class_docstr: Multi-layer perceptron classifier with logistic sigmoid activations
  
  Parameters
  ------------
  eta : float (default: 0.5)
      Learning rate (between 0.0 and 1.0)
  epochs : int (default: 50)
      Passes over the training dataset.
      Prior to each epoch, the dataset is shuffled
      if `minibatches > 1` to prevent cycles in stochastic gradient descent.
  hidden_layers : list (default: [50])
      Number of units per hidden layer. By default 50 units in the
      first hidden layer. At the moment only 1 hidden layer is supported
  n_classes : int (default: None)
      A positive integer to declare the number of class labels
      if not all class labels are present in a partial training set.
      Gets the number of class labels automatically if None.
  l1 : float (default: 0.0)
      L1 regularization strength
  l2 : float (default: 0.0)
      L2 regularization strength
  momentum : float (default: 0.0)
      Momentum constant. Factor multiplied with the
      gradient of the previous epoch t-1 to improve
      learning speed
      w(t) := w(t) - (grad(t) + momentum * grad(t-1))
  decrease_const : float (default: 0.0)
      Decrease constant. Shrinks the learning rate
      after each epoch via eta / (1 + epoch*decrease_const)
  minibatches : int (default: 1)
      Divide the training data into *k* minibatches
      for accelerated stochastic gradient descent learning.
      Gradient Descent Learning if `minibatches` = 1
      Stochastic Gradient Descent learning if `minibatches` = len(y)
      Minibatch learning if `minibatches` > 1
  random_seed : int (default: None)
      Set random state for shuffling and initializing the weights.
  print_progress : int (default: 0)
      Prints progress in fitting to stderr.
      0: No output
      1: Epochs elapsed and cost
      2: 1 plus time elapsed
      3: 2 plus estimated time until completion
  
  Attributes
  -----------
  w_ : 2d-array, shape=[n_features, n_classes]
      Weights after fitting.
  b_ : 1D-array, shape=[n_classes]
      Bias units after fitting.
  cost_ : list
      List of floats; the mean categorical cross entropy
      cost after each epoch.
  
  Examples
  -----------
  For usage examples, please see
  https://rasbt.github.io/mlxtend/user_guide/classifier/MultiLayerPerceptron/
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code: def _predict(self, X):
      net_1, act_1, net_out, act_out = self._feedforward(X)
      y_pred = np.argmax(net_out, axis=1)
      return y_pred
  
file_path: mlxtend/mlxtend/classifier/multilayerperceptron.py
imports: from time import time
  import numpy
  from scipy.special import expit
  from .._base import _BaseModel, _Classifier, _IterativeModel, _MultiClass, _MultiLayer
label_desc: Use image node_id 5 for calling the MultiLayerPerceptron obj's underlying member method code with example usage: obj._predict(X) and returns: y_pred