class_docstr: The Perceptron. One layer neural network classifier.
  
  Parameters:
  -----------
  n_iterations: float
      The number of training iterations the algorithm will tune the weights for.
  activation_function: class
      The activation that shall be used for each neuron.
      Possible choices: Sigmoid, ExpLU, ReLU, LeakyReLU, SoftPlus, TanH
  loss: class
      The loss function used to assess the model's performance.
      Possible choices: SquareLoss, CrossEntropy
  learning_rate: float
      The step length that will be used when updating the weights.
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code: def predict(self, X):
      y_pred = self.activation_func(X.dot(self.W) + self.w0)
      return y_pred
  
file_path: ML-From-Scratch/mlfromscratch/supervised_learning/perceptron.py
imports: from __future__ import print_function, division
  import math
  import numpy
  from mlfromscratch.utils import train_test_split, to_categorical, normalize, accuracy_score
  from mlfromscratch.deep_learning.activation_functions import Sigmoid, ReLU, SoftPlus, LeakyReLU, TanH, ELU
  from mlfromscratch.deep_learning.loss_functions import CrossEntropy, SquareLoss
  from mlfromscratch.utils import Plot
  from mlfromscratch.utils.misc import bar_widgets
  import progressbar
label_desc: Use image node_id 3 for calling the Perceptron obj's underlying member method code with example usage: obj.predict(X) and returns: y_pred