new_contents: import numpy as np
  from keras.models import Sequential
  from keras.layers import Dense, Activation, Dropout
  from keras.models import load_model
  from keras.optimizers import RMSprop
  
  from keras import regularizers
  from keras.constraints import *
  from keras.callbacks import *
  
  import os
  os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
  
  x = np.random.rand(10000)*100
  y = np.random.rand(10000)*100
  train = np.array([[i, j] for i, j in zip(x, y)])
  z = x**2 + y**2
  
  def create_model():
  	model = Sequential()
  	model.add(Dense(200, activation='sigmoid', input_dim=2, kernel_regularizer=regularizers.l2(0.001), bias_regularizer=regularizers.l1(0.001), bias_constraint=max_norm(2, 0)))
  	model.add(Dense(100, activation='sigmoid', kernel_regularizer=regularizers.l2(0.001), bias_regularizer=regularizers.l1(0.001)))
  	model.add(Dense(100, activation='relu', kernel_regularizer=regularizers.l2(0.001), bias_regularizer=regularizers.l1(0.001)))
  	model.add(Dense(25, activation='relu', kernel_regularizer=regularizers.l2(0.001), bias_regularizer=regularizers.l1(0.001)))
  	model.add(Dense(1, activation='relu'))
  
  	rms = RMSprop(lr=0.005, rho=0.9, epsilon=1e-08, decay=0.0001)
  	model.compile(loss='mean_absolute_error', optimizer=rms)
  	return model
  
  model = create_model()
  model.fit(train, z, epochs=150, batch_size=5)
  model.save('my_model.h5')
  # model = load_model('my_model.h5')
  s = np.array([[5, 5], [25, 25], [50, 50], [2, 2], [20, 20], [100, 100]])
  print(model.predict(s))
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