Menu
I am implementing a neural network classifier, to print loss and accuracy of this NN I'm using:
score = model.evaluate(x_test, y_test, verbose=False)
model.metrics_names
print('Test score: ', score[0]) #Loss on test
print('Test accuracy: ', score[1])
I would to know how Keras calculate the loss of a model. Specially whether it is evaluated on the first (and only) step of the test set. I have search on keras.io, but I don't have find anything about it.
770
Accuracy calculates the percentage of predicted values (yPred) that match with actual values (yTrue). For a record, if the predicted value is equal to the actual value, it is considered accurate. We then calculate Accuracy by dividing the number of accurately predicted records by the total number of records.
Keras can separate a portion of your training data into a validation dataset and evaluate the performance of your model on that validation dataset in each epoch. You can do this by setting the validation_split argument on the fit() function to a percentage of the size of your training dataset.
Evaluation is a process during development of the model to check whether the model is best fit for the given problem and corresponding data. Keras model provides a function, evaluate which does the evaluation of the model.
From the documentation:
evaluate
Computes the loss on some input data, batch by batch.
Returns
Scalar test loss (if the model has no metrics) or list of scalars (if the model computes other metrics). The attribute
model.metrics_nameswill give you the display labels for the scalar outputs.
So it returns you either a single value that represents a loss, or a list of values that correspond to different metrics that were added to your model. These values are calculated based on the whole test set, i. e. all values in x_test and y_test.
110
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
