问如何在python keras中计算张量上的浮点数的熵

EN

class entropy_measure(Layer):

    def __init__(self, beta,batch, **kwargs):
        self.beta = beta
        self.batch = batch
        self.uses_learning_phase = True
        self.supports_masking = True
        super(entropy_measure, self).__init__(**kwargs)

    def call(self, x):
        return K.in_train_phase(self.rev_entropy(x, self.beta,self.batch), x)

    def get_config(self):
        config = {'beta': self.beta}
        base_config = super(entropy_measure, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))

    def rev_entropy(self, x, beta,batch):

        for i in x:
            i = pd.Series(i)
            p_data = i.value_counts()  # counts occurrence of each value
            entropy = entropy(p_data)  # get entropy from counts
            rev = 1/(1+entropy)
            return rev

        new_f_w_t = x * (rev.reshape(rev.shape[0], 1))*beta

        return new_f_w_t