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a guest Feb 19th, 2019 61 Never
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  1. gen = generator()
  2. loss, mae = model.evaluate_generator(gen, 1000)
  3. #This produces Test set MAE:  1.80
  4.      
  5. gen = generator()
  6. predictions = model.predict_generator(gen, 1000).flatten()
  7.      
  8. gen = generator()
  9. y_test = np.zeros((1000))
  10. i = 0
  11. while i < 1000:
  12.     p = next(gen)[1]
  13.     y_test[i] = p
  14.     i += 1
  15.      
  16. # Manually compute MAE
  17. np.mean(np.abs(predictions - y_test))
  18.  
  19. # This produces 13.74
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