CODEWITHSUNDEEP
keras
Aryo Pradipta Gema
Aryo Pradipta Gema

Reputation: 1230

Categorical crossentropy need to use categorical_accuracy or accuracy as the metrics in keras?

I'm currently doing a research for multi class classification. I used categorical crossentropy and i've got a really good result using accuracy as the metrics of the experiment. When i try to use categorical_accuracy, it gives a slightly worse accuracy (1% below). My question will be, is it ok to use accuracy metrics for categorical crossentropy loss instead of the categorical_accuracy?

Upvotes: 13

Views: 13671

Answers (1)

dhinckley
dhinckley

Reputation: 2135

Keras detects the output_shape and automatically determines which accuracy to use when accuracy is specified. For multi-class classification, categorical_accuracy will be used internally. From the source:

            if metric == 'accuracy' or metric == 'acc':
                # custom handling of accuracy
                # (because of class mode duality)
                output_shape = self.internal_output_shapes[i]
                acc_fn = None
                if output_shape[-1] == 1 or self.loss_functions[i] == losses.binary_crossentropy:
                    # case: binary accuracy
                    acc_fn = metrics_module.binary_accuracy
                elif self.loss_functions[i] == losses.sparse_categorical_crossentropy:
                    # case: categorical accuracy with sparse targets
                    acc_fn = metrics_module.sparse_categorical_accuracy
                else:
                    acc_fn = metrics_module.categorical_accuracy

The 1% difference you are seeing can likely be attributed to run-to-run variation, as stochastic gradient descent will encounter different minima, unless the same random seed is used.

Upvotes: 31

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