tensorflow/models · error · ValueError

Boundaries length is equal to learning rate levels length{le

Error message

Boundaries length is equal to learning rate levels length{len(self.boundaries)} != {len(self.values)}

What it means

Error "Boundaries length is equal to learning rate levels length{len(self.boundaries)} != {len(self.values)}" thrown in tensorflow/models.

Source

Thrown at official/modeling/optimization/lr_schedule.py:445

        increasing entries, and with all elements having the same type as the
        optimizer step.
      values: A list of `Tensor`s or `float`s that specifies the
        values for the intervals defined by `boundaries`. It should have one
        more element than `boundaries`, and all elements should have the same
        type.
      offset: The offset when computing the power decay.
      name: Optional, name of learning rate schedule.
    """
    super().__init__()
    self.values = values
    self.boundaries = boundaries
    self.offset = offset
    self.name = name

    if len(self.values) < 1:
      raise ValueError(f"Expect non empty {self.values}")
    if len(self.boundaries) != len(self.values):
      raise ValueError(
          "Boundaries length is equal to learning rate levels length"
          f"{len(self.boundaries)} != {len(self.values)}")

    self.total_steps = (
        [boundaries[i + 1] - boundaries[i] for i in range(len(boundaries) - 1)
        ] + [0])

  def __call__(self, global_step):
    with tf.name_scope(self.name or "StepCosineDecayWithOffset"):
      global_step = tf.cast(global_step - self.offset, tf.float32)
      lr_levels = self.values
      lr_steps = self.boundaries
      level_total_steps = self.total_steps
      num_levels = len(lr_levels)

      init_lr = lr_levels[0]
      next_init_lr = lr_levels[1] if num_levels > 1 else 0.

View on GitHub (pinned to e006f5f0d5)

When it happens

Trigger: Thrown at official/modeling/optimization/lr_schedule.py:445 when the library encounters an invalid state.

Common situations: See trigger scenarios.

AI-assisted analysis of tensorflow/models@e006f5f0d5 (2026-08-24). Data as JSON: /api/errors/c8918d890024ba01. Report an issue: GitHub.