0.961...]])
  >>> clf.predict(X_test[:5, :])
  array([1, 0, 1, 0, 1])
  >>> clf.score(X_test, y_test)
  0.8...
docstr: Private score method without input validation
docstr_tok: ["Private","score","method","without","input","validation"]
code: def _score(self, X, y):
      """Private score method without input validation"""
      # Input validation would remove feature names, so we disable it
      return accuracy_score(y, self._predict(X, check_input=False))
  
imports: import warnings
  from abc import ABCMeta, abstractmethod
  from itertools import chain
  from numbers import Integral, Real
  import numpy
  import scipy.optimize
  from ..base import BaseEstimator, ClassifierMixin, RegressorMixin, _fit_context, is_classifier
  from ..exceptions import ConvergenceWarning
  from ..metrics import accuracy_score, r2_score
  from ..model_selection import train_test_split
  from ..preprocessing import LabelBinarizer
  from ..utils import _safe_indexing, check_random_state, column_or_1d, gen_batches, shuffle
  from ..utils._param_validation import Interval, Options, StrOptions
  from ..utils.extmath import safe_sparse_dot
  from ..utils.metaestimators import available_if
  from ..utils.multiclass import _check_partial_fit_first_call, type_of_target, unique_labels
  from ..utils.optimize import _check_optimize_result
  from ..utils.validation import check_is_fitted
  from ._base import ACTIVATIONS, DERIVATIVES, LOSS_FUNCTIONS
  from ._stochastic_optimizers import AdamOptimizer, SGDOptimizer
label_desc: Use image node_id 5 for calling the MLPClassifier obj's underlying member method code with example usage: obj._score(X, y) and returns: accuracy_score