Tensorflow 2.0 自定义训练循环的学习率

当我使用tensorflow 2.0自定义训练循环时，是否有任何函数或方法可以显示学习率？

这是张量流指南的示例：

def train_step(images, labels):
  with tf.GradientTape() as tape:
    predictions = model(images)
    loss = loss_object(labels, predictions)
  gradients = tape.gradient(loss, model.trainable_variables)
  optimizer.apply_gradients(zip(gradients, model.trainable_variables))

  train_loss(loss)
  train_accuracy(labels, predictions)

当模型训练时，如何从优化器中检索当前的学习率？

我将非常感谢您提供的任何帮助。 :)

在 Tensorflow 2.1 中，Optimizer 类有一个未记录的方法_decayed_lr（参见定义here)，您可以通过提供要转换为的变量类型来在训练循环中调用它：

current_learning_rate = optimizer._decayed_lr(tf.float32)

这里还有一个更完整的 TensorBoard 示例。

train_step_count = 0
summary_writer = tf.summary.create_file_writer('logs/')
def train_step(images, labels):
  train_step_count += 1
  with tf.GradientTape() as tape:
    predictions = model(images)
    loss = loss_object(labels, predictions)
  gradients = tape.gradient(loss, model.trainable_variables)
  optimizer.apply_gradients(zip(gradients, model.trainable_variables))

  # optimizer._decayed_lr(tf.float32) is the current Learning Rate.
  # You can save it to TensorBoard like so:
  with summary_writer.as_default():
    tf.summary.scalar('learning_rate',
                      optimizer._decayed_lr(tf.float32),
                      step=train_step_count)