# larq.metrics¶

We add metrics specific to extremely quantized networks using a larq.context.metrics_scope rather than through the metrics parameter of model.compile(), where most common metrics reside. This is because, to calculate metrics like the flip_ratio, we need a layer's kernel or activation and not just the y_true and y_pred that Keras passes to metrics defined in the usual way.

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### FlipRatio¶

larq.metrics.FlipRatio(values_dtype="int8", name="flip_ratio", dtype=None)


Computes the mean ratio of changed values in a given tensor.

Example

m = metrics.FlipRatio()
m.update_state((1, 1))  # result: 0
m.update_state((2, 2))  # result: 1
m.update_state((1, 2))  # result: 0.75
print('Final result: ', m.result().numpy())  # Final result: 0.75


Arguments

• name: Name of the metric.
• values_dtype: Data type of the tensor for which to track changes.
• dtype: Data type of the moving mean.