[Python] Tensoflow F1 score metrics

class F1Score(tf.keras.metrics.Metric):
    def __init__(self, name='F1Score', **kwargs):
        super(F1Score, self).__init__(name=name, **kwargs)
        self.f1score = self.add_weight(name='F1Score', initializer='zeros')
        self.count = self.add_weight(name='F1ScoreCount', initializer='zeros')

    def update_state(self, y_true, y_pred, sample_weight=None):
        y_true = tf.cast(y_true, tf.bool)
        y_pred = tf.cast(y_pred, tf.bool)

        true_positives = tf.logical_and(tf.equal(y_true, True), tf.equal(y_pred, True))
        true_positives = tf.cast(true_positives, self.dtype)
        count_true_positives = tf.reduce_sum(true_positives)

        possible_positives = tf.cast(y_true, self.dtype)
        count_possible_positives = tf.reduce_sum(possible_positives)

        predicted_positives = tf.cast(y_pred, self.dtype)
        count_predicted_positives = tf.reduce_sum(predicted_positives)

        precision = count_true_positives / (count_predicted_positives + K.epsilon())
        recall = count_true_positives / (count_possible_positives + K.epsilon())
        f1_cal = 2*(precision*recall)/(precision + recall + K.epsilon())

        self.count.assign_add(1)
        a = 1.0 / self.count
        b = 1.0 - a
        self.f1score.assign(a*f1_cal+b*self.f1score)

    def result(self):
        return self.f1score