text: import pandas as pd
  import numpy as np
  import random
  from sklearn.model_selection import train_test_split
  from sklearn.metrics import accuracy_score
  from keras.models import Sequential
  from keras.layers import Dense
  
  
  # Could make a generalized instance of the Model Keras class for a GNN
  # New Type of Neural Network
  class GeneticNeuralNetwork(Sequential):
      # Constructor
      def __init__(self, child_weights=None):
          # Initialize Sequential Model Super Class
          super().__init__()
          # If no weights provided randomly generate them
          if child_weights is None:
              # Layers are created and randomly generated
              layer1 = Dense(4, input_shape=(4,), activation='sigmoid')
              layer2 = Dense(2, activation='sigmoid')
              layer3 = Dense(1, activation='sigmoid')
              # Layers are added to the model
              self.add(layer1)
              self.add(layer2)
              self.add(layer3)
          # If weights are provided set them within the layers
          else:
              # Set weights within the layers
              self.add(
                  Dense(
                      4,
                      input_shape=(4,),
                      activation='sigmoid',
                      weights=[child_weights[0], np.zeros(4)])
                  )
              self.add(
                  Dense(
                   2,
                   activation='sigmoid',
                   weights=[child_weights[1], np.zeros(2)])
              )
              self.add(
                  Dense(
                   1,
                   activation='sigmoid',
                   weights=[child_weights[2], np.zeros(1)])
              )
  
      # Function for forward propagating a row vector of a matrix
      def forward_propagation(self, X_train, y_train):
          # Forward propagation
          y_hat = self.predict(X_train.values)
          # Compute fitness score
          self.fitness = accuracy_score(y_train, y_hat.round())
  
      # Standard Backpropagation
      def compile_train(self, epochs):
          self.compile(
                        optimizer='rmsprop',
                        loss='binary_crossentropy',
                        metrics=['accuracy']
                        )
          self.fit(X_train.values, y_train.values, epochs=epochs)
  
  
  # Chance to mutate weights
  def mutation(child_weights):
      # Add a chance for random mutation
      selection = random.randint(0, len(child_weights)-1)
      mut = random.uniform(0, 1)
      if mut >= .5:
          child_weights[selection] *= random.randint(2, 5)
      else:
          # No mutation
          pass
  
  
  # Crossover traits between two Genetic Neural Networks
  def dynamic_crossover(nn1, nn2):
      # Lists for respective weights
      nn1_weights = []
      nn2_weights = []
      child_weights = []
      # Get all weights from all layers in the first network
      for layer in nn1.layers:
          nn1_weights.append(layer.get_weights()[0])
  
      # Get all weights from all layers in the second network
      for layer in nn2.layers:
          nn2_weights.append(layer.get_weights()[0])
  
      # Iterate through all weights from all layers for crossover
      for i in range(0, len(nn1_weights)):
          # Get single point to split the matrix in parents based on # of cols
          split = random.randint(0, np.shape(nn1_weights[i])[1]-1)
          # Iterate through after a single point and set the remaing cols to nn_2
          for j in range(split, np.shape(nn1_weights[i])[1]-1):
              nn1_weights[i][:, j] = nn2_weights[i][:, j]
  
          # After crossover add weights to child
          child_weights.append(nn1_weights[i])
  
      # Add a chance for mutation
      mutation(child_weights)
  
      # Create and return child object
      child = GeneticNeuralNetwork(child_weights)
      return child
  
  
  # Read Data
  data = pd.read_csv('assets/banknote.csv')
  # Create Matrix of Independent Variables
  X = data.drop(['Y'], axis=1)
  # Create Vector of Dependent Variable
  y = data['Y']
  # Create a Train Test Split for Genetic Optimization
  X_train, X_test, y_train, y_test = train_test_split(X, y)
  # Create a List of all active GeneticNeuralNetworks
  networks = []
  pool = []
  # Track Generations
  generation = 0
  # Initial Population
  n = 20
  
  # Generate n randomly weighted neural networks
  for i in range(0, n):
      networks.append(GeneticNeuralNetwork())
  
  # Cache Max Fitness
  max_fitness = 0
  
  # Max Fitness Weights
  optimal_weights = []
  
  # Evolution Loop
  while max_fitness < .9:
      # Log the current generation
      generation += 1
      print('Generation: ', generation)
  
      # Forward propagate the neural networks to compute a fitness score
      for nn in networks:
          # Propagate to calculate fitness score
          nn.forward_propagation(X_train, y_train)
          # Add to pool after calculating fitness
          pool.append(nn)
  
      # Clear for propagation of next children
      networks.clear()
  
      # Sort based on fitness
      pool = sorted(pool, key=lambda x: x.fitness)
      pool.reverse()
  
      # Find Max Fitness and Log Associated Weights
      for i in range(0, len(pool)):
          # If there is a new max fitness among the population
          if pool[i].fitness > max_fitness:
              max_fitness = pool[i].fitness
              print('Max Fitness: ', max_fitness)
              # Reset optimal_weights
              optimal_weights = []
              # Iterate through layers, get weights, and append to optimal
              for layer in pool[i].layers:
                  optimal_weights.append(layer.get_weights()[0])
              print(optimal_weights)
  
      # Crossover, top 5 randomly select 2 partners for child
      for i in range(0, 5):
          for j in range(0, 2):
              # Create a child and add to networks
              temp = dynamic_crossover(pool[i], random.choice(pool))
              # Add to networks to calculate fitness score next iteration
              networks.append(temp)
  
  # Create a Genetic Neural Network with optimal initial weights
  gnn = GeneticNeuralNetwork(optimal_weights)
  gnn.compile_train(10)
  
  # Test the Genetic Neural Network Out of Sample
  y_hat = gnn.predict(X_test.values)
  print('Test Accuracy: %.2f' % accuracy_score(y_test, y_hat.round()))
  