text: from sklearn.neural_network import MLPRegressor
  from sklearn.metrics import mean_squared_error
  
  import numpy as np
  
  # Set Data and Train
  X_train = np.arange(0.0, 10, 0.01).reshape(-1, 1)
  y_train = np.sinc(X_train).ravel()
  
  model = MLPRegressor(
      hidden_layer_sizes=(10,10),
      activation='tanh',
      solver='lbfgs',
      max_iter=10000
  )
  
  model.fit(X_train, y_train)
  
  # Test
  X_test = np.arange(-0.1, 11, 0.01).reshape(-1, 1)
  y_test = np.sinc(X_test).ravel()
  y_pred = model.predict(X_test)
  
  error = np.sqrt(mean_squared_error(y_test, y_pred))
  print('Accuracy: %.8f' % model.score(X_test, y_pred))
  print('RMSE: %.8f' % error)