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Could someone explain why the loss returned is different than the loss printed during the evaluation?

They are the same in the Tensorflow documentation https://www.tensorflow.org/guide/keras/train_and_evaluate

Code:

results = model.evaluate(test_data, test_target, verbose=2)
print("test loss, test acc:", results)

Output:

45/1 - 0s - loss: 1.2592 - mae: 0.7602
test loss, test acc: [1.05335361427731, 0.76020277]
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The model.evaluate function predicts the output for the given input and then computes the metrics function specified in the model.compile.Based on y_true and y_pred and returns the computed metric value as the output.

You can use model.metrics_names property of your model to find out what each of those values corresponds to.

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  • $\begingroup$ I have the following metrics ['loss', 'mae'] but it doesnt explain why the loss printed in verbose is different than the loss in the returned object $\endgroup$ – Marc Aug 25 '20 at 12:38
  • $\begingroup$ check this link $\endgroup$ – Shiv Aug 25 '20 at 17:01
  • $\begingroup$ I don't think it's related, my issue is not the different between evaluate and predict but between the loss printed with verbose by evaluate and the loss returned by evaluate $\endgroup$ – Marc Aug 27 '20 at 11:45

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