是否可以基于批次标签（y_true）分布来更新每个批次的学习率？

In order to implement a learning rate schedule, as described in this paper, I believe I need a way to update the learning rate during training, each batch, by a value calcuated from the label distribution of the true labels in the batch (y_true as it's typically denoted in keras/tensorflow)

哪里...      x模型的输出      y相应的地面真相标签      Βm个样本的小批量（例如64个）      ny地面真相标签y的整个训练样本量      ny-1逆标签频率

我关注的公式部分是α和Δθ之间的部分

我可以在自定义损失函数中轻松实现这一目标，但是我不知道如何提高损失率的学习率（如果可以的话）。

def loss(y_true, y_pred):
    y = math_ops.argmax(y_true, axis=1)
    freqs = tf.gather(lf, y)  # equal to lf[y] if `lf` and `y` were numpy array's
    inv_freqs = math_ops.pow(freqs, -1)
    E = 1 / math_ops.reduce_sum(inv_freqs)  # value to use when updating learning rate

where ...

lf the sample frequencies for each class. e.g. 2 classes, c0 = 10 examples, c1 = 100 --> lf == [10, 100]

有什么花哨的方法可以更新优化器的学习率，例如可以通过CallBack进行哪些操作？

def on_batch_begin(self, batch, log):
    # note: batch is just an incremented value to indicate batch index
    self.model.optimizer.lr  # learning rate, can be modified from callback

在此先感谢您的帮助！