code: def main():
      data = datasets.load_digits()
      X = normalize(data.data)
      y = data.target
  
      # Convert the nominal y values to binary
      y = to_categorical(y)
  
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, test_size=0.4, seed=1
      )
  
      # MLP
      clf = MultilayerPerceptron(
          n_hidden=16, n_iterations=1000, learning_rate=0.01
      )
  
      clf.fit(X_train, y_train)
      y_pred = np.argmax(clf.predict(X_test), axis=1)
      y_test = np.argmax(y_test, axis=1)
  
      accuracy = accuracy_score(y_test, y_pred)
      print("Accuracy:", accuracy)
  
      # Reduce dimension to two using PCA and plot the results
      Plot().plot_in_2d(
          X_test,
          y_pred,
          title="Multilayer Perceptron",
          accuracy=accuracy,
          legend_labels=np.unique(y),
      )
  
file_path: ML-From-Scratch/mlfromscratch/supervised_learning/multilayer_perceptron.py
imports: from __future__ import print_function, division
  import numpy
  import math
  from sklearn import datasets
  from mlfromscratch.utils import train_test_split, to_categorical, normalize, accuracy_score, Plot
  from mlfromscratch.deep_learning.activation_functions import Sigmoid, Softmax
  from mlfromscratch.deep_learning.loss_functions import CrossEntropy
label_desc: Use image node_id 1 for calling a global function with example usage: main() without return types