parameters: (predictions, labels)
return_statement: return 100.0 - (
        100.0 *
        numpy.sum(numpy.argmax(predictions, 1) == labels) /
        predictions.shape[0])
docstring: Return the error rate based on dense predictions and sparse labels.
docstring_summary: Return the error rate based on dense predictions and sparse labels.
docstring_tokens: Return the error rate based on dense predictions and sparse labels .
function: def error_rate(predictions, labels):
    """Return the error rate based on dense predictions and sparse labels."""
    return 100.0 - (
        100.0 *
        numpy.sum(numpy.argmax(predictions, 1) == labels) /
        predictions.shape[0])
function_tokens: def error_rate ( predictions , labels ) : return 100.0 - ( 100.0 * numpy . sum ( numpy . argmax ( predictions , 1 ) == labels ) / predictions . shape [ 0 ] )