text: from keras.models import Sequential
  from keras.layers import Dense
  from keras.optimizers import Adam
  from keras import losses, activations, metrics
  import random
  import numpy as np
  
  dataset = list()
  answers = list()
  
  for i in range(100000):
      value_x = random.random()
      value_y = random.random()
      dataset.append([value_x, value_y])
      if value_y > 0.5:
          answers.append([1])
      else:
          answers.append([0])
  
  model = Sequential()
  model.add(Dense(20, activation=activations.relu, input_dim=2))
  model.add(Dense(1, activation=activations.sigmoid))
  model.compile(optimizer=Adam(learning_rate=0.001), loss=losses.binary_crossentropy, metrics=[metrics.binary_accuracy])
  
  model.fit(np.array(dataset), np.array(answers), 100, 200)
  model.save_weights("model")